Effects of decision-based learning on student performance in introductory physics: The mediating roles of cognitive load and self-testing

Abstract DBL 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.