Learning analytics for student homework activities during a long break: Evidence from K-12 education in Japan

Abstract

Learning Analytics (LA) is an emergent field that aims to better understand students and provide intelligence to learners, teachers, and administrators using learning log data. Although the use of technology in class is increasing in the K-12 sector and tertiary education, cases of effective implementation of LA in secondary schools have rarely been reported. This study offers an example of LA implemented in a junior high Math class during long vacations in Japan. This paper comprises two studies: first, we analyzed 121 students’ answer logs and their exam performance after vacation by the K-means clustering method. We found that students’ progress patterns were categorized into four types of engagement—early, late, high, and low—and the early and high-engagement groups obtained significantly higher scores than the low-engagement group. In the second study, we implemented a real-time dashboard that visualizes students’ progress patterns and gives students insights about their progress during the vacation period. We found that the dashboard significantly increased students’ interactions with the assignment, and the questionnaire survey determined that the LA dashboard motivated students to learn during the long vacation period. Considering the previous studies of LA, we estimate that LA-based interventions enhance students’ self-regulation skills, which is crucial for learning during long vacation periods. Our study offers a novel approach to implementing LA in K-12 education.