Design, Implementation and Evaluation of Microlearning for Business Education Curricula

Thesis Abstract

This study investigates the design, implementation, and evaluation of microlearning interventions within undergraduate and postgraduate business education curricula to address gaps in knowledge retention, engagement, and time-efficient learning among diverse student populations. The problem addressed centers on traditional lecture-centric delivery failing to sustain long-term retention and practical application of core business concepts in fast-paced learning environments. The aim is to develop a scalable microlearning framework tailored to business education that enhances cognitive processing, retention, and task performance, while preserving pedagogical rigor and accreditation standards. Specific objectives include (1) designing a modular microlearning curriculum aligned with key business competencies (e.g., strategic management, financial literacy, data-driven decision-making) and evidence-based instructional design principles; (2) implementing the microlearning modