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EMPOWERING STUDENTS’ SCIENTIFIC REASONING ABOUT
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ANALYSES

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Hasan Abdelkareem

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EMPOWERING STUDENTS’ SCIENTIFIC REASONING ABOUT ENERGY
THROUGH EXPERIMENTATION AND DATA ANALYSES

By

Hasan Abdelkareem

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Department of Curriculum, Teaching, and Educational Policy

2008

ABSTRACT

EMPOWERING STUDENTS’ SCIENTIFIC REASONING ABOUT ENERGY
THROUGH EXPERIMENTATION AND DATA ANALYSES

By
Hasan Abdelkareem

The goal of this study was to explore how middle school learners reason fi'om the
scientific data that they collect while conducting energy related activities. Specifically,
the targeted data analysis skills were: realizing the variables of investigation, finding
patterns in data, and developing model-based reasoning about energy. The study was
conducted as part of a science curriculum design project called IQWST- Investigation
and Questioning our World through Science and Technology.

Two experienced science teachers and their 30 middle school students fi'om an
independent school participated in this study by piloting a designed unit about energy.
Data was collected through classroom observations and in-depth clinical interviews.
Those interviews were hold towards the end of the energy unit enactment with a focus
group of six students. Participants responded to two types of questions: direct data that
were similar to the investigations they conducted in the classroom and indirect data that
were represented by energy scenarios. Students’ reasoning from data was then analyzed
and synthesized using a special coding technique in order to answer the research
questions.

Findings of this research have shown three types of results:
a) Middle school learners have shown a tendency to use force—dynamic causation

about energy (e.g., energy is associated with living things, an enabler to do work

b)

and activity, and a materialized thing that moves like fluid). Very few students
were able to reason about energy using a model-based perspective.

Participants were able to reason about scientific data on the local level (i.e.,
connecting data with direct observations) but rarely were they able to transform
these local skills into global reasoning that implies developing models about
energy.

Although the two science teachers were able to enact the designed activities
through dialogical approach, there was no evidence that they had tendency to

reinforce model-based reasoning among their students.

T o my wife, Raba.

iv

Acknowledgements

I

Many people have participated in this accomplishment. At the top of my
acknowledgments list is my adviser, Andy Anderson. Dr. Anderson provided me with
unique opportunities for doing research in science education. His continuous support,
encouragement, and wisdom have always been with me through this journey in order to
achieve my goals. I deeply appreciate your time and support, Andy. I would like also to
thank all my committee members: David Fortus, Lynn Fendler, and Randi Stanulis. Dr.
Fortus did not hesitate when I told him that I am interested in conducting my research
through his IQWST project. His comments, feedback, and support were very important
for facilitating the data collection process. My deepest thanks to Dr. Fendler, who taught
me the first graduate course at Michigan State University. Her critical perspectives about
teaching and learning encouraged me to think differently about my own experience as a
science teacher and as a teacher educator as well. I appreciate Dr. Stanulis comments and
support not only while doing this study, but also in many other occasions. Many thanks,
Randi.

I would like to thank the two science teachers and their students who welcomed me
very nicely to their classrooms. Indeed, I tasted a different style of teaching and learning
science there. My visits to your classes were very productive and enjoyable occasions; I
never felt like a stranger in your school.

I would like to thank my family members. My wife, Raba, and my five children:
Maysam, Mahmoud, lhab, Issam, and Dana. I know how you were patiently accepting

my commitments and assignments as a graduate student. I am proud that in spite of the

many challenges and difficulties that. we faced in those four years, we together got the
mission done. Thank you so much guys and I dedicate this dissertation for you. From the
bottom of my heart I thank my family in Palestine for their unlimited support and
encouragement through my graduate school study. I feel so lucky to have such a
supportive mother, sisters, and brothers.

My sincere acknowledgments go to my American friends who helped our family in
many different ways. Very special thanks to Stefanie Kendal, Anne Schneller, Sally
McClintock, Peter Briggs, Brooke Stokdyk, and Rosemary Max for their continuous

support.

TABLE OF CONTENT

List of Tables ................................................................................................ [X
List of figures ............................................................. . ................................... X
CHAPTER ONE: INTRODUCTION: ........................ 1
Theoretical framework ..................................................................................................... 3
Data Analysis as part of inquiry-based Learning: ............................................................ 8
Definition of the Problem: ............................................................................................. 12
The significance of this study ........................................................................................ 13
Definitions of the main terms: ....................................................................................... 14
Chapter Summary .......................................................................................................... 16
Dissertation overview .................................................................................................... 16
CHAPTER TWO: REVIEW OF LITERATURE ................................ 18
Introduction: ................................................................................................................... 18
Inquiry-based learning and constructivism: ................................................................... 18
Considering Scientific Data as evidence: ...................................................................... 21
Students’ reasoning from scientific data: ....................................................................... 23
Anomalous Data and conceptual change: ...................................................................... 27
Science-Related professions and reasoning from data ................................................... 28
Research on students’ conception of energy: ................................................................. 30
Why should energy be in science curricula? ........................................................... 31

What can be done? ................................................................................................... 35

This study: ................................................................................................................ 36
Chapter Summary .......................................................................................................... 37
CHAPTER THREE :METHODOLOGY ............................................. 38
The context of this study ................................................................................................ 38

0 The IQWST project: .......................................................................................... 38

0 The Energy Unit: ............................................................................................... 39

0 Study Site: ......................................................................................................... 4O

0 Sample of the study: .......................................................................................... 41
Instruments and data sources ......................................................................................... 42
A) Classroom Observation: ..................................................................................... 42

B) Field Notes: ..................................................................................................... 42

C) Clinical interviews: ............................................................................................. 43

D) The interview protocol: ....................................................................................... 44
Data Management and Data Analysis ............................................................................ 47

0 Analyzing data from interviews: .......................................................................... 47

0 Analyzing data from classroom observations: ...................................................... 50
Chapter Summary .......................................................................................................... 52

vii

CHAPTER FOUR: FINDINGS & RESULTS. ............... . .................. 54

Findings about learners’ data analysis skills .................................................................. 54
A) Reasoning about investigation variables: ......................................................... 55

B) Realizingpatterns in data: ........................ 59

C) Drawing conclusions and making evidence-based arguments: ........................ 62
Students’ reasoning about energy .................................................................................. 63
1. Energy is associated with living things: ........................................................... 63

2. Energy as enabler to do things: ........................................................................ 65

3. Energy transformation and conversion (ETD & ECD): ................................... 67

4. Integrating in-school formal knowledge with out-of-school experiences: ....... 70
Overall results for cases ................................................................................................. 71
Pedagogical aspects of data analysis .............................................................................. 75

0 Teachers ’ ways of exploring investigations variables: ..................................... 75

0 Finding patterns and drawing conclusions: ..................................................... 78

o Treating conflict with anomalous data: ............................................................ 81
Chapter Summary .......................................................................................................... 84
CHAPTER FIVE: DISCUSSION AND IMPLICATIONS ........ 85
Findings Summary: ........................................................................................................ 85
Discussing the results of the study ................................................................................. 87
A) Local to global reasoning fiom data: ............................................................... 87

B) F orce-dynamic causation to model-based reasoning: ...................................... 88

C) From conceptual change to conceptual integration: ........................................ 90
Study Limitations: .................................................................................................... 91
Implications: ............................................................................................................ 92
Reflection ....................................................................................................................... 94
Chapter Summary .......................................................................................................... 96
APPENDICES .......... ............. .......................... 97
Appendix A: Description of the Energy Unit ................................................................ 97
Appendix B: The interview protocol ............................................................................. 99
Appendix C: Exemplar sheet about Coding students’ response .................................. 104
REFERENCES .. .................................................................................... 105

viii

LIST OF TABLES

Table 1: Contrasts between constructivist and traditional learning environments. . .........21

Table 2: Data analysis skills and their distribution among the interview questions ...... 46
Table 3: Participants data analysis skills codes .. .......................................................... 49
Table 4: Participants’ reasoning about energy codes .......................................... 50
Table 5: Coding the teaching practices that are embedded in reasoning from data ...... 52
Table 6: Students’ performance in data analysis skills ........................................ 72
Table 7: A summary of students’ reasoning about energy in two scenarios ............... 74
Table 8: The energy unit lessons and activities ................................................ 98
Table 9: The interview data table ................................... I ............................... 99
Table 10: An example of coding the students’ data analyses skills ........................ 104

ix

LIST OF FIGURES

Figure 1: Knowledge and practices of model-based reasoning .............................. 5
Figure 2: Long-Term objectives of science education ....................................... 19
Figure 3: A model for measurement, data, and evidence .................................... 23
Figure 4: Energy conversion models for a falling book and a compressed spring ....... 68
Figure 5: Energy transfer models (ETD) for two different students ....................... 69
Figure 6: Wood in fire place .................................................................... 100

Figure 7: A graph of the relationship between burned wood and a room’s temperature.101
Figure 8: A picture of the elastic energy activity ............................................. 101
Figure 9: A graph of the relationship between springs types and moving distance. . 102

Figure 10: A picture of a car at a gasoline station ............................................. 102

Chapter One

Introduction

A couple of months ago, I watched the docuinentary movie An Inconvenient Truth as a
part of my participation in the Environmental Literacy project at Michigan State
University. The goal of the documentary, which was produced by former US. Vice
President Al Gore and his colleagues, was to make the public aware of the crises of
climate changes. The main scientific point was the negative impacts of the increased
concentration of the carbon dioxide (C02) in the atmosphere. This is the main argument

of the movie producers:

Carbon dioxide and other gases warm the surface of the planet naturally by trapping
solar heat in the atmosphere. This is a good thing because it keeps our planet
habitable. However, by burning fossil fuels such as coal, gas and oil and clearing
forests we have dramatically increased the amount of carbon dioxide in the Earth’s
atmosphere and temperatures are rising. The vast majority of scientists agree that
global warming is real, it’s already happening and that it is the result of our activities
and not a natural occurrence. The evidence is overwhelming and undeniable. We’re
already seeing changes. Glaciers are melting, plants and animals are being forced
fi'om their habitat, and the number of severe storms and droughts is increasing. (Gore,

2008)

That is, Gore and his colleagues relied heavily on observation and scientific data to
support their essential argument regarding the relationship between the cause (CO2) and
the effect (i.e., Earth’s temperature). The movie’s authors have used considerable
amounts of data and various types of graphical representations to support their main
claim about the two variables. Reflecting on one of the graphs that illustrates how both
C02 and the Earth’s temperature have been changing for the last 650,000 years, Gore
concluded that “the relationship [between carbon dioxide and temperature] is actually
very complicated, but there is one relationship that is far more powerful than the others
and it’s this: when there is more carbon dioxide, the temperature gets warmer because it

traps more heat from the Sun inside” (Gore, 2008).

However, the An Inconvenient Truth movie caused an ongoing debate among
scientists, particularly about the controversial relationship between C02 and the Earth’s
temperature. For instance, Tim Ball, a climatology professor, argued that temperature
increased significantly till 1940 when human production of CO2 was relatively low (Ball,
2008). Then, in the post war years, when the industries and the human production of CO2
were increasing, the global temperature was going down. Using a similar reasoning,
professor Lan Clark (2008), an Earth scientist from the University of Ottawa, argued that
carbon dioxide cannot be causing temperature changes, but rather it is a product of
temperature changes. If the average citizen had been watching the previous debate, he
might have been confused about making decisions regarding which argument is

scientifically acceptable.

In spite of the fact that I do have a sincere commitment towards environmental issues,
the purposes of the previous example is not directly related to environmental crises or to
the political debate over global warming. Furthermore, I am not trying to defend Gore’s
or his Opponents’ arguments in spite of the fact that they are very interesting. Rather, I am
using that introduction as an example of how the same observations and the same
scientific data could be analyzed and interpreted by scientists and other public
communities differently. Making sense of scientific data and building evidence-based
arguments are essential areas in science learning (Duschl & et a1., 2007; Fortus &
Shwartz, 2006). As a result, data analysis and reasoning from evidence is a cornerstone in

inquiry-based science classrooms.

The purpose of this study is to explore the learning and teaching aspects that are
associated with one of the inquiry practices: learners’ skills in data analysis that are
mobilized in classroom conversations about energy-related investigations. In this chapter,
I will address the research theoretical framework, study problem, research questions, and
the significance of the study and its goal. I will finally close with an overall summary of

this dissertation to give the reader a sense about what will be coming next.

Theoretical framework

In their last insightfully comprehensive report, which they called Taking Science to
schools: Learning and Teaching Science in Grades K-8, Duschl (2007) and his
colleagues stated that:

Science is both a body of knowledge that represents current understanding of natural

systems and the process whereby that body of knowledge has been established and

being continually extended, refined, and revised. Both elements are essential: one

cannot make progress in science without understanding of both (p. 26).

That is, science consists of two integrated parts: one refers to the final products, which
represent tentative facts and the accurate scientific stories, whereas the other represents
the practices that scientists usually use to reach their ultimate goals. Consequently,
addressing both the body of knowledge and the processes that facilitate its formation are
essential in the field of science education. Furthermore, I argue that for orchestrating
effective science classrooms, the body of knowledge and the process of acquiring it goes
hand in hand. This study addresses an essential scientific practice about learners’
reasoning from scientific data while conducting various activities about energy. In fact,
there is a general agreement among science educators that data analysis skills are
essential embedded practices in the inquiry-based learning activities (J eong, Songer, &
Lee, 2007; NRC, 2000).

The following diagram includes both the body of scientific knowledge and the process
that is used by scientists to develop it (Anderson, 2007). There are two important points
that should be noted in this diagram. The first is related to the triangle, whereas the
second explains the direction of the arrows at both sides of it.

At the bottom of the diagram are the unlimited ranges of experiences. These
experiences include ecosystems, physical phenomena, living and non living things, and
all other unorganized types of data and observations (i.e., knowledge and experience).
We experience these almost on a daily basis even without giving them any attention. As a
result, learners come to school with a wide range of experiences about their real world

throughout their communication with it. Thus, one of the major goals of school science, I

believe, should be finding a reasonable equilibrium between these experiences, the
intuitive knowledge which usually is loaded with many misconceptions about science,
and the formal school science. However, one should keep in mind that there are many
differences between the way the scientists and learners approach science, but I will talk

about such differences in another area.

   
   
 
 
  

  

Models
Theories

Inquiry: Using
Learning knowledge:
from Data Application

Figure 1: Knowledge and practices of model-based reasoning (Anderson, 2007, p. 18)

In fact, the accumulative bodies of scientific knowledge were formed by collecting
and analyzing data through long periods of time. Scientists start by making observations
and taking measurements, which ultimately form the foundation of making evidence-
based claims. These experiences might be gained in the short term, as in a classroom
setting, or through longer periods of time forming what Kuhn (1962) called paradigms—
the accepted norms of knowledge discourse in any field. These observations could be

organized, classified, and rearranged in different ways, such as tables, graphs, and

specific kinds of classifications. For example, we say that plants are living things which
use the sunlight to make their food independently through photosynthesis. Animals are
also living things but they have a different life cycle and food production. However, both
plants and animals are related to each other and they are both parts of a bigger system
that represents food chains and energy flow in ecosystems. This sort of scientific
reasoning is an example of how we realize patterns among our various experiences and
observations.

In the last stage scientists draw conclusions and build models and theories relying on
solid evidence. Borrowing from Taking Science to School report, “the body of knowledge
includes specific facts integrated and articulated into highly developed and well-tested
theories. These theories, in turn, can explain bodies of data and predict outcomes of
experiments” (p. 26). Of course, one should keep in mind that these are tentative theories
and laws and they are never completed or finalized. Hence, they are likely to be modified
and changed as new data or empirical evidence develops. According to the previous
diagram, theories and models are represented at the top of the diagram because they are
abstract representations of knowledge that can explain huge amounts of observations and
patterns. Developing models and theories in that way is inquiry that scientists do so often.
Moving from the bottom of the triangle (the real world experiences and observations)
towards the top is a complicated process. In other words, it is impossible to separate the
“body of knowledge” from the “process” that facilitates producing it.

In fact, this process of inquiry is embedded within the scientists’ techniques and
work when they move between the material world and the abstract models. Once a theory

or a model has been tested and developed, it becomes a powerful tool to explain other

kinds of data that are governed by the same patterns. Thus, scientists use theory and
models in order to make sense of new observations and experiences that they did not
necessarily investigate before (i.e. application). In spite of the fact that both approaches
(inquiry and application) are valid venues for constructing meaningful learning, science
educators support the notion of inquiry-based learning. There is an assumption that
helping students make sense of their world and reinforcing them to modify their
worldview of science is more accessible through inquiry. However, the two communities,
scientists and school learners, work in two different contexts.

Students don’t necessarily explore their world the same way that scientists do. For
instance, students rely heavily on their daily life experiences, thus their patterns and
theories are influenced by such observations. When scientists develop their patterns and
models, on the other hand, their personal experiences and observations have less control
on their thinking. Furthermore, the purpose of doing inquiry in the two communities is
not quite similar. Scientists usually work towards developing, testing, or discovering new
theories. In the classroom setting, science teaching usually focuses on helping students
integrate formal science with their personal experience (i.e. conceptual change) or
confirming some facts that they already know. Yet, these are fruitful strategies for doing
inquiry. Finally, there is an important distinction between scientists’ and learners’ ways
of reasoning: force-dynamic causation versus the model-based reasoning.

