Date of Award:
Doctor of Philosophy (PhD)
Instructional Technology and Learning Sciences
Problem-based learning is a student-centered, inquiry-based approach that builds problem-solving skills. Reviews of problem-based learning, as compared to traditional lecture-based learning, report modest positive gains in cognitive outcomes. Many metaanalyses have been conducted to analyze the effectiveness of problem-based learning, but none have examined self-directed learning in the context of problem-based learning. The purpose of this study was to conduct a meta-analysis across all disciplines examining the extent to which problem-based learning engenders self-directed learning compared to a lecture-based approach.
This study used a random effects model meta-analysis using 75 outcomes from 38 studies. Results indicated a statistically significant, z(74) = 7.11, p = 0.01, overall medium effect size (g = 0.45) favoring problem-based learning. A test of heterogeneity indicated genuine variance across outcomes (Q = 559.57, df = 74, p < 0.01). Subgroup analyses indicate positive effect sizes for the four components of self-directed learning with two being statistically significant: personal autonomy, g = 0.51, z(47) = 6.4, p = 0.01, and independent pursuit of learning, g = 0.66, z(2) = 3.49, p = 0.01. Two emergent subgroups were also examined. From the 23 subgroup components, 12 reported statistically significant effect size estimates above 0. Findings and conclusions provided the first synthesis of conative and affective outcomes in problem-based learning by specifically analyzing self-directed learning. From this synthesis, practitioners learn that problem-based learning promotes conative and affective skills in self-directed learning.
Leary, Heather M., "Self-Directed Learning in Problem-Based Learning Versus Traditional Lecture-Based Learning: A Meta-Analysis" (2012). All Graduate Theses and Dissertations. 1173.
Copyright for this work is retained by the student. If you have any questions regarding the inclusion of this work in the Digital Commons, please email us at .