Assessing Active Learning Impacts in Introductory Economics Courses

Thesis Abstract

This study investigates the effects of active learning strategies on student engagement, conceptual understanding, and performance in introductory economics courses within a large public university context. The problem addressed concerns persistent gaps between student-centric pedagogies and traditional didactic instruction, which have been linked to lower retention of economic reasoning and diminished problem-solving proficiency among undergraduates. The primary aim is to evaluate whether structured active learning interventions improve learning outcomes and to identify mechanisms through which these effects operate. Specific objectives are (1) to measure differences in conceptual mastery of core economics concepts between active-learning and traditional sections; (2) to assess changes in student engagement, motivation, and self-efficacy; (3) to examine the moderating role of prior mathematics preparedness and baseline economics interest; (4) to evaluate instructors’ perceptions of feasibility, workload, and fidelity of implementation; and (5) to develop a model linking active-learning practices to academic performance via engagement and self-efficacy. The methodology employs a quasi-experimental, mixed-methods design conducted across two academic terms in the fall and spring semesters of 2024 in five introductory microeconomics and macroeconomics sections. The population comprises first- and second-year undergraduates enrolled in these courses, with a target sample of 600 students (approximately 300 in active-learning sections and 300 in traditional sections). Data collection instruments include (i) standardized concept inventories tailored to introductory economics, (ii) the Student Engagement Instrument (SEI) complemented by the Motivated Strategies for Learning Questionnaire (MSLQ), (iii) course performance metrics (midterm and final examination scores, assignment grades), (iv) instructor fidelity checklists based on a predefined active-learning protocol, and (v) semi-structured interviews with 12 instructors and 24 students to capture experiential insights. Validity and reliability will be established through pilot testing of the concept inventories (Cronbach’s alpha > .70), DV practitioners’ validation of SEI and MSLQ scales, and triangulation across quantitative and qualitative data. Data analysis will proceed as follows descriptive statistics to summarize student characteristics and outcome measures; multilevel linear regression models to compare learning gains while accounting for clustering by class and instructor; a difference-in-differences approach to isolate the impact of active learning from secular trends; and structural equation modeling to test the hypothesized mediation pathways from active-learning exposure to engagement, self-efficacy, and performance. Theoretical framing integrates Kolb’s experiential learning theory and Self-Determination Theory to explain how active-learning activities foster autonomy, competence, and relatedness, thereby enhancing engagement and achievement. The analysis will also incorporate a qualitative thematic analysis of interview transcripts to elucidate contextual factors influencing implementation fidelity and perceived value. Expected findings indicate that students in active-learning sections will exhibit greater conceptual mastery, higher engagement, and improved course performance relative to traditional sections, with effect sizes in the small-to-moderate range (Cohen’s d ? 0.25–0.50) after controlling for baseline characteristics. Mediation analyses are anticipated to reveal that increases in engagement and self-efficacy partially transmit the effect of active learning to performance. Subgroup analyses may reveal larger benefits for students with lower prior mathematics preparedness or lower initial interest in economics, suggesting differential equity effects. The study contributes to knowledge by providing robust empirical evidence on the effectiveness and mechanisms of active-learning interventions in foundational economics education, offering a validated mixed-methods framework for evaluating pedagogical innovations in large-enrollment settings, and informing policy recommendations for curriculum design, instructor professional development, and scalable implementation. In terms of implications, the findings are expected to support the expansion of structured active-learning modules (e.g., problem-based tutorials, clicker-assisted case discussions, and collaborative data analysis labs) within introductory economics programs, complemented by targeted faculty development and ongoing fidelity monitoring. The conclusion emphasizes that, when implemented with theoretical grounding and rigorous fidelity, active learning can produce meaningful gains in understanding and engagement for diverse student populations, with practical guidance for educators seeking to optimize introductory economics instruction. Recommendations include adopting a standardized active-learning protocol across sections, investing in instructor training and resource development, and conducting regular outcome assessments to sustain improvements in student learning trajectories.