Model-based reasoning means using scientific theories, principles and laws, and
sophisticated models while explaining how things happen. Scientific models have three
interrelated characteristics. The first one is the metaphorical nature of them. Both

scientists and students use metaphorical narratives to describe their models. We say that

water molecules contain the H (Hydrogen) arranged around 0 (Oxygen) like the ears on
the human head. These are useful stories that help us by connecting abstract scientific
narratives with concrete examples in real world. The second characteristic is that those
models have constraints. These are rules and principles that govern a model. For
example, we say when a physical change happens in the water state, the molecules are
rearranged and their motion becomes slower. Lastly, scientific models have many
representational forms such as diagrams, chemical equations, maps and cross sections.
Unlike students, scientists have access to these three aspects of scientific models and thus
they can explain how things happen using model-based reasoning.

There is evidence in research that students in different stages have a tendency to use
force-dynamic causation rather than model-based reasoning (J in & Anderson, 2007).
Force-dynarnic causation usually explains why things are happening in a specific way
using natural tendency approaches (e.g. plants need water to grow; we eat food because it
gives our bodies energy). According to this perspective, scientific phenomena occur
because there are enablers that force them to happen. Finally, force-dynamic learners
focus on macroscopic and visible parts of systems rather than realizing hidden parts and

mechanisms.

Data Analysis as part of inquiry-based Learning

Generally speaking, people collect and analyze data in order to have a piece of
evidence (Duggan & Gott, 2002) that helps them make appropriate decisions in different
circumstances. In fact, our daily lives have many examples where understanding data in

order to make decisions, or to understand some claims that are supported by this data, is

crucial. Perhaps the An Inconvenient Truth documentary movie is a good example in this
regard. An informed citizen has to decide, as a result of watching this movie, which
argument or data sounds more convincing to him. Along the same line Kanari and Miller
(2004) stated that:
Claims are made, for instance, that certain foodstuffs increase (or decrease) the risk
of certain diseases or conditions, or that the use of mobile phones increases the risk
of brain tumors. Deciding what to make of such claims, and whether to act or not on
them requires some understanding of how to interpret data and assess its
trustworthiness” (p.768).
The previous examples reflect science educators’ perspectives about the relationship
between teaching science and citizenship (Lee, 1997).

Similarly, science related professions require also dealing with data analysis on a daily
basis manner to make appropriate decisions. For example, the first step that a physician
usually makes is collecting data from his or her clients. This data collection includes:
measuring the body temperature, blood pressure, and any additional information that the
patient reports. Advanced data analysis, such as testing a blood sample or taking an X-ray
photo, might be necessary in order to make sure that the available data are “good enough”
(Aikenhead, 2004) to make further decisions. A similar case is when a mechanic tries to
solve a problem in order to fix a broken car. He ultimately has to make decisions and take
actions to solve a problematic situation.

However, the situation in the science classroom is different. Although data collection
and analysis in the previously mentioned examples have many common aspects, they

differ in purposes, techniques, and consequences. A mechanic or a physician collects data

in order to find a solution for specific problem such as a broken car or a sick person. Put
differently, he is not necessarily trying to test or change his own theory. Rather, his main
purpose is to integrate the data he collects with his own theory to make further decisions
regarding the casein hand. In a science classroom, on the other hand, learners gather and
analyze data in order to change their institutive or previous theories (e. g. conceptual
change) or to test the correctness of their predictions (F ortus & Shwartz, 2006).
Conducting scientific investigations for conceptual change is essential in inquiry-based
learning environments (Driver & et al., 1994; Chinn & Malhotra, 2002). The main
assumption in this case is that learners come to classrooms with a wide range of
experience about the material world around them. This knowledge could be advanced (or
even modified) as a result of meaningful engagement opportunities in well-designed
activities (White & Frederiksen, 1998).

Learning and teaching science through engaging students in inquiry investigations has
recently been one of the most important fields for research in science education as a
response to the recent calls for national curriculum reforms (Kanari & Millar, 2004;
Reiser & et al., 2003; Blumenfeld & Krajcik, 2006). According to the National Research
Council, developing sufficient understanding about inquiry and its practices is one of the
essential standards in science education (NRC, 1996). The American Association for the
Advancement of Science (AAAS) has also pointed to the importance of developing
“habits of mind” through engaging learners in inquiry-based activities (AAAS, 1989).

Although science educators have been using the term “inquiry” in different ways,
there is a general agreement about the main characteristics of the concept. The National

Research Council, for example, pointed to five aspects of inquiry:

10

a) Learner engages in scientifically oriented questions.

b) Learner gives priority to evidence in responding to questions.

0) Learner formulates explanations fiom evidence.

d) Learner connects explanations to scientific knowledge.

e) Learner communicates and justifies explanations (NRC, 2000, p. 25).

Along the same lines, many science educators called for introducing learners to
opportunities where they can ask and refine questions, design and conduct experiments,
and collect and analyze data in order to make evidence-based arguments (Chinn &
Malhotra, 2002; Gott & Doggan, 1996). According to Edelson (2000), inquiry skills
should include “formulating hypotheses, collecting and evaluating the evidence, and
defending conclusions based on evidence” (p.362).

Looking closely at the previous educators’ suggestions and the way they identify
inquiry characteristics, one may draw two important assumptions. First, students are
capable agents and they can effectively participate in developing meaningfirl knowledge.
In other words, not only they can advance content understanding, but also they can feel
kind of knowledge ownership. By doing so, authoritative resources, such as textbooks
and teachers as experts, may not keep the traditional positions. The second assumption is
related to designing science curriculum materials that reinforce inquiry-based learning. In
fact, this is a big challenge as these materials should be educative for teachers (Callopy,
2003; Davis & Krajcik, 2004) and at the same time should open new rooms for students
to work like scientists. Having said that, science teachers’ roles could be seen as
mediators and dialogue facilitators between formal curriculum materials and students’

recent knowledge.

11

Definition of the Problem:

In spite of the fact that the inquiry-based learning has been widely studied and
described in science education literature, studies that were conducted about specific
aspects of this approach, such as students’ skills in data analysis, still necessary. Thus, I
believe that exploring students’ reasoning from data and how they communicate with
various types of it is. essential in order to understand the functioning of the inquiry-based
environments. Additionally, this study is an attempt to shine some light on both scientific
practices (data analysis skills) as they are nested in students’ reasoning about energy
(content knowledge).

The central goal of this research is to answer the following major question: how are
data analysis tasks exhibited by middle school students and implemented by science
teachers while conducting scientific investigations about energy?

While this is the key question, these are other supportive sub-questions:
RQI: What are the general data analysis skills that have been developed by learners after
participating in the energy unit activities? The basic targeted skills include:

a) Reasoning about investigation variables.

b) Finding patterns in scientific data.

0) Developing model-based reasoning about energy.

RQZ: What are the pedagogical aspects that are associated with those data analysis skills?

12

The significance of this study:

In the last three years, I participated in designing science curriculum materials for
middle school learners through a project called IQWSTl (Investing and Questioning our
World through Science and Technology). This project aimed at developing curriculum
materials in many interrelated fields including earth science, chemistry, and physics. I
participated in developing light and energy lessons as a member of the physics team. The
focus of this research was the Energy Unit. This unit, which will be described in detail in
the methodology chapter, was designed for middle school learners.

As a team of science educators, our main goal was designing inquiry—based lessons
about different types of energy and how energy is transformed from one type to another.
Engaging learners in hands-on activities that promote reasoning from data skills while
learning about energy concepts was essential goal in that curriculum. Additionally, our
focus was designing educative materials for science teachers (Schneider & Krajck, 2001).
The significance of this research lies not only in its focus on a comer stone in inquiry-
based learning (i.e. middle school students reasoning about scientific data and energy),
but also it takes into consideration the teaching practices that are associated with it.
Linking students’ learning in a specific area, such as energy, and teaching practices is an
interesting approach in terms of building an integrated picture about science classrooms
discourse. Furthermore, it will also provide valuable opportunities for deliberation
considerations for our work as curriculum developers. Thus, the feedback and results of

this research might be useful for additional revisions in IQWST curriculum.

 

1 More details about IQWST project will be presented in the third chapter when I discuss the
study methodology and procedures.

13

Definitions of the main terms

This is a short description about the main terms and concepts that I am using in this
study:

Curriculum materials:

These are the lesson plans that were developed by the IQWST team of researchers. In
this research, the curriculum materials include the written texts, the supportive apparatus
and equipments. It also includes the Internet and videos demonstrations that were used
for conducting activities to reinforce learning energy concepts.

Data:

In the context of this study, the term data refers to the outcomes that are produced as a
result of learners’ engagement in a scientific investigation. For instance, it may mean a
student-generated table of results that shows how much a cube of Play-Dob was smashed
as a result of dropping different masses fiom specific elevation. Data could also mean
observations, like watching a video that shows a specific phenomenon about energy.
Although the term data is often used to describe outcomes of scientific activities and
experiments in school settings, learners “observe” various kinds of informal data in their
daily lives (e.g. intuitive knowledge). Examples of these observations include different
types of motion, human activities, plants, animals, and various scientific phenomena. In
other words, data includes in-class formal activities and outside experiences as well.
Data analysis:

A cognitive process that refers to a set of skills and abilities that help a learner make
sense of specific kind data. This includes realizing the variables of an investigation,

finding the pattern, drawing appropriate conclusions, and building evidence-based

l4

arguments. In this study, data analysis is contextualized within the energy concepts and
processes. Furthermore, I use the terms data analysis and reasoning from data
interchangeably. It’s important to differentiate between “data analysis” that refer to my
work as a researcher and “data analysis skills” that refers to students’ reasoning from the
data that they collect in the classroom while exploring energy concepts.

Data representations:

For the purposes of this research, data representations are visual scientific expressions
that summarize data in organized forms such as tables and graphs. It also means other
types of diagrams such as Energy Conversion Diagrams (ECD) and Energy
Transformation Diagrams (ETD).

Model-based reasoning:

The concept model refers to a set of ideas, or even to a physical picture, that a learner
holds about a phenomenon or a theory. Models are helpful tools for explanations,
predictions, and putting coherent representation about scientific concepts and processes
together. By the same token, model-based reasoning means the ability to reason about
scientific phenomenon attempting to explain how it happens. This includes using
metaphors, realizing the system roles and its macroscopic and microscopic parts, and
using abstract scientific representations.

F orce-dynamic causation:

Force-dynamic causation is a term that refers to a reasoning way that justifies why things
happen rather than explaining how an event or a scientific phenomenon occurs. This type
of reasoning, as an opposite of the model-based reasoning, focuses more on cause and

effect approach (e.g., cars need gas to move from one place to another). Learners who

15

rely on this type of reasoning usually focus on the visible parts of the system, give more

attention to natural tendency, and so often trust their intuitive experiences.

Chapter Summary

Although the processes of data gathering, organization, and analysis are important
areas in science education, this study pay specific attention to the data analysis skills.
Those skills are very important for learners in order to reason from evidence and for
scientific argurnentations. The goal of this research is to give many examples of how
middle school learners reason from different sets of data that are related to energy
activities. Furthermore, informed citizens need basic skills in this regard so that they can
participate in science driven issues in their community. This reflects the importance of
science as a tool for citizenship especially in today’s world where various sets of data and

information are delivered through technology and media.

Dissertation overview

Chapter 2:

In chapter 2, I review the relevant literature that has been done about students’ reasoning
from data (i.e. inquiry practices). I also will be giving specific attention to literature that
focused on students’ concepts of energy (content) in order to situate this study with
literature. I finally refer to some research that has been conducted about data analysis

outside school contexts, such as science professional fields.

16

Chapter 3:

Here, I explain the general context of the study including: participants, school, teachers,
and the IQWST curriculum. Additionally, I explain my data collection methodology
through observations and clinical interviews. I finally, discuss the data analysis
techniques that I have used to synthesize the findings and results of this study.

Chapter 4:

This is the chapter that tells the story about my dissertation. It will be used to help the
reader make connections about the various parts of the research. The results that I
concluded from participants’ interviews and the classroom observation are summarized in
this chapter. Three areas of results are reported, two of them about learning aspects of
data analysis and energy and one about science teaching.

Chapter 5:

The last chapter of this dissertation has two main parts: the discussion of the main
findings, where I integrate this research with the field, and the reflection part. In this later
one, I reflect on my actions and decisions as a researcher. Additionally, I discuss the

research implementations for teaching and learning, curriculum, and for future research.

17

Chapter Two

Review of Literature

Introduction:

Making sense of scientific data is an essential tool for scientists, professionals, and
informed citizens. Looking closely at the literature that has been reported on data analysis
and scientific reasoning, which is also called reasoning from data or evidence-based
argurnentations; I organize this chapter around three main themes. The first focuses on
situating reasoning from data within a wider domain about scientific knowledge
acquisition. It also addresses the theoretical aspects of data analysis and my literature
review regarding learners’ reasoning from data. A particular attention will be given to
energy because it represents the scientific topic under investigation. The second area
highlights literature about science-related professionals’ reasoning from data. This area
illustrates the relationship between schooling and learners’ future careers. Finally, I will
report on some literature that considers reasoning from scientific data as a part of

scientific literacy and citizenship. These areas are represented in the following diagram.

Inquiry-based learning and constructivism:

I see the inquiry-based learning approach as a tool for promoting both meaning
construction and sense making of the real world experiences. As I have explained in the
first chapter, inquiry approach opens additional rooms that help learners investigate
scientific phenomena about their real world experiences. By doing so, they have more

responsibilities and additional tools while learning science. I also believe that science

18

educators’ calls for adopting inquiry approach in science teaching and learning are rooted

in constructivism, where learning is constructed and integrated in a meaningful manner.

 

 

 

 

 

 

 

 

Canonical
Science
Data Analysis
skills and .
evidence-based Inquiry-based
. learning).
argumentation
In-Use scientific
' ~ knowledge
Science for Preparing "
citizenship professionals '
Public
engagement
in science

 

Figure 2: Long-Term objectives of science education.

Constructivism is a learning theory that explains how learners depend on their

faculties, which include their own senses and former experiences, in addition to external

supportive tools to construct meaningful knowledge (Brooks & Brooks, 1993). Students

come to science classroom with a wide range of concepts and worldviews about the

material world around them. In spite of the fact that these intuitive experiences are

legitimate, many of these worldviews and ideas are not accepted from scientific point of

view, i.e. misconceptions. When they are introduced to scientific situations that

investigate particular phenomena, students relay heavily on these personal experiences to

make sense of a new situation. Rooted in cognitive psychology, consiructivists assume

that learners are capable agents who are responsible for their own learning. This occurs

19

 

while interacting actively with the new experience and through social communication
with other learners (V ygotsky, 1978; Trumpet, 1990). From Piagetian developmental
perspective, the disequilibrium between new experiences and preexisting knowledge
facilitates the learning process. Consequently, a change in the leamer’s schemas and
frameworks happens by what he calls accommodation and assimilation (Piaget, 1968).
These two terms refer to the internal cognitive interaction between a leamer’s previous
and current knowledge.

Considerable literature in science education departs fiom this cognitive perspective
reporting on conceptual change (Posner & et al., 1982) or students’ ways of reasoning
from anomalous data (Chin & Brewer, 1993). The conceptual change perspective
assumes that clarifying the contrast between the new concept, which should be fi'uitful
and scientifically powerful, and the leamer’s recent knowledge would be helpful in
advancing the current knowledge (Resnick, 1987; Driver & et al., 1994). Anomalous data
is the data that was collected by learners but sounded contrasting their predictions or
expectations. In fact, many science educators referred to this kind of data as a sufficient
tool for the conceptual change.

Adopting constructivism and inquiry-based learning for science learning and
teaching requires considerable changes in the teaching methods. It also assumes
reconsiderations of curricula designers’ work and education policy (Shwartz & et al.,
2005). The following table summarizes the main differences between the constructivist
and traditional science learning and teaching environment (Brooks & Brooks, 1993). A
vivid difference that is illustrated by this table is educators’ beliefs regarding knowledge,

learners, and the teachers’ roles. However, what is missed from such a comparison is the

20

big picture about the relationship between schooling and society. Additionally, one

should keep in mind that although educators tend to support constructivism and inquiry

approaches, this does not mean that they have magical solutions for the classrooms

complexities and limitations. I believe that every single classroom is a unique context that

has its own circumstances.

Table 1: Contrasts between constructivist and traditional learning environments

 

Traditional Classrooms

Constructivist environment

 

Knowledge is transferred from
authoritative resources (e. g. teachers and

textbooks) to learners.

Knowledge is constructed through social

interaction and meaningful activities.

 

Students work individually with more
emphasis on returning back what they

memorize.

Students work in groups and share
knowledge construction through a learning

community.

 

Curricular activities rely heavily on

textbooks and workbooks

Activities are conducted using various

sources of data and manipulative materials.

