ENHANCING GRAPHICAL LITERACY SKILLS IN THE HIGH SCHOOL SCIENCE
CLASSROOM VIA AUTHENTIC, INTENSIVE DATA COLLECTION AND GRAPHICAL
REPRESENTATION EXPOSURE
By
Anthony Palmeri

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Biological Sciences – Interdepartmental – Master of Science
2013

ABSTRACT
ENHANCING GRAPHICAL LITERACY SKILLS IN THE HIGH SCHOOL SCIENCE
CLASSROOM VIA AUTHENTIC, INTENSIVE DATA COLLECTION AND GRAPHICAL
REPRESENTATION EXPOSURE
By
Anthony Palmeri
This research project was developed to provide extensive practice and exposure to data
collection and data representation in a high school science classroom. The student population
engaged in this study included 40 high school sophomores enrolled in two microbiology classes.
Laboratory investigations and activities were deliberately designed to include quantitative data
collection that necessitated organization and graphical representation. These activities were
embedded into the curriculum and conducted in conjunction with the normal and expected
course content, rather than as a separate entity. It was expected that routine practice with graph
construction and interpretation would result in improved competency when graphing data and
proficiency in analyzing graphs.
To objectively test the effectiveness in achieving this goal, a pre-test and post-test that
included graph construction, interpretation, interpolation, extrapolation, and analysis was
administered. Based on the results of a paired T-Test, graphical literacy was significantly
enhanced by extensive practice and exposure to data representation.

ACKNOWLEDGEMENTS

Several individuals deserve recognition for the accomplishment of this project. First and
foremost, I would like to thank Dr. Merle Heidemann for her assistance, support, and
encouragement related to the required coursework, development of curricular activities, and the
completion of this thesis. Also, I would like to thank Dr. Ken Nadler for his wealth of historical
knowledge that enabled me to engineer and develop unique quantification methods for the
laboratory investigations which I employed. My initial experiences with Chuck Elzinga’s
ecology course were inspirational and left a lasting, positive impression which I carried
throughout this Master’s program. I am in debt to friends and colleagues Ben Copper and Joe
Salerno, with whom I roomed with for 3 consecutive summers. These surrogate brothers
provided inspiration, emotional support, general perspective, and were always willing to lend a
hand (or an ear) whenever problems or uncertainties arose. Finally, I would like to thank my
mother and father, Krystyna and Leonard Palmeri, who back me in any of my pursuits, this thesis
included.

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TABLE OF CONTENTS
LIST OF TABLES………………………………………………………………………...

v

LIST OF FIGURES…………………………………………………………………….....

vii

INTRODUCTION…………………………………………………………………………
Rationale and Statement of the Problem…………………………………………..
Class Descriptions and Demographics…………………………………………….

1
1
14

IMPLEMENTATION……………………………………………………………………...
General Considerations…………………………………………………………….
Discussion and Analysis of Activities……………………………………………..
Investigation #1 (Grow Toy)……………………………………………………….
Investigation #2 (Osmosis in Potato and Cantaloupe)……………………………..
Investigation #3 (Photosynthesis Lab – Bubble Rate)……………………………..
Investigation #4 (Photosynthesis Lab – Disk Assay)………………………………
Investigation #5 (Respiration Rate – Yeast)……………………………………….
Investigation #6 (Lactic Acid Buildup)…………………………………………….
Investigation #7 (Enzyme Activity – Temperature and pH)……………………….

16
16
19
19
21
23
25
26
28
29

RESULTS AND ANALYSIS………………………………………………………………
Pre-Test and Post-Test Administration…………………………………………….
Data-Analysis – Combined Pre-Test and Post-Test……………………………….
Data Analysis – Part 1 and Part 2 Pre-Test and Post-Test…………………………

32
32
32
35

DISCUSSION………………………………………………………………………………
Discussion of Data…………………………………………………………………
General Discussion………………………………………………………………...
Discussion of Investigations……………………………………………………….
Limitations and Considerations for the Future…………………………………….

38
38
39
44
47

CONCLUSIONS…………………………………………………………………………..

52

APPENDICES……………………………………………………………………………..
Appendix A: Laboratory Investigations and Activities………………………….....
Appendix B: Student Examples…………………………………………………...
Appendix C: Parent Letter and Consent Form…………………………………….
Appendix D: Assessment Tools……………….......................................................

53
54
81
92
97

REFERENCES…………………………………………………………………………….

113

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LIST OF TABLES

TABLE 1: Incorporation of Graphing Activities into the Curriculum……………………... 17
The respective conceptual units of study and the particular graphing activities that were
embedded within each. A brief description of each graphing task is also included.

TABLE 2: Pre-Test and Post-Test Data from all Assessment Components ……………….. 36
All students’ test scores improved when comparing the combined pre and posttest scores. One student achieved a lower post-test score on Part 1 of the test
compared to the initial pre-test score. Five students did worse on Part 2 of the test
and 12 students achieved grades on Part 2 of the test that were equal to their
initial scores. Green shading is used to represent an increase, yellow indicates no
change, and red denotes a decrease.
TABLE 3: Results of Statistical Analysis of a Paired T-Test on Pre-Tests and Post-Tests… 37
The combined tests as well as the individual components were analyzed and the
corresponding p-values signify statistical significance for both Part 1 and Part 2
of the assessment
TABLE 4: Salt Mixtures…………………………………………………………………….. 55
TABLE 5: Grow Toy Mass Data……………………………………………………………. 56
TABLE 6: Grow Toy Length Data………………………………………………………….. 56
TABLE 7: Potato Mass Data………………………………………………………………... 59
TABLE 8: Cantaloupe Mass Data…………………………………………………………... 59
TABLE 9: Experiment 1 Data – Distance from Light………………………………………. 63
TABLE 10: Experiment 2 Data – Color of Light…………………………………………… 63
TABLE 11: Sodium Bicarbonate Data……………………………………………………… 67
TABLE 12: Pure Water Data……………………………………………………………….. 67
TABLE 13: Fermentation Rate Data……………………………………………………….. 70
TABLE 14: Lactic Acid Activity Data……………………………………………………... 73
TABLE 15: Six-Well Plate Organization…………………………………………………… 75
TABLE 16: Temperature Data – Starch Degradation by Enzyme Amylase………………... 78
TABLE 17: pH Data- Starch Degradation by Enzyme Amylase………………………….... 78
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TABLE 18: Part1, Question #1 Data……………………………………………………….. 97
TABLE 19: Part1, Question #1 Graph……………………………………………………… 97
TABLE 20: Part 2, Question #6 Calorie Use Data…………………………………………. 105
TABLE 21: Scoring Rubric: Graphical Literacy Pre-Assessment: Part 1………………….. 110
TABLE 22: Scoring Rubric: Graphical Literacy Pre-Assessment: Part 2………………….. 111

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LIST OF FIGURES

FIGURE 1: Investigation #1 Exemplar …………………………………………………...
This investigation necessitated 2 separate Y-axes – one for the change in mass
and the other for the change in length. Different scale values allowed students to
recognize that the changes in both parameters were closely related. For
interpretation of the references to color in this and all other figures, the reader is
referred to the electronic version of this thesis.

20

FIGURE 2: Investigation #2 Exemplar …………………………………………………...
The graph produced in this investigation was unique because it needed to include
both positive and negative values on the Y-axis.

22

FIGURE 3: Investigation #3 Exemplar……………………………………………………
Different laboratory groups were assigned to test one of the investigated
variables. Afterwards, groups shared data and graphed the results from both
experiments

24

FIGURE 4: Investigation #5 Exemplar …………………………………………………...
The experiment was flawed with several problems, so the instructor provided
some additional data to graph. The complex nature of the representation was
intentional

27

FIGURE 5: Investigation #7 Exemplar …………………………………………………...
Different variables (temperature and pH) were tested on two consecutive class
periods. Students were required to reference both graphs to identify ideal
conditions for the enzyme α-amylase

30

FIGURE 6: Percent Difference between Combined Pre-Test and Post-Test Scores ……..
The range of improvement yielded on students’ pre-test and post-test scores. All
participating students demonstrated improvement when evaluating the combined
scores of Part 1 and Part 2 of the assessment.

34

FIGURE 7: Common Scale Issues (1)…………………………........................................... 40
Some students had difficulty with the establishment of an initial “0” value on the
X and Y axes when graphing data from the investigations
FIGURE 8: Common Scale Issues (2)…………………………………………………….
Some students had difficulty establishing and applying a consisent scale
increment on the assigned graphing tasks incorporated in each investigation

vii

41

FIGURE 9: Scaling Issues on the Pre-Test………………………........................................ 42
These students peformed better than most students on Question #1 of Part 1 of the
assessment. However, these examples illustrate how students have a tendency to
“assign” values to lines based on the provided data points, rather than using a
consistent and logical incremental value.
FIGURE 10: Graph Titling………………………................................................................ 43
Although students understood that titling their graph was required, many students
did not provide meaningful titles that described the representation adequately.
This student simply titled their graph “Respiration Lab”. The author noted that
the titles of graphs gradually improved during the course of this study.
FIGURE 11: Grow Toy Measurement..................................................................................

55

FIGURE 12: Potato...............................................................................................................

58

FIGURE 13: Cantaloupe………………………...................................................................

58

FIGURE 14: Photosynthesis Lab Apparatus Illustration......................................................

61

FIGURE 15: Floating Disk Assay Apparatus Illustration..................................................... 66
FIGURE 16: Respiration Lab Apparatus Illustration............................................................ 69
FIGURE 17: Exercise 1……………………………………………………………………. 72
FIGURE 18: Exercise 2……………………………………………………………………. 72
FIGURE 19: Six-Well Plate………………………………………………………………..

75

FIGURE 20: Graduated Pipette……………………………………………………………

75

FIGURE 21: Investigation #1 Student Example (1)……………………………………….

81

FIGURE 22: Investigation #1 Student Example (2)……………………………………….

82

FIGURE 23: Investigation #1 Student Example (3)……………………………………….

83

FIGURE 24: Investigation #2 Student Example…………………………………………..

84

FIGURE 25: Investigation #3 Student Example (Color of Light)…………………………

85

FIGURE 26: Investigation #3 Student Example (Distance from Light)…………………..

86

FIGURE 27: Investigation #5 Student Example (1)………………………………………

87

FIGURE 28: Investigation #5 Student Example (2)………………………………………

88

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FIGURE 29: Investigation #5 Student Example (3)………………………………………… 89
FIGURE 30: Investigation #7 Student Example……………………………………………. 90
FIGURE 31: High School Letterhead – Parental Consent Form (1)……………………….. 92
FIGURE 32: High School Letterhead – Parental Consent Form (2)……………………….. 95
FIGURE 33: Part 1, Question #2 Graph – Photosynthesis When Exposed to Light……….. 98
FIGURE 34: Part1, Question #3 Graph…………………………………………………….. 99
FIGURE 35: Part1, Question #3 Illustration……………………………………………….. 100
FIGURE 36: Part 1, Question #4 Graph – Spring Stretch of 2 Different Springs………….. 100
FIGURE 37: Part 2, Question #1 Graph – Growth of Bacteria at 37 Degrees Celsius……... 101
FIGURE 38: Part 2 Question #2 Graph – Caloric Intake………………………………….... 102
FIGURE 39: Part2, Question #4 Graphs – Effectiveness of Different Antibiotics………..... 103
FIGURE 40: Part 2, Question #5 Graph – Solubility of Various Substances………………. 104
FIGURE 41: Part 2, Question #6 Graph Answers………………………………………….. 105
FIGURE 42: Part 2, Question #7 Graph – Methane Production of Bacteria……………….. 106
FIGURE 43: Part 2, Question #8 Graph – Digestion of Starch by Amylase……………….. 107
FIGURE 44: Part 2, Question #9 Graph – Pollution……………………………………….. 108
FIGURE 45: Part 2, Question #10 Graph – Osmosis in Zucchini and Cantaloupe………… 109

ix

INTRODUCTION
Rationale and Statement of Problem
In the evolving world in which we live, the body of scientific knowledge is continually
growing as researchers across the globe investigate various systems and seek resolutions to
questions and problems. Each discipline becomes seemingly more compartmentalized as each
investigator’s focus becomes more specific to their related research field. Thus, a crucial
dynamic in the scientific community is effective collaboration and communication. To digest this
continual stream of progressive research, it is desired that this communication be condensed into
a convenient form. The “language” of science often involves data representations including
charts, tables, diagrams, and graphs that offer an analyzed and summarized version of an
investigation. In describing graphs, one researcher says, “They present concepts in a concise
manner or give at glance information which would require a great deal of descriptive context”
(as cited in Berg and Smith, 1994). In many contexts, graphs can be considered communicative
devices (Padilla, McKenzie, and Shaw, 1986). Experts of the scientific discipline become wellversed at constructing, dissecting, and analyzing these types of communicative data
representations. Many important scientific discoveries and understandings are not immediately
recognized. Rather, these findings are identified by careful analysis of data. When unorganized,
data tend to be awkward and difficult to comprehend. Graphs, however, force us to see important
or unusual details that we never expected (Friel, Curcio, and Bright, 2001).
One overarching goal of a dedicated science educator is to build disciplinary literacy and
subject-specific knowledge and comprehension. In conventional terms, literacy refers to reading
and writing proficiencies. However, in the discipline of science one must also consider the
importance of data collection, data representation, and data analysis as it applies to

1

understanding the process of science and perhaps to the comprehension of specific concepts. In
fact, experts propose that “using graphical representations are critical to being scientifically
literate” (Coleman, McTigue, and Smolkin, 2011). The graphs themselves are representations of
quantitative scientific information and serve as tools in detecting patterns which inform the
viewer about the phenomenon that is under investigation (Roth and McGinn, 1997). Clearly, in a
discipline that relies on empirical, quantitative evidence, establishing proficiencies with graph
construction and interpretation could be considered essential.
Scientific literature and even high school textbooks provide evidence of the importance
of visual representations such as graphs in conveying information. In a random survey, it was
determined that, on average, there are 1.46 to 1.38 graphics per page in typical scientific journals
and high school science textbooks (McTigue and Flowers, 2011). Graphs are excellent tools for
depicting quantitative data and make it easier to understand a scientific concept (Shah and
Hoeffner, 2002). Of course, this assumes that the viewer has the capability of analyzing the
information contained within (Tairab and Khalaf Al-Naqbi, 2004). Research has consistently
shown that students of all ages have difficulty comprehending graphs and this lack of proficiency
“is a handicap and a limiting factor in learning scientific concepts” (Ates and Stevens, 2003).
Significant effort has been put forth in attempting to find cause for this general lack of
competency with regard to graphical literacy. It has been argued that interpretation and even
graph construction skills require abstract reasoning ability. Thus, some researchers are resolute
that abilities related to graphing proficiencies are dependent on developmental cognitive abilities
(as cited in Friel et al., 2001). These researchers might insist that it is entirely expected that
young learners, those in elementary and middle school, are incapable of constructing and
analyzing certain graphs effectively. Traditional research in science shows graphing as a
2

compilation of individual abilities and skills, and graphs themselves serve as mental
representations to the learner (Roth and McGinn, 1997). In the tradition of the Piagetian
philosophy, the lack of proficiency many students have with graph construction and
interpretation can be attributed to the learner lacking “logical-thinking structures”. Support for
this reasoning is reinforced by research that demonstrate the correlation between sixth, seventh,
and twelfth grade students’ logical reasoning ability and graphing achievement (as cited in Roth
and McGinn, 1997).
However, others argue that graphing proficiencies are not “exclusive” to cognitive
ability. Like many other skills, meaningful practice is necessary before one can be expected to
master and become proficient at graphing. Logically, some argue that students who are not
exposed to experiences requiring graph construction or evaluation will certainly not be good at it
(Friel et al., 2001). Some argue that individuals need a certain level of knowledge before they are
able to legitimately participate in practicing graphing (Roth and McGinn, 1997). This sort of
reasoning does not appeal to the educational practitioner who is interested in systematically
assisting students and working with them in skill development. Proponents who view graphing
ability as a “practice driven” process insist that if students are expected to acquire graphing
competence they surely must “participate actively in the development and maintenance of this
practice” (Ibid).
Reasonably, it can be assumed that practice would positively impact the acquisition of a
desired skill. This belief is fundamental in the premises behind the study reported here. In the
arena of science education it is valid to ask the question: How much “meaningful” graphing
practice are students exposed to during a general K-12 education? Beginning with the elementary
level, self-contained classroom teachers typically do not have extensive preparation in the
3

