The machine learning algorithm and the interpretable mathematical model identified the same task-solving strategies from students' gaze data¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Comparison of MLA and IMM results showed convergence in the strategies identified from gaze data on single histograms. → Boels 2024

Evidence¶

Boels 2024¶

Boels, L., Garcia Moreno-Esteva, E., Bakker, A., & Drijvers, P. (2024). Automated Gaze-Based Identification of Students' Strategies in Histogram Tasks through an Interpretable Mathematical Model and a Machine Learning Algorithm. International Journal of Artificial Intelligence in Education, 34, 931-973. https://doi.org/10.1007/s40593-023-00368-9

q2 · i? · design · r2

The third phase of the study compared the random forest MLA results with the IMM results and with the previous qualitative study's strategy classifications. The authors report that "the MLA and IMM identi- fied the same task-solving strategies."

"The MLA (1) performed well but is a black box. The IMM (2) is transparent, performed well, and is theoretically meaningful. The comparison (3) showed that the MLA and IMM identi- fied the same task-solving strategies."

Discussion¶

Related Claims¶

• Students' horizontal or vertical gaze patterns on the graph area of single histograms are indicative of their task-specific strategies for estimating the mean — related
• An interpretable mathematical model based on saccade lengths and angles performs well, is transparent, and is theoretically meaningful for identifying students' histogram strategies — related