 

Teachers seek correct answers as a valid

way for evaluating students’ learning

Assessments include multiple dimensional
tools including portfolios, student

exhibitions and informal observation.

 

Scientific activities concentrate mainly
on confirmation labs that require
following step-by-step procedural

learning.

 

 

Activities emphasize big ideas
investigations around main concepts and
problems taking learners prior knowledge

into consideration.

 

 

Considering Scientific Data as evidence:

The term data is used by many communities, particularly scientists and

mathematicians, to refer to various meanings. Mathematicians, for instance, use the term

21

data as quantitative representations and therefore data analysis form this perspective
means the ability to reason statistically (Leinhardt, Zaslavsky & Stein, 1990; Ridgway,
Nicholson, & McCusker, 2007). Many math educators have also used statistical literacy
to explain the importance of data analysis in mathematics curricula (Ben-Zvi, Arcavi,
2001; Nicholson, Ridgway& McCusker, 2007). In science education, the term data is
used in a broader way that includes qualitative observations in addition to quantitative
representations. Furthermore, in science classrooms data is contextualized within
investigations that support learners’ understanding of scientific process or phenomenon.
Regardless of the context in which the term data is used, an important question will be:
when can data be trusted and treated as solid evidence?

Evidence-based reasoning is one of the most essential aspects while conducting
scientific investigations and developing scientific argumentations (Lubben & Miller;
1996). Perhaps the work of Gott and his colleagues is a good example that shows how
data should be treated in order be seen as evidence. From their perspective, “evidence is
data that have been scrutinized by various methods of validity and reliability” (p.257). In
other words, evidence has a higher degree than data and it’s more creditable. Data
reliability refers to consistency in readings over time or by mutable measures. Validity
deals with the question: does a reading measure what claimed to be measured?

The following diagram, which should be read from center to out, illustrates that a
datum is consisted of several measurements (or readings). The accumulated datum
elements can produce data. In school settings, for example, a datmn could be one
measure (e. g. the measure of a gas volume). Once this process is repeated many times

and considering another variable, such as the temperature, then we produce a data table.

22

Further, a datum could be quantitative (like measuring the mass of an object) or
qualitative such as observing a boiling water. The outer ring of the model, “wider societal
issues,” means that the final meaning of evidence might be influenced by the social
context. For example, the evidence about some environmental issues, such as global

warming, is so often related to political and social perspectives.

A single measurement |

Measuring A datum
can involve more than
one measurement

 

 

 
 
 
 
 
 
 
  
 
 
 
 
 

 

 

 

- Relationship between
i 5 data which may, or
may not, exhibit a
pattern

 

’ Comparison with other
data

 

L—

Wider social issue

 

 

 

Figure 3: A model for measurement, data, and evidence (Gott, Duggan & Roberts, 2003, p. 268).

Students ’ reasoning from scientific data

Many studies addressed to the importance of students’ reasoning from data while
conducting scientific investigations (Chinn & Malhotra, 2002; Kanari & Miller, 2004;
Varelas, 1997; Lubben & Miller, 1996; Vellom & Anderson, 1999). In other studies,
research in this regard focused on students’ conceptual changes and treating anomalous

data (V arelas, 1997; Chinn & Malhotra, 2002). These two trends of research are located

23

within the canonical science as illustrated in figure 2 at the beginning of this chapter.
Next, I introduce examples of these studies.

Frequently, science education researchers explored how learners reason about the
variables that are involved in scientific phenomena. The assumption was that grasping the
relationship between those variables is essential for finding patterns and drawing
conclusions. Kanari & Miller (2004), for example, studied how 60 participants, ages 10,
12, 14 years-old, interpreted data and reasoned about investigation variables. In that
study, students carried out activities where a dependent variable (the oscillation period of
a pendulum) may or may not co-vary with other independent variables (e.g. the length of
the pendulum rope or its mass). Participants were supposed to find a pattern that supports
the scientific conclusion about the relationship between the variables under investigation.
The pendulum oscillation period depends only on its length (co-variation variable), the
mass does not affect the oscillation time (non-covariation variable).

Two interesting results were reached by the previous study. First, there were no
significant differences in participants’ reasoning in terms of the tasks contexts or the
participants’ ages. I am concerned about the conclusion about the context. I noticed that
participants have conducted two investigations within the same topic: force and motion.
Thus, it was not clear to me if these were really different contexts. Many studies found
that the context of investigation play essential role in learners’ reasoning abilities (Kuhn,
Amsel, and O’loughlin, 1988; Koslowski, 1996). Furthermore, the participants’ ages in
Kanari & Miller’s study were close, thus they will likely have similar reasoning abilities.
The second result was that very few participants were able to show sufficient reasoning

about non-covariant situations. I found many other studies that support such conclusion

24

(Chin & Brewer 1998; Masnik & Morris, 2002). The non-covariant situations are very
complicated even for high school learners. Many researchers critiqued the formal
curricula focus on co-variation examples and leaving opportunities for other types of
investigations are rare (Chinn & Malhotra, 2002; Kanari & Miller, 2004).

In a similar study, Koslowski (1996) found that learners’ abilities in realizing
patterns in data depend on their hypotheses about the relationship between variables. If
students can imagine a plausible reason why two variables might covary, then a small
amount of data showing no covariation is unlikely to make them reject the hypothesis that
the variables covary. By the same token, if they cannot imagine a relationship linking two
variables, a small amount of data showing covariation is unlikely make them conclude
that they are related. Put differently, people treat their ideas about the relationships
between variables as working hypotheses, and modify them when confronted with
anomalies in a way that takes into account their theory and data together.

Along the same lines, Kuhn, Amsel, and O’loughlin (1988) have examined how
students at different ages evaluate given theories using given evidence, and the influence
of the students’ own theories on this process. They introduced students to a problem,
elicited their theories about it, and then asked them to say whether given pieces of data
supported or conflicted with that theory and explain their reasoning. Findings suggested
that the process of theory and evidence coordination undergoes developmental change
(conceptual change). This means that many students don not consider the possibility that
their theory might be false or that alternative theories are even exist. Other studies found

that when data don’t clearly point to a clear conclusion, students often impose patterns on

25

their data, based on their prior expectations (Leinhardt, Zaslavsky & Stein, 1990;
Ridgway, Nicholson, & McCusker, 2007).

In one of the most recent studies, Jeong and her colleagues (2007) researched how a
group of sixth graders reason about “evidentiary competence” within the atmospheric
science. They tested six aspects that are related to evidence-based arguments: priority,
relevancy, objectivity, repeatability of evidence, interpretation of tables, and examples.
Results showed that students’ understanding of scientific evidence and the data collection
process was quite weak. However, students’ reasoning about data was assessed
depending on their responses to various sets of data that they did not collect. I argue that
using written tests, which usually call for responding to given data, may not be a
sufficient tool for drawing accurate conclusions about students’ reasoning. There is
evidence in research that students make more sense if data was collected by them as part
of an investigation that sounds interesting to them (Lubben & Millar, 1996; Schauble,
1996)

Other examples of research in this regard focused on students reasoning fi'om various
data sets that are varied in size and on how students identify errors in data. Masnik &
Morris (2002) researched how American students (from 3rd grade till college levels)
reasoned from data sets that varied in sample size, consistency, and the variability about
the mean. They found that students from all ages had greater confidence in conclusion
drawn from larger data sets. Furthermore, learners tended to ignore the data that were
inconsistent or did not fit with the average.

Lubben et al., (2001) explored how South Afiican freshman students, who were

studying physics, reasoned about different sets of data. They found that participants could

26

be described as “point” or “set” learners. The latter participants showed better
understanding of error, using a set of values to represent a measured quantity rather than
seeking evidence that one single measurement was correct or the best in the set. In other
place of literature such differentiation was called as “local” against “global” reasoning
from data (Ben-Zvi & Arcavi, 2001). Global learners not only were more confidants in
their conclusions, but also they were more successful in transforming scientific

knowledge from one context to another.

Anomalous Data and conceptual change:

Anomalous data are observations or pieces of collected data that scientifically valid
and yet are contrasting with the leamer’s theory about a phenomenon2 (Austin, Bell &
Darrials, 1991; Chin & Brewer, 1998). Many examples regarding reasoning from data
refer to the conceptual change to report on the interaction between learners pre-existing
knowledge and anomalous data. Chinn & Malotra (2002) attempted to understand why
students don’t change their previous scientific views about a phenomenon even after
collecting anomalous data. In order to help learners change their misconceptions, the
researchers pointed to the importance of making accurate observations, correct
interpretation, generalization, and retention. For instance, incomplete observations or
interpretations might block the new data and therefore a learner may stay with his own
theory. There is evidence in research that although students may be able to carry out

simple investigations, they often collect insufficient or inadequate data (Schauble, 1996).

 

2 For example, many people believe that if a heavy and a light objects are dropped from the same
elevation, then the massive object will reach the ground first. If an accurate experiment was
conducted from reasonable height, it will prove that the two reach the ground together. Such data
is anomalous for those who hold that misconception.

27

As a result, they either draw conclusions that are inconsistent with their data or prefer to
stay with their intuitive conceptualizations (i.e. their own theories).

However, I am not sure about the efficiency of the conceptual change in such-cases,
especially when it comes to the learners’ authoritative intuitive knowledge that has been
developed through considerable period of time. I alternatively support the notion of
conceptual development and integration. Learners’ own framework and worldviews
about science are very resistive and thus replacing them is a complicated process. Perhaps
we need to put more emphasize on what Duit (1983) called the “ground level” of
negations in order to integrate the out-of-school experiences with the formal science
knowledge. There are many recent calls for valuing what students know in order to
develop more realistic and accessible science curriculum rather than imposing experts’
agendas. Examples of these approaches are the learning progression new trends in

science education research (J in & Anderson, 2008; Covitt & Gunkel, 2008).

Science-Related professions and reasoning from data:

An essential goal that is assumed by science curriculum designers and education
policy makers is to prepare students for science related careers in industry, government,
engineering and health professions. Curriculum developers expect students to integrate
scientific content into their own thinking so that this content is accessible later when
students are employed in a science-rich workplace (Aikenhead, 2004). This relationship
between future occupation and formal science was addressed by some literature. This

kind of literature concentrated on how professionals in some fields collect and analyze

28

data while dealing with science related circumstances (Aikenhead, 2004; Gott, Duggan &
Johnson, 1 999).

An insightful study that was conducted by Glen Aikenhead about nurses’ reasoning
from the data they collect in a hospital surgical unit. The main idea in his study was
investigating to what degree is the in-school canonical science (sometimes he called it
pure science) could be practical in terms of decision-making for those who work in
science-related professions such as nurses. It was noticed that from the nurses point of
view data become evidence if it follow a specific trend and is also supported by another
source (e. g. fever and blood pressure). Interestingly, only one of the six nurses in the
study used canonical science content in their clinical reasoning. The authors’
differentiation between the school formal knowledge, “knowing that,” and the procedural
knowledge, “knowing how” (p.243) sounds interesting. Although professional employees
use data in a way that differs from the classroom context, school science and inquiry
practices could form important bridges between learners’ future careers and schooling.

Duggan & Gott (2002) found that most of the scientific conceptual understanding
used by employees was learned in the job, not in high school or university courses. They
called this kind as “knowledge-in-use” (p.674). Such a conclusion was also supported by
other studies. Chinn et al., (2004) found that “School learning is focused predominantly
on declarative knowledge [knowing that], while workplace is focused predominantly on
procedural knowledge [knowing how]. The science found in workplace differed
significantly from the science learned in schools” (p.129). Although this contrast sounds
reasonable, I don’t agree with some educators’ calls for putting a greater emphasis on the

explicit teaching of the procedural understanding related to data and evidence, and a

29

reduced emphasis on teaching canonical science content. Most of the procedural
knowledge concepts would be learned in the job site, thus emphasizing procedural
knowledge on favor of canonical science may not guarantee developing learners’ skills in
terms of reasoning about evidence.

In addition to students in school settings and employees in different occupations,
reasoning from data is also important for average citizens. Tyler and her colleagues
reminded us that “J udgrnents about evidence are often central in the interactions between
science and the public.” (p. 817). For example, parents deciding whether or not to have
their infant child immunized, taking a position regarding debates over some public issues
related to science, an deciding which policy argument to support require sort of reasoning
about evidence and scientific data. Duggan and Gott (2002) have insightfully argued that
sense making of science issues in general, and understanding concepts of evidence

specifically, lie at the heart of scientific literacy as part of citizenship.

Research on students ’ conception of energy:

Energy is a shady and perhaps one of the most complicated concepts in science.
There are many reasons that make it such confusing and difficult to understand. In the
one hand, students experience many out-of-school experiences about energy. These
experiences reinforce them to construct intuitive knowledge about the concept energy. In
stores, for example, they see different kinds of food and drinks labeled by “energy
drinks” names, or explaining how much energy a specific food contains. Furthermore,
mass media send messages to public that there are some wars and conflicts in this world
because of the energy resources. To make things even worse, we use the term in our daily

discourse by saying: today, I am really tired and I don’t have energy to do any work!

30

Together, these circumstances likely shape the learner’s powerful and resistive
conceptualizations about energy.

On the other hand, the school formal science curriculum introduces students to a
totally different language about energy. In school, students learn about energy using what
Aikenhead called “decorative knowledge” about energy: kinetic energy is the energy of
motion; gravitational energy depends on an object’s mass and elevation; electrical energy
depends on the electrons movement; heat is a form of energy; energy is conserved,but it
could be transformed from one system to another, and the list continues. They also
supposed to learn how to calculate the amount of energy by implying the roles of energy
conservation and transformation. It is not a surprise, then, that students develop a long list

of energy synonyms and meanings such as force, power, momentum, and work.

Why should energy be in science curricula?

In spite of my previous introduction, energy is still considered as an important topic
that is virtually introduced in all science courses (Watts, 1983; Nordine, F ortus &
Kraj cik, 2005). According to Duit (1984), there are four basic aspects of the concept
energy. These aspects or “energy quadriga” (p.185), represent the four basic scientific
principles about energy: energy transformation, conservation, transport, and energy
degradation. Science educators as well as scientists in various fields draw on these
principles in order make sense and simplify complicated systems that include energy.
Duit & Haeussler (1994) have nicely summarized the historical roots of the term:
In the early nineteenth century, the science research program was much influenced
by the romanticist idea of intimate interrelation between the forces of nature.

Researchers investigated the change of electricity into magnetisms, magnetism into

31

electricity, heat into electricity, electricity to heat and many other phenomena. The
concept of energy may be viewed as the scientific way of expressing the idea of a
unifying, overarching romanticist force of nature. It is important to know that this
idea is still at the center of the contemporary science energy concept. When we
speak of energy today, we have to be aware that the changes are occurring at the
phenomenological level. The very idea of energy is that the amount of energy does
not change despite all changes at the phenomenological level. At the conceptual
level, there is only a change of energy’s manifestations that are usually called
energy forms (p.186).

I brought the previous quotation for two reasons. First it gives the reader a sense
about two of the main principles about energy: conservation and conversion. The second
reason is showing the abstractness nature of the concept. In other words, we observe and
experience the “phenomenological” aspects of energy and this adds more complicity to
the term (Lijnse, 1990; Trumper, 1993). The other two aspects of energy, transport and
degradation, are associated within the two others. Frequently, energy is manifest in
various places and thus it is viewed as ‘transport” or “flow” through systems.
Degradation is used as an indicator of energy behavior in closed systems to indicate that
if any process is taking place, such a chemical reaction or a moving car, the amount of
energy is conserved but the usefulness of energy declines.

There is a general agreement among science educators regarding the difficulty and
abstractness of the term energy (Duit, 1984; Watts, 1983; Lijnse, 1990; Duit & Haeussler,
1994). Frequently, studies on learners’ understanding of energy and other related topics

(e. g. heat, photosynthesis and carbon cycle) gave cumulative evidence about

32

rrrisunderstanding and confusion about energy. Furthermore, research shows those
students conceptions of energy is also influenced by the context or the place in which
they live.

One of the earlier long-term studies was done by Duit in Germany. He followed 300
of sixth graders, before they were introduced to physics course or any instruction about
energy, for four years. After they were taught about energy, he found that “students in
grade 10 were able to give a lot more examples for energy forms than in grade 6. But did
not mean that the students learned the idea of energy transformation” (p.191). He reached
two interesting conclusions: students usually don’t learn the basic aspects of the energy
concepts and they do not necessarily transform energy language as taught in school into
real life situations. However, using written tests, as in his case, may not give students real
chances to express their ideas. Thus, more specific findings about students reasoning
were found when learners were interviewed.

An example of these studies was conducted by Solomon (1983a, 1983b). In the first
study, the author tried to see how students use the concept using their own language.
Three main frequent findings were summarized by her: energy is associated with living
things, energy is associated with motion and activity, and finally energy is used to run
machines. Some of participants in the study have also expressed some concerns about
energy conservation. In the second study, Solomon argued that students’ informal
knowledge (e. g. daily life experiences) has too much influence on the way that they
reason about energy. Similarly, Arzi (1988) interviewed middle school learners and

asked them to explain what they think about energy when they see a box of chocolate or

33

yogurt. She found that participants have severe difficulties in using the school science
about energy to explain that situation.