discipline of science. Furthermore, visual representations, in general, have played a minor role
compared to other forms of communication, including written and oral. In a comprehensive
study involving the self-reported use of graphical representations in elementary science teaching,
researchers concluded that elementary “teachers are not likely to employ graphical
representations to their fullest potential when teaching science” (Coleman, McTigue, and
Smolkin, 2011). The practice involving graphs that does exist can be considered limited in
frequency and in complexity. Furthermore, the skills and cognizant thought processes needed to
analyze graphical representations is generally not taught explicitly. Thus, there is a distinct
possibility that a typical student does not receive the exposure required to cultivate these skills
needed for effective graph construction and analysis.
One would think that the aforementioned prevalence of graphical representations in
scientific literature and high school textbooks would provide significant amounts of practice and
exposure to a typical student. However, it has been pointed out that “Textbook examples of
graphs often are too pre-processed” (Friel et al., 2001). When the purpose of graphical
representations and their precise role in a science text is considered, it is understandable that
these graphs are deliberately designed to be “processed” easily. Obviously, graphs embedded in
a text are provided to assist with comprehension of whichever topic or concept is contained
within that section. Indeed, significant efforts have been made to determine which factors in
graph design might contribution to enhanced comprehension.
Researchers have concluded that, when designing graphs for ease of integration into
one’s knowledge base, the “number of cycles of processing required to interpret the graph”
should be minimized (Ratwani, Trafton, and Boehm-Davis, 2008). The dual nature of graphs is
to teach and present data. There are two general considerations relative to the learner’s analysis
4

of a graph – visual integration and cognitive integration. With respect to visual integration, it is
suggested that “visual clusters” of information in texts be presented so that they can be easily
framed. The boundaries of various features of the graph should be highlighted or bolded such
that they are perceptibly distinct to the viewer. Even the selection of color needs to be intentional
when designing a graph to facilitate visual integration. “Spectral color palettes” that are easy to
discriminate should be incorporated rather than minor adjustments in shading or intensity
(Ratwani et al., 2008).
Likewise, designing graphs for efficient cognitive integration also requires calculated
attention to design details. Researchers have concluded that “graphs should be designed to
reduce the amount of processing needed to reason with the visual clusters formed during visual
integration” (Ratwani et al., 2008). Intuitively, one understands that when a graph is overly
complex it requires the viewer to transition between the various regions and features contained
within the representation. More complicated graphs inherently depend on additional cycles of
visual integration and cognitive integration (Ratwani et al., 2008). As these transitional
requirements increase, it is more likely for the student to make processing errors. It is also
possible that when graphs contain too many intricacies the cognitive load presented might be
beyond the ability of the viewer.
When considering the implications of graph cognition research, it is understandable that
textbook publishers carefully choose how data should be represented. For integration and
assistance with understanding, graph simplification is the general theme. In a comprehensive
review of the research, it was determined that “Non-informative features are unhelpful and often
distracting” and certain unnecessary features that have been categorized as “chart junk” should
be minimized (Shah and Hoeffner, 2002). It is even suggested that some traditional features,
5

such as graph keys and legends be eliminated. These features “require that graph readers keep
referents in memory” and “pose special demands on working memory” (Shah and Hoeffner,
2002). An implication has been that, if possible, graph features should be labeled directly, rather
than using an auxiliary key or legend.
The careful considerations involved in selecting graphical representations for textbooks
are an indirect acknowledgement that students have difficulties with graph comprehension. In the
educational community, however, the relevance of graphs is also presented directly. State
benchmarks and National Science Education Standards emphasizing graphical literacy serve as
evidence that skills related to graphing are understood and valued (Coleman et al., 2011).
Review of the Michigan Department of Education’s Grade Level Content Expectations (version
1/09) and High School Content Expectations (version 10/06) reveal that data organization,
analysis, and comprehension are universally critical for each respective content discipline
(physics, chemistry, biology, and earth science). The inclusion of graphical literacy in content
expectations is entirely justified because it is unlikely students will develop such skills unless
they are directly addressed by the curriculum (Padilla et al., 1986).
Of course, in the domain of public education, the ultimate importance of any skill might
be gaged by how this ability translates into achievement on standardized tests. After all, student
achievement on standardized tests determines the meeting of Adequate Yearly Progress (AYP) in
conjunction with the much discussed No Child Left Behind legislation. When viewed in this
light, the importance of developing graph construction and analysis skills is highlighted once
again.

6

Researchers investigating how necessary diagrammatic literacy skills are for success on
standardized tests exposed some remarkable statistics. In the analysis of 985 standardized test
questions collected randomly from various state assessments for late-elementary to middle
school grade levels, researchers discovered that 52.7% of test questions involved graphical
representations (Yeh, and McTigue, 2009). Of these questions involving graphics, 79.5% of the
representations contained information needed in successfully answering the question. The most
prevalent types of representation included graphs and data tables, and the frequency of such
representations was found to increase as students advance in grade level. Thus, a student’s ability
to proficiently extract information and meaning from graphs is imperative if success on
standardized tests is expected.
Recently, there has been a concern that students in this country are falling behind and our
nation will lose its innovative identity and competitive edge in the disciplines of science and
engineering. Thus, the development of “critical thinking” skills has been advocated and
recognized as a priority in the future of the educational system. Those researching the
development of critical thinking skills and their assessment believe that a more multidisciplinary
approach that includes the analysis of visual representations is necessary (Malamitsa, Kokkotas,
and Kasoutas, 2008). The interpretive skills needed for effective critical thinking are intimately
connected to all forms of literacy, including that which is graphical. Using a standardized
assessment tool, The Test for Everyday Reasoning (TER), researchers analyzing various age
groups determined “a strong relationship” between graph and chart interpretation and the
assessed critical thinking skills (Ibid). Thus, it is advocated that visual literacy, which includes
graph interpretation, should be taught in conjunction with critical thinking. Researchers warn that

7

teachers often assume visual representations are “self-explanatory” and that students can
decipher them without being provided any support (Ibid).
With this in mind, intentional instruction regarding graph analysis is required to develop
graphical literacy that will enhance critical thinking (Fencl, 2010). In one study, “carefully
designed” hands-on experiences which incorporated graphing resulted in significant gains in the
critical reasoning skills of university students (Fencl, 2010). A curricular experience with
embedded practice and routine use of graph analysis skills resulted in students being able to
apply the same skills in a “straightforward but unfamiliar” situation (Fencl, 2010). The
crossovers of such abilities are conditional on intentionally designed activities and the skill must
be practiced repeatedly. The implications of this are particularly important to classroom teachers
– a curriculum designed with these considerations can produce students that are capable of
applying graph analysis skills to the frequent instances encountered on standardized tests and
college entrance exams.
One might question why exactly “intentional” practice is necessary for the acquisition of
these skills, especially to those that translate to standardized test achievement. The
considerations involved in the design of representations to assist with graph comprehension have
been detailed extensively. However, the motives involved in graph implementation on
standardized tests, compared with those in textbooks, appear as a conflict. Obviously, textbooks
attempt to enhance comprehension, while standardized tests seek to assess skills, abilities, and
understanding. Rather than intentionally simplifying graphs, standardized tests are likely to
incorporate graphs which pose visual and cognitive obstacles. Graphs, charts, and tables on the
ACT, for example, often require the student to evaluate and cross-reference multiple figures
when answering a question. Likewise, standardized assessments often present multifaceted
8

graphs that include multiple data sets and relate several variables at one time. For instance,
graphs displaying three continuous variables are seldom encountered by a typical student, and
consequently, reflect a complex analysis task (Friel et al., 2001). As the complexity of the graphs
increases, it requires students to internalize many different referents, increasing the time and
processing required while evaluating the representation (Ratwani, 2008). As one would expect,
children and even adults are likely to “make systematic errors interpreting graphs, especially
when graphs do not explicitly depict the relevant quantitative information” (Shah and Hoeffner,
2002).
When it comes to graphical analysis, it is important to understand specifically what
challenges students when evaluating graphs. Generally, students perform better when they are
assessed on the ability in identifying a “specific piece of interpretations” that are directly
indicated by the graph (Tairab and Khalaf Al-Naqbi, 2004). For example, in a graph of
population growth students are typically capable of recognizing the maximum population value,
or they can identify when the population grew at the greatest rate. However, students
demonstrate relatively poor skill when interpreting graphs in a quantitative manner and making
broad descriptions of what a graph is communicating. Finally, researchers have determined that
students lack an understanding of the different types of data representations (Tairab and Khalaf
Al-Naqbi, 2004). Thus, students are unaware of when and how various graphs are used to display
quantitative information.
The capabilities demonstrated in graph interpretation are not equal among different types
of representation. For students, reading and analyzing circle graphs, or pie charts, tends to be a
less challenging task (Ates & Stevens, 2003). Bar graphs also tend to be easier for students to
decipher, and the writer has noted that middle school and high school students tend to have an
9

inclination to favor this type of representation. The author speculates that in earlier educational
experiences, bar graphs were probably most likely to be employed. It is ventured that overemphasizing one type of representation “may lead students to conceive all graphs as having that
form” (Meverech and Kramarsky, 1997). Perhaps the propensity for bar graphs also relates to the
fact that line graphs tend to be the most difficult for students to interpret. It has been proposed
that the construction and interpretation of line graphs requires “abstract reasoning skills” (Padilla
et al., 1986). Obviously, line graphs are preferred when attempting to depict trends and changes
rather than simple comparisons and the use of line graphs is extensive in the scientific domain.
Since little can be done regarding the specific cognitive ability of a student, the logical
approach would include support in developing these “abstract reasoning skills” related to
graphing. As elaborated, much research has been dedicated to the reading and interpretation of
graphs by students. Friel and others explain that few researchers have explored other
considerations, including “graph construction or invention or graph choice” (2001). Very little is
understood how statistical laboratory investigations that include graph creation relates to
students’ comprehension. Researchers speculate that instruction within an appropriate context
“may promote a high level of graph comprehension that includes flexibility, fluid, and
generalizable understanding of graphs and their use” (Friel et al., 2001). The outright
implication, therefore, is that students who are engaged in formulating graphical representations
are likely more prepared to convey their understanding of represented data, relating it to other
information, and successfully answering questions that are posed (Friel et al., 2001).
When engaging students in authentic laboratory investigations, the learners at least have
some perspective and understanding of context – and they understand how the data were
collected and what the data are attempting to represent. One study that illustrates the importance
10

of contextual knowledge involved having research scientists analyzing graphs that were
unrelated to their own precise discipline. In this study, researchers determined that “One’s
extensive interaction with phenomenon and representational means seems to be prerequisite for
competent graphing practices” (Roth and Bowen, 2001). The conclusion, therefore, is that
students need more than just instruction that focuses on the mechanical construction aspects of
graphing. Instead, the graphical representation should be tied directly to occurrences that they are
familiar with.
Generally, standardized assessments are delivered in a multiple-choice format. Given this
reality, one might assume that direct and frequent exposure to existing standardized-test style
graphs and subsequent questions would be the most efficient strategy for developing learners’
measured graph comprehension skills. Tairab and Al-Naqbi, while studying tenth grade science
students’ competencies with graphs, recognized that students tend to perform better at
interpretation tasks in comparison to the constructive processes (2004). The suggestion from this
research is that teachers ought to specifically practice construction and interpretation in unison.
Students should be exposed to “varied and rich” activities that involve graphing data. Other
researchers also identify value in having students construct graphs, because it provides
enhancements in their examination capabilities. Berg and Smith (1994) venture that “perhaps the
act of construction leads to higher levels of cognitive engagement by forcing students to attend to
the local processes”. Therefore, the environment of an effective science classroom should unite
the progressions of data collection, graph construction, and corresponding critical analysis.
Another important consideration that one must ponder is that graph construction and
graphical analysis skills are not isolated and unique to the scientific discipline. Obviously, graphs
are intimately tied to the field of mathematics, but graphs can also be encountered in other
11

traditional high school courses including those such as social studies and history. Thus,
incorporation of graphing into a science curriculum might also pay dividends by teaching
processing skills that can be applied in multiple subject areas. This proposition has been
supported by research. In a comprehensive study examining graphing ability, grade school
students tended to do well on determining appropriate X and Y coordinates and point plotting.
This exhibited proficiency, some speculate, was due to the fact that these skills overlap into the
science and math curriculums and have been emphasized early in the student’s educational
experiences (Padilla et al., 1986). Hochberg and Gabric go as far as describing math and science
as a “provably necessary symbiosis” (2010). These researchers developed overlapping biology
and math lessons, with the mathematical, qualitative components being “interwoven” into the
science concepts under investigation. Not surprisingly, graphing was included in these activities.
Other practitioners seek to use graphing in other unique and interesting ways. The strategy of
“creative graphing” is an activity that allows students to organize and represent information
graphically. This method can occur in multiple curricular settings, even those involving writing
and literature. One proponent explains that the process of re-organizing complex information
into appropriate graphs is a highly effective learning strategy for many students (Johnson, 1989).
Certainly, an interdisciplinary perspective that cultivates graphical literacy in multiple subject
areas can be entirely warranted.
The rationale for this research project includes a novel approach: Including authentic data
collection and graph construction into a science curriculum with the expectation that students’
proficiency with graphical representation will improve. With the incorporation of laboratory
activities that generate authentic data, it is expected that learners will develop a sense of
“ownership” for the data they collect, thereby establishing a realistic context for graphing
12

practice to be conducted. During the course of a laboratory investigation, having students
experimenting and collecting their own data inherently makes them familiar with the context
(Friel et al., 2001). Researchers have recognized that a viewer’s knowledge of context influences
their ability in scrutinizing and making sense of graphs (Shah and Hoeffner, 2002). Thus, the
approach employed in this study ensures all students will be familiar with the concept being
investigated, the setup and organization of the laboratory apparatus, and the methodology used in
collecting data.
The researcher considered developing an independent unit of study that focused entirely
on graph construction and analysis, without the inclusion of curricular content. However, this
approach would provide disruptions in the host school’s accepted curricular scheme and
sequence. It can be noted that this was the researcher’s first experience teaching the respective
courses in which this study was implemented. Because the author had never taught these courses,
seamlessly embedding the developed activities, rather than retooling the entire curriculum, was a
logical approach. In addition, based on the conclusions of experts, the chosen inclusive approach
to teaching graph construction skills and analysis was justified.
The extensive practice and exposure to graph construction embedded within the content
of a biology course is aligned with the suggestions of experts. Shah and Hoeffner, following their
review of graph comprehension research, decided that “Graphical literacy skills should be taught
in the context of science” (2002). Obviously, students can not be expected to tacitly acquire
graph comprehension knowledge, and researchers suggest that explicit instruction with graphical
representations should be entrenched within authentic, scientific inquiry (Coleman, 2011).

13

Teachers of science in the K-12 setting are encouraged to mimic the scientific process by
including genuine laboratory investigations. However, it is the experience of the writer that often
times the quantitative element of collecting empirical evidence is ignored. Many times, “lab
activities” rely on subjective observations rather than the analysis of concrete, quantitative data.
Understandably, some investigations are difficult to quantify, or the equipment needed for
quantification is too expensive and or unavailable to the typical educator. Thus, for this study,
activities that generate data that could be quantified, graphed, and analyzed using ordinarily
available materials were developed. The approach of this study is to provide authentic, intensive
data collection experiences and routine exposure to graph construction and graph analysis
problems. Fundamentally, the author expects that student-constructed graphs accomplished in the
context of learning science will also provide the most legitimate opportunities for graphical
analysis. It is hypothesized that exposure to authentic data collection and routine graphing
practice will positively support improvements in students’ measured graphical literacy skills. The
null hypothesis is that these efforts will have no effect.

