Assessing learning effort with hand motion tracking methods

Technology has enabled various alternative educational platforms, such as online courses. Compared to human instructors in traditional educational environments, alternative platforms often show a limited capacity to evaluate the learning progress of students and implement intervention strategies based on the evaluation. Here, we tested participants' hand motions, recorded using a computer mouse and a touchscreen, to determine if the data can predict learners' struggles. Hand motions of participants concurrently performing arithmetic and motor tasks were examined to investigate how the hand motions varied depending on the difficulty of the arithmetic task. The results indicated that working memory load affected both the temporal and spatial features of hand motions, which were predictive of participants' level of working memory load. These findings demonstrate that the assessment of struggles in learning may be achieved at relatively high accuracy with input devices commonly used by learners to access online education systems.