Perhaps the study of Lijnse (1990), a Netherlands science educator, was one of the
most comprehensive ones in this regard. Following are her findings about students’ views
of energy:

0 Human centered energy. This is an example of the frequent occurrence of
anthropomorphic thinking, which with. respect to energy especially finds
expression in the idea of being energetic, having a lot of energy to be active,
or having lost all energy and therefore feeling exhausted.

- A depository model of energy. This idea suggests a notion of energy as a
source of power, as a cause of activity, and as such stored in objects that
possess energy.

0 Energy as an ingredient or as a product. In these cases energy is not so much
considered to be a cause, but rather a result of, or a reaction to, something.
Energy is found in food but only becomes active after you have eaten it. Or,
energy is stored in coal but manifested only after combustion.

0 Energy as an obvious activity. In this notion, energy is identified with the
presence of the activity, particularly motion. Energy is motion.

0 Energy is fimctional. In many situations energy is conceived to be a general
kind of fuel, as something that is especially useful and has something to do
with technology.

0 Energy is some kind of fluid. Energy is then materialized as a kind of fluid that

flows and may enter and/or leave something. (p. 574).

One of the most recent studies was done by Liu and McKeough (2005). They studied
the development of the energy concepts among elementary (as young as grade three)
through high school American students. They analyzed the 27 items of energy related

questions in the Third International Mathematics and Science Study (TIMS S). They

34

found that some younger learners’ views of energy were concentrated around the idea of
energy as an activity cause (the ability to do work). However, complicated aspects of
energy such as transformation, conservation, and degradation were not accessible before

high school level.

What can be done?

The previous examples about students’ reasoning about energy have consistently
reported learners’ difficulties about energy. The question becomes, then, what are the
areas that sound difficult for learners and how could reasoning fi'om data be advanced in
order to make this abstract concept more accessible for them? In fact, answering such a
question is not simple. In spite of that fact, there are many promising attempts in that
direction. These attempts depart from the assumption that both well—designed curriculum
materials and good instruction may make a difference.
Duit and Haeussler (1994) proposed integrating the everyday conceptions of energy
with the science formal concepts. From their point of view:
The main focus should be the understanding of energy issues in everyday life and
society as well as energy concerned behavior. If this is the focus, it has far reaching
consequences for the conceptual change we have to arrange. We have to reject the
idea of replacing students’ conceptions by scientific ones. If we were to try (it
would most probably not work anyway), students would learn a conception that is
at least partly contradictory to the life-world energy conceptions. Hence, it would
not be very useful in life-world contexts” (p.197).

The authors were proposing a new approach of teaching energy. This approach aims at

combining issues of STS (Science Technology and Society) and constructivism. The

35

believe that starting with students’ ideas and ways of talking about energy, which they
called ground level, and moving towards more theoretical aspects of energy could be
effective. However, their approach did not introduce sufficient suggestions in terms of
curriculum materials or teaching strategies.

A recent study that was conducted by Nordine and his colleagues (2006) concluded
that curriculum materials play essential role in developing learners’ concepts about
energy. The authors stated, “We believe that the right curricular design can help students
develop an understanding of the rather abstract energy concept. By providing them with
adequate supports, we feel that middle school students can exhibit the kind of energy
conceptions that many insist out of their reach (p.15). After interviewing participants
multiple times, most participants have shown considerable progression towards energy
conversion after the unit enactment. Yet, this study did not tell us anything about the

pedagogical aspects that were developed through the unit enactment.

This study:

Looking back at the previously stated literature, one may realize the interesting work that
has been done about students’ reasoning fi'om data or about their concepts of energy.
However, I noticed that many of these studies, especially those about students’ reasoning
from data were de-contextualized. In many cases, participants were introduced to ready-
made data rather than primary data (data that is collected by them). Furthermore, the ‘
previous research focused basically on learners’ data analyses skills with less emphasis
on the scientific knowledge. In other cases, research about energy focused on the
students’ concepts and ideas about energy without giving enough attention to the

pedagogical aspects. This research is an attempt to combine both the inquiry skills, such

36

as data analysis and finding patterns in data, with the context of teaching and learning

energy.
Chapter Summary

Although science stakeholders practice data analysis differently (i.e., depending on
their purpose and context), yet it is a powerful tool that might develop our scientific
knowledge and abilities to make evidence-based decisions. Considerable research has
shown that a limited portion of the canonical school science is transformable fiom formal
settings into science-rich places. In today’s world, average citizens find themselves
involved in scientific arguments about scientific issues such as global warming, food
quality, and medication efficiency. Consequently, they need sort of ability, which should
be guaranteed through scientific literacy, to reason about various types of data and to be
able to engage in evidence-based argumentations as part of their citizenship.

Previous research has frequently reported that energy is an abstract concept and
students have many difficulties in gasping its scientific principles. There are some new
trends in teaching about energy that take into consideration cuniculum materials,
teaching methods. The recent study aims at focusing on students’ reasoning about data

while trying to develop models about energy.

37

Chapter Three

Methodology

In this chapter, I describe the following three related areas about the study procedures
and methodology: the context of the Study, data collection and the research instruments,
and the data management and analyses procedures. Under the study context title, I will be
discussing the IQWST project, participants, and the study site. Then, I will explain the
tools of the study and the data sources with particular attention to the observations and
clinical interviews. In the last part, I explain the data analysis techniques and how the
research questions were answered. Finally, I summarize this chapter with some details
about the methodological considerations and decisions that I deliberately made while

collecting and analyzing my data. I will start by describing the context of the study.
The context of this study

0 The IQWST project:

This study is a part of a long-term project called IQWST (Investigating and
Questioning our World through Science and Technology). The main goal of the IQWST
project is to desigr research-based science curriculum materials for upper elementary and
middle school learners. In this project, many goups of researchers have been working
towards developing curriculum materials in biology, chemistry, earth science, and
physics. The IQWST approach emphasizes on common “big pictures” among those
disciplines. In this context, big pictures mean central scientific concepts that have

powerful overlap among many areas.

38

The physics team has been working on developing three units: the light unit light for
sixth gade, the energy unit for seventh gade, and lastly the force and motion unit for the
eighth gade. Energy is one of the “big ideas” that is supposed to be addressed across
gade levels within the same content, which means that energy will be addressed within
the three physics units, and also across subject matters such as biology and chemistry. In
that sense, IQWST approach emphasizes on developing cuniculum materials that

reinforce interdisciplinary nature of science.

0 The Energy Unitj:

The Energy Unit is consisted of fourteen lessons that are organized around four
central questions. Organizing the unit around driving questions aims at keeping learners’
attention focused around an interesting scientific phenomenon about the main topic:
energy (Fortus, 1997). For example, the first question, “why do some things stop while
others keep going? ” introduces students to many scientific concepts and principles that
are related to kinetic, gavitational, and elastic energies. Other lessons focus on other
types of energy such as chemical, electrical, and thermal energy and how it might be
transformed from one type to another. Each of the fourteen lessons is built around two or
three activities that require hands-on experience. Additionally, the activities are desigred
in a way that requires the science teachers to adopt guided inquiry teaching.

There are many assumptions that could be noticed in the IQWST cuniculum. These
assumptions reflect the curriculum developers’ beliefs about scientific knowledge,
teaching, and learning. One of these assumptions is the importance of the hands-on and

inquiry-based investigations. Departing from this point, most of the energy unit lessons

 

3 Detailed descriptions about the Energy Unit are available in Appendix A

39

include some of these activities. While conducting these activities, students make
predictions, collect, organize and analyze data in order to make evidence-based
arguments and draw conclusions about energy. Another vivid assumption is the
importance of integating the formal cuniculum and the learners’ intuitive knowledge.
There are many examples in the lessons to reinforce such an approach: readings,
demonstrations, and homework assigrments. Furthermore, the IQWST curriculum
materials take into consideration the teacher’s content knowledge. The “teacher
backgound knowledge” readings, to name some examples, were explicitly addressed to
promote the idea of considering the teacher as a learner. The general assumption in
IQWST, I believe, is that engaging students in those well-organized activities, in addition
to support teachers with educative materials, will help learners gasp new concepts and
processes about energy. In spite of all these assumptions, one should keep in mind the
context of the classroom and the complicity of the enactment process. Learning outcomes
and classroom environments, even with best materials and expert teachers, are

unpredictable.

0 Study Site:

This study took place at one of the American suburban independent schools in mid
Michigan. Although I agee with Stake’s (2005) perspective that in qualitative research
we don’t usually have a long list of choices, this site was convincing for many reasons.
At the top of these reasons was the logistic support that the IQWST project has received
from the school staff and the science teachers. The head of the school stated its vision as
“a community that values learning as a life-long process and teaching as an art.” In

addition to this philosophy, there are many interesting characteristics about the school

40

that should be reported here. First, the schedule of the classes was dynamic, which meant
to have extended lessons in specific days. Additionally, the average class size was around
15 students. These classes were well-equipped in terms of technology and scientific
apparatus. Another reason- for selecting this school was the teachers’ willingress to enact
the Energy Unit. The two teachers were enthusiastic and highly-committed to integate
their teaching with research. In fact, having researchers in their classes is not an unusual
experience for them. Both teachers are experienced and they have been teaching science

for more than 25 years.

0 Sample of the study:

Students from two eighth gade sections participated in this research. The total number of
students in the two classes was 30 students: 12 in the first section and 18 in second one.
The majority of those eighth ‘gaders were females; only 7 boys were enrolled in both
classes. However, for the interview purposes, which will be described later, I selected
only 6 students (three from each class). Taking students’ academic level into
consideration, I cooperated with the teachers to select 2 students from high, medium, and
low levels (one participant of each level from each section). Having students from
various levels was meant to provide the study with a reasonable range of data in terms of
students’ skills and abilities. All participants were informed in advance about the
interviews and that their participation was optional, they all ageed to be part of this

research.

41

Instruments and data sources

A) Classroom Observation:

For the purposes of this study, I depended on two main data collection tools: classroom
observations and clinical interviews. Keeping the research questions in mind, I decided to
observe the lessons and activities that were explicitly designed to address data analysis
aspects and skills. In fact, I ended up observing more than 80% of the Energy Unit
enactment. I found myself following what Johnson (2002) has called the productive
diversions of observing. These rich opportunities were fruitful as two expert science
teachers have enacted the same lessons in the same building. Observation was very
important for this study, it served as a tool that enabled me to make sense of the
enactment of the IQWST curriculum. Such experience could not be reached otherwise.

Technically speaking, the classroom observation was such a helpful tool for
achieving two goals. First, reporting on students’ engagement in the data analysis
activities. Without observation as part of the research methodology, it would be difficult
to know how students have interacted with scientific data and how they made sense of it.
Furthermore, the classroom observations were productive venues in developing the
interview protocol. The second goal of these observations was to report on how were the
data analysis tasks orchestrated by the two science teachers in order to answer the second

research question.

B) Field Notes:

In addition to my silent camera, I continuously took field notes fi'om every class.

These notes were short descriptions about the class flow. As a member of the IQWST

42

developer team, who was familiar with what’s going on, I was indirectly involved. Thus I
was a partial participant even without direct involvement in the class. While taking notes,
I paid more attention to the students’ tasks, questions, and the way they communicated
with each other and with their teachers about data. For instance, the way in which
students analyze data for a specific activity, their acceptance or rejection for a pattern,
and their conclusions, were reported. This was a helpful tool to remind me about some
interesting moments in the class flow in order to observe them again for further analysis.
However, one should keep in mind that “our own perspective color what we see when we
look” (Hatch, 2002, p. 79). Consequently, “each [observation] is shaped by the observer
and what she or he brings to the task, the situation and its conditions for observing, and
what is being observed” (W eiland, 2005). Although those educators were reflecting on
observation in other qualitative areas such as ethnogaphic research, I believe that their

comments are valid here as well.

C) Clinical interviews:

Collecting data through clinical in-depth interviews was necessary for this study
purposes. This tool was the main source of data to answer the first research question.
Furthermore, it helped me synthesize a comprehensive picture about learners’ reasoning
about data in different contexts (e. g. classroom and interview settings). These interviews
were held towards the end of the Energy Unit enactment.

After arranging the time of the interviews with the science teachers, each of the six
participants was interviewed at the school site. At the beginning of the interview, I
explained its purpose and how the data that I was collecting would be used for research.

In order to remind the interviewees about the original activities that they have earlier

43

conducted in the class, I used the classroom projector to help them visualize the situation.
The interview protocol included many pictures and tables that were related to the
questions. All interviews were audiotaped for further analysis. The time of each interview
was varied between 20-30 minutes. The length of the interview depended on the
discussion flow and how participants interacted with data. Next, I explain the interview

questions and scenarios.

D) The interview protocol:

The interview protocol has been modified many times so that it meets the research
goals. For instance, the initial version of interview questions focused on data that were
collected by learners while conducting energy-related activities. However, additional
questions and scenarios were added to that version in order to include intuitive
experience about energy. The final version of the questions and scenarios consisted of
four parts. Some of these questions addressed direct experiences from the classroom
activities. The goal of this kind of questions was investigating participants’ reasoning
from data skills. By doing so, I was able to have another deeper take on students’
classroom discussions about data analysis. The other type of questions was more general.
I desigred some energy scenarios that represent interesting aspects of energy transfer and
processes. These questions were meant to see how students reason about energy from a
broader perspective. Following is a detailed description of the interview scenarios and
questions.

The first question aimed at exploring students’ reasoning from quantitative data set
that was represented in a table. Furthermore, this numerical data was directly related to

two of the activities that students have conducted while investigating the factors that

44

influence gavitational and kinetic energies. The goal of this set of questions was
addressing the following data analysis skills: 1) identifying the independent variables,
such as mass and elevation, and correlating them to energy (gavitation and kinetic), 2)
finding a pattern in the data, 3) drawing apprOpriate scientific conclusion, 4) comparing
two data sets and identifying problems in data, and 5) presenting energy conversion
diagams (ECD). However, these skills were not restricted to this question. Many of them
were included in other questions as well.

Unlike the first set of questions, scenario B represented indirect data about energy.
However, it was relevant to chemical and thermal energy concepts, which have already
been addressed in the curriculum. The scenario was presented by a picture of a fire place
in an apartment. A set of follow up questions then were asked in order to see whether
participants were able to transform the formal in-school science to out-school contexts
(i.e. integating formal with intuitive knowledge). Additionally, this scenario aimed at
exploring students’ skills in analyzing scientific data that was represented in gaphs.

The third set of the interview questions, scenario C, was directly relevant to the
elastic energy conceptions. It was desigred as a result of the classroom confusion in order
to have a deeper look at the participants’ reasoning. My observation of that activity has
shown that this scenario was problematic from learning and teaching point of view. Thus,
it has been re-introduced again in the interview. Like in scenario A, this set of questions
addressed the following data analysis skills: 1) reasoning about the investigation
variables, 2) treating gaphical data and representing ECD, 3) making evidence-based

scientific arguments, and 4) exploring learners’ reasoning about anomalous data.

45

Finally, the last set of questions aimed at exploring learners’ general reasoning about

energy. Like the second scenario, scenario D starts with a picture of a car at a gasoline

station. The questions in this scenario focused on: energy conversion by explaining how

the gasoline was consumed in the car, representing the Energy Transformation Diagam

(ETD), and tracing energy through the system. Using a fire place at home or filling a car

with gasoline is “intuitive experiences” about energy. Learners are familiar with them,

but they are not necessarily correlating them with scientifically acceptable reasoning (See

appendix B for more details about the interview questions).

The following table summarizes the four scenarios with a brief description of the

targeted data analysis skills. Although the scientific knowledge varies from one scenario

to another, one should expect that many data analysis skills are common among them.

Table2: Data analysis skills and their distribution among the interview questions.

 

 

 

 

 

how students reason about energy
and its conversion.

— The question calls for transforming in-
class experience into general models.

 

S . Reasoning from data skills
cenarro Description of the question
addressed in the scenario
- Data in this question is represented in l- Reasoning about variables of
a quantitative form (table). The table investigation.
mainly focused on gavitational and 2- Realizing patterns from a data
A. kinetic energies. table.
Gravitational - The variables in this question are mass 3- Comparing (evaluating) two sets
and Kinetic and height. of data.
energy - Learners are supposed to conclude 4-Representing ECD; energy
how elevation and mass affect conversion diagam
gavitation and kinetic energy. 5- Making predictions from current
data.
- This scenario is represented by a 1- Realizing the pattern of a graph.
picture of wood in the fire place in an 2- Reading a gaph locally and
B. Wood in apartment. globally.
fire place - The goal of this question is exploring 3- Transforming local

understanding to a general
model-based reasoning.
4- Drawing conclusions from data.

 

46

 

Table2 (Continued).