Class Descriptions and Demographics
This study was administered at Avondale High School within the Avondale School
District, a suburban community located approximately 21 miles north of Detroit, Michigan. The
Avondale District spans across multiple municipalities, including portions of Auburn Hills,
Bloomfield Township, Rochester Hills, and Troy. Avondale High School has a student
population of approximately 1160 students and includes a diverse student body composed of
65% White/Caucasian, 21% Black/African American, 8% Asian/Pacific Islander, 5% Hispanic,

14

and 1% Multiracial. Approximately 24% of students qualify for free or reduced lunch and
Avondale High School receives Title I funding. Average class sizes included 26 students for the
2011-2012 school year, and the graduation rate was reported at 97% for 2010-2011. Of those,
72% of students attended college directly after graduating. ACT scores for the 2011-2012 school
year included an average composite of 21.2, and 21.7 for the science portion.
At Avondale High School the biology curriculum is split into two separate semester
courses, “Microbiology” and “Macrobiology”. The framework for each course’s curriculum is
structured based on the Michigan Department of Education’s High School Content Expectations
for Biology (rev. 10/06). Content expectations are appropriately divided and distributed into each
course. This research project was initiated in two separate Microbiology classes during the first
semester of the school year. Each class was composed mostly of tenth grade sophomores, but a
few high-achieving ninth grade freshman students were also present. Both classes included
special education students that qualified for accommodations.
At the beginning of the semester, consent forms were distributed and students were given
a timeframe of approximately 2 weeks to return them. In addition, during an introductory open
house, visiting parents were made aware of this study. In the first class section, 21 of 25 students
consented and in the second section 19 of 22 students provided consent. This established a
participation of 85% of enrolled Microbiology students, which can be considered a highly
representative sample

15

IMPLEMENTATION
General Considerations
During the preparation of this study and development of activities contained within, there
were some uncertainties regarding the teaching schedule that the writer would undertake. A
potential transfer to the district’s middle school was a distinct possibility that was thoroughly
considered. Thus, concepts that provided opportunities for graphing were selected such that
activities could be modified and translated into the seventh grade curriculum if needed. It was
determined that osmosis, photosynthesis, cellular respiration, and enzyme activity were concepts
that lend themselves to quantitative analysis and presented overlap in both curriculums.
The range of concepts investigated spanned approximately 9 weeks of academic
instruction. Thus, each activity was strategically implemented into each respective unit of study
at the most opportunistic and logical time. In general, these activities were implemented with the
learner already having some background knowledge provided by other planned curricular
activities and perhaps some prior learning experiences. Again, the approach was not to
drastically alter the ebb and flow of the accepted curriculum, but rather to embed these authentic,
data-intensive laboratory investigations that required graph construction and analysis. Table 1
illustrates the unit-by-unit break down and specifies which activities were developed for the
project reported here. After each activity, laboratory worksheets were collected, assessed, and
returned to students. Particular attention was applied to graphs which were constructed.
Individual students might receive specific comments that corresponded to the graph they
submitted. Also, while assessing student submissions, the instructor kept note of common errors
that signified conceptual errors or processing deficiencies. Upon returning graphs and laboratory

16

worksheets to students, the instructor methodically described these consistent errors. Examples
of common errors were illustrated or projected onto the board such that students could selfevaluate their own graphs. In some instances, exemplars were displayed in each class to model
appropriate use of scale and graph organization.
Before engaging in any planned graphing activities, the pre-test that included two
separate components was administered. Each part of the pre-test was administered on two
separate but consecutive days. This decision was made due to time considerations and to avoid
testing fatigue. The pre-tests were conducted during the second week of school, during the first
unit of study. All enrolled students completed this test before consent forms were returned.

Table 1: Incorporation of Graphing Activities into the Curriculum
The respective conceptual units of study and the particular graphing activities that were
embedded within each. A brief description of each graphing task is also included.
Unit of Study

UNIT I
Scientific
Processes
“Chemistry of
Life”
UNIT II
“Cells”

Embedded Activities

Investigation #1 (Grow Toy)
How does dissolved salt concentration
affect the change in size (length) and
mass of a grow-toy?
Investigation #2 (Osmosis in Potato and
Cantaloupe)
A comparison of the osmolarity of
potato and cantaloupe. Determining
what would be considered a hypotonic
or hypertonic solution for each. Using
the constructed graph to determine
isotonic concentrations for each cell
type and testing this prediction.
17

Description of Graphing
Tasks
A 2-line graph that
incorporates 2 independent
y-axes (± % change in
length and mass). X-axis –
% Salt concentration (m/v)
A 2-line graph that
incorporates ± % change in
mass on the y-axis and
potato and % Sucrose
concentration (m/v) on the
x-axis. This is a unique task
because it includes positive
and negative values on the
y-axis.

Table 1 (cont’d)
UNIT III
“Cellular
Respiration and
Photosynthesis”

UNIT III
“Cellular
Respiration and
Photosynthesis”

Investigation #3 (Photosynthesis Lab –
Bubble Rate)
Determining how intensity (distance
from light source) and the color of light
(wavelength) affects the photosynthetic
rate of an aquatic plant.
Investigation #4 (Photosynthesis Lab –
Disk Assay)
Determining how the availability of
carbon dioxide affects the rate of
photosynthesis. (Addition of sodium
bicarbonate)
Investigation #5 (Respiration Rate –
Yeast)
Testing the respiratory rate of yeast
when exposed to various sugar
concentrations at a standardized
temperature. Rate determined by
measuring CO2 production via the
movement of an “indicator” in a small
diameter tube.
Investigation #6 (Lactic Acid Buildup)

UNIT III/IV

Subjects squeezing a tennis ball with
their non-dominant hand until fatigued
related to aerobic and anaerobic
respiration
Investigation #7 (Enzyme Activity –
Temperature and pH)
Investigating how pH and temperature
influence the digestion of starch by the
enzyme amylase

18

2 separate graphs (one that
relates to intensity and the
other wavelength). Each
graph includes 2 separately
graphed lines. The slope of
each line representing the
rate of photosynthesis
A 2-line line graph that
compares the photosynthetic
rate (with a supplied carbon
dioxide source and without)

A line graph that includes
multiple lines (Variable %
sugar concentrations)

A bar graph that compares
the results of various
students participating in the
activity

One graph comparing the %
starch digested (y-axis) at
various temperatures (xaxis). Another graph
comparing the starch
digested in different pH
solutions

Discussion and Analysis of Activities
Investigation #1 (Grow Toy) – (Appendix A)
During the first unit of study, as is typically done in many science courses, a general
overview of the scientific method was presented to students. The dynamic nature of the scientific
process was studied and students engaged in various activities to recognize many relevant skills,
such as observation, question generation, and measurement. However, there were not any
existing activities that incorporated graphing. The “Grow Toy” investigation was included
towards the end of the first unit because it provided a general review of the scientific method and
it required the use of these tangible skills. For the purposes of this study, it provided the first real,
authentic investigation that included data collection and graphical representation.
To facilitate and streamline the execution of this investigation, tap water was pre-mixed
with precise amounts of sodium chloride to establish the required salt concentrations (2.5%,
5.0%, 7.5%, 10.0%, and 15.0%). Large containers of distilled water and each solution were
provided to students, relevant measurements were made, and “Grow-Toys” were soaked in each
respective solution for 24 hours. The following day, students removed the toys, dried them,
conducted post-soaking measurements, and calculated respective changes in mass and length.
These mathematical calculations posed a significant challenge for several students. However,
working in collaborative groups supported those who struggled.
Students were instructed to construct a graph that represented both the changes in mass
and the changes in length. This necessitated that students utilize two separate y-axes, one for the
percent change in mass and the other for the percent change in length. Figure 1 is a student
example produced during this activity. Since this was the first exposure to this type of
19

Figure 1: Investigation #1 Exemplar
This investigation necessitated 2 separate Y-axes – one for the change in mass and the
other for the change in length. Different scale values allowed students to recognize that
the changes in both parameters were closely related. For interpretation of the references
to color in this and all other figures, the reader is referred to the electronic version of
this thesis.

1

3

2

5
4
Figure Legend:
1. Graph Title
2. Left Y-axis - % change in length
3. Right Y-axis - % change in mass
4. X-axis - % salt concentration
5. Graph key

representation, it was expected that students might struggle. Some direct instruction to scaffold
the graph construction process was provided as well as some individual instruction to struggling
20

students.
On the initial pre-test students had a tremendously difficult time with the construction of
a similar graph. Thus, this graphing exercise made them aware of how a graph can be designed
to deal with multiple variables. One common mistake noted by the instructor was errors in the
scale utilized on the X-axis. Although the salt concentrations were conveniently chosen, these
increments were not uniform. Many students made conceptual errors in equally spacing data
points horizontally (even though the data is not quantitatively spaced equally). After assessing
each student’s submission, graphs and laboratory worksheets were returned, the common
mistakes were explained, and exemplars were provided for reference.

Investigation #2 (Osmosis in Potato and Cantaloupe) – (Appendix A)
The second unit of study focused on cells and incorporated the concepts of molecular
movement across cell membranes. Specifically, the concept of osmosis was investigated. In a
prior laboratory activity, students quantitatively analyzed osmosis via a popular activity –
subjecting an egg to a hypertonic solution (corn syrup) and hypotonic solution (distilled water).
Although this investigation provided some practice with data and provided insight into the
concept, it did not include any data representation. It was logical, therefore, to build on prior
knowledge and segue into a more structured, data-intensive examination of osmosis.
Different concentrations of sucrose (5%, 10%, 20%, and 30%) we pre-mixed by the
instructor. Due to the limited time allotted per class period, such preparatory steps were done for
logistical considerations. Students in selected laboratory groups cut potatoes using a vegetable
cutter to ensure consistency in size. Similar to Investigation #1, students measured the initial
21

Figure 2: Investigation #2 Exemplar
The graph produced in this investigation was unique because it needed to include both
positive and negative values on the Y-axis.
1

4
2
Figure Legend:
1. Graph Title
2. Y-axis - % change in mass
3. X-axis - % sucrose
concentration
4. Graph Key (Potato,
Cantaloupe)

3
mass of each sample and then soaked a sample in each respective solution (with variable sucrose
concentrations). Because of cost considerations, each group did not repeat this procedure with
the cantaloupe. Instead, the instructor conducted the process with the cantaloupe and gathered a
“classroom data set” that all groups could use and evaluate.
The following class period, samples were removed, dried, and measured, and percent
changes in mass were calculated. The instructor used the cantaloupe sample as an opportunity to
model how to conduct the measuring and calculation process. As a class, the calculations were
performed for the cantaloupe so that students could use these data in their graphical analysis.
22

Although difficulties were encountered, the researcher noted that there seemed to be fewer
struggles (in comparison to Investigation #1). Students then were tasked with organizing and
representing both data sets (potato and cantaloupe) onto a single graph. One unique aspect of this
investigation is that, depending on the sucrose concentration, some samples gained mass while
others lost mass. Therefore, it was necessary for students to construct a graph that allowed for the
graphing of negative values on the y-axis. A student-produced example is provided in Figure 2.
Using their constructed graphs, students answered a variety of post lab questions (See
Appendix A). In addition, students were asked to use their graphs in predicting an isotonic solute
concentration for each sample type. This concentration corresponded with the respective line’s
intersection of the X-axis. Several predictions were solicited from students and solutions were
mixed to test these predictions. It was determined that student predictions were accurate in
establishing an approximately isotonic concentration.
Investigation #3 (Photosynthesis Lab – Bubble Rate) – (Appendix A)
The third unit of study provided the best opportunity to incorporate data-rich laboratory
activities. The concepts of photosynthesis and cellular respiration lend themselves to laboratory
investigations. In this first laboratory exercise investigating photosynthesis, students were
distributed into collaborative groups. The groups were then divided such that some groups
investigated how light intensity affects the photosynthetic rate of an aquatic plant. The remaining
groups investigated how the color of light affects the photosynthetic rate. The laboratory
apparatus was set up similar to how it is illustrated in Appendix A. However, instead of using a
separate heat sink, the test tubes were submerged in a large beaker of water, a strategy that

23

proved effective. In this
Figure 3: Investigation #3 Exemplar
Different laboratory groups were assigned to test one of the
investigated variables. Afterwards, groups shared data and
graphed the results from both experiments

activity, the rate of
oxygen bubble
production was related to

1
4

2

3
5
8

Figure Legend:
1. Graph Title- “The
Effect of Distance of
Light on the
Photosynthetic
Rate”
2. Y-axis – Number of
Bubble
3. X-axis – Time
(minutes)
4. Graph Key – (10cm
distance, 30cm
distance)
5. Graph Title – “The
Effect of Color of
Light on the
Photosynthetic
Rate”
6. Y-axis – Number of
Bubble
7. X-axis – Time
(minutes)
8. Graph Key – (White
Light, Green Light)

6

the overall rate of the
photosynthetic process.
The data
collection involved in
this activity included
recording the time it took
to produce each
consecutive oxygen
bubble. Some students
varied the aquatic plant’s
distance from the light
source while other
groups tested the color,
or wavelength, of the
light. This was
accomplished by
wrapping the entire test
vessel with green,

7

transparent cellophane.
24

One problem encountered was that not all of the aquatic plant samples were photosynthetically
active enough to produce consistent and reliable oxygen bubbles. Thus, a few groups had to
combine with each other in order for all students to collect useful data. After data were collected,
groups that tested different variables shared data.
Students were instructed to graph the results of each experiment on separate graphs as
illustrated in Figure 3. The quantification method resulted in a data set that required the “bubble
number” to be graphed as a function of time. In each graph, two separate lines were included. In
the experiment investigating light intensity, the data collected at a close distance and the data
collected at a further distance were graphed as individual lines. Likewise, the same was done on
the second graph, one line resulting from the white light, and one line from the green light. In
this investigation the concept that the slope of a graphed line represented a rate was emphasized.
Thus, students calculated and compared slope values as a component of the post-lab questions.
Investigation #4 (Photosynthesis Lab – Disk Assay) – (Appendix A)
Following Investigation #3, the second laboratory activity examining photosynthesis
involved carbon dioxide as the tested variable. A large batch of 0.2% sodium bicarbonate was
premixed to ease the process of material distribution. Students were randomly distributed into
laboratory groups. A deviation from the illustrated laboratory apparatus (Appendix A) was the
absence of a heat sink because of a lack of availability. Heat production from the incandescent
light bulbs was a concern, so they were exchanged in favor of compact florescent bulbs (CFL).
The writer believes that the laboratory results were reliable with this modification.
In the presence of light, oxygen production causes the spinach leaf disks to become
buoyant. For quantification, students counted the number of floating disks each minute. The
25

concept of a carbon dioxide source being critical to the photosynthetic process was verified
unanimously among groups. Some groups collected very regular data with the disks floating up
consistently. There were a few groups that observed more erratic results – short intervals where
many disks became buoyant and other time interval during which no additional disks floated.
However, in all groups the supply of carbon dioxide (from the dissolved sodium bicarbonate)
resulted in more floating spinach disks compared to the samples that only included water.
Students then had to graph the number of floating disks as a function of time for each
data set. This produced 2 separate lines on the graph – one for the sample that included sodium
bicarbonate, and the control sample that only used tap water. As described, some groups
collected data that resulted in nearly linear graphs. Other groups had more erratic data.
Regardless, this activity provided the desired graphing practice, and it supported the conceptual
ideas being investigated.
Investigation #5 (Respiration Rate – Yeast) – (Appendix A)
Continuing in Unit III, the concepts of aerobic cellular respiration and anaerobic
fermentation were introduced. Prior to this lab investigation, a fermentation activity utilizing
yeast was conducted as part of an existing “mini-lab”. Thus, students already had some
background knowledge concerning the topic. In Investigation #5, a small-diameter tube
connected to a rubber stopper is inserted into a test tube containing a yeast culture. The sucrose
concentration provided to the yeast was varied in this experiment. A small drop of colored
solution introduced into the tubing acted as an “indicator”. Carbon dioxide gas produced during
fermentation forces the indicator to move through the tubing. The distance that the indicator
traveled through the transparent tubing (per unit time) was used to quantify the respiratory rate.

26

Large beakers containing pre-mixed concentrations of sucrose were situated in a hot-water bath
Figure 4: Investigation #5 Exemplar
The experiment was flawed with several problems, so
the instructor provided some additional data to graph.
The complex nature of the representation was
intentional

set at 50°C.
Students were
randomly distributed into
laboratory groups for this

1

experiment. Following the

4

supplied laboratory
procedure, students tested
various concentrations of

2

sucrose and collected data
using the laboratory apparatus
(see Appendix A). Some
adjustments were made while
this experiment was
conducted. Several groups
had difficulties because the
3

carbon dioxide production

Figure Legend:
1. Graph Title – “How Sucrose Concentration
Affects Alcohol Fermentation in Yeast”
2. Y-axis – Distance (cm)
3. X-axis – Time (minutes)
4. Graph Key – coded for various sucrose
concentrations

was excessive, either blowing
past the indicator, or moving
it at a speed that impeded
data collection. However,
most groups were eventually

able to collect usable data. The researcher introduces several modifications to the procedure that
27

could be useful in the future. The standardized temperature of 50°C could be reduced, or the
amount of yeast utilized could be less, perhaps 0.5 grams instead of 1.0 grams. These
adjustments would likely reduce the rate of carbon dioxide production and facilitate data
collection.
In addition to data collected by students, the instructor also supplied students with
previously obtained data. Students were instructed to represent all of this quantitative
information on a single graph, as illustrated in Figure 4. The distance the indicator traveled in
each trial was graphed as a function of time, resulting in a graph that included multiple lines.
Due to the excessive carbon dioxide production in most trials, there was not a significant
distinction in the slope (rate) of each respective line. On a positive note, the nature of the graphs
produced during this activity did provide much desired, intentional practice involving complex
representations.
Investigation #6 (Lactic Acid Buildup) – (Appendix A)
Cellular respiration was one of the fundamental concepts covered in this unit of study.
Aerobic respiration and various forms of fermentation were detailed during note taking and when
engaged in certain activities, such as those experienced with Investigation #5. Thus,
Investigation #6 was introduced when students already had significant background knowledge
concerning the topic.
In this activity, students squeezed a tennis ball with their non-dominant hand. The total
time and the number of squeezes were counted until the student’s hand became fatigued. The
reality of muscle fatigue is related to lactic acid build-up from the fermentation process. This
activity is not entirely authentic; however, it does involve data collection and graphical
28

representation. After collecting their data, students produced a bar graph comparing the number
of squeezes among peers in the class. Also, the time elapsed before fatigue was experienced was
also represented with a line graph. This activity provided students with practice constructing
appropriate scales. Inevitably, one of the students in each class produced data that might be
considered an “outlier”, forcing students to adjust and compensate with the design and chosen
scale increment.
Investigation #7 (Enzyme Activity – Temperature and pH) – (Appendix A)
Investigation #7 was conducted immediately after the completion of Unit III, but before
beginning Unit IV. Although not assessed in the previous unit, the concept of starch being
broken down into sugar related closely with cellular respiration. This laboratory investigation
spanned two consecutive days. The first component of the lab examined how temperature affects
the activity of the enzyme amylase. On the second day, students determined how pH influences
the enzyme. Prior to the lab, a large volume of buffered starch solution, an acidic iodine solution,
and a stock solution of α-Amylase were prepared. The enzyme and starch solutions were placed
under variable temperature treatments.
Students were randomly divided into laboratory groups for the execution of this lab. For
each trial, students obtained starch and enzyme from each respective temperature treatment. 1.0
mL of the enzyme was introduced into the starch test tube, mixed, and allowed to catalyze for 1.5
minutes. At the conclusion of this time period, 10 drops of the acidic iodine was added to stop
the decomposition reaction and the purple-black color that developed indicated the relative
amount of starch remaining. To quantify these remaining concentrations, the contents of the test
tube were poured into a six-well plate. Students proceeded to follow this same procedure for the

29

remaining temperature
Figure 5: Investigation #7 Exemplar
Different variables (temperature and pH) were tested on
two consecutive class periods. Students were required to
reference both graphs to identify ideal conditions for the
enzyme α-amylase

treatments. Afterwards, the
six-well plates were
scanned and these images
files were copied into each

1

student’s folder on the
district’s intranet.
The second day of
the lab was conducted in

2

3

the same manner, except
pH was varied using

4

acetate and phosphate
buffered solutions and all
samples were exposed to
ambient temperature.
5

6

Based on pre-lab
experimentation, the

Figure Legend:
1. Graph Title – “Effect of Temperature…”
2. Y-axis – % starch degraded

instructor had students run

3. X-axis – Temperature (C°)

As was done before, each

4. Graph Title – “Effect of pH…”
5. Y-axis - % starch degraded
6. X-axis - pH

each trial for two minutes.

group prepared a six-well
plate, which was scanned
prior to the end of the
30

class period.
To quantify the concentration of starch, the scanned images of the six-well plates were
analyzed using the software program Image-J. Due to complications, it was decided that
quantification with Image J would be conducted as a large-group classroom activity. A few of
the experimental examples were selected and quantified and average values were calculated for
each respective temperature and pH value. Afterwards, students used these values to determine
the remaining starch concentrations – this was accomplished via a standardized curve that the
instructor had prepared.
Using the quantified results, students determined the percent of starch degraded and
constructed two individual graphs – one that depicted the results of the temperature experiment,
and the other which illustrated the results of the pH experiment. In each graph, the percent starch
degraded was graphed as a function of the appropriate independent variable as illustrated in
Figure 5.

31

RESULTS AND ANALYSIS
Pre-Test and Post-Test Administration
The pre-test and post-test utilized in this study was administered to all students enrolled
in the microbiology classes as part of a planned curricular activity. These tests were composed of
two separate and distinct parts. “Part 1” included four questions that assessed graph construction
skills, point plotting, and interpretation. This portion of the test had a maximum value of 20
points and was assessed via a rubric (Appendix D). “Part 2” of the assessment consisted of 10
multiple-choice questions which incorporated supplied graphs. Answering these questions relied
on the student analyzing and extracting information explicitly from the graphs. Each question
was valued at 2 points, resulting in a combined worth of 20 points.
Part 1 and Part 2 of the assessment were administered on two separate but consecutive
days. This decision was made because the researcher considered the significant amount of time
that might be required in completing the graph construction problems encountered on Part 1 of
the test. Pre-tests were administered at the beginning of the semester, before any of the related
graphing investigations were introduced. As with the pre-test, post-testing was completed on two
separate instructional periods – this was conducted after the relevant activities were
accomplished. During the administration of these tests the researcher was unaware of which
students had returned their consent forms, or which students had granted or denied consent to
participate.
Data Analysis – Combined Pre-Test and Post-Test
Forty students consented to participate in this study, representing 85% of the students
enrolled in the instructor’s two microbiology classes. Each participating student’s scores from
32

Part 1 and Part 2 of the pre-test were added to establish a “combined” score. The pre-test yielded
an average combined score of 57.8%. The corresponding average for the post-test was
determined to be 85.2%, representing a substantial improvement. All participating students
achieved a combined post-test score that exceeded their pre-test score, and the average increase
was calculated at 27.4%. These increases ranged between 52.5% and 3.75%. Figure 6 illustrates
how much each student improved on the post-test in comparison to the pre-test. Table 2 lists
each student’s combined scores (as a percentage) on both tests, as well as the percent change
between each. To determine whether the differences between combined pre-test and post-test
scores were statistically significant, a paired t-test was performed. The paired t-test yielded a pvalue of 0.000. Thus, the null hypothesis is rejected and the alternative hypothesis was accepted
– there is a statistical difference between achievements when comparing pre-test and post-test
scores.

33

Figure 6: Percent Difference Between Combined Pre-Test and Post-Test Scores
The range of improvement yielded on students’ pre-test and post-test scores. All participating students demonstrated improvement
when evaluating the combined scores of Part 1 and Part 2 of the assessment.

34

Data Analysis – Part 1 and Part 2 Pre-Test and Post-Test
Although the difference in combined scores appeared impressive, the researcher
recognized that many of the students attained this improvement due to the influence of Part 1.
This component tested graph construction and related skills. Thus, the decision was made to
individually analyze Part 1 and Part 2 of the assessments. Regarding the pre-test, Part 1 of the
assessment yielded an average of 43.9%. On the post-test, the participants’ scores increased
impressively to an 88.6% average. Only one student managed a post-test score less than what
they achieved on the pre-test (See Table 1). Using a paired t-test, a p-value of 0.000 was obtained
from the data comparison. Not surprisingly, the differences identified in Part 1 of the assessment
proved to be statistically significant.
The researcher questioned whether there was truly a statistical difference between the
pre-test and post-test scores that were obtained from Part 2 of the assessment. The initial pre-test
of Part 2 resulted in a student average of 71.8%. On the subsequent post-test, the average
climbed to 81.8%, resulting in a mean increase of 10.0%. The 40 student sample was subjected
to a paired t-test, yielding a p-value of 0.001. Thus, there was indeed a statistical difference
between Part 2 of the assessment’s pre-test and post-test scores. Interestingly, five students did
worse on the multiple choice graphical analysis questions that made up this component of the
test. In addition, 12 students produced scores identical to their pre-test scores. The test scores
from each component of the assessment are listed in Table 2.

35

Table 2: Pre-Test and Post-Test Data from all Assessment Components
All students’ test scores improved when comparing the combined pre and post-test scores.
One student achieved a lower post-test score on Part 1 of the test compared to the initial pretest score. Five students did worse on Part 2 of the test and 12 students achieved grades on
Part 2 of the test that were equal to their initial scores. Green shading is used to represent
an increase, yellow indicates no change, and red denotes a decrease.
Tests Combined
Stu.
#

Pre

2957
5521
2987
2989
3002
3019
3052
3104
5437
3136
3154
3161
3213
3309
3317
3357
3359
3398
3408
3012
3018
6740
3037
3058
3086
3933
6943
3121

67.5% 97.5%
77.5% 95.0%
70.0% 87.5%
57.5% 75.0%
43.8% 87.5%
51.3% 75.0%
68.8% 78.8%
57.5% 90.0%
31.3% 71.3%
58.8% 90.0%
66.3% 80.0%
46.3% 50.0%
68.8% 100.0%
78.8% 95.0%
61.3% 87.5%
48.8% 80.0%
70.0% 97.5%
58.8% 73.8%
95.0% 100.0%
80.0% 100.0%
36.3% 66.3%
35.0% 86.3%
87.5% 97.5%
75.0% 95.0%
73.8% 97.5%
36.3% 72.5%
45.0% 97.5%
42.5% 82.5%

Post

+/30.0%
17.5%
17.5%
17.5%
43.8%
23.8%
10.0%
32.5%
40.0%
31.3%
13.8%
3.8%
31.3%
16.3%
26.3%
31.3%
27.5%
15.0%
5.0%
20.0%
30.0%
51.3%
10.0%
20.0%
23.8%
36.3%
52.5%
40.0%

Graph Construction
(Part 1)
Pre
Post
+/45.0%
75.0%
70.0%
25.0%
27.5%
42.5%
57.5%
35.0%
12.5%
37.5%
32.5%
62.5%
77.5%
57.5%
42.5%
27.5%
80.0%
67.5%
90.0%
70.0%
22.5%
10.0%
85.0%
60.0%
57.5%
22.5%
30.0%
35.0%

95.0%
100.0%
95.0%
70.0%
85.0%
80.0%
87.5%
100.0%
62.5%
100.0%
100.0%
60.0%
100.0%
100.0%
95.0%
90.0%
95.0%
87.5%
100.0%
100.0%
72.5%
92.5%
95.0%
100.0%
95.0%
95.0%
95.0%
85.0%
36

Graph Analysis
(Part 2)
Pre
Post
+/-

50.0% 90.0% 100.0%
25.0% 80.0% 90.0%
25.0% 70.0% 80.0%
45.0% 90.0% 80.0%
57.5% 60.0% 90.0%
37.5% 60.0% 70.0%
30.0% 80.0% 70.0%
65.0% 80.0% 80.0%
50.0% 50.0% 80.0%
62.5% 80.0% 80.0%
67.5% 100.0% 60.0%
-2.5% 30.0% 40.0%
22.5% 60.0% 100.0%
42.5% 100.0% 90.0%
52.5% 80.0% 80.0%
62.5% 70.0% 70.0%
15.0% 60.0% 100.0%
20.0% 50.0% 60.0%
10.0% 100.0% 100.0%
30.0% 90.0% 100.0%
50.0% 50.0% 60.0%
82.5% 60.0% 80.0%
10.0% 90.0% 100.0%
40.0% 90.0% 90.0%
37.5% 90.0% 100.0%
72.5% 50.0% 50.0%
65.0% 60.0% 100.0%
50.0% 50.0% 80.0%

10.0%
10.0%
10.0%
-10.0%
30.0%
10.0%
-10.0%
0.0%
30.0%
0.0%
-40.0%
10.0%
40.0%
-10.0%
0.0%
0.0%
40.0%
10.0%
0.0%
10.0%
10.0%
20.0%
10.0%
0.0%
10.0%
0.0%
40.0%
30.0%

Table 2 (Cont’d)
3144
3145
3163
3279
3283
3332
3334
3335
3419
3443
3469
3491
Avg.

78.8% 100.0%
60.0% 92.5%
37.5% 88.8%
28.8% 75.0%
31.3% 61.3%
65.0% 75.0%
52.5% 92.5%
47.5% 91.3%
33.8% 52.5%
63.8% 92.5%
76.3% 100.0%
50.0% 78.8%
57.8% 85.2%

21.3%
32.5%
51.3%
46.3%
30.0%
10.0%
40.0%
43.8%
18.8%
28.8%
23.8%
28.8%
27.3%

57.5% 100.0%
30.0% 85.0%
35.0% 87.5%
27.5% 90.0%
12.5% 62.5%
30.0% 70.0%
35.0% 95.0%
35.0% 82.5%
17.5% 55.0%
37.5% 95.0%
52.5% 100.0%
30.0% 87.5%
43.9% 88.6%

42.5% 100.0% 100.0%
55.0% 90.0% 100.0%
52.5% 40.0% 90.0%
62.5% 30.0% 60.0%
50.0% 50.0% 60.0%
40.0% 100.0% 80.0%
60.0% 70.0% 90.0%
47.5% 60.0% 100.0%
37.5% 50.0% 50.0%
57.5% 90.0% 90.0%
47.5% 100.0% 100.0%
57.5% 70.0% 70.0%
44.6% 71.8% 81.8%

0.0%
10.0%
50.0%
30.0%
10.0%
-20.0%
20.0%
40.0%
0.0%
0.0%
0.0%
0.0%
10.0%

Table 3: Results of Statistical Analysis of a Paired T-Test on Pre-Tests and Post-Tests
The combined tests as well as the individual components were analyzed and the
corresponding p-values signify statistical significance for both Part 1 and Part 2 of the
assessment
Statistical Analysis Using a Paired T-Test
Tests Component(s)

Null Hypothesis

P-Value

Results

Combined (Part 1 + Part 2)

Tpre = Tpost

0.000

Part 1 (Graph Construction)

Tpre = Tpost

0.000

Part 2 (Graph Analysis)

Tpre = Tpost

0.001

Statistical Difference
(Reject Null)
Statistical Difference
(Reject Null)
Statistical Difference
(Reject Null)

37

DISCUSSION
Discussion of Data
The null hypothesis that exposure to authentic data collection and routine graphing
practice would not have an effect on students’ measured graphical literacy skills was not
supported. Analyses from both components of the administered pre-test and post-tests proved to
be statistical significant, as indicated in Table 3. Paired t-tests performed on Part 1, Part 2, and
the combined test components yielded respective p-values of 0.000, 0.001, and 0.000. Based on
these results, it can be assumed that embedding data collection and authentic graph construction
activities into a science curriculum positively influence student proficiencies constructing,
analyzing, and deciphering graphs.
The most significant and impressive gains were observed with students’ graph
construction skills. The first question posed the greatest challenge encountered on the graph
construction component of the assessments. Given appropriate data, students had to construct a
particularly difficult graph that included bivariate data, necessitating the inclusion of two
separate y-axes. It is entirely possible that this was a student’s first ever encounter with the
construction of this style of representation. The researcher noted that several students appeared
overwhelmed by the graphing task and did not know how to proceed with the organization of
such a graph. The struggles on Question #1 of the assessment certainly were responsible for the
low 43.9% average achieved on Part 1. This study’s exposure to complex representations may
have provided the necessary knowledge in dealing with multiple variables and data sets,
contributing to the much-improved post-test average of 88.6%. Only one student did worse on
the subsequent post-test. This individual was a special education student that is easily distracted

38

and often has difficulty focusing. The author suspects that this student was having these
particular issues during the completion of the post-test.
With regard to the graphical analysis component of the assessment, average
improvements were far less than those attained on Part 1. Although a statistical difference was
realized, the 10.0% average increase paled in comparison to the improvement on the graph
construction element. In reflection, the author recognized that the graphical analysis questions
were not developed such that they were sufficiently difficult. The average pre-test score of
71.8% was higher than the writer expected or desired. Six students actually achieved a perfect
100.0% on the pretest and an additional eight students scored 90.0%. These 14 pre-test scores
represented 35% of the participating sample. Mathematically, it was impossible or unlikely for
some students to demonstrate any improvement on Part 2 of the assessment due to their high
initial achievement.
General Discussion
The majority of the writer’s experience is teaching at the middle school level. An
involuntary transfer resulted in the researcher conducting this study in an unfamiliar high school
setting. Without sufficient experience teaching high school grade levels, it was unknown what
level of difficulty would represent a substantial challenge to most students. Also, the possibility
of being transferred back to the middle school was presented by administrators during the
development of activities and during the design of pre-test and post-test assessments. As with the
design of each investigative activity, the researcher considered the assessment as something that
might require modification to make it more appropriate for middle school students. A
combination of these influences might be responsible for the author overestimating the difficulty

39

of the graph analysis questions. If this
Figure 7: Common Scale Issues (1)
Some students had difficulty with the
establishment of an initial “0” value on the X
and Y axes when graphing data from the
investigations

study were repeated at the high school
level, it is suggested that the difficulty
of these questions be amplified such
that questions mimic those presented
on the ACT.

