AI-assisted evaluation of problem-solving performance using eye movement and handwriting

Abstract

With the ability to predict learning behaviors, artificial intelligence (AI) is increasingly involved in assessing the performance of problem solving. This study explored the potential of AI to predict whether mathematics problems could be solved based on eye movements and handwriting in a digital problem-solving environment. Sixty-one students participated in the experiment. The goal is to examine whether types of eye movement features (AOI-based and fixation-based features), levels of information while solving problems (separated and integrated steps), and handwriting could impact the performance of AI. The results indicated that fixation-based features outperform AOI-based features. Furthermore, information in separated steps could provide higher accuracy than that in integrated steps. Inconsistent patterns between human and AI-based assessment of solvers’ answers are discussed.

Keywords:

• Digital problem solving
• eye movement
• handwriting
• long short-term memory
• artificial intelligence

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was financially supported by the projects from the Ministry of Science and Technology, Taiwan, under contract number NST C 112-2410-H-003-072-MY3, NST C 112-2926-I-011-502-G, and the National Taiwan Normal University (NT NU) within the framework of the Higher Education Sprout Project by the Ministry of Education(MOE) in Taiwan.