 

 

 

- This question is about the elastic energy 1- Reasoning about variables.
C. Springs’ in springs. It is designed to extend the 2- Realizing patterns in
motion (elastic students reasoning about anomalous gaphical representations.
energy). data. 3- Representing ETD.
- This is direct experience about a similar 4- Drawmg evidence-based
activity that was conducted in the class. arguments
D. “A car’s - Qualitative data that is represented by a 1- Reasoning about energy
gasoline tank picture of a car filling gasoline. conversion in the system
being filled with - Intuitive experience and indirect (Model-based reasoning
gas” scenario. association with the classroom from qualitative data).
activities. 2- Representing ETD, energy
transformation diagfls

 

 

 

 

Data Management and Data Analysis

This study is a qualitative research that focused on specific data analysis skills as
they were addressed by the interview protocol. The collected data was analyzed using a
special technique in order to answer the research questions. The ultimate goal was to
synthesize an exemplar table and examples among students’ reasoning patterns that were
identified by the clinical interviews and the classroom observations. In the following
sections, which I called data management, I explain the process of the data analysis. It

includes organizing, filtering, and analyzing/synthesizing aspects.

0 Analyzing data from interviews:

The main purpose of the clinical interviews was to report on how middle school learners
reason about specific sets of scientific data about energy. The previous table, table 2,

connects the interview scenarios with the data analysis skills among them. Thus, I used

47

that table as a guidance to classify students’ responses. Following is the detailed

procedures that I did:

1.

Transcribing the interviews data: the first step was transcribing the six interviews,
I did that process and this was helpful in terms of making more sense of what the
students have said. The interviewer questions, the researcher, and the students’
response were colored differently in order to manage them for further analysis.
Summarizing responses: The next step was summarizing each of the six
interviewee’s responses according to the data analysis skills as they were
proposed by each of the four scenarios (1 called the scenarios A, B, C, D). The
first scenario, for example, was divided into the following sub-questions: Al
(Reasoning about variables- mass & elevation), A2 (Finding a pattern in data), A3
(Connecting data with energy), A4 (drawing Energy Conversion Diagam (ECD)
model), A5 (comparing goup A’s and B’s data), and A6 (Making predictions
from recent data). These sub-skills were the columns of one of the four excel
sheets (one sheet for each scenario). The rows of that sheet represented the six
cases. The result of each scenario was a matrix that contains all response in each
sub questions. For instance, the first matrix contained 36 cells, each one of them
has the actual response of the interviewee in that sub-question.

Cross comparisons and coding: The next step was the making the cross
comparisons between cases. The goal of such comparisons was to find a general
pattern among participants’ reasoning skills. There were two kinds of
comparisons: the first one focused on students’ data analysis skills. These skills

were distributed between the four scenarios. Thus, all items that were focusing on

48

the same skill (e. g.. reasoning about variables or finding patterns in data) were
classified together. To make sense of theses data, I used a special coding schema
to classify the responses according to their scientific appropriateness and
according to their ranking if compared with each other’s. These codes are

explained in the following table. An exemplar of that process is shown in

 

 

 

 

 

appendix C.
Table 3: Participants data analysis skills codes
Code Kind of response

3 Participants response is accurate and complete

2 Response is almost accurate, one part is missed

1 The response is partially accurate, more than one

part are not included.
0 The response is irrelevant to the question.

 

 

 

 

Taking the classified skills together, I generated a table that contains all cases and
each response codes. That table was then used to report on students’ data analysis
skills and also to make comparisons among the cases.

4. Comparisons of participants ' reasoning about energy: The second type of
comparisons was focused making on qualitative comparisons between the
students’ response that mainly came fiom the second and the third scenarios. All
responses were summarized in an exemplar table. To find a general pattern among
responses, participants’ similar responses were categorized together. I then
followed the work of Duit & Haeussler (1994), Heejun (1994) and Anderson

(2008) in order to give the participants response special codes. The following

table summarizes these codes.

49

Table 4: Participants’ reasoning about energy codes.

 

 

 

 

 

 

 

 

Code Participant’s reasoning about energy
EL Energy is needed for life or activity (vitalism)
EA Energy is an enabler to do things (agency)
EM Energy is a synonym of momentum or force (motion)
EI Energy is an ingedient or a product (stored in or a cause for)
EF Energy is some kind of fluid (materialized)
MBR Model-Based Reasoning tendency.
F DC Force-dynamic Causation

 

 

 

 

The results of this coding process were used to report on the general patterns among

participants in terms of their ideas about energy.

0 Analyzing data from classroom observations:

The data that were collected fiom the classroom observations was a supportive tool that
has two goals. First, it was used to report on teachers’ strategies mediating the dialogue
between students and data. Secondly, they were also used to track students’ reasoning in
both the classroom discussions and the participants’ responses in the interviews. For
practical and simplification purposes, I decided to focus on the enactment of three main
activities of the Energy Unit. These three activities were selected because they are
directly related to the interview scenarios. Then I selected the teaching episodes that are
relevant to the data analysis or reasoning about energy. These periods were varied from 5
to 10 minutes in the videos of the intended classes.

A similar technique was used here again. I first transcribed the focus episodes and
then they were colored differently to separate the teachers’ talks from the students’

responses. The dialogue between teachers and students in this case was more open than

50

the discussion in the interviews. In this case we have the entire class rather than one
participant. Thus, students’ responses here don’t necessarily refer to the same students
who were interviewed, but they still from the same section. Furthermore, the discussion
in these episodes was not continues (i.e. it was distributed mainly before, within, and
after conducting the activities). All these new circumstances were taken into
consideration. while analyzing the data from the classroom episodes.

Again, the students’ reactions were classified together and were divided into two
related areas: reasoning about data, and ideas about energy. I implemented the previous
coding techniques (tables 3 & 4) whenever this was possible’. Furthermore, I linked the
interviewees’ reasoning in the class with their reasoning in the interviews.

The teachers’ talks were coded differently. After separating students’ and teachers’
talks, I classified the teachers ideologue according to period time of the activity: a) before
conducting the activity (treating variables and dealing with learners’ predictions), b)
conducting the activity (measurements and data consistency), and c) after the activity
(drawing conclusions and treating anomalous data). The following table shows the codes
that were used to find the pattern of the teachers’ practices. I used these codes in order to
decide whether this aspect were available in the teacher’s dialogue or not. The results
fiom these periods were used to introduce descriptive examples about the teaching

aspects in this regard.

 

4 Students’ reasoning in the teaching episodes was not necessarily similar to their responses in the

interviews. Yet, their discussions, predictions, and conclusions about data and energy were used
for coding.

51

Table 5: Coding the teaching practices that are embedded in reasoning fi'om data.

 

Code

Description

Central pedagogical question

 

Variables of investigation

0 How do teachers address the variables of
investigation?
0 How do they respond to learners’

predictions, misconceptions?

 

PF

Patterns Finding

o What are the teachers’ decisions when
patterns are apparent?
o How conclusion and evidence-based

arguments were discussed?

 

Anomalous data

0 When data is anomalous, what are the

teacher’s decisions?

 

 

 

Model-based reasoning

 

o Are there indicators of teaching strategies

that ask for model-based reasoning?

 

Chapter Summary

This study aimed at exploring middle school learners’ reasoning from scientific

data while conducting activities about energy. This unit was desigred by a goup of

science education researchers in a project called IQWST (Investigating and Questioning

our World through Science and Technology). Two classes, around 30 students,

participated in this research. Data were collected using two main methods: interview and

classroom observation. A focus goup of six participants was interviewed for in-depth

follow up investigation using a specific protocol.

In this study, many decisions were deliberately made for economical and pragmatic

reasons. For instance, having six students in the clinical interviews was an important

52

 

decision from the time management perspective. This convincing sample was “good
enough” to give an example about their reasoning from selective stets of data.
Interviewing students through their school day should take into account their
commitments to other classes besides science and the school schedule. Furthermore,
traveling a long distance in order to hold these interviews on time was an issue for me as
a researcher. Thus, the idea was to minimize the number of interviewees and the
interview times without influencing the richness of the collected data.

Similarly, the classroom activities that I have selected were relevant to the interview
questions in terms of data analysis skills and the scientific content. Although the purpose
of these three focus classroom activities was not to evaluate the teachers’ performance or
the IQWST curriculum, they were analyzed as supportive data in order to link the

learning and the teaching aspects of data analysis.

53

Chapter Four
Findings & Results

The main goal of this study was to report on middle school learners’ general abilities
in data analysis skills while learning about energy. In this chapter, I will synthesize the
study results from the clinical interviews and the field observations. The study findings
will be organized around four major highlights. The first area is about learners’ skills in
data analysis from a general perspective (i.e. how they reason from data sets). In this
case, I will be giving more emphasis on the analysis skills and less focus on the concepts
that have something to do with energy. The second kind of results is about participants’
reasoning from data with specific attention to energy and the learners’ conceptualization
about it. In the third part, I will summarize the pattern of the six participants’ responses in
terms of data analysis skills. The goal of this area will be an attempt to see if participants’
are able to reason about energy using model-based scientific reasoning (as a contrast with
the force-dynamic causation). Finally, I will report on some pedagogical aspects that
emerged from my observation of the energy unit enactment in order to correlate that with

the other areas about data analysis skills.

Findings about learners ’ data analysis skills

This area included the participants’ reasoning from data skills that were addressed in
tables 2 in the analytical chapter in order to answer the first'research question (RQl).
Students reflected different levels of performance in this regard. In this part, I report on
participants’ abilities in realizing the variables under investigation, finding a pattern in

data and reading data locally and globally, drawing conclusions and evidence-based

54

argumentations about scientific phenomena, and treating various types of data

representations.

A) Reasoning about investigation variables:

Realizing the variables under investigation is essential because other data analysis skills,
such as realizing the pattern and making accurate conclusions, depend on it. This skill
was explored throughout three sub-questions of the clinical interview scenarios (see for
examples questions A1, C1, and C2). Findings in this regard showed that most
participants were able to reason about the investigations variable at the local level (i.e., the
direct observations that learners made). Being able to reason about those variables locally
means that they can identify the relationship between variables as cause and effect (e.g.,
more mass implies more smoosh on the Play-Dob). However, when it comes to reasoning
scientifically about correlating those variables to scientific concepts, such as gavitational
or kinetic energy, a few of them were able to give adequate justifications. In other words,
students tended to correlate variables to observations on the local levels, such as the
“smoosh” factor, rather than general models. The cross comparisons in this regard
showed that only one interviewee was able to give a complete scientific explanation
about the variables of investigation and to also correlate them to gavitational and kinetic
energiess. Following are some examples of the participants’ response when they were

asked to explain some variables.

 

5 Gravitational energy (GE) depends on two factors: mass (m) and height (h) and it could be
mathematically represented by the equation GE = mgh, where g is the gavity acceleration
constant. Similarly, kinetic energy (KE) depends on mass (m), and velocity (v). The mathematical

representation of kinetic energy is KE= l/2 mv2 . In IQWST, these mathematical equations were

deliberately avoided as the developer team believed that introducing them will be difficult for
learners at this age.

55

In the first scenario, which includes dropping different masses from different
elevations, I asked participants to reason about the mass and height as variables. Chandler
is one of the students who explained that sufficiently.

Me: Why do you think that we have dropped five diflerent masses from two diflerent

elevations in this activity?

Chandler: I think that we used 5 diflerent masses to see how mass aflect the Play-

Doh block height (the “smoosh ”), mass was the only variable. We also dropped

them fiom two difi'erent elevations to see how elevation aflects how much energy we

have.
In our follow up discussion, Chandler explained that the “smoosh” factor is important
because it is an indicator of kinetic energy. Interestingly, most students were able to point
to the “smoosh” of the Play-Dob, but rarely did they connect it with either gavitational
or kinetic energies. For instance, Maddy, one of the high level students according to her
school scores, reasoned about the variables “logically.” When I asked her the same
question, she said: “I think that the logical answer and the logical claim is when you drop
it [the book] with more mass and with more height then it will have more smoosh.”
Again, talking about the “smoosh” factor, which refers to the difference between the
Play-Doh cube’s height before and after the collision, was a typical response for most
participants. This is an indicator of the participants’ local reasoning about data.
Connecting direct observations with the gavitational and kinetic energies means that a
learner has developed an advanced ability of reasoning that connects direct observations

with scientific conclusions.

56

Similarly, in another scenario questions (i.e., C1 &C2), students were also required
to reason about the investigations variables. In this activity, the dependent variable was
the height that a spring jumps as an indicator of how much elastic energy it has. That
distance depends on two factors: the spring hardness, which usually is called K by
physicists, and the distance of compression (X)6. Looking at students’ explanations, one
might see that they were confused about the independent variables. A reasonable source
of this confusion might be their pre-assumption that “softest springs can go higher
because they are easier to compress.” Blake is one of the students who insightfully
reasoned about the variables and yet has such an assumption. This is a part of our
discussion:

Me: Why did we use three springs? ‘

Blake: to find if the um, the hardness of the spring aflected the elastic energy of it.
Me: Why do you think that the teacher asked you first to compress the springs for 1 cm
and then for 2 cm?

Blake: to see if also the distance of the compression in the spring aflects the elastic
energy, the higher [it jumps], the more elastic energy [it has].

Me: what did we measure in this activity?

Blake: we measured the distance.

Me: which distance?

Blake: Um, the spring jumps.

Me: Why did you measure distance?

Blake: as evidence for our claim that soft springs jump higher than hard springs and
that the more you compress, the higher it goes.

Although Blake’s last comment about the relationship between hardness and distance

is inaccurate, he showed a sufficient understanding of the variables involved in this

 

6 Theoretically, the stored elastic energy in a spring that has been compressed for X
distance, and has a K factor is 1/2 KXZ.

57

activity. For example, be specifically mentioned the hardness of the springs (i.e., the K
factor) and the distance of compression and their relationship with elastic energy. Most
other students were confused about the hardness of the spring (K) and the distance of
compression and thus they were not able to differentiate between the two variables. Like
in the “smoosh” factor in the previous question, most students in this scenario mentioned
the importance of the distance that a spring jumps, but without connecting this indicator
with the elastic energy.

Looking closely at the results, one may conclude that participants from various
academic levels have reasoned about the investigation variables differently. Whereas
high and middle level students were able to reason sufficiently about the variables or they
missed one important part, lower level students introduce naive explanations. For
example, when I asked Amma to explain why we dropped different masses from different
elevations she said, “Probably to get different results, because if like you use the same
ones, you will have the same stuff the whole time.” Such a response was also found in the
discussion with Austen while explaining the elastic energy variables. Justifying why we
used different springs, he said “Um, each of them are going to give different results.
Because some of them [the springs] are lighter than the other and some are heavier, some
is harder to compress.”

The previous examples suggest that some participants’ were confused about
separating dependant variables from the independent ones. Such confusion about the
variables under investigation and their association with the central variable of the activity
might negatively influence learners’ reasoning in other relevant areas such as realizing

patterns in data. Without gasping the relationship between investigation variables, which

58

one is the cause and which one reflects the effect, realizing a pattern in a data set rrright
be impossible. In the following part, I will report on results that have to do with this

particular area.

B) Realizing patterns in data:

Seeing the pattern in scientific data is an essential skill. Not only because it enables
learners to understand the relationship between variables, but it also helps them draw
sufficient conclusions about their inquiry. Final results of this research showed that
students’ skills in terms of finding a pattern in data have two main characteristics: relying
heavily on the mathematical consistency and the authority of their intuitive knowledge.
While the first refers to the quantitative nature of the used data sets, the latter has
something to do with participants’ responses to anomalous data that contrasted their pre-
assumptions. In this study, participants were required to find a pattern in various data
sets. For instance, in the first scenario data was presented in a table (see the data table in
the first scenario in appendix B) that shows the relationship between independent
variables (i.e. mass and height) and the smoosh factor as an indicator for gavitational
energy.

Four of the six participants expressed their concerns about the mathematical
consistency in the data table. Mathematical consistency here means a constant rate of
change (e. g. a constant change in the “smoosh” factor as a result of increasing the mass
for one bound in each trial). I noticed that if students did not see that rate of change

between variables, then they likely will not consider a pattern in data7. For instance, when

 

7 In the first scenario, the general pattern was about the relationship between the independent
variables (mass and height) and the dependant one (smoosh factor) as the indicator of
gavitational and kinetic energy.

59

I asked participants if they see a pattern in the data table in the first scenario, most of
them focused on specific trials that they considered “inconsistent” with the rest of the
outcomes in the table. Maddy, one of the outstanding students, said that “I don’t see a
pattern except for in two [trials] of them; in the first elevation and then in three for the 20
inches [height].” A similar inference was found in Amma’s response: “Well, not really
because the first column has the same thing, I don’t really think that there is consistency.”
However, Chandler was the only student who realized the pattern and explained that by
saying, “Yes, I see that. I think that if you increase the mass, the smoosh factor increases,
and I think that the 20 inches elevation generally has more smoosh factor than the 10
inches.” In other words, Chandler was reasoning about a general pattern in the data rather
than concentrating on local specific points.

Finding the pattern in data depends also on the type of the representations. Results of
this study show that realizing the pattern in gaphical representations, for example, was
much easier than finding the pattern in a table. Most of participants realized the pattern
that was gaphically introduced to them about the relationship between the number of
burned logs of wood and the room temperature. One of the students concluded that
“There is a pattern in the gaph; I think that the Elm slope stays steady: it always has the
same consistency going up. For the Oak wood, it stays the same till five [logs of wood],
after that it has bi gger slope and it burns much harder.” Mathematically speaking,
realizing a pattern in gaphs was more accessible to learners than tables since gaphs
usually-show the general trends, whereas tables show details about particular points.