1

The author was impressed with
the improved competency that students
demonstrated with graph construction.
In addition to the presented empirical
statistical data, the instructor

2

subjectively noted improvements
during the course of the semester.

3

Basic improvements included
understanding the orientation of axes
and variables. Students exhibited a
4

more deliberate effort to label axes,
denote units, and provide useful titles.

Figure Legend:
1. X-axis – Values adjusted for corrected
origin
2. Y-axis – Time (minutes)
3. Y-axis – Shifted to accommodate the
provided values
4. X-axis – Time (minutes)

Finally, students became more skilled
at devising logical scales, utilizing
space on the provided graph paper, and
generally making their produced
graphs more aesthetically pleasing.
40

Errors in scale design were noted in both the assignments and in the assessments used in this
study. The researcher subjectively noted improved skill dealing with scale during the course of
the study. Figure 7, Figure 8, and Figure 9 illustrate some of the common deficiencies
encountered.
Figure 8: Common Scale Issues (2)
Some students had difficulty establishing and applying a consisent scale increment on
the assigned graphing tasks incorporated in each investigation

1

2
3

4
Figure Legend:
1. Y-axis – negative % change in mass
2. X-axis – % sucrose concentration (not spaced proportionally)
3. Y-axis – negative % change in mass
4. X-axis – % sucrose concentration (not spaced proportionally)

41

Figure 9: Scale Issues on the Pre-Test
These students peformed better than most students on Question #1 of Part 1 of the
assessment. However, these examples illustrate how students have a tendency to “assign”
values to lines based on the provided data points, rather than using a consistent and
logical incremental value.

1

2

3

4

Figure Legend:
1. Y-axis – methane concentration (ppb)
2. X-axis – Year – values that are not spaced incrementally
3. Y-axis – methane concentration (ppb)
4. X-axis – Year – values that are not spaced incrementally

To aid in graph analysis, the instructor provided immediate feedback by assessing student
submissions and returning them in short time periods. Having tangible, student-produced
examples as a reference was a considerably effective method in teaching and evaluating graphs
for information and meaning. As pointed out by Coleman and others, students cannot be

42

Figure 10: Graph Titling
Although students understood that titling their graph was
required, many students did not provide meaningful titles that
described the representation adequately. This student simply
titled their graph “Respiration Lab”. The author noted that the
titles of graphs gradually improved during the course of this
study.
1
4

expected to
accidently gain these
skills, rather,
graphing should be
taught within the
context of science
and embedded into
the curriculum

2

(2011). Further
research has
supported the
strategy of
systematically
3
teaching students to
Figure Legend:
1. Graph Title – “Respiration Lab”
2. Y-axis – Distance
3. X-axis – Time (minutes)
4. Graph Key – coded for various sucrose concentrations

deal with
quantitative
information and
graphical

representation (Tairab and Khalaf Al-Naqbi, 2004). The instructor included explicit, direct
instruction regarding graph construction before tasking students with the included graphing
activities and such instruction was also presented after returning work to students. This provided
the researcher with an opportunity to explain common mistakes and flaws that were identified
when students attempted to evaluate their produced graphs. During several of the investigations

43

utilized in this study, the concept of relating the slope of a plotted line to a functional rate was
introduced and emphasized. A conceptual error that was uncovered on the post-test included
responses to Question #3 on Part 1 of the assessment. Several students confused the relative
amount (height graphed) with the concept of slope being equivalent to a respective rate. This
phenomenon has been identified historically in elementary-aged learners and is an error that has
been described as slope-height confusion (Shah and Hoeffner, 2002).
In several instances, severe graph construction misunderstandings required one-on-one
direct tutoring to support deficient students. Confusing the orientation of the axes, erratic
changes in scale increments, and the inability to accurately plot points summarizes the majority
of these deficiencies.

Discussion of Investigations
Investigation #1 employing the Grow-Toys was successful and well-liked by students.
The produced graph utilizing two separate y-axes was unique and corresponded to the most
challenging graph construction problem on the measured assessments (Question #1 of Part 1). As
graph cognition researchers have identified, scale considerations should reflect the goal of either
understanding relative relationships or absolute information and students “have difficulty
translating between different graphic scales.” (Shah, Mayer, and Hegarty, 1999). Since the
objective of this graphing activity was to compare relative changes in size and mass, the
differences in scaling were justified.
The instructor will consider the possibility of conducting this activity using a guided
inquiry approach. For this method to be successful, significant emphasis would have to be placed
44

on the quantitative components that allow for the investigation to serve as an effective graphing
exercise. In the arena of physics education, practitioners advocate using data collection and
graphing as a means of “discovering” physical laws by recognizing mathematical relationships
between variables (Oakes, 1997). Indeed, the student-produced graphs in this activity yielded a
perfectly inverse relationship.
Investigation #2 involving osmosis in potato and cantaloupe produced exceptional data
that lent itself to graph construction and analysis. This comprehensive laboratory investigation
supplied the precise type of context that experts advocate for when attempting genuine statistical
examinations (Friel, et al., 2001). The graphs that students produced were highly reliable and
allowed the instructor to ask students for interpolated values. Students determined isotonic
sucrose concentrations for each cell type (potato and cantaloupe). Solutions were mixed based on
these student estimates and samples were soaked for 24 hours to test the validity of each isotonic
prediction. To the delight of the researcher, the tested samples yielded almost no change in mass.
There is tremendous value in having students utilize graphs they’ve produced for the purposes of
interpolation and extrapolation. The researcher aims to devise more of these types of auxiliary,
predictive activities with student-produced graphs. Some practitioners even promote the idea that
students should predict what the general shape of their graph will look like before actually
graphing the data (Connery, 2007). There is merit in this approach because it introduces another
added element of predicting and testing predictions, a process that is fundamental to the
scientific process.
Overall, Investigation #3 went relatively well except for a few groups which inherited
Elodea that was uncooperative. It is unknown why some of the aquatic plant samples produced
consistent oxygen and others produced very little. Fortunately, groups were combined and all
45

students were able to obtain data. During the second semester of the school year, the author had
some success “starving” the aquatic plants of light for several days prior to attempting the
laboratory activity. The instructor is also investigating the possibility of utilizing dense algal
cultures in glass laboratory flasks that would utilize a similar setup. Continuing with
photosynthesis activities, Investigation #4 allowed for the examination of additional variables.
The floating disk assay was a successful, quantitative exercise.
Investigation #5 was riddled with the most issues. The researcher believes that using finediameter tubing and indicator movement to quantify carbon dioxide gas production is a great
concept. To make data collection and quantification more reliable, some adjustments need to be
considered. Rapid carbon dioxide production resulted in excessive pressure and gas blowing past
the indicator. Thus, one consideration involves using less yeast – this would result in less carbon
dioxide production. Another possibility is using a lower standardized temperature, but this would
require additional time for the yeast to become active from its dormant state. Obviously, within
the time constraints of a typical classroom period, the availability of time would be the biggest
concern. A potential adjustment with the indicator would include the use of insoluble substances
such that the carbon dioxide gas cannot push past.
Investigation #7 was a laboratory activity that could be considered complex but very
authentic. This investigation required en extraordinary amount of pre-lab preparation to facilitate
completion in the high school setting. The timing of each trial needed to be exact and uniform
addition of the enzyme with a graduated pipette was required. Some laboratory groups performed
the investigation perfectly, achieving a six-well plate with a spectrum of remaining starch
concentrations. A few of the groups made procedural errors that affected the data collected.

46

Because of technical issues, the possibility of individually analyzing and quantifying
laboratory results was abandoned. Thus, in a whole-group classroom setting, the instructor used
three or four of the most visually acceptable six-well plate images, analyzed them via Image J,
and averaged the results from each trial. Students used the collective Image J values, in
conjunction with a provided standardized curve, to quantify the remaining starch concentrations.
In hindsight, this may be a better method when dealing with quantifying the lab results. The
nature of this investigation makes it very likely for some students to derive flawed data and the
subsequently graphed results would certainly not supported conceptual understanding of the
concept investigated.
Investigation #7 was unique in that graphs produced represented the comparative results
of two separate experiments. The skill of associating two separate graphs certainly could be
helpful for students, particularly since tests like the ACT routinely require such processing. In
one study, Smith and Gentner (2011) determined that comparing contrasting examples promoted
the understanding of graphical representations. The author expects that more experiences with
graph comparison should be provided. For example, in a structured activity, students from
dissimilar laboratory groups could evaluate each other’s graphs and evaluate whether or not
similar results (and conclusions) were achieved.

Limitations and Considerations for the Future
These investigations and graphing exercises reported here will definitely be utilized in the
future. Some adjustments are required; however, the goal of providing realistic, authentic
laboratory investigations that require data collection and the intentional practicing of complex
47

graph construction and graph analysis was accomplished. In fact, the writer believes that
additional data and graphing enrichment should be incorporated into the curriculum. The school
year surrounding this study was the first year the instructor taught this microbiology course.
Aside from the newly introduced investigations contained within this study, there was only one
existing activity that provided any exposure or practice with graphical representations.
While requiring considerable effort, more investigations that correspond to other relevant
scientific concepts should be developed and made available to practitioners. The lack of existing,
authentic data-driven investigations that incorporate graphing in the microbiology curriculum of
the writer’s high school is, perhaps, not entirely unusual. Some researchers believe that new
teachers entering the teaching profession are not qualified to provide such authentic experiences.
Bowen and Roth (2005) for example, studied pre-service teacher’s ability, given various
scenarios, to develop appropriately designed quantitative investigations. They concluded that,
“pre-service teachers need more experience in engaging in data and graph interpretation practices
originating in activities that provide the degree of variation in and complexity of data present in
realistic investigations” and “despite considerable preparation, and for many, despite bachelor of
science degrees, pre-service teachers do not enact the (‘‘authentic’’) practices that scientists
routinely do when asked to interpret data or graphs.”
The conclusions of Bowen and Roth perhaps should not be generalized. However, this
insight presents some unfortunate realities that exist. True, it is possible that some teachers lack
the necessary knowledge and understanding to implement authentic data collection and data
representation activities. Instead of dwelling on this deficiency, appropriate interventions should
be conducted. The development of these types of activities involves intensive time commitments.
Furthermore, logistical considerations are the tail that wags the dog when a teacher is expected to
48

introduce “authentic” laboratory investigations in class sizes in upwards of 30 students. In the
collaborative atmosphere of education, well-designed quantitative investigations should be
shared amongst colleagues. Teacher preparation programs, outreach coordinators, university
affiliates, and district curriculum designers should also understand the value in data-driven
investigations and graphing exercises based on authentic data. The availability of curricular
resources would certainly help classroom teachers to address the need for quantitative laboratory
examinations.
One noticeable element missing from this study is the incorporation of computer-assisted
graphing instruction and graphical representations generated via software programs. Regarding
computer-assisted graphing instruction, there is some contradiction in the available literature. In
one study, two different hands-on, instructional modules were used to teach two groups of
students – one that included computer applications and another that did not (Ates and Stevens,
2003). It was determined that there was not any significant difference in measured graphing
comprehension; both treatments were equally effective. Conversely, other research has
determined that microcomputer-based laboratories (MBLs) are effective in teaching students
graph comprehension relative to kinematic concepts encountered in physics classrooms (Brasell
and Rowe, 1993). Of course, one benefit of introducing technology is that it can incite interest
from students and engage them to a greater extent. The author considered having students
produce some of the incorporated data representations with Microsoft® Excel graphs. Some
researchers, however, warn that computer graphing programs allow students to “demonstrate a
lack of understanding of the relationships among the graph, the type of data, the purpose of
analysis, and the judgment task” (Friel et al., 2001). In the writer’s experience, sometimes the
immediate data organization and representation offered by computer programs produces a graph
49

without requiring the student to consciously decide axes and orientation. For this reason, and for
the sake of consistency among the incorporated investigations, computer-aided graphing was not
utilized.
Another potential criticism surrounding this study is the contextual considerations. It
might be argued that the improved proficiencies on the assessment were the result of students’
enhanced knowledge of the corresponding content, rather than greater skill, aptitude, and
familiarity with data representation. Indeed, many of the questions contained within the pre and
post-test involved concepts that were investigated as part of the existing, planned microbiology
curriculum. Conceptual understanding of photosynthesis, for example, would occur without the
inclusion of the graphing activities incorporated by this study. Thus, there is a possibility that
improved performances could be attributed to this generally attained knowledge. However, the
assessments also included questions involving unfamiliar concepts that were not directly
investigated. One suggested solution to this dilemma is to devise an assessment that is entirely
composed of graphing analysis questions that do not correspond with topics that have been
taught. In this way it would be possible to determine if students are truly able to apply their
knowledge of data representation to new scenarios to which they are unaccustomed.
The author has been transferred back to the district’s middle school for the upcoming
school year. Thus, translating these activities so that they are useful with younger learners is a
challenge. Based on prior experience, the writer believes that seventh grade students are, in
general, capable of completing similar types of graphing tasks. Researchers in the field of graph
comprehension have debated whether proficiency with graphs is a matter of cognitive ability or
practice. Those that insist on the cognitive considerations reference evidence of improved
capabilities as a learner transition from the middle to high school ages (as cited in Friel et al.,
50

2001). However, the instructor choses to adopt the former approach and is confident that routine,
meaningful and authentic practice with graphs are necessary in the development of
corresponding skills, regardless of age. Obviously the ultimate proficiency of an average seventh
grade student cannot be compared to that of a high school senior. The considerations of
intellectual capability are understood, but this educator seeks to develop learners’ skills to the
maximum extent, regardless of what the individual’s supposed cognitive ceiling might be.

