Adaptive Practicing Design for Self-paced Online Learning

Abstract

Self-paced online learning (SPOL) provides great flexibility of learning anytime, anywhere and at any pace, yet it brings some inherent learning barriers because of the distance between students and instructors. This study recognizes two major learning barriers: challenges for instructors to provide proactive support and the need for higher self-directed learning skills in students. Such learning barriers can lead to students’ academic struggle and failure. This study suggests three key learning strategies to alleviate learning barriers: a) increasing students’ self-awareness of learning, b) identifying struggling students, and c) facilitating students’ mastery learning. Focusing on the domain of Data Structures and Algorithms in the computer science discipline, this study proposes systematically designing and embedding adaptive formative assessment in SPOL courses to implement these strategies. Formative assessment is a fundamental process within the learning process. With an adaptive mechanism embedded, it can detect a student’s knowledge state more accurately and efficiently. Furthermore, adaptive formative assessment can facilitate students’ mastery learning if adaptive formative assessment is designed for practicing. During this mastery learning process with adaptive practicing, it is possible to identify one kind of struggling student – wheel-spinning students, who persistently work on problems or exercises without progressing towards mastery. Different from the research on adaptive assessment that mainly focuses on Intelligent Tutoring Systems, this study investigates an effective adaptive practicing mechanism in the context of self-paced online learning. A prototype of an adaptive practicing system is being created to test the effectiveness of this mechanism in implementing those suggested learning strategies.