Students with correct answers show significantly higher fixation counts in task-relevant areas than students with incorrect answers¶

Claim · All claims
Evidence · 1 study · associational r1 · q2 · i3 large
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i3 The first five eye tracking metrics lead to significant differences between correct and incorrect groups with medium and strong effects (e.g., r = 0.71 for relative fixation count in relevant areas). → Kennel 2025

Evidence¶

Kennel 2025¶

Kennel, K., Ishimaru, S., Küchemann, S., Steinert, S., Kuhn, J., & Ruzika, S. (2025). Gaze-Based Prediction of Students' Math Difficulties - A Time Dynamic Machine Learning Approach to Enable Early Individual Assistance. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-024-00447-5

q2 · i3 · associational · r1

Mann-Whitney U test comparison of eye tracking metrics between 34 correct and 109 incorrect solvers. Table 2 reports r = 0.71 for relative fixation count in relevant areas and r = 0.68 for fixation count in relevant areas.

"The first five metrics lead to significant differences between the two groups with medium and strong effects, respectively."

Discussion¶

Related Claims¶

• In one eye-tracking experiment, revising math-relevant visuals changes fixation length on them differently by prior knowledge group, with shorter fixations under the revised visuals for low prior knowledge students — related
• The five difficulty groups differ significantly on all 15 eye tracking metrics — related