In addition to their tendency to use mathematical reasoning while finding patterns,

participants in this study have also relied heavily on their intuitive experiences. In other

60

words, when learners have some experience about data, they trust their own theories
rather than the pattern that they see. For example, when students were asked if they see a
pattern in a gaph that represents different kinds of springs and the distance they travel,
all students were able to see the pattern. However, when it comes to correlating that
pattern to scientific conclusion about the spring hardness, most of participants gave a
wrong explanation. A typical response was similar to Maddy’ 5, “Well, for each spring the
distance goes down, I think that the hardest one is nmnber 5 because the hardest one
should spring the least amount of distance, and number 1 is the softest because it jumps
most.” Only one student has drawn the appropriate conclusion which is the opposite of
what Maddy said. In fact, reasoning about this particular question was also influenced by
what happened in the class when students conducted the activity. A safe conclusion that
one might draw here is the difference between scientists’ and learners’ ways of seeing
patterns in data. Scientists usually trust their data and consequently they don’t consider
their personal experiences. In the classroom settings, on the other hand, finding patterns
by students is usually governed by their personal experiences.

While reasoning about patterns in data, some students focused on comparing the two
goups’ results (goup A dropped masses from a10-inch elevation whereas goup B
dropped them from a 20-inch elevation). From Kit’s perspective, “In the first one [goup
A’s data] yeh, because it changes a little bit each time, but for goup B it [the smoosh]
stays the same for the 3 and for the 4 bounds, and it does not seem to make sense.”
Along the same lines, when participants were asked to compare the results of the two
goups, all of them were able to identify at least one “illogical result” fiom the three that

were included in the data table. Surprisingly, none of the students accepted the results as

61

they were, in spite of the fact that it’s experimentally possible to have such results due to
human or instrumental errors. In order to fix these problems, the interviewees suggested
making more trials in order to have accurate results that fit with the pattern. Although
identifying experimental error was not an essential goal of this study, comparing data sets

could be seen as part of the data analysis skills.

C) Drawing conclusions and making evidence-based arguments:

Central to inquiry-based learning and data analysis is the learners’ ability to draw
appropriate conclusions and to make evidence-based arguments. Noticeably, high and
middle academic level students were able to draw sufficient evidence-based arguments
fiom data. For example, we asked participants what they might conclude from the gaph
that shows Oak and Elm burning, Kit concluded that “Oak will probably be more
efficient than Elrm because temperature increases more each time for the same amount of
logs. It’s probably more efficient to burn like two logs of Oak than three logs of Elm.” In
a similar question but in a different scenario, she gave another example when she
connected a claim with evidence. Reflecting about evidence that she used for elastic
energy she added “um, we measured the height that the spring went like when it went
over the ruler. We did that to see how much energy it has when it's going up and coming
back, the higher bounce then probably the more energy.”

Students’ abilities to draw conclusions varied from one context to another. For
example, when the conclusion was about activities with “transparent data,” they did not
have troubles connecting claims with evidence. Example of that kind of data is the
gavitational and kinetic energies. In such cases, most participants were able to draw

appropriate conclusions and predictions. It seems that in this kind of activities, whether a

62

learner relied on force-dynamic causation or model-based reasoning, he can come with a
sufficient conclusion. In other data sets, such as the spring or the car at the gasoline
station scenario, there is a need for more advanced reasoning (i.e., model-based
reasoning). These stances calls for realizing more complicated scientific laws and

principles about energy conversion.

Students ’ reasoning about energy

Results of this part mainly come from the second and fourth scenarios because they
were represented in the form of qualitative data. Furthermore, unlike data in the rest of
the interview questions, these two scenarios were deliberately desigred to see how
participants reason about intuitive data. That is, data that are indirectly related to energy
as part of the formal curriculum but still valid general examples about energy. The most
frequent findings that can be reported fi'om students’ responses in this regard were:
energy is associated with living things; energy is an agent that helps us do something,
energy is something that is similar to matter (fluid) that moves through conversion. Next,

I will discuss these areas with examples of students’ responses.

1. Energy is associated with living things:

In the second scenario, participants were asked to reflect on a picture that shows
wood in the fireplace. Frequently, students thought that energy is associated with living
things (e. g. wood had energy when it was alive). For instance, when I asked participants
if they think that wood has any type of energy, most of them said that it has a small

amount of chemical energy stored in it. From their perspective, it should have more

63

energy when it is part of a living tree. Results suggested that associating energy with
living things was common among students regardless of their academic levels. However,
high achiever students seemed to have more access to more developed reasoning in
respect of tracing energy and explaining its conversion through systems. Following is a
short portion of the conversation with Amma, while discussing that scenario:

Me: [after introducing the picture of the fireplace] : do you think that wood has any
type of energy?
Amma: Um the wood, maybe there is a little bit of chemical energy.
Me: Why a little bit?
Amma: because the, [silence]
Me: if there is any energy, could you please tell me where from did it come?
Amma: Um, the heat it comes fi'om the fact that the wood used to be alive, like
Me: so, once it was alive it has energy?
Amm: yeh, once it was alive it has to have energy to grow, but when you cut it there
probably still some energy left, but not a lot.
Me: could you please explain to me any energy transformation when you burn
something like wood?
Amma: probably, those to be the wood and then the fire is both chemical and
thermal energy, and as it is just burning it gain, like it gain the thermal, as it burns
it gain thermal but it might loose the chemical.
A similar response was found in Austen’s reasoning, in his words “wood does not have
energy at all because it is just passed away; not doing anything else it is just staying

there.

However, a few students seem to have more access to sophisticated reasoning,
especially in terms of tracing energy and scientific explanation. The term sophisticated
here means the ability to tell an accurate scientific explanation in away that reflect sort of
model-based reasoning. For example, one of the students showed an acceptable scientific

narrative explaining that:

64

Wood has chemical energy, which is like potential energy. It originally comes from
the Sun, the Sun gives this energy to the leaves which perform the photosynthesis
and all that stuff to make the tree better gows, and then later when the tree is cut

down, these blocks of wood still have energy stored there.

Another student thought that energy has been transformed to the tree by some carriers;
she explained that by saying “I think it [the wood] has chemical energy that comes fiom
its surroundings. It has to gow and it needs energy, so when you cut it, it still has some
energy that comes from the Sun, soil, the surroundings.” The previous quotations show
also that students see energy as something that could be gained, lost, or travel through

systems. This might reflect their confusion between energy and matter.

2. Energy as enabler to do things:

In addition to their ideas about energy as something that is associated with living
things, students tend to picture energy as a tool that enables us to do things. This could be
vividly concluded from participants’ explanations when they were tracing energy in a car.
Reflecting on a picture of a car at a gasoline station, four of the six interviewees used
terms that refer to force-dynamic causation“. In fact, in this scenario it was even hard to
draw a line that differentiates between students’ reasoning in terms of their academic

levels. For example, one of the outstanding students in the class said:

 

8 I am using the term force-dynamic causation as opposite to model-based reasoning. In the first
one, learners usually depend on their accounts and experiences to explain why a scientific
phenomenon happens. Examples of these include: plants need light to gow; gasoline is used by
the car to help it move from one place to another. So often, they depend on their intuitive and
daily life experiences to explain things. The model-based reasoners, on the other hand, rely on
more sophisticated scientific models to explain how a phenomenon happens. They also can
smoothly go back and forth between the formal science constraints and principles and the
intuitive science suing representations and scientific laws.

65

Well, the gas was used to power the car and the engine of the car. Then, I mean it
probably came out of the car through the exhaust. When the car is done with it, it
probably has some device that like leads it to the exhaust pipe and then come out in

smoke; I guess it gets transferred into other parts of the car like the engine.

A similar response was also expressed by a student who steadily tended to use force-
dymarnic causation. According to him, “It [the gasoline] is burned off, it's not really
burned off, but it is just like run away to the exhaust. I am not totally sure about where
does it go but the car used it up and threw it away.” However, in spite of the fact that
students from various academic levels have similar explanations, especially when it
comes to intuitive experiences, yet a few of them have access to richer terminology.

Blake, an average student according to the formal assessment perspective, was a good
example of model-based learner. In his words:
The gas is sort of chemical energy and that cherniCal energy was burned in the
internal combustion of the engine. This made the pistons move up and down, which
made the car move to the Upper Peninsula. This process produces carbon dioxide
and monoxide as a waste gas and also produces lots of heat. The heat is in the

engine and above it.

When I asked him to say more about energy conversion that happens inside the car, he
pursued “if you stick a camera inside the pistons you can see light, and sound energy. It’s
coming from the motion inside the pistons when the engine is working and also all the

gear shifting and all that stuff.”

66

3. Energy transformation and conversion (ETD & E CD) :

A central goal of the energy unit was helping learners understand the main principle
that energy is conserved, but it might be transformed from one type to another. To
develop such an understanding, IQWST curriculum gave a special attention to scientific
models and representations. Examples of these models included the Energy Conversion
Diagams (ECD) and the Energy Transformation Diagam (ETD). The first one, the
ECD, emphasizes a sort of quantitative reasoning and it pushes learners to predict how
much energy of each type might be involved in a specific process. The second one, ETD,
concentrates on qualitative predictions, particularly when the system is complicated and
making quantitative estimations is irnpossibleg. Next, I introduce some examples about
participants reasoning in this regard.

Three areas of the interview scenarios required participants to draw ECD or ETD.
Furthermore, they were asked to explain what they mean by their models in terms of
energy types and its ratios. Students were able to insightfully represent and reason about
cases that they have similar experience with. Except for one of them, all interviewees
have drawn an acceptable ECD for a free-falling and a compressed spring. They were
able to identify what are the energy types and how they were converted from one type to
another. The following diagam shows how Kit translated the motion of a falling book

and a launched spring into qualitative representations.

 

9 For example, in some activities students dropped masses fi'om known heights. Depending on
some indicators of gavitational energy, such as elevation and mass, students can draw a pie chart
that shows that it has 50% of gavitational energy and 50% of kinetic energy at the middle of the
trip. Similarly, in a process that making estimations is impossible, such as in a chemical reaction,
students rather can realize the kinds of energy might be involved in that process.

67

 

 

 

 

Figure 4: (Students work) Kit’s ECD for a falling book (left) and for a spring (right).

Reflecting on her first drawing, which represents how a falling book gavitational
energy is converted into kinetic energy, Kit explained that by saying:
In [position] #1, it’s probably all gavitational energy because it’s the highest point.
The second position # 2, there is kinetic energy since it falls and it says half of the
way and it’s loosing some gavitational energy. The third one is probably a lot of
kinetic energy but still (hah?), I am trying to figure out what [type of energy is that],
I can’t remember but when it touches the play-dob, um, I think thermal energy

maybe?

Looking at her drawings, Kit explanation is accurate in terms of the types of energy that
are involved here. In fact, she was the only one who considered having thermal energy
(TE) as a result of the collision between the book and the play-doh. However, her
problem was in representing the ratios of kinetic and gavitational energy in the second

position for both the falling book and jumping spring.

68

Students reasoning in the cases of the energy transformation diagams (ETD) was
different from the other type. In general, high academic level students were able to
identify more types of energy with more sophisticated models of energy conversion. In
the following diagam, there are two main differences between the upper and the lower
models: first, the number of the energy types that are involved and the second in the
arrows directions. The first model shows that there are only three types of energy
conversion involved in the car scenario: chemical, kinetic (the student used the term
speed) and sound energies. Further, it pictures energy as fluid that changes from one type
to another in a linear direction. The second model has three additional types of energy
and looks more complicated as indicated by the directions of the arrows. Most students’

models were similar to the linear kind with additional types of energy.

 

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69

4. Integrating in-school formal knowledge with out-of-school experiences:

An interesting result of this study was learners’ confusion while integating their
formal activities in the energy unit with similar phenomena from their daily lives. An
example of that is the question that asks students if they see similarities between burning
wood in the fireplace and the chemical reaction in the class activity. Although they all see
some similarities between the two cases, many of them were confused about the energy-
matter conceptualization. For some students like Amma, the thermal and chemical
energies are “mixed together” in order to start burning wood. Additionally, some types of
energy stay there and others disappear, “when you burn wood you start with chemical
and thermal energy together, and when it is just burning it's only thermal and it looses the
chemical [energy]. The chemical reaction and burning are similar because they both are
gaining thermal energy.” Along the same lines, another student thinks that the heat of fire
is produced as a result of “mixing the flame of the match and the wood together.” Again,
such ideas support the conclusion that students in this age can’t draw clear lines between
matter and energy.

In very rare cases, some students were able to use a scientific explanation that
reflects their ability to transfer formal scientific knowledge to general contexts. For
example, from Blake’s point of view:

When you burn wood, chemical energy is converted into heat, light, sound and
kinetic energies. Chemical reaction and wood burning are alike because they both
start with chemical energy and produces thermal energy. They also may produce

other new products such as ash and carbon dioxide.

70

Overall results for cases

In the two previous sections I focused on particular examples from the six
participants’ reasoning regarding data analysis and ideas about energy. However, those
selective examples may not give the reader an overall picture about the participants’
reasoning. Thus, it is also interesting to see if there is any sort of overlap between
students reasoning in one area, such as the data analysis skills, and another (i.e. students’
ideas about energy). I will summarize the results in two tables: the first focuses on the
data analysis skills (questions 1 & 3 in the interview) whereas the second table
summarizes the students’ ideas about energy (see scenarios 2 & 4).

Table # 6 results suggest that students’ data analysis skills not only depended on their
academic levels"), but also they varied from one skill to another. The trend of the table
shows that while learners were able to see patterns in data that is represented by gaphs,
realizing the pattern in a data table was less accessible for most students. Another
noticeable result in this part is the contrast between some students’ reasoning as reported
by the interview and their performance through traditional assessment. According to the
table the results, the overall best result was for a B-level student. This result is reasonable
since the research interview questions were focused on specific areas of data analysis
skills, whereby science teachers have other areas of interest (i.e. content knowledge). The

rest of the results were generally consistent with the students’ levels.

 

10 Students’ levels (A, B, & C) represent their academic grades depending on the school tests and
their teachers’ recommendation. For more details about the codes, see the data analysis section in
the previous chapter.

71

Table 6: Students’ performance in data analysis skills

 

 

 

 

 

 

 

 

 

Skills Reasoning Finding patterns in data Evidence-
about based
Student variable argument 2:31;“
(Level) A1‘ C1 C2 A2 BS C5 B4 C3
(table) (L.graph) (charts)

Maddy (A) 2 2 3 2 2 3 3 3 20
Chandler(A) 3 2 2 3 3 3 3 3 22
Blake (B) 3 3 3 2 3 3 3 3 23
Kit (B) 2 1 1 2 3 3 3 3 18
Amma (C) 1 2 1 1 3 3 2 1 14
Austen (C) 2 0 1 2 2 3 2 2 14
Total 13 10 11 12 16 18 16 15

 

 

 

 

 

 

 

 

 

 

 

 

* This represents the first sub-question in scenario #1.

Another interesting question would be: what students’ reasoning about energy looks
like within the entire goup? The following table aims at reporting general comparisons
among participants in order to answer that question. In addition to what was stated in the
second section of the results about learners’ ideas about energy, a deeper look at this table
illustrates two additional findings. First, naive ideas about energy, such as its association
with living things and seeing it as an agent, could be found even among the best students.
Maddy, one of the A-level students, thought that “other things give wood energy” and
this energy originally comes from the match. She further believes that “the gas was used
to power the car” and the gasoline eventually “comes out” once the car is done with it.
However, she drew an energy conversion diagam that has seven types of energy and
shows the process as a continuous cycle.

Secondly, the cross comparison results suggest that there is some sort of horizontal

consistency among students’ reasoning about energy. Put differently, there is evidence

72

that those who reasoned superficially about one aspect of energy have done the same in
other aspects. For example, students who believe that energy is associated with living
things, which in fact was explicitly expressed by four of them, have also pictured energy
as an enabler that helps the car move. Furthermore, their models of energy conversion
were linear (e. g. one direction arrows). Similarly, students who tended to use more
developed models about energy, were also able to explain energy conversion and to talk
about many invisible parts of the system. Blake’s and Chandler’s responses in the table
reasoning are examples about that.

Taken together, there is an interesting contrast between the two tables. In the one
hand, the pattern in the first table shows that many participants reflect considerable
reasoning about scientific data. It’s true that some skills like comprehending the
relationship between variables were not accessible for lower level students; most
participants were able to realize patterns, make scientific predictions, and draw
appropriate conclusions from data sets. On the other hand, the second exemplar table
indicates that most students still have naive conceptions about energy. It seems that
although learners may collect and analyze reliable data and involve in the inquiry
practices, this does not guarantee adequate reasoning about energy. Learners may
develop many skills on the local level (i.e., reasoning bout variables and patterns), yet

moving towards global models about energy stay limited and difficult.

