IEEE 20th International Conference on Advanced Learning Technologies (ICALT2020), Tartu, Estonia, 6 - 09 July 2020
Self-direction skills in the context of learning can be supported with data in this digital era. This study analyzes the behaviors of learners during a self-directed reading and summarization task. Our work investigates an undergraduate course (n=72) where students worked on a reading and summarizing assignment while planning and monitoring the task in GOAL, a platform synthesizing learner's activity data from learning and physical activity contexts. This study focuses on the initial cohort analysis of the students' behavior based on the fine grain interaction data collected in the different systems using visual analytics techniques. Such a data-rich narrative of self-directed in-semester activities is not discussed yet in the literature to our knowledge. We discuss the implications of the trends that is found in our collected dataset for designing AI-support for self-direction skills with the GOAL platform and the scope of deeper analysis to further understand the process.