51

CONCLUSION
The employed measurement tools indicate that exposure to authentic data collection and
routine graphing practice positively supports students’ graph construction and graph evaluation
skills. Analyses from Part 1 and Part 2 of the administered pre-test and post-test proved to be
statistically significant. Students achieved the greatest gains in the graph construction skills
assessed on Part 1 of the assessment. The added, subjective observations of the author also
identified improved competency organizing and representing data in graphs. The less-than
desired difficulty and subsequent high achievement on Part 2 of the pre-test mathematically
limited how much improvement could be demonstrated on the subsequent post-test. This study
validates the importance of graph construction and analysis practice and the author expects that
greater gains in achievement would have occurred with a more rigorous assessment. It is
probable that the skills developed during this study will be useful in the future, when the
participating students encounter new, unfamiliar representations. Ideally, the exposure received
during the completion of these investigations will provide confidence to the learner when
handling data and it will allow them to logically organize and construct an appropriate
representation that communicates quantitative information in the same way that a scientist
would. Likewise, when facing an extraordinarily complex representation, the author is optimistic
that students will not be overwhelmed or frustrated. Rather, they might draw from the
constructive experiences included from this study and analytically “deconstruct” and decipher
graphs using the appropriately gained skills.

52

APPENDICES

53

APPENDIX A:

LABORATORY INVESTIGATIONS AND ACTIVITIES

54

Name:____________________________________________ Date: _____________ Hour: _____

Exercising the Scientific Method

Figure 11: Grow Toy Measurement

Description of the Problem:

Measured length (cm)

While browsing the aisles at a local retail store, you
stumble upon various “grow toys”. The product
manufactures’ claim that these creatures are capable of
growing to 600% of their original size when placed in
water. However, you are curious if this 600% is regarding
the toy’s length, or perhaps mass. In addition, you have
heard that the maximum growth size can be affected by
the amount of dissolved substances in the local municipal
water supplies.

Question:
How does the concentration of dissolved salt in water affect the growth of “grow toys?”
Hypothesis:

__________________________________________________________
Materials:




Distilled Water
Salt (NaCl)
Plastic containers





Tape
Markers
Stirring rod

Procedure:
Note: For 0%, use pure distilled water. Tap water can be
used for the other mixtures
1)
2)

3)

Prepare various concentrations of salt water specified in
the table.
Measure out the appropriate amount of salt, pour the salt
into the beaker, and add tap water to the 500 mL mark.
Thoroughly mix until the salt has dissolved.
Pour the salt water into one of the plastic containers.
Label the container, identifying the salt concentration and
your group members.
55





Electronic scale
Metric ruler
Beaker (1000mL)

Table 4: Salt Mixtures
% Salt Conc.
(m/v)
0%
2.5%
5.0%
7.5%
10.0%
15.0%

Amount of
Salt (g) in
500mL H2O
0.0g
12.5
25.0
37.5
50.0
75.0

4)
5)

Carefully rinse the beaker before preparing the next salt water mixture.
Measure the initial mass of one of the “grow” toys. Record this value in the provided data
table for 0% salt concentration.
6) Measure the initial length (see diagram) and record.
7) Place the measured grow toy in the 0% container (pure distilled water).
8) Repeat steps 5 through 7. Measure and place “grow” toys into the 2.5%, 5.0%, 7.5%,
10.0%, and 15.0% salt solutions. Make careful measurements and ensure that the
correct toy is placed in each respective container.
9) After 24 hours (or 48 hours): Remove each grow toy from the soaking container. Gently
pat-dry.
10) Measure and record the length and mass of each grow toy.
11) Calculate the percent change in mass and percent change in length for each grow toy.
Data:
Table 5: Grow Toy Mass Data
Change in Mass of “Grow Toy”
Salt
Conc.
(%)
0%
2.5 %
5.0 %
7.5 %
10.0 %
15.0 %

Initial
Mass (g)

Mass @
24hrs. (g)

Use the following formula to
calculate the % Change in Mass:

% Change
±

((Mass @ 24hrs.)  ( Initial Mass))
Initial Mass

Table 6: Grow Toy Length Data
Change in Length of “Grow Toy”
Salt
Conc.
(%)
0%
2.5 %
5.0 %
7.5 %
10.0 %
15.0 %

Initial
Length
(cm)

Length @
24hrs. (cm)

% Change
±

Use the following formula to
calculate the %
Change in Length:
(( Length @ 24hrs.)  ( Initial Length ))
Initial Length

56

Post-Experiment Questions: (Preliminary Analysis)
1) What parameter showed the greatest change? Mass or length?

2) Are these “grow toys” capable of growing up to 600% or their original size (as advertised)?
EXPLAIN!

3) Imagine the manufacture wants you to clarify to growth claims in order to avoid a falseadvertising lawsuit. What should the toy’s packaging claim as far as the growth potential?
(Be specific).

Analyze:
Represent the data collected in the experiment by graphing. You will graph both the change in
mass and change in length on the same graph! Separate Y-axes are required.

Conclusions: (By analyzing your graph, summarize what you found out. Accept or Reject your
hypothesis)

________________________________________________________________
________________________________________________________________
________________________________________________________________
________________________________________________________________
________________________________________________________________
________________________________________________________________
________________________________________________________________
57

Name: ________________________________________________________ Date: _____________ Hour:

Figure 12: Potato

Figure 13: Cantaloupe
Determining the Osmolarity of Various Plant Cells

Introduction:
As you have already learned, osmosis is the movement of water (solvent) across a selectively
permeable membrane (such as the cell membrane). Although water molecules can move in and
out of a cell, many dissolved solutes cannot freely diffuse across the cell membrane. These sugar
and starch molecules are “trapped” within the boundaries of the cell. During osmosis, water
always moves from an area of low solute concentration to an area of high solute concentration. If
the solute concentration is high, then the water (solvent) concentration is low. Alternatively, if
the solute concentration is low, then the water concentration is high. Remember the following
tonicity designations:


Hypertonic: The solution has a higher solute concentration than the inner cell



Hypotonic: The solution has a lower solute concentration than the inner cell



Isotonic:

The solute concentration inside the cell and in the surrounding solution is equal

Osmolarity is the measure of solute concentration. In the cytoplasm of a cell there can be a
variety of different dissolved solutes, such as starches, sugars, or salts. In today’s laboratory our
goal is to determine the solute concentration inside of potato and cantaloupe cells. To
accomplish this, we will soak potato and cantaloupe in various concentrations of sucrose for 24
hours. The observed gain (or loss) in mass will allow us to estimate each respective cell’s
osmolarity.
Predictions:
1. Which of the two cell types do you think will have a higher solute concentration?

2. Estimate the solute concentration (in percent) of each:




Potato ______%

Cantaloupe ______%

Procedure:
1. Prepare 5 different cups (about 200mL) with the following sucrose concentrations (m/v mass sucrose / volume water):
58








0% (distilled water)
5% sucrose
10% sucrose

20% sucrose
30% sucrose

2. Using the provided cork-borer, punch out 5 equally sized sections of potato and 5 equally
sized sections of cantaloupe. Cut each so they are equal in length (about 6 cm long).
3. Gently pat-dry each potato and cantaloupe cylinder.
4. Use a digital scale, measure and record the mass of each cylinder and place them in the
appropriate sucrose container. Be careful they don’t get mixed up!!!
5. Wait 24 hours: carefully remove, dry and measure the mass of each cylinder. Record the
data.
Data:

Table 7: Potato Mass Data

Table 8: Cantaloupe Mass Data

Potato
Sucrose
Conc.
(%)
0%
5%
10 %
20 %
30 %

Initial
Mass
(g)

Mass @
24hrs.
(g)

Cantaloupe
%
Change
±

Sucrose
Conc.
(%)
0%
5%
10 %
20 %
30 %

Initial
Mass
(g)

Mass @
24hrs.
(g)

%
Change
±

Use the following formula to calculate the % Change in mass:

% Change 

((Mass @ 24hrs.)  ( Initial Mass))
 100
Initial Mass

Post-Lab Graphing:
Graph the % change in mass for both the potato and cantaloupe. The graph should be a 2-line,
line graph that has both a positive and negative Y-axis. Color-code each line, label the axes
appropriately, and include a title.

59

Post-Lab Questions:
1. Looking at the graph you produced, what is the approximate solute concentration in the

potato and cantaloupe cells?





Potato - _______%

Cantaloupe - _______%

2. Which plant gained the most mass (%) in distilled water (0% sucrose)? What does this tell

you about the relative solute concentrations inside each? (compare)

3. Is it possible for a specific sucrose concentration to be hypertonic to potato, but hypotonic to

cantaloupe? (Look at your graph!)

4. Name a specific sucrose concentration in which one of these cell types would gain mass and

the other would lose mass. Explain why this is so!

60

Name: _________________________________________ Date: _____________ Hour: _______

Aquatic Plants’ Response to Various Gradients and Wavelengths of Photosynthetically Active
Radiation
Figure 14: Photosynthesis Lab Apparatus Illustration

Heat Sink

BubbleCounting
Test Tube

Incandescent
Light

Photosynthesis
Vessel (Filled with
Elodea)
Background:
The process of photosynthesis requires electromagnetic energy to convert low-energy molecules,
carbon dioxide and water, into the simple sugar glucose, an energy-rich molecule. During
photosynthesis, oxygen gas is also produced (to the delight of aerobic organisms). The
chemical equation that describes photosynthesis is written below:
Light
6CO2 + 6H2O

C6H12O6 + 6O2

Purpose:
1. To determine how light intensity affects the rate of photosynthesis.
2. To determine how the wavelength of light affects the rate of photosynthesis.
Laboratory Apparatus:

61

A 150W incandescent bulb will provide a continuous source of intense light. A large test-tube
will be filled with the aquatic plant Elodea. An air-tight rubber stopper (with hole) attached to a
small-diameter tube will provide the means to collect oxygen gas produced via photosynthesis.
With the end of the tube immersed into a second test-tube, counting oxygen bubbles provides a
useful method of quantifying the rate of photosynthesis. The distance of the light source to the
Elodea can be varied to change the intensity of the electromagnetic radiation. To test different
wavelengths of light, the test tube filled with Elodea may be surrounded with various light filters
or wrapped with different colors of cellophane.
Question: How does distance from the light (light intensity) affect the photosynthetic rate?
Procedure – Experiment #1:
1. Mix a large quantity 2% sodium bicarbonate solution (this should already be completed).
2. Place equal amounts of Elodea into two large test tubes. Fill each test tube with the sodium
bicarbonate solution.
3. Wrap one of the Elodea test tubes with 2 layers of aluminum foil to establish an experimental
control. Light does not penetrate the foil!
4. Situate both test tubes at equal distances from the light source (15cm). Insert the rubber
stopper and tube apparatus into each test tube.
5. Insert the end of the air tubing into a separate test tube that is elevated above the Elodea
containers.
6. Turn on the light source. Wait 5 minutes before collecting data.
7. Once a bubble is released, start the stopwatch. Record the time each bubble releases. (Does
this for 8 minutes or until 8 bubbles have been released – whichever occurs first).
8. Move the test tubes until they are 25 cm away from the light source. Wait 5 minutes! Repeat
step #7.
9. Move the test tubes until they are 35 cm away from the light source. Wait 5 minutes! Repeat
step #7.
Question: How does the wavelength of light (Color) affect the photosynthetic rate?
Procedure – Experiment #2:
1.
2.
3.
4.

Mix a large quantity 2% sodium bicarbonate solution (this should already be completed).
Place multiple sprigs of Elodea into a large test tube.
Fill the test tube with the sodium bicarbonate solution.
Wrap the test tube containing Elodea with clear cellophane and place it at a distance 15 cm
from the light source. Insert the rubber stopper and tube apparatus into the test tube.
5. Insert the end of the air tubing into a separate test tube that is elevated above the Elodea
vessel.
6. Turn on the light source. Wait 5 minutes before collecting data.
62

7. Once a bubble is released, start the stopwatch. Record the time each bubble releases. (Does
this for 8 minutes or until 8 bubbles have been released – whichever occurs first).
8. Wrap the Elodea vessel with red, green, or blue cellophane. Wait 5 minutes! Repeat step #7.
9. Test as many different colors that time will allow.
Table 9: Experiment 1 Data – Distance from Light
Distance from
Light (cm)

Time for Bubble to Form and Release (minutes)
0

15 cm
25 cm
35 cm

0
0
0

st

nd

rd

th

th

th

th

th

th

th

1
2
3
4
5
6
7
8
Bubble Bubble Bubble Bubble Bubble Bubble Bubble Bubble

Table 10: Experiment 2 Data – Color of Light
Color of Light

Time for Bubble to Form and Release (minutes)
0

White (no
cellophane)

st

nd

rd

th

th

th

1
2
3
4
5
6
7
8
Bubble Bubble Bubble Bubble Bubble Bubble Bubble Bubble

0
0
0

Post-Lab Graphing:
1. Graph the results of each experiment. Be sure to use the correct style of graph. Include a
graph title and label each axis.
Post-Lab Questions:
st

1. Identify the independent and dependent variables in the 1 experiment?


Independent Variable:

______________________________



Dependent Variable:

______________________________
nd

2. Identify the independent and dependent variables in the 2


Independent Variable:

experiment?

______________________________
63



Dependent Variable:

______________________________

3. Looking at your graph, explain the trend witnessed during the first experiment – How does
distance from the light source affect the rate of photosynthesis?
4. A classmate of yours predicted that if you double the distance, then the photosynthetic rate
would be cut in half. Based on your data, do you agree with this reasoning?

5. (2

nd

Experiment) How did the cellophane color affect the photosynthetic rate?

nd

6. (2 Experiment) Which color do you predict would result in the lowest photosynthetic rate?
EXPLAIN!

7. Imagine that a strand of Elodea produces 210 molecules of glucose in 8 minutes. How many
oxygen molecules must have been produced during this time?

st

8. Calculate the slope value of each line graphed in the 1 experiment:


Slope 15cm: _________



Slope 25cm: _________



Slope 35cm: _________

9. Compare the slope values you calculated in question 8. This is the photosynthetic rate! How
does the photosynthetic rate at 15cm compare to the rate at 35cm?

64

10. 100 molecules of glucose were produced in a certain amount of time when 35 cm from the
light source. Predict how many glucose molecules would have been produced in the same
amount of time IF the Elodea was as distance of 15 cm from the light source.

65

Name: ___________________________________________ Date: _____________ Hour: _____

Assessing photosynthetic activities with a leaf disk assay
Figure 15: Floating Disk Assay Apparatus Illustration
Heat Sink

Beakers containing
spinach disks

Distance
Background:

The process of photosynthesis converts carbon dioxide gas and water into the simple sugar
glucose and oxygen gas. Chemically, this is represented in the equation below:
Light
6CO2 + 6H2O

C6H12O6 + 6O2

Purpose:
To investigate how different variables affect the photosynthetic process.
Laboratory Apparatus:
Spinach disks will be subjected to a vacuum, thereby expelling all gasses in the leaf tissue – this
will cause the spinach disks to sink when placed in the beakers containing water. A 150W
incandescent light with shine through a glass, water-filled heat sink. Oxygen production and
accumulation in the leaf tissues (due to photosynthesis) will make the leaf disks buoyant again,
causing them to eventually float to the surface. The time required for disks to float allows for
scientific measurement.
Question: How does CO2 concentration affect the rate of Photosynthesis?

66

Procedure:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.