73

 

 

 
 

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74

Pedagogical aspects of data analysis

In the previous sections, I attempted to help the reader make sense of the learning
aspects of data analysis within the context of energy. In this part, I pay more attention to
the process of enacting the designed activities with particular emphasis on three areas:
discussing investigation variables, looking at pattern in data and drawing conclusions
from it, and treating anomalous data. The purpose of focusing on those three activities is
primarily to compare how the participants reasoned about data in the classroom as well as
in interviews and to report how the teachers responded to their interaction with the caitiff
data. In other words, it is not to evaluate the teachers’ enactment or to evaluate the
IQWST designed activities.

However, these teaching aspects are helpful because they form some interesting
aspects of the comprehensive story. One should keep in mind that this unit was enacted
by two experienced, committed, and enthusiastic science teachers. They patiently went
the last mile in order to help their students make sense of a shady scientific concept, like
energy, through guided inquiry and continuous dialogical process. Among many other
significant characteristics, those teachers willingness’ to learn new scientific topics was

remarkable.

0 Teachers ' ways of exploring investigations variables:

Findings from classroom observations suggest that the two science teachers who
enacted this unit have followed a dynamic, but a well-organized, technique while
translating the designed activities into classroom experiences. In spite of the fact that the

energy lessons were orchestrated differently, the teachers tended to use the following

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strategy in a systematic manner: housekeeping, introducing the activity through
discussing students’ intuitive knowledge, data collection in groups, and drawing
conclusions. The longest period in terms of the class flow was usually the introductory
discussion about students’ ideas. So often, teachers tended to use them as a scaffolding
transition to introduce learners to new scientific concepts about energy. Within that
introductory discussion, I focused my analyses on the variables of investigation because
they sounded central in most energy activities.

Anne, one of the two science teachers who enacted the unit, used to discuss the
investigation variables in order to make data collection more accessible to all groups.
However, when an investigation has multiple variables involved, such as height, mass,
and speed, she relies heavily on pushing students to reason in a way that is consistent
with the designed curricula. Following is an example of her discussion when she was
trying to help students isolate the variables that might affect the kinetic energy of a
falling book:

Teacher: let ’s go back a second, what is kinetic energy?

Students: motion.

T: motion, do you think about kinetic energy for those textboon while standing there?
A student: no, unless we drop them.

T: that ’s a great idea! We are going to do that in a minute. But let me remind you
about the list [ of factors] that you came up with yesterday. Who can read it for the
class?

S: mass, speed, weight, height, movement, motion, power, density, and gravitation.

T: when we talked about the volleyball, I heard two words in most of the groups:
mass and speed. Those are the two that we are going to look at, let ’s start with speed.

We are going to drop books on the play-doh and look at something called “smoosh "

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factor, could you please go to activity 2.2 and read the purpose silently? (Teacher

and students read).

Ann then explained the purpose of the activity was to learn about the factors that affect
how much kinetic energy an object might have. Furthermore, students wrote their
predictions about the activity’s central question: How can we use play-doh to determine
whether there is a relationship between the M of a moving book and the amount of
kinetic energy it has? As one might expect, students came with many ideas that were not
necessarily overlapping with the teacher’s agenda. This was obvious to me when the
teacher responded to one of the student’s suggestions while answering the central

question.

T: who can give me one sentence as a response to that question, what can we do to
figure this out?

S: drop the textbooks on the play-doh from diflerent heights.

T: 0k, what can we get from that, then?

S2: the higher the book, the faster it will be.

T: Will you be able to see the speed?

SS: no.

T: does everybody ’s idea include diflerent heights?

Some students replied by yes, but others said no.

T: let us say this, we are going to look at height, but we are not going to do that
today. What we want to do is somehow change the speed, but not height. You ’re

making an assumption or maybe from your experience in terms of what height do to

speed.

Scientifically speaking, dropping books from different elevations is a valid suggestion to
change speed. Height will however be the central variable in the second lesson when

students will be investigating gravitational energy. Consequently, the teacher was more

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committed to the designed curriculum in spite of the dialogical approach that she has
shown while discussing students’ initial thoughts. Interestingly, discussing the
investigation variables was not enacted similarly by the two teachers in spite of the fact
they refer to same activities. The second science teacher preferred to be more
straightforward in terms of stating the variables and discussing them. So often, she listed
the dependent, independent, and control variables on the board without developing

controversial arguments about them.

0 Finding patterns and drawing conclusions:

There is solid evidence that the two science teachers who implemented the energy unit
were deliberately reinforcing students’ reasoning from scientific data in order to develop
evidence-based arguments. According to findings from the classroom observations in
most of the focusing teaching episodes, the teachers have explicitly discussed the terms
evidence, pattern in data, and scientific conclusion. Regarding helping students realize
patterns in the data, teachers have reflected two different roles while mediating the
discussion between students and data. Frequently, students were able to make appropriate
predictions about what they think the results might be. In other words, finding the pattern
in data was not used as a method to inquire an unknown result, rather it was collected to
reassure the prediction that students have made (i.e. confirmation lab). In such a case,
teachers did not face problematic situation and thus students accepted the pattern in data
and were able to draw appropriate scientific conclusions. In other investigations, although
they were limited in numbers, teachers were confused when they faced a situation when
students collect anomalous data. Anomalous here means having accurate scientific data

that contrasts with learners’ predictions. In this latter case, the powerful students’

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intuitive experiences and the teacher content knowledge were the main sources of
trouble. Consequently, patterns in data were not realized insightfully and sometimes the
data was rejected. Next, I will give an example that illustrates each case.

While introducing the gravitational energy, Chris, the second science teacher,
reminded her students about the last activity, the kinetic energy, and about its central
variables: mass and speed. With a smooth transition, she returned back to the arguable
variable they brought in the previous class, “because you mentioned elevation last time, I
told you that we’ll come to that later.” Like their peers in Ann’s class, students in this
class have also argued that elevation could be a factor that can change the kinetic energy
of a falling object. In the following discussion, Chris was trying to force students to make
scientific predictions about the relationship between a falling object’s elevation and how

much gavitational energy it might has.

T: Now, in this activity you are going to make a prediction. You are going to try
and determine: if I throw objects at diflerent heights, how it influences the smoosh
factor of the play —doh? I don 't want you to say that it will aflect the smoosh factor,
I don 't want you to make a lucky guess, but I want you to explain why do you think
so? Remember, it is OK if it 's a wrong guess.

T (after 4 minutes of writing predictions): Can some one give their opinion?
Chandler: um, the higher the drop is, the more smoosh will be because when the
book is higher it has more energy.

T: OK, the higher the book is the more energy it has. Anybody else?

Sarah: the higher you have the book, the faster it drop down and having more
kinetic energy and better impact.

T: anybody have anything diflerent? They are pretty the same thing?

T (as nobody responded): Why do you think it will have more smoosh?

Kit: because it has more momentum as it falls.

Alex: because of gravity.

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T: what about gravity?

Alex: because it pulls it back down.

T: 0K.

Chandler: like if you drop a penny on your head from 2-feet elevation nothing will
happen to you, but if you drop it from 100 —feet height, it might kill you.

T: 0K, those are great ideas! So, what do you thing about the relationship between
height and energy, then?

Kit: the higher you drop the book the more speed it will gain.

T (repeats the answer): 0K.

Again, students have valid predictions and legitimate ideas about motion and energy
even before carrying the activities out. However, they may not necessarily collect the
data the way that teachers expected. For instance, many students were throwing the books
rather than dropping theml 1. Other students were measuring the thickness of the play-doh
inaccurately. In spite of that, students were able to find a general trend that there is a
positive relationship between gravitational energy and its two factors: mass and height.
Thus, the conclusion that “the higher and the heavier an object is, the more gravitational
energy it will have,” was reached by all groups.

Looking closely at some inconsistent results, where the lighter book or the one that
was dropped fiom less height seemed to have more “smoosh” factors, the teacher’s
justification that “maybe the weather temperature has affected the play-doh” did not
sound convincing. All groups used the same play-doh and even if its condition has
changed, this has nothing to do with odd results. In fact, one of the students’ reasoning

sounds more reasonable when he said, “perhaps the ten centimeters elevation was not big

 

11 By doing so, the falling object will have initial velocity (i.e. V0 35 0) instead of the free falling
supposed case, and thus there is another variable other than the height.

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enough to show real differences in the outcomes.” The teacher did not pay considerable

attention to his insightful suggestion.

0 Treating conflict with anomalous data:

Patterns in data are usually easier for students to realize and for teachers to negotiate
as long as they are consistent with students’ predictions. These pre-existing thoughts are
usually developed in along period of time throughout learners experiences with the
world around them. This was vivid in most of the focus activities that had something to
do with motion and falling objects (i.e. gravitational and kinetic energy). However, if
data show a trend that contrasts with students’ conceptualization, then science teachers
may find themselves in a controversial situation. This particularly becomes more
complicated if the teachers themselves don’t have sufficient content knowledge in that
area. The teachers faced such a situation while explaining the free falling objects and
elastic energy examples”. The following example explains how the teacher and students
interacted with data when it showed a pattern that is opposite to their predictions.

While conducting the elastic energy activity, students thought that soft springs will
jump higher than hard springs. This is not an unusual prediction because students were
referring to daily life experiences that imply pushing soft springs is easier than the
heavier ones. However, in the spring activity there were additional variables besides the

spring hardness such as the distance of compression and the spring’s mass. This perhaps

 

12 Free falling phenomenon happens when an object falls under the influence of the gravity force.
Students’ misconception in this regard includes their thinking that heavy objects fall faster than
light objects (for example, a 10 Kg block versus a penny). Some students, however, think the
opposite: because the penny is lighter it falls faster than the heavy object. Theoretically, the two
objects fall at the same speed under gravity acceleration, unless the shape of them was influenced
by air resistance.

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was the main source of the confusion. The following discussion took place when students
reported the data they collected after conducting an investigating about three springs.

T: When you did this experiment, which one was the easiest to compress?

S1: spring number one.

T: number one, did it go the highest, the lowest, or something in the middle?

S1: it was the highest.

T: so, for you, your easy spring was the highest. 0K, how bout this table (asking
another group)?

S2: number one was the easiest, but it went like (trying to read the result).

T: 0K, which one went the highest?

S2: number three.

T: number three, and that was the hardest to compress. All right, Steven what about
your spring?

Steven: the heaviest went the highest.

T: your heaviest went the highest. 0K, Alah, what did your group decide?

Alah: number three went the highest.

T: UK, number three. What about this group?

Kit: number one was the highest, but that was the average! We have like spring
number three was sometimes higher.

T: you want the average, though. So, for your average the easiest one was the
highest.

In fact, most groups found that the harder the spring was, the higher distance it
reached. Only one group found the opposite. In spite of this fact, the teacher decided that,
“we don’t have any conclusive evidence and the groups’ results were split.” Furthermore,
she did not pay enough attention to the student who was trying to explain the problem in
her average. Kit was right when she explained that “number one was the highest, but this
was the average.” If there was an extreme wrong measurement in the three trials, this

likely might lead to misleading average. The result in such a case was mathematically

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reasonable, but scientifically inaccurate. An interesting opportunity for measurement
errors and its implications from science and mathematics perspectives was missed.

The discussion that followed reporting the results was important because it could be
linked to the students’ reasoning in the clinical interviews. According to Chandler’s
response in the class, “the hardest requires more energy to push it down and thus it has
more elastic energy stored in it.” In fact, Chandler was the only interviewee who drew an
appropriate conclusion about the same scenario. Students who resisted the new data, like
Austen, still think that “the soft spring is easier to compress and so it makes sense to me
that it went higher.” Again, this kind of reasoning was found in most of the interviews.
Thus, finding a similar line of reasoning in the classroom discussion was not a surprise
for me.

Looking closely at the previous results from the classrooms dialogue, I noticed that
the two teachers did not show explicit teaching strategies that motivate students to use
model-based reasoning. It is not clear, however, if their lack of content knowledge, such
as the physics of the moving spring for instance, or the nature of the activities themselves
was the reason for that. Again, patterns in some activities in the energy unit, such as the
kinetic and gravitation energy activities, could be realized whether learners used force-
dynamic or model-based reasoning. Other activities or scenarios, such as patterns in the
spring’s motion, were sophisticated in terms of their physics and thus using force-

dynamic causation was not helpfirl to find the patternl3 . My study design, however, did

 

13 A force dynamic learner will say because you pushed the spring more, it has stored more
elastic energy, thus softer springs go higher. A model-based learner would say that the hardness
of the spring is important in this case. There is a difference between pushing two different springs
by the same force and pushing them for the same distance. He also realizes many factors that
matter here: the hardness of the springs, the mass, and the distance of compression.
Understanding the relationship between these factors is not like the case of dropping books.

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not include data collection methods that give the two science teachers reflection

opportunities to justify their actions and decisions.

Chapter Summary

Findings of this study show a line of consistency among students’ reasoning about
investigation variables and patterns findings in the interviews and the classroom
observations. Many examples of confusion and difficulties were reported in both cases.
Furthermore, students’ ideas about energy, particularly as reported in the general
scenarios, are still governed by force-dynamic reasoning. They see energy as something
that is associated with living things, a helper that enable us do our work, or a fluid that
moves from a place to another. In other words, transforming local knowledge into more

complicated scenarios is not clear.

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Chapter Five

Discussion and Implications

In this commentary chapter I address two main topics. First, I discuss the findings
of this study, particularly learners’ reasoning from data and their worldviews about
energy. Additionally, I correlate those results to literature. It is an attempt to make some
interesting contrasts that were revealed from this study. The second part, which I called
reflection, is another way about thinking “outside the box.” This part is about my
experiences as a researcher. Next, I will summarize the main findings before to help the

reader make sense of the discussion that follows.

Findings Summary:

Major findings of this study could be summarized in the following three areas:

0 Reasoning from data results:

a) The final findings found that most participants were confused about treating the
variables under investigations. They concentrated on local observations (e. g., the
smoosh factor) rather than moving towards general model reasoning about kinetic and
gravitational energy.

b) The second result in this regard was finding patterns in data. Participants’ skills here
were influenced by many factors: mathematical consistency in data, and the type of

data representation (tables versus graphs), and students intuitive knowledge.

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c) Most participants were able to develop evidence-based argumentation as long as the
data was representing a simple scenario, once data refers to complicated scenarios
only high level students could draw valid evidence-based arguments.

d) Students were able to draw both Energy Conversion Diagrams (ECD) and to till
accurate scientific narrative about energy conversion. However, when it comes to the
Energy T ransfonnation Diagrams (ETD) that were new to them, the models were
mostly simplified and one directional for most students, some sophisticated models
drawn by two of them.

0 Participants ’ ideas about energy: Findings in this regard were to some extent similar to

other findings in the literature about students’ conceptualization of energy.

a) Energy is associated with life.

b) Energy is an enabler (or agent) that helps us do things, or machines run.

c) Energy is materialized, it could be mixed or be a product of something.

(1) Energy is synonym for other scientific concepts such as momentum, force, and
power.

0 Pedagogical aspects of reasoning from data and energy:

a) Teachers have shown high commitment towards the designed curriculum. Yet, the
two teachers’ enactment process was different.

b) Teachers and students have smoothly treated the less “trouble-making” data: the
data that show a vivid pattern. However, when data did not meet students’
predictions or when it was anomalous to them, then the dialogue seems to be

governed by students pre-existing knowledge.

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Discussing the results of the study

Having summarized the previous results, it seems that this study have brought to the
surface two of the most complicated fields in science education: reasoning from scientific
data and students ideas about energy. According to the recent study findings as well as
the literature that I have reviewed, there are two vivid conclusions that confidently could
be drawn and they are: a) The evidence that has been reported regarding students’
difficulties (i.e. misconception or confusion) about energy is much stronger than a few
promising cases. B) This study and relevant literature have also pointed to another line of
consistency: learners’ informal tools of developing scientific knowledge are likely
situated within out-of-school contexts and these contexts have more authoritative
influence on their ideas about energy than the formal science.

How could we understand the findings of this study in terms of students reasoning
from scientific data and about energy? In order to answer this question, I will integrate
the recent results with other findings from literature. To simplify the discussion, I will
focus on some interesting contrasts that have been consistency found in this research and

the relevant literature

A) Local to global reasoning from data:

Students have shown that they are fluent in terms of local data analysis. For example,
they all were able to read local points in a graph or a specific point in a data table. In this
regard, locality of reasoning also means focusing on direct observations (the changes in
the smoosh factor) without necessarily than connecting that effect with kinetic and
gravitational energy. Finding a pattern in data requires, however, the learner to move

from local to more general reasoning that implies realizing the relationship between

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variables that are involved in an investigation. Many previous studies have shown that the
more variables an experiment includes, the more confusion students might face (J eong,
Songer, Lee,2007; Leinhardt, Zaslavsky, Stein, 1990; Lubben, & Millar, 1996) While
reasoning about gravitational or kinetic energy activities, students came up with a long
list of variables that could be influential in both cases. The fact that there are many
factors, such as height, mass, speed, isolating these variables one from another is rather a
difficult task, particularly when energy is converted fi'om gravitational to kinetic or the

opposite.

