Assessing Students’ Interpretations of Histograms Before and After Interpreting Dotplots: A Gaze-Based Machine Learning Analysis
Boels, Lonneke; Lyford, Alex; Bakker, Arthur; Drijvers, Paul
(2023) Frontline Learning Research, volume 11, issue 2, pp. 1 - 30
(Article)
Abstract
Many students persistently misinterpret histograms. Literature suggests that having students solve dotplot items may prepare for interpreting histograms, as interpreting dotplots can help students realize that the statistical variable is presented on the horizontal axis. In this study, we explore a special case of this suggestion, namely, how students’ histogram
... read more
interpretations alter during an assessment. The research question is: In what way do secondary school students’ histogram interpretations change after solving dotplot items? Two histogram items were solved before solving dotplot items and two after. Students were asked to estimate or compare arithmetic means. Students’ gaze data, answers, and cued retrospective verbal reports were collected. We used students’ gaze data on four histogram items as inputs for a machine learning algorithm (MLA; random forest). Results show that the MLA can quite accurately classify whether students’ gaze data belonged to an item solved before or after the dotplot items. Moreover, the direction (e.g., almost vertical) and length of students’ saccades were different on the before and after items. These changes can indicate a change in strategies. A plausible explanation is that solving dotplot items creates readiness for learning and that reflecting on the solution strategy during recall then brings new insights. This study has implications for assessments and homework. Novel in the study is its use of spatial gaze data and its use of an MLA for finding differences in gazes that are relevant for changes in students’ task-specific strategies.
show less
Download/Full Text
Keywords: Eye-Tracking, Histogram and Dotplot, Practice Effect, Random Forest, Statistics Education, Education
ISSN: 2295-3159
Publisher: European Association for Research on Learning and Instruction
Note: Funding Information: This research is funded with a Doctoral Grant for Teachers from the Dutch Research Council (NWO), number 023.007.023 awarded to Lonneke Boels. Any opinions, findings, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Dutch Research Council. We thank the following people for their contributions to this study. Nathalie Kuijpers for checking the document on style and English, Ciera Lamb for proofreading a previous version for American English, Anna Shvarts for assisting during the last day of data collection, Wim Van Dooren for his contribution to the design of the eye-tracking study, Rutmer Ebbes for his contribution to the pilot eye-tracking study (Boels et al., 2018), Aline Boels for the programming of the HTML-files containing the items, Juri Boels for transcribing almost all verbal reports, Iljo Boels for exporting gaze plots and heatmaps, Willem den Boer for writing the macros for processing the eye-tracking data. Furthermore, LB thanks all the people organizing and contributing to the eye-tracking seminars of the UU, especially Ellen Kok, Margot van Wermeskerken, Roy Hessels, Ignace Hooge, and Jos Jaspers. LB also thanks the Faculty of Social and Behavioral Sciences for lending the laptop and Tobii-XII-60 eye tracker for this research. Publisher Copyright: © 2023, European Association for Research on Learning and Instruction. All rights reserved.
(Peer reviewed)
See more statistics about this item