Combining Clickstream Analyses and Graph-Modeled Data Clustering for Identifying Common Response Processes

Esther Ulitzsch (), Qiwei He, Vincent Ulitzsch, Hendrik Molter, André Nichterlein, Rolf Niedermeier and Steffi Pohl
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Esther Ulitzsch: IPN – Leibniz Institute for Science and Mathematics Education
Qiwei He: Educational Testing Service
Vincent Ulitzsch: Technische Universität Berlin
Hendrik Molter: Technische Universität Berlin
André Nichterlein: Technische Universität Berlin
Rolf Niedermeier: Technische Universität Berlin
Steffi Pohl: Freie Universität Berlin

Psychometrika, 2021, vol. 86, issue 1, No 7, 190-214

Abstract: Abstract Complex interactive test items are becoming more widely used in assessments. Being computer-administered, assessments using interactive items allow logging time-stamped action sequences. These sequences pose a rich source of information that may facilitate investigating how examinees approach an item and arrive at their given response. There is a rich body of research leveraging action sequence data for investigating examinees’ behavior. However, the associated timing data have been considered mainly on the item-level, if at all. Considering timing data on the action-level in addition to action sequences, however, has vast potential to support a more fine-grained assessment of examinees’ behavior. We provide an approach that jointly considers action sequences and action-level times for identifying common response processes. In doing so, we integrate tools from clickstream analyses and graph-modeled data clustering with psychometrics. In our approach, we (a) provide similarity measures that are based on both actions and the associated action-level timing data and (b) subsequently employ cluster edge deletion for identifying homogeneous, interpretable, well-separated groups of action patterns, each describing a common response process. Guidelines on how to apply the approach are provided. The approach and its utility are illustrated on a complex problem-solving item from PIAAC 2012.

Keywords: action sequences; response times; complex problem solving; cluster editing (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11336-020-09743-0

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