B) F orce-dynamic causation to model-based reasoning:

Some scenarios of this study sounded more accessible to students than others. For
example, when they were asked about their predictions about the gravitational energy
scenario, most students were able to do that. However, when it comes to more
complicated scenarios, such as the spring activity or the fireplace, the number of students
who can reason from model-based perspective dramatically decreases. This kind of
reasoning requires multi-level of understanding. This includes: realizing the constraints
and principles, dealing with multiple representations, making connections among the
different parts of the system. In fact, many research groups have found that even students
in high school level many not be able to reason that way (Lin & Hu, 2003; J in &
Anderson, 2007). Learners in middle school level have tendency to focus on visible,
macroscopic, and natural tendency of scientific phenomena.

It is not clear whether IQWST curriculum designers were deliberately aiming at
pushing students towards this kind of reasoning. There is solid evidence in this study that

students were able to collect and analyze data through the guided inquiry teaching. They

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further were able to reason about energy transformation representations, such as ECD and
ETD, sufficiently. In a sense, they are partially model-based learners. Anderson’s (2007)
diagram that illustrates developing models from observations does not work in school and
scientists communities in the same way. Thus, force-dynamic causation, especially in a
topic such as energy, is not an unusual finding.

The wood in the fireplace and the car on gasoline station scenarios have strongly
shown that middle school learners reason about energy from a force-dynamic causation
point of view. There are many considerations that should be discussed here. First, unlike
the other two scenarios, those two are not directly related to the IQWST energy unit.
They rather are general questions that were basically designed to investigate
participants” reasoning about energy. Thus, transforming learners’ understating about
energy into these scenarios is not expected.

Aikenhead (2004) have insightfully explained why the possibility of transform us
very rare, “the transformation of canonical science knowledge into useful everyday
knowledge requires that it be deconstructed fiom its universal context [the energy
concepts in the classroom] and then reconstructed according to the idiosyncratic demands
of everyday context [such as the car at a gasoline station].” This transformative
knowledge, from Aikenhead’s perspective, “has agency in other situations, whereas
canonical content knowledge found in science courses would seldom have agency.”
(p.260). I see this process of the “deconstruction” and “reconstruction” of the scientific

knowledge is very similar to model-based reasoning.

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C) From conceptual change to conceptual integration:

Perhaps participants’ worldviews about energy were the most vivid findings of this
study in terms of its overlap with the similar literature. Students ideas about energy are
powerful, legitimate, and were developed mostly in out-school contexts. Like many other
science education researchers (Resnick, 1987; Driver & eta], 1994; Duit & Haeussler,
1994), I believe in developing these ideas, departing from constructivist point of view,
rather than conceptual change perspective. In their outstanding analytical piece about
how learners construct knowledge in the daily life settings, Driver and his colleagues
stated that:

As far as people’s everyday experiences are concerned, the informal ideas are
often perfectly adequate to interpret and guide action. Fires do burn down to result
in small pile of ash- a widely used way of getting rid of unwanted rubbish. If you
want to keep a piano moving across the floor you do need to keep up a constant
push. It is not surprising that ideas that are used and found useful are then
represented in everyday language. Perhaps such as being “as light as air” or
something being “completely burned up” both reflect and give further support to

underlying informal ideas” (p8).

This is very similar to children ideas about energy. The car at the gasoline station is
a systematic experience that most children see on daily bases. The “gasoline is gone or
just get burned” is a common language that may not be simply replaced, but rather
advanced. This kind of advancement may be achievable goal through a dialogical process
between the “common sense” knowledge and an authority (usually the teacher).

According Resnick (1987), there are two conditions for productive negotiations:

9O

providing support and guidance and at the same time listening carefully to the learners.
By doing so, “teaching from this perspective is thus a learning process for the teacher and
he is the often hard-pressed tour guide mediating between children’s everyday world and
the world of science” (p.11).

Looking back at the two science teachers who enacted this unit, they indeed were
mediating the dialogue between students’ world and science world. Through my
observations, I was enjoying a different type of teaching science. They went the last mile
while listening to their students’ ideas. Their willingness to learn science from the energy
unit and other resources was noticeable. However, firrther research is perhaps needed in
order to explore their classroom actions and decisions regarding specific findings of this

study. Next, I will discuss some of the study limitations.

Study Limitations:

This study has many limitations in terms of its results, data collection methodology,
and its context. First, this is a case study that focused only on six participants and
therefore its findings may not be generalized. Rather than providing comprehensive
results about middle school learners’ skills in data analysis and worldviews about energy,
it gives an example about that. Perhaps collecting data through additional tools, such as
written assessment, and including a bigger sample will provide additional venues for
understanding how students from different academic levels reason about scientific data.
Furthermore, the results of the recent study were related to the energy activities. The
interview questions and scenarios were also consistent with these types of activities.
Giving the abstractness and students’ difficulties about energy, it might be interesting to

pay more attention to more accessible and concrete scientific concepts.

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Secondly, the study was conducted in an independent school that has many special
characteristics. Examples of these are the considerable flexibility in the school schedule
and the length of classes’ periods, the number of students in each class, the technological
infrastructure, and the general educational policy. These aspects many not be found in
other schools. In fact, my participation in IQWST has shown that the context of teaching
and learning (e. g., the teachers, learners, and local community) plays a critical role in the
process of curriculum enactment. Therefore, the results of this study are associated with
its context.

Finally, the study tools were limited to observations and interviews. Again, another
valuable dimension might be added if the two teachers who participated in this study
were given the opportunity to reflect on their actions and decisions regarding data
analysis. My data collection methods did not include interviewing the two teachers for
economic and procedural reasons. The goal was collecting sufficient data that provide me
. with valid sources for answering the research questions. Thus, we have to keep in mind
that this research results don’t include teachers’ strategies of curriculum enactment or
teaching materials evaluation. Wilend (2006) reminded us that in qualitative research
“data is typically collected with interviews and observations (listening and looking) often
as part of some of fieldwork. But not all the information we collect turns to be data”
(p.7). In my case, it is true that not all the collected information was used as data, but it

seems that using additional methods might enrich the findings of such research.

Implications:

In spite of the previously mentioned limitations, this study has many implications for

teaching and learning science, curriculum, and for future research. The results of this

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study show that middle school learners rely on force-dynamic causation about energy.
Although this was not a surprising outcome, especially from middle school learners,
science teachers can play an essential role in order to reinforce model-based reasoning. In
spite of the fact that classroom observation have shown that the two science teachers
were using productive dialogical approach in teaching science, there is no evidence that
they were reinforcing model-based reasoning about energy. Students have used a long list
of synonyms and concepts to talk about energy such as: momentum, gravity, force, and
power. Frequently, learners have used these terms as appropriate language for their force-
dynarnic causation, particularly when they attempted to correlate informal science with
school science (Solomon, 1983a). Perhaps professional development, with more
emphasis on content knowledge, can play a significant role in terms of advancing science
teachers’ pedagogical knowledge that is related to model-based reasoning.

This research findings have also many implications for curriculum materials and
cuniculum design as well. Many previous studies have suggested that school curricula
should include wide range of reasoning from data opportunities (Kanari and Millar
(2004); Nicholson, Ridgway, & McCusker, 2007). In school science, learners are often
asked to explore situations where two variables covary. The existence of a pattern or a
trend is often immediately clear fi'om the data they collect.- They are rarely asked to
investigate the effect of a variable that does not (in fact) covary with the outcome. Thus,
conducting investigations from both types will enhance their reasoning from data
abilities. Furthermore, I think that Duit’s & Haeussler’s (1994) recommendation to

integrate science, technology, and society (STS) within the science curricula might be

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useful in linking the out-of-school knowledge and the formal learning settings,
particularly in topics such as energy.

Like most of research in education, this study is an incomplete story. It opened new
directions for future investigations. For instance, the following questions remain
unanswered and they might be interesting future projects: How do learners from other
school contexts, such as public schools, and other grade levels reason about scientific
data? Would learners’ skills and abilities be different from the recent case if the concept
under investigation was other than energy? What are the teachers’ considerations and

justifications that have shaped their enactment of this energy unit?

Reflection

0 Reflection on the tools:

What additionally could we learn if students’ reasoning from data and their ideas about
energy was assessed using written assessment? The main tool that was used for data
collection was the interview. It is important to note that these interviews were not
identical in spite of using a special protocol. In many cases, I learned fi'om the previous
interview. So, I used such an opportunity to extend the dialogue (with the next
interviewee. In fact, I don’t see in this any problem, I rather consider that a legitimate
way to learn from my own work. In interviews, we use our authoritative position. We add
more questions, seek deeper explanations and justifications, and we also may decide to

stop asking more questions. This is not the case in the classroom observations.

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Unlike the interviews, observation as a data collection tool takes away most of the
researcher authorities. Once you observe, you can’t lead the dialogue in spite of the very
fact that you are a selective observer. The teacher, his students, and the curriculum
materials interact in a complicated social way so that it becomes impossible to predict
what might happen after a minute. Classrooms are uncertain environments. Although we
design activities that need well-known materials, we may know the teacher’s abilities and
background, but we hardly know something about learners’ and their worldviews about
science. Researchers are selectively making their decisions. We decide which group to
focus on, which portion of data to pay more attention to it. For somebody else, perhaps
the moments that we have ignored are more significant and interesting than what we
reported.

0 Reflection on experience:

There is a unique thing that most of graduate students, particularly those who came
from international backgrounds, learn while doing their research: experiencing education
of the others (i.e. the American). I did this research in an American school and this
experience was another PhD. In addition to that school, I visited many other schools in
rural, inner-city, and suburban areas. As a result of these different experiences, I have
advanced my own understanding of the meaning of the term “context.” I remember how
my first experience inside an American classroom was! By that time, I did not feel that
there was any “good” teaching or learning. My own vision of teaching and learning was
developed through many years of teaching science and math in Palestine. Thus, it was
like the glasses on my eyes, filtering what I see. In fact, this unique experience is very

important in answering many questions about teaching and learning in different cultures

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and contexts. Yet, the initial questions about the meanings of teaching and learning are

still there; in fact the list is now longer.

Chapter Summary

Students in middle school students draw from a wide range of out-school experience and
they legitimately use this discourse to talk about science. In spite of the fact that they can
treat scientific data and use models to reason about energy conservation and conversion,
they are not necessarily depending on model-based reasoning. Rather, they have tendency
to relay on force-dynamic causation. Science teachers can orchestrate dialogical

strategies to bridge the out-of-school informal knowledge with the formal school science.

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Appendices

Appendix A: Description of the Energy Unit

The energy unit is designed for middle school level students. The entire unit is
organized around a driving question (Fortus, 2007).This question is supposed to be “a
rich and open-ended question that uses everyday language to connect with authentic
interests and curiosities students have about the world” (p.ii). The energy unit driving
question is: “Why do some things stop and others keep going?” The 14 lessons of the
unit are then organized around three learning sets (LS). Each of these sets emphasizes a
specific area of the big driving question and at the same time has some sort of unifying
potential for some types of energy. The following table summarizes the three learning
sets including their driving questions, the types of energy in each set, and the scientific
principles that are addressed by the national standards.

According IQWST designing point of view, learning science has the following aspects:

1. Learners engage in guided-inquiry activities. Each of these activities addresses an
interesting idea about energy. Learning science is a social process and thus
IQWST curricula gives priority for working in projects through small groups.

2. Students collect, organize, and analysis quantitative and qualitative observations
in order to advance their learning about energy types and principles.

3. Learners involve in many design activities, such as designing Rube Goldberg
machine, to help them make sense of energy and its conversion.

4. Curriculrun materials should be simple, accessible, and attractive for learners. It
should be also educative for teachers and their professional development.
Technology plays essential role in learning and therefore computers, movies, and

other apparatus are used in most activities.

97

 

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Appendix B: The interview protocol

A) The Gravitational and kinetic energy activities:

Remind students about the activity in which they dropped different masses (books) from
different elevations in order to find the “smoos ” factor. Then show him/her the play-dob
cube and how they took the measurements. While introducing the following data table,
I’ll explain to the interviewee that group “A” dropped 5 different masses (each was
dropped three times) from a 10 inches height, whereas group B did the same activity but

 

 

 

 

 

 

from 20 inches height.
Table 9: The interview data table
Group A’s results Group B’s results
DroppLegl)maSS Average of “smoosh” [mm] Average of “smoos ” [mm]
( Height = 10 inch) ( Height = 20 inch)

1 4 mm 4 mm

2 7 mm 11 mm
3 10 mm 13 mm
4 15 mm 13 mm
5 20 mm 25 mm

 

 

 

 

 

I would like to focus on the data in this table (give him/her one minute and then start
probing the following questions).

1.
2.
3. How do you connect this data (event) with energy? What kinds of energy are

Why do you think that the class has used 5 different masses? Two different
elevations?
Do you see any pattern in the data? Explain that to me.

involved in this activity? How?

I would like you to draw an ECD for the dropped book at positions 1, 2, and 3.
(Identify these positions to be at the beginning of the free falling, the middle, and
just before the book hits the play-dob. I will draw a diagram on the board as an
example of this scenario).

What do you think about group A’s data and group B’s? If the interviewee stuck
here, ask him if he sees any problem (or anything strange or disagreement) with
the results. If he identified a problem in the measurements, ask additional
questions if we can correct it and how.

99

6. Assuming that the books were dropped freely (free falling objects), do you think
that the previous results (data) will change if the books were thrown? Why? Why
not?

B) Chemical Energy and Thermal energy:

Show the following picture of the fire place and introduce to the question by saying that
many American families prefer using wood for heating their homes in winter.

 

Figure 6: Wood in fire place picture.

1) Do you think that wood has any type of energy? Could you please explain that to
me? Where did this energy come from?
2) Could you please explain the energy transformation that happens when we burn
wood?
3) Do you see any similarities between burning wood and the chemical reaction
activity (remind interviewee about that activity)?
The following graph shows the change in the room temperature as a result of burning two
kinds of wood: Oak and Elm wood in the fire place. I would like you to concentrate on
the graph and then I’ll ask you some questions about it.
4) What will the temperature increase be if we burn 4 logs of oak?
5) Which wood is better for your fire place? Why?
6) Do you see any pattern in the graph? Explain that to me.

Probe with the students to see if they can realize the relationship between the mass of

substance (wood) and the heat increase as a result of converting chemical energy into
heat. .

100

 

 

Relationship between wood burning and room

 

temperature
0
a
8
0
.E
2
5 —-— Oak wood
E —0— Elm wood
a.
E
H
5
c4
1 2 3 4 5 6
Burned wood (Logs)

 

 

 

Figure 7: A graph of the Relationship between burned wood and a room’s temperature.
C) Elastic energy activity:
Remind students about the activity 4.3 about elastic energy; show him/her the apparatus

(springs, stand, and the ruler) and the following picture. Then ask the following
questions:

 

Figure 8: A picture of the elastic energy activity.

1) Why did we use three different springs? What was the difference between them?

2) Why do you think that we compressed the springs for 1 cm at first and then in the
second route we compressed it 2 cm?

3) What did you measure? What was the evidence that your group used to differentiate
between elastic energies of the three springs?

Imagine that we have 5 different springs (SI, 82, S3, S4, and SS). Each of these springs

was compressed for the same distance, say 1 cm, the class then reported the following

results about the flown distance for each spring.

Give the interviewee one minute to focus on the graph and then ask start by asking the

following questions:

101

a) According to the graph, which spring has around 38 cm average flown?

b) Could you please describe the graph?

c) Could you please rank the springs from the hardest to the softest?

d) I would like to draw ECD for springs in two positions: once it’s compressed, at the
middle of the distance of flown, at the heights point (show these position on a diagram on
the board as I did in the book question).

I would like to draw ECD for springs in two positions: once it’s compressed, at the
middle of the distance of flown, at the heights point (show these position on a diagram on
the board as I did in the book question).

 

 

Distance in cm
N
O

Spfing

 

 

 

Figure 9: A graph of the relationship between springs types and moving distance

D) A car on a gas station scenario:

 

Figure 10: A picture of a car on a gasoline station

102

I will first show the picture to the left and then I’ll tell this short narrative: Your dad filled
the tank of his car before traveling from Ann Arbor to the Upper Peninsula in Michigan
with 20 gallons of gasoline (around 150 lb). After the trip, he has to fill the tank again
because the indicator of the gasoline indicator pointed to E, which means empty. After
telling the story, I’ll ask the following questions:

1. I would like you to think about where did the gasoline go?

2. Can you think about all types of energy transfers that have been taking place in
the car while it’s traveling? I’ll ask the interviewee more questions whenever he
mentions something like KB, GE, light energy, or thermal energy.

3. What are the ETDs, which stands for energy transfer diagrams, that you are
thinking about in this case? You may draw anything that represents those?

If the student’s responses were very limited (or irrelevant) in the first stage, I will
show him the picture on the left side in order to encourage him/her for extended
responses. '

a) What kinds of energy transfer process that are happening here?

b) Can you explain one of these to me?

o) I would like to draw a general ETD (energy transfer diagram) starting from the
gasoline pump to show all the processes that you are drinking about.

103

 

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