Differences of online learning behaviors and eye-movement between students having different personality traits

The information technologies are integrated into education so that mass data is available reflecting each action of students in online environments. Numerous studies have exploited these data to do the learning analytics.In this paper, we aim at achieving the show of personalized indicators for students per personality trait on the learning analytics dashboard (LAD) and present the preliminary results. First, we employ learning behavior engagement (LBE) to describe students' learning behaviors, exploited to analyze the significant differences among students having different personality traits. In experiments, fifteen behavioral indicators are tested. The experimental results show that there are significant differences about some behavioral indicators among personality traits. Second, some of these behavioral indicators are presented on the LAD and distributed in each area of interest (AOI). Hence, students can visualize their behavioral data that they care about in AOIs anytime in the learning process. Through the analysis of eye-movement including the fixation duration, fixation count, heat map and track map, we have found that there are significant differences about some visual indicators in AOIs. This is partly consistent with the results of behavioral indicators.