Frontline Learning Research <p>Frontline Learning Research (FLR) welcomes risk-taking and explorative studies that provide input for theoretical, empirical and/or methodological renewal within the field of research on learning and instruction. The journal offers a distinctive opening for foundational research and an arena for studies that promote new ideas, methodologies or discoveries. Read about what is frontline under <a href="" target="_blank" rel="noopener">Aims and scope</a></p> <p>ISSN 2295-3159</p> en-US <p>FLR adopts the Attribution-NonCommercial-NoDerivs Creative Common License (BY-NC-ND). That is, Copyright for articles published in this journal is retained by the authors with, however, first publication rights granted to the journal. By virtue of their appearance in this open access journal, articles are free to use, with proper attribution, in educational and other non-commercial settings.</p> (Nina B. Dohn) (Havva Akcaoglu) Mon, 21 Aug 2023 05:14:10 -0700 OJS 60 Assessing Students’ Interpretations of Histograms Before and After Interpreting Dotplots: A Gaze-Based Machine Learning Analysis <div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p>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 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.</p> </div> </div> </div> Lonneke Boels, Alex Lyford, Arthur Bakker, Paul Drijvers Copyright (c) 2023 Frontline Learning Research Tue, 05 Sep 2023 00:00:00 -0700