Effects of real-time analytics-based personalized scaffolds on students’ self-regulated learning

Self-Regulated Learning (SRL) is related to increased learning performance. Scaffolding learners in their SRL activities a computer-based environment can help improve outcomes, because students do not always regulate spontaneously. Based on theoretical assumptions, scaffolds should be continuously adaptive and personalized students' ongoing progress order promote SRL. The present study aimed investigate the effects of analytics-based scaffolds, facilitated by rule-based artificial intelligence (AI) system, process outcomes real-time measurement support using trace data. Using pre-post experimental design, received (n = 36), generalized 32), or no 30) during learning. Findings indicated that induced more activities, but were found outcomes. Process models large similarities temporal structure between groups which may explain why group differences performance observed. In conclusion, informed students’ measured supported with AI are first step towards supports incorporating has further developed future research.