Mix a solution of 0.2% (w/v) sodium bicarbonate. (Pre-lab)
Prepare 3 beakers: 2 with 250 mL of 0.2% sodium bicarbonate and 1 with 250 mL of fresh
water.
Cut out about 100 disks from the spinach leaves – avoid the veins!
Place approximately 30 disks into a 60 mL syringe filled with 0.2% sodium bicarbonate
solution (you may “borrow” this amount from your beakers).
Evacuate all air from the syringe, cap the end, and pull to evacuate all air from the spinach
disks. Hold the syringe at a vacuum for about 15 seconds.
Release the syringe – all disks should sink to the bottom of the syringe. Repeat step 5 if
needed.
Remove the spinach disks and place 20 disks into the appropriate beaker
Rinse the syringe. Place the beaker containing leaf disks into a dark area.
Repeat the procedure such that you have 2 beakers of 0.2% sodium bicarbonate each with 20
spinach disks and 1 beaker with pure water containing 20 spinach disks.
Cover one of the 0.2% beakers with aluminum foil to block out all light. This will serve as a
control.
Situate the uncovered 0.2% bicarbonate beaker and the pure-water beaker an equal distance
of 20cm away from the light source.
Turn on the light and start your stopwatch.
Record the number of disks floating every minute in the data table below:

Data Collection – Experiment #1:
Table 11: Sodium Bicarbonate Data

Table 12: Pure Water Data

0.2% Sodium Bicarbonate Solution
Time (min)

# of Disks
Floating

Pure Water

Percentage
Floating (%)

Time (min)

1
2
3
4
5
6
7
8
9
10

1
2
3
4
5
6
7
8
9
10

67

# of Disks
Floating

Percentage
Floating (%)

Question:
Check the beaker that was covered. How many disks floated to the top when light was not
available for photosynthesis?
Post-Lab Graphing:
Graph the results of this experiment on the attached sheet. Be sure to label each axis and indicate
the appropriate units. Provide a title and a key. This should be a 2-line line graph!

Post-Lab Questions:
1.

What are the independent and dependent variables in this experiment?


Independent Variable:

_____________________________



Dependent Variable:

_____________________________

2.

Why did we cover up one of the beakers in this experiment?

3.

Based on the results of this experiment, what conclusions can you make?

4.

What is needed for photosynthesis to occur?

5.

How many carbon dioxide molecules and how many water molecules are needed to produce
one glucose molecule during photosynthesis?

6.

What are the products of photosynthesis?

68

Name: ___________________________________________ Date: _____________ Hour: _____

The conversion of sugar to cellular energy (ATP) by fungal organisms (yeast)
Figure 16: Respiration Lab Apparatus Illustration
Distance

Meter Stick

Indicator

Yeast Culture
(Respiring in
Sucrose Solution)

Background:
Yeasts are eukaryotic organisms that belong to the Fungi Kingdom. Many yeast, like the specie
we are experimenting with today, are capable of both aerobic respiration (with oxygen) and
fermentation (without oxygen). During respiration and fermentation, sugars are metabolized and
broken down to release the energy stored in the chemical bonds of the sugar molecules. During
aerobic respiration, water and carbon dioxide are produced. There are several different pathways
of anaerobic respiration, one of which is fermentation. During fermentation, ethanol and carbon
dioxide are produced.
Purpose:
1. To discover how the available concentration of sugar affects the respiration rate of yeast
cells.
69

2. Possible learning extension – Determining how temperature affects the respiration of
yeast cells.
Laboratory Apparatus:
In this lab, small-diameter tube will be connected to a rubber stopper that is inserted into a test
tube containing a yeast culture. The sucrose concentration will be varied in this experiment. A
small drop of indicator solution will be introduced into the tubing. Carbon dioxide gas produced
during respiration will force the indicator to move through the tubing. The distance that the
indicator travels down the transparent tubing (per unit time) can be used to quantify the
respiratory rate.
Question: How does the sugar concentration affect the respiratory rate of yeast cells?
Procedure:
1. Mix 4%, 2%, 1%, and 0.5% sucrose solutions. (These should already be available when you
arrive to class)
2. Fill the test tube approximately half way with 4% sucrose solution.
3. Add 1.0g of yeast to the culture test tube and mix rapidly with a straw.
4. Add additional 4% sucrose solution up to the line marked on the test tube.
5. Start your stopwatch. At 5 minutes insert the rubber stopper.
6. Gently add a small amount of indicator solution into the transparent plastic tubing.
7. At 7 minutes stop and reset your stopwatch.
8. Attach the tubing to the rubber stopper.
9. Start the stopwatch when the indicator reaches the “0 cm” mark on the meter stick.
10. Record the distance the indicator travels every 30 seconds, for 3 minutes, or until it has
traveled 50cm.
11. Repeat steps 2 through 10 except with the 2%, 1%, and 0.5% sucrose solutions.
Table 13: Fermentation Rate Data
Distance Indicator Traveled @ Time (minutes)
% Sucrose Concentration
(m/v)
0.5%

0.0
min
0

1.0%

0

2.0%

0

4.0%

0.5
min

1.0
min

0

70

1.5
min

2.0
min

2.5
min

3.0
min

Post-Lab Graphing:
Graph the results of this experiment. Be sure to use the correct style of graph. Include a graph
title and label each axis.
Post-Lab Questions:
1. What are the independent and dependent variables in this experiment?


Independent Variable:

_________________________



Dependent Variable:

_________________________

2. What gas is being produced during the process of respiration? (what pushed the indicator
through the clear tubing?)

3. Describe the results of this experiment. Make a declarative statement that answers the
question being investigated.

4. Why do you think that the respiration rate might slow down if we were to continue this
experiment for a long period of time?

5. Which sugar concentration yielded the greatest rate of respiration?

6. What are some potential errors in this experiment?

7. What are other variables that could be testing using this laboratory setup?

8. Describe the graph that you produced. Be specific!

71

Name: ___________________________________________ Date: _____________ Hour: _____
Figure 17: Exercise 1
Apparatus Illustration

Figure 18: Exercise 2
Apparatus Illustration

Background:
Lactic acid in muscle that causes pain and soreness is the result of lactic acid fermentation, a
process that occurs when muscles are overused. As athletes build up their strength and
endurance, they don’t experience lactic acid build up often. When a person uses muscles they
have not used in a while, or have not conditioned to work in a specific way, lactic acid can build
up as a byproduct of anaerobic metabolism. This means the body is trying to obtain ATP to give
energy to the muscles, but it does not receive oxygen fast enough to support the breakdown of
sugar in the presence of oxygen. So, the body does the next best thing and still obtains ATP, but
does so without the presence of oxygen. This method is not as efficient and does not generate a
lot of ATP, but it does generate some. The byproduct of this method can be lactic acid build up
in muscle tissues.

Materials:
1 tennis ball
1 stopwatch
1 partner
1 non-dominant hand
Hypothesis:
After reading the background information, predict how many times you will be able to squeeze a
tennis ball with your non-dominant hand (the one you DON’T write with) before you just can’t
squeeze any more.
____________________________________________________________________
____________________________________________________________________
Procedure:
1. Using the hand you don’t write with, squeeze a tennis ball as fast as you can and as many
times as you can until you just can’t squeeze any more. Be sure to also time yourself.
2. Write the number of squeezes in the data table.
3. Write the time it took you to complete your squeezes in the data table.
4. Repeat steps 2 – 4 for your lab partner.
72

5. Place your squeeze number and time taken and your partner’s squeeze number and time
taken on the board.
6. Record 6 other people’s data (you choose), as seen on the board, and transfer to the data
table.
7. Create a neat bar graph of class data for squeeze number. You should have eight bars in
total on your graph, including you and your partner. Your x axis on the bar graph should
be students by initials.
Your y axis on the bar graph should be squeezes by number.
8. Create a neat line graph of class time. You should have eight points in total on your
graph, including you and your partner. Your x axis for the line graph should be students
by initials (there should be 8 people). Your y axis for the line graph should be time in
minutes.

Data table:
Table 14: Lactic Acid Activity Data
Name

Number of
squeezes

Time taken
(min)

Questions:

1. Why did you have to stop squeezing?

73

2. What substance was accumulating in your muscle cells because of your over exertion?

3. What molecules were your muscle cells deprived of that resulted in your having to stop
squeezing the tennis ball?

4. Looking at your bar graph, how did your results compare to the results of the class?

5. What is the name of the process that was demonstrated in this lab activity?

6. Is the process that occurred in this lab activity aerobic or anaerobic?

74

Name: ___________________________________________ Date: _____________ Hour: _____

Determining how pH and Temperature Affect the
Activity of α-Amylase (a Starch-Degrading Enzyme)

Figure 19: Six-Well Plate
Apparatus Illustration

1

2

3

6

5

Figure 20: Graduated Pipette

4

1.0 mL mark on
the Graduated
Pipette
Table 15: Six-Well Plate Organization
EXP # 1 (Temp)
1
Coldest
2
3
4
5
6
Hottest

EXP # 2 (pH)
1
pH 3.0
2
pH 4.0
3
pH 5.0
4
pH 6.0
5
pH 7.0
6
pH 8.0

Introduction:
Enzymes control numerous reactions involved in the metabolic activities of different organisms.
One well-known enzyme is amylase, an enzyme that is responsible for catalyzing the
breakdown of starches into sugars. Amylase can be found in human saliva and other organs
associated with the digestions of food. As you might expect, plants also contain amylase. Many
plants store starch, which must be broken down first into simple sugars and eventually converted
into cellular energy. Amylase is the enzyme responsible for the initial breakdown of starch.
Purpose / Question:
In today’s laboratory, you will investigate how 2 different factors affect the activity of the
enzyme amylase. The 2 variables we are testing are pH and temperature. Upon completing this
exercise, we hope to answer the following question:
What are the optimal pH and temperature conditions that allow
amylase to break down starch the fastest?
75

Basic Laboratory Explanation and Preparation:
The enzyme amylase will be added to a starch solution. After a given amount of time, an acidic
iodine solution will be added to stop the starch breakdown. Any remaining starch will turn
blue/purple in the presence of the iodine. We will measure the relative darkness with a software
program (Image J) to determine how much starch was degraded and how much remains.

Before conducting this laboratory investigation, it is assumed that the following solutions and
materials will be prepared:
 A large volume of 0.1m Phosphate Buffer (pH = 6.0)
 Appropriated 0.1M Phosphate and Acetate buffers with pH values of 3.0, 4.0, 5.0, 6.0, 7.0,
and 8.0
 α-Amylase stock solution (2.5 mg/mL) – Dilute as needed.
 20 g/L soluble potato starch stock solution
 Buffered starch solutions – Mix 1:1 to achieve 10g/L buffered starch solutions (pH 3.0 –
8.0)
 Iodine IKI dropper bottles (6.0 g KI + 0.6 g I in 100 mL DI – Diluted 1:20 w/0.05M
Hydrochloric Acid)

Procedure – Experiment #1 (Temperature):
1. In locations around the laboratory there should be water baths holding test tubes
filled with 5.0 mL of starch solution and a second test tube containing a small
volume of the amylase enzyme.
2. Remove a set of test tubes from the lowest temperature water bath (0°C) and quickly
3.
4.
5.
6.
7.

return to your lab table.
Prepare your stopwatch.
Using a graduated pipette, transfer 1.0 mL of amylase enzyme into the test tube
containing the starch solution. Gently swirl the test tube to mix.
Immediately start the stopwatch.
After 1.5 minutes add 10 drops of the acidic Iodine solution (this stops the reaction).
Pour the contents of the starch test tube into the 6-well plate. (See the diagram on the
1st page)

8. Repeat this procedure at the various temperatures that are available.
9. Scan the 6 well plate in the flat-bed scanner – this will be completed by Mr. Palmeri.
The scanned image will be available in the X drive.
10. Analyze the results using Image-J in the computer lab (use the procedure below).
76

Procedure – Experiment #2 (pH):
1. In the laboratory there will be 6 different stations for obtaining starch solution (pH 3.0, 4.0,
5.0, 6.0, 7.0, and 8.0). Start with the lowest pH.
2. RINSE YOUR TEST TUBE!!!
3. Go to the pH 3.0 station. Transfer 5.0 mL of starch solution into you test tube. Return to your
lab-station.
4. Prepare your stopwatch.
5. Transfer 1.0 mL of the amylase enzyme into the test tube containing starch solution.
6. Start your stopwatch.
7. After 2.0 minutes add 10 drops of the acidic Iodine solution (this stops the reaction).
8. Pour the contents of the starch test tube into the 6-well plate. (See the Diagram above)
9. Repeat this procedure at the various temperatures that are available.
10. Scan the 6-well plate in the flat-bed scanner – this will be completed by Mr. Palmeri. The
scanned image will be available in the X drive.
11. Analyze the results using Image-J in the computer lab (use the procedure below).
Image J Instructions:
1. Open the Image J program from the computer’s desktop.
2. Click “File” → click “Open” → look in the “X Drive – Palmeri Folder” for the Image of
the 6-well plate that Mr. Palmeri scanned.
3. Once the image is opened, click on “Image” → “Color” → click on “Split Channels”
4. Close out the red and green channel images. Zoom in on the appropriate portion of the blue
channel image.
5. Select the “Oval” or elliptical tool.
6. On the image, starting with well #1, select a large and uniformly dark circular area.
7. Click “Analyze” → Click “Measure”. Numeric values will post in a new window called
“Results”.
8. Repeat this procedure for all of the wells.
9. Record the mean value in the result window into the data table on this lab worksheet.

Analysis:
1. Use the standard curve provided to determine the starch concentrations at each
temperature and pH.
2. Graph the results of each experiment separately in the graphs included on this lab
worksheet.

77

Data:
Table 16: Temperature Data – Starch
Degradation by Enzyme Amylase

Table 17: pH Data- Starch Degradation by
Enzyme Amylase

Experiment #1 - Temperature
Temperature
(C°)

Mean
Image J
Value

Experiment #2 - pH

Starch
Concentration
(g/L)

pH

Mean
Image J
Value

Starch
Concentration
(g/L)

3.0
4.0
5.0
6.0
7.0
8.0
Use the “Mean Image J Value” and the “Standard Curve” below to determine the starch
concentration
Standard Curve:

Prior to conducting this lab, establish a standardized curve

 Starting with the most concentrated starch sample, perform a stepwise dilution to achieve a consistent range of starch solutions. Add
ten drops of acidic iodine to develop color in each sample. Using
ImageJ, determine quantitative values and convert these values to
established starch concentrations. Students will reference this curve
when analyzing their obtained values (when determining their
remaining starch concentrations related to ImageJ values).

Post-Lab Graphing:
Graph the results of each experiment on the following page. Plot the respective points and
connect with a smooth line. Remember to set your graphs up correctly (Independent variable on
the X-axis). Label each axis, indicate the units, and include a title for each graph.

78

Name: ___________________________________________ Date: _____________ Hour: _____

Determining how pH and Temperature Affect the Activity of α-Amylase (a
Starch-Degrading Enzyme)
Post-Lab Questions:
1. What are the independent and dependent variables in each experiment?
Experiment #1:


Independent Variable: ________________________________________



Dependent Variable:

________________________________________

Experiment #2:


Independent Variable: ________________________________________



Dependent Variable:

________________________________________

2.

Looking at your graph, what temperature do you expect amylase works best?

3.

Looking at your graph, what pH do you expect amylase would work best?

4. Predict: If you conduct this experiment with a starch sample that is 30°C, what do you
estimate the remaining starch concentration would be?

5. If you conduct this experiment with a starch sample that has a pH of 7.5, what do you
estimate the remaining starch concentration would be?
6. Summarize the results from each experiment (summarize what each graph is “saying”).

