Learning progressions are used to describe how students' understanding of a topic progresses over time and to classify the progress of students into steps or levels. This study applies Item Response Theory (IRT) based methods to investigate how to design learning progression-based science assessments. The research questions of this study are: 1) how to use items in different formats to classify students into levels on the learning progression, 2) how to design a test to give good information about students' progress through the learning progression of a particular construct and 3) what characteristics of test items support their use for assessing students' levels. Data used for this study were collected from 1500 elementary and secondary school students during 2009-2010. The written assessment was developed in several formats such as the Constructed Response (CR) items, Ordered Multiple Choice (OMC) and Multiple True or False (MTF) items. The followings are the main findings from this study. The OMC, MTF and CR items might measure different components of the construct. A single construct explained most of the variance in students' performances. However, additional dimensions in terms of item format can explain certain amount of the variance in student performance. So additional dimensions need to be considered when we want to capture the differences in students' performances on different types of items targeting the understanding of the same underlying progression. Items in each item format need to be improved in certain ways to classify students more accurately into the learning progression levels. This study establishes some general steps that can be followed to design other learning progression-based tests as well. For example, first, the boundaries between levels on the IRT scale can be defined by using the means of the item thresholds across a set of good items. Second, items in multiple formats can be selected to achieve the information criterion at all the defined boundaries. This ensures the accuracy of the classification. Third, when item threshold parameters vary a bit, the scoring rubrics and the items need to be reviewed to make the threshold parameters similar across items. This is because one important design criterion of the learning progression-based items is that ideally, a student should be at the same level across items, which means that the item threshold parameters (d1, d2 and d3) should be similar across items. To design a learning progression-based science assessment, we need to understand whether the assessment measures a single construct or several constructs and how items are associated with the constructs being measured. Results from dimension analyses indicate that items of different carbon transforming processes measure different aspects of the carbon cycle construct. However, items of different practices assess the same construct. In general, there are high correlations among different processes or practices. It is not clear whether the strong correlations are due to the inherent links among these process/practice dimensions or due to the fact that the student sample does not show much variation in these process/practice dimensions. Future data are needed to examine the dimensionalities in terms of process/practice in detail. Finally, based on item characteristics analysis, recommendations are made to write more discriminative CR items and better OMC, MTF options. Item writers can follow these recommendations to write better learning progression-based items.
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