Abstract
AbstractDBL is a novel pedagogical approach intended to improve students’ conditional knowledge and problem-solving skills by exposing them to a sequence of branching learning decisions. The DBL software provided students with ample opportunities to engage in the expert decision-making processes involved in complex problem-solving and to receive just-in-time instruction and scaffolds at each decision point. The purpose of this study was to examine the effects of decision-based learning (DBL) on undergraduate students’ learning performance in introductory physics courses as well as the mediating roles of cognitive load and self-testing for such effects. We used a quasi-experimental posttest design across two sections of an online introductory physics course including a total N = 390 participants. Contrary to our initial hypothesis, DBL instruction did not have a direct effect on cognitive load and had no indirect effect on student performance through cognitive load. Results also indicated that while DBL did not directly impact students’ physics performance, self-testing positively mediated the relationship between DBL and student performance. Our findings underscore the importance of students’ use of self-testing which plays a crucial role when engaging with DBL as it can influence effort input towards the domain task and thereby optimize learning performance.
Funder
Seoul National University
Publisher
Springer Science and Business Media LLC