__________________________________________________________
__________________________________________________________
__________________________________________________________
__________________________________________________________
79

APPENDIX B:

STUDENT EXAMPLES

80

Figure 21: Investigation #1 Student Example (1)
1

3
2

4
Figure Legend:
1. Graph Title – “Percent Growth of That’s Gross Objects”
2. Left Y-axis – % change in length
3. Right Y-axis – % change in mass
4. X-axis – % salt concentration

81

Figure 22: Investigation #1 Student Example (2)

1

2
3

5
4
Figure Legend:
1. Graph Title – “Expanding Feet in Different Saltwater Concentrations”
2. Left Y-axis – % of length change
3. Right Y-axis – % of mass change
4. X-axis – % salt concentration
5. Graph Key – color coded (mass change, length change)

82

Figure 23: Investigation #1 Student Example (3)

1

5

3

2

4

Figure Legend:
1. Graph Title – “How Salt Concentration Affects the Growth of Feet”
2. Left Y-axis – % change in length
3. Right Y-axis – % change in mass
4. X-axis – % salt concentration
5. Graph Key – color coded (% change in mass, % change in length)

83

Figure 24: Investigation #2 Student Example

1

4
2

3
Figure Legend:
1. Graph Title – “How Sucrose Concentration Affects Mass of Potatoes and Cantaloupes”
2. Y-axis – Percent Change in Mass (grams)
3. X-axis - Sucrose Concentration (%)
4. Graph Key – color coded (Potato, Cantaloupe)

84

Figure 25: Investigation #3 Student Example (Color of Light)

1
2

Figure Legend:
1. Graph Title – “The Effect of Color of Light on the Photosynthetic Rate”
2. Graph Key – color coded – (white, green)

85

Figure 26: Investigation #3 Student Example (Distance from Light)

1

2

Figure Legend:
1. Graph Title – “The Effect of the Distance of Light on the Photosynthetic Rate”
2. Graph Key – color coded – (Distance of 10 cm, Distance of 30 cm)

86

Figure 27: Investigation #5 Student Example (1)

1
4

2

3
Figure Legend:
1. Graph Title – “How Sucrose Concentration Affects Alcohol Fermentation in Yeast”
2. Y-axis – Distance (cm)
3. X-axis – Time (minutes)
4. Graph Key – color coded for various sucrose concentrations

87

Figure 28: Investigation #5 Student Example (2)

1
4

2

3
Figure Legend:
1. Graph Title – “Respiration Lab”
2. Y-axis – Distance (mm)
3. X-axis – Time (minutes)
4. Graph Key – coded for various sucrose concentrations

88

Figure 29: Investigation #5 Student Example (3)

1

4

2

3

Figure Legend:
1. Graph Title – “Rate of Cellular Respiration”
2. Y-axis – Distance indicator travelled (cm)
3. X-axis – Time (minutes)
4. Graph Key – coded for various sucrose concentrations

89

Figure 30: Investigation #7 Student Example
1

2

3

4

5

6

Figure Legend:
1. Graph Title – “Effect of Temperature…”
2. Y-axis – % starch degraded

4. Graph Title – “Effect of pH…”
5. Y-axis – % starch degraded

3. X-axis – Temperature (C°)

6. X-axis - pH

90

APPENDIX C:

PARENT LETTER AND CONSENT FORM

91

Figure 31: High School Letterhead – Parental Consent Form (1)

PARENTAL CONSENT AND STUDENT ASSENT FORM

Dear Students and Parents/Guardians:
I would like to take this opportunity to welcome you back to school and invite you to participate
in a research project, “Enhancing Graphical Literacy Skills in the High School Science
Classroom via Authentic, Intense Data Collection and Graphical Representation Exposure”
that I will conduct as part of the microbiology class this semester. My name is Mr. Anthony
Palmeri. I am your science teacher for the first semester of the 2012- 2013 school year and I am
also a master’s degree student at Michigan State University. Researchers are required to
provide a consent form like this to inform you about the study, to convey that participation is
voluntary, to explain risks and benefits of participation, and to empower you to make an
informed decision. You should feel free to ask the researchers any questions you may have.
What is the purpose of this research? I have been working on effective ways to incorporate
authentic data collection, graph construction and analysis skills and I plan to study the results of
this teaching approach on student comprehension and retention of the material. The results of
this research will contribute to teachers’ understandings about the best way to teach about
science topics. Completion of this research project will also help me to earn my master’s degree
in Michigan State University’s Division of Math and Science Education (DSME).
What will students do? You will participate in laboratory investigations activities that will
require graph construction and subsequent analysis. These activities will encompass several
different units of study, but primarily will focus on the following topics: Photosynthesis, cellular
respiration, osmosis, and enzyme catalyzed reactions. You will complete all of the usual
assignments, laboratory experiments and activities, computer analysis, class demonstrations,
92

graphing assessments and pretests/posttests just as you do for any other unit of instruction.
There are no unique research activities – participation in this study will not increase or
decrease the amount of work that students do. I will simply make copies of students’ work for
my research purposes. I am asking for permission from both students and parents/guardians
(one parent/guardian is sufficient) to use copies of student work for my research purposes. This
project will continue from September 2012 until January 2013.
What are the potential benefits? My reason for doing this research is to learn more about
improving the quality of science instruction. I won’t know about the effectiveness of my
teaching methods until I analyze my research results. If the results are positive, I can further
intensify data and graphing activities in other science topics taught in this course. I anticipate
that you will benefit by better learning and remembering of course content and you will be
more capable of both creating graphs and deciphering those you encounter in the future. Also,
enhancing graphical literacy should certainly benefit you when taking college entrance exams
such as the ACT. I will report the results in my master’s thesis so that other teachers and their
students can benefit from my research.
What are the potential risks? There are no foreseeable risks associated with completing course
assignments, laboratory experiments and activities, computer analysis, class demonstrations,
and pretests/posttests. In fact, completing course work should be very beneficial to students.
Another person will store the consent forms (where you say “yes” or “no”) in a locked file
cabinet that will not be opened until after I have assigned the grades for this unit of instruction.
That way I will not know who agrees to participate in the research until after grades are issued.
In the meantime, I will save all of your written work. Later I will analyze the written work only
for students who have agreed to participate in the study and whose parents/guardians have
consented.
How will privacy and confidentiality be protected? Information about you will be protected to
the maximum extent allowable by law. Students’ names will not be reported in my master’s
thesis or in any other dissemination of the results of this research. Instead, the data will consist
of class averages and samples of student work that do not include names. After I analyze the
data to determine class averages and choose samples of student work for presentation in the
thesis, I will destroy the copies of student’s original assignments, tests, etc. The only people
who will have access to the data are me, my thesis committee at MSU, and the Institutional
Review Board at MSU. The data will be stored on password-protected computers (during the
study) and in a locked file cabinet in Dr. Heidemann’s locked office at MSU (after the study) for
at least three years after the completion of the study.
What are your rights to participate, say no, or withdraw? Participation in this research is
completely voluntary. You have the right to say “no”. You may change your mind at any time
93

and withdraw. If either the student or parent/guardian requests to withdraw, the student’s
information will not be used in this study. There are no penalties for saying “no” or choosing to
withdraw.
Who can you contact with questions and concerns? If you have concerns or questions about
this study, such as scientific issues, please contact the researcher, [Mr. Anthony Palmeri: 2800
Waukegan Street, Auburn Hills, MI 48326; anthony.palmeri@avondale.k12.mi.us; (248) 5376100 and /or Dr. Merle Heidemann: 354 Farm Lane #118 , Michigan State University, East
Lansing, MI 48824; heidema2@msu.edu; 517-432-2152 x 107].
If you have questions or concerns about your role and rights as a research participant, would
like to obtain information or offer input, or would like to register a complaint about this study,
you may contact, anonymously if you wish, the Michigan State University’s Human Research
Protection Program at 517-355-2180, Fax 517-432-4503, or e-mail irb@msu.edu or regular mail
at 207 Olds Hall, MSU, East Lansing, MI 48824.
How should I submit this consent form? If you agree to participate in this study, please
complete the attached form. Both the student and parent/guardian must sign the form. Return
the form to the Main Office at Avondale High School by 9/18/2012.

94

Figure 32: High School Letterhead – Parental Consent Form (2)

Name of science course: Microbiology
Teacher: Mr. Anthony Palmeri
School: Avondale High School
Parents/guardians should complete this following consent information:
I voluntarily agree to have ____________________________________ participate in this study.
(print student name)
Please check all that apply:
Data:
I give Mr. Anthony Palmeri permission to use data generated from my child’s work in this
class for his thesis project. All data from my child shall remain confidential.
I do not wish to have my child’s work used in this thesis project. I acknowledge that my
child’s work will be graded in the same manner regardless of their participation in this
research.
Photography, audiotaping, or videotaping:
I give Mr. Anthony Palmeri permission to use photos, audiotapes, or videotapes of my child
in the class room doing work related to this thesis project. I understand that my child will
not be identified.
I do not wish to have my child’s images used at any time during this thesis project.
Signatures:
____________________________________________
(Parent/Guardian Signature)
I voluntarily agree to participate in this thesis project.

_________________________
(Date)

________________________________________________
__________________________
(Student Signature)
(Date)
***Important***
Seal this form in the attached envelope and return to the Avondale High School Main Office.

95

APPENDIX D:

ASSESSMENT TOOLS

96

Name: ___________________________________________ Date: _____________ Hour: _____
Graphical Literacy Pre-Assessment: Part 1
Table 18: Part1, Question #1 Data
Graphing Problem #1:
Methane is a greenhouse gas. Using different data
collection methods, climate scientists have
determined the atmospheric methane concentrations
and average temperatures in a high mountain glacier
during a 275 year long period (See Figure 1).
Instructions:
Correctly graph both the methane concentration and
average temperature on the graph below. Include all
relevant parameters that would be important to a
scientist viewing your graph.

Table 19: Part1, Question #1 Graph

97

Year
1700
1725
1750
1790
1805
1825
1850
1860
1875
1900
1925
1950
1975

Methane
Average
Concentration Temperature
(ppb)
(°C)
460
7
410
4
645
16
450
10
440
6
390
5
350
4
700
15
505
9
400
8
390
9
375
10
605
13

Graphing Problem #2:
A student conducts a photosynthesis experiment to see how the wattage of a light bulb affects the
amount of oxygen produced during photosynthesis. Draw a line representing the photosynthetic
rate for the 40W bulb (assuming it is greater than that of the 20W bulb).
Figure 33: Part1, Question #2 Graph- Photosynthesis of a Plant When Exposed to Light

98

Graphing Problem #3:
The enzyme catalase speeds up the decomposition of hydrogen peroxide in living things. Water
and oxygen are produced when catalase breaks down hydrogen peroxide. Catalase works slower
at extremely low and extremely high temperatures. Catalase works best at intermediate
temperatures.
If the activity of catalase was graphed, how would you expect the graph to look? Draw a
line that represents the shape you would expect:

Oxygen Produced

Figure 34: Part1, Question #3 Graph

Temperature

99

Graphing Problem #4:
You have 2 different springs, spring “A” and spring “B”. When 5
kilograms are suspended from spring “A” it stretches 10 cm.
When 5 kilograms are suspended from spring “B” is stretches
4.0 cm. The distance a spring stretches due to the amount of weight
pulling on it is a linear relationship. You can assume that these
springs do not stretch at all (0cm) when there is no weight pulling on
them.
Graph the spring-stretch data below and answer the questions that follow:
Figure 36: Part1, Question #4 Graph – Spring Stretch of
2 Different Springs

Figure 35: Part1, Question
#3 Illustration
A

Questions:
1. About what distance would spring “A” stretch by an 8.0 kg mass?
2. About what distance would spring “B” stretch by an 8.0 kg mass?
100

B

Name: ___________________________________________ Date: _____________ Hour: _____
Graphical Literacy Pre-Assessment: Part 2
1. A culture tube is inoculated and incubated at 37°C. The resulting bacterial growth is graphed
below:

Figure 37: Part2, Question #1 Graph – Growth of Bacteria at 37 Degrees Celsius

At what time is the number of bacteria increasing at the greatest rate?
A. 16 hours
B. 11 hours

C. 8 hours
D. 5 hours

101

2. The graph below shows how the average daily calorie needs of young people changes as the
get older.

Figure 38: Part2, Question #2 Graph – Caloric Intake

How many more average daily Calories are needed by a 17-year old male than by a 17-year old
female?
A. 300
C. 2700
B. 500
D. 3000
3. Look at graph provided in the previous question (#2). Which statement is supported by the
graph?
A. At age 14, a female needs more daily Calories than a male
B. At age 9, a female needs the same daily Calories as a male
C. An 11-year old child needs twice as many daily Calories as a 6-year old child
D. An 8-year old female needs fewer daily Calories than a 5-year old male.

102

4. Some species of bacteria are harmful. Antibiotics are chemicals that kill bacteria. Some
bacteria are resistant to antibiotics and are not killed by these chemicals. The graphs below
show the results of a controlled experiment that was conducted to determine the effectiveness
of 3 different antibiotics against a specific type of bacteria.
Figure 39: Part2, Question #4 Graphs – Effectiveness of Different Antibiotics

Which conclusion is best supported by the information in the graphs?
A. Antibiotic A continuously slowed the growth
B. Antibiotic B was least effective in controlling the growth
C. Antibiotic C was most effective in controlling the growth
D. Antibiotic A and B slowed the growth

103

5. The solubility curves below show how many grams of each substance can be dissolved into
100 grams of water at various temperatures.
Figure 40: Part 2, Question #5 Graph – Solubility of Various Substances

Based on the graph above, which of the following statements is TRUE?
A. At 20°C, the maximum amount of KClO3 that can dissolve in 100 grams of water is 20
grams
B. At 20°C, the maximum amount of NaNO3 that can dissolve in 100 grams of water is 45
grams
C. At 50°C, approximately three times as much NH4Cl can dissolve compared to NH3
D. At 50°C, approximately twice as much KNO3 can dissolve compared to KCl
104

6. The data table on the right shows the number of
calories used while doing a variety of exercises.
According to the table, which graph below
illustrates the calories used for 1 hour of jogging
followed by 2 hours of walking?

Figure 41: Part 2, Question #6 Graph Answers

105

Table 20: Part 2, Question #6
Calorie Use Data

7. Many Bacteria produce methane (CH4) as a byproduct when they grow. A measured amount
of methane-producing bacteria was placed in four test tubes, each containing a different sugar
(fructose, sucrose, lactose, glucose). What conclusion can you draw from this graph?

Figure 42: Part 2, Question #7 Graph – Methane Production of Bacteria

A. These bacteria cannot use lactose very efficiently
B. Growth is fastest with sucrose
C. All of the sugars were used up after 40 minutes
D. At 60 minutes all of the bacteria were dead

106

8. Amylase is an enzyme that catalyzes the breakdown of starch. Study the graph below:
Figure 43: Part 2, Question #8 Graph – Digestion
of Starch by Amylase

According to the graph, addition of the enzyme amylase caused the reaction to:
A. Slow down
B. Take in heat

C. Give off heat
D. Speed up

107

9. The graph at the right shows the amount of pollutants removed by trees.
According to this graph, trees are able to remove the greatest amount of which pollutant
during the month of October?
Figure 44: Part 2, Question #9 Graph – Pollution

A. Ozone
B. Particulates
C. NO2
D. SO2

108

10. In an osmosis experiment, equally sized zucchini and cantaloupe pieces were soaked in
various concentrations of sugar. The percent change in mass at each concentration was
determined and the results were graphed:

Figure 45: Part 2, Question #10 Graph – Osmosis in Zucchini and Cantaloupe

Based on this graph, at what sugar concentration will cantaloupe show no change in mass?
A.
B.
C.
D.

0.0%
7.5%
14.0%
23.0%

109

Table 21: Scoring Rubric: Graphical Literacy Pre-Assessment: Part 1
Part 1 of the Assessment has a total value of 20 points and will be scored as follows:
Graphing Problem #1:
Point Value
(MAX → partial)
2/1/.5
1/.5
1/.5
2/0
1/.5
1/.5
2/1/.5/0
2/1/.5/0
Total Possible =
12 pts.

2 individual Y axes Labeled (Methane) and (Temperature) with
units annotated on each
X-Axis (Year) Labeled with units annotated on X axis
Key is provided
Axes oriented appropriately
Each axis uses an appropriate and consistent increment
A suitable title is provided
A line showing methane concentration is graphed correctly
A line showing average temperature is graphed correctly

Graphing Problem #2:
Point Value
(MAX → partial)
1/.5
1
Total Possible =
2 pts.

Line (Linear) is drawn
Slope is greater than the 20W bulb

Graphing Problem #3:
Point Value
(MAX → partial)
2/1/.5
Oxygen level is indicated highest at some moderate temperature
value
1/.5
Smooth, bell-shaped curve is drawn
Total Possible =
3 pts.
Graphing Problem #4:
Point Value
(MAX → partial)
1
1
.5
.5
Total Possible =
3 pts.

Line for spring A is graphed correctly
Line for spring B is graphed correctly
Question 1 answer: 16±0.5 cm
Question 2 answer: 6.5±0.5 cm

110

Table 22: Scoring Rubric: Graphical
Literacy Pre-Assessment: Part 2
Question
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.

Answer
D
A
B
C
D
B
A
D
B
C

Part 2 of the Assessment has a total value of 20 points.
Each correct response is worth 2 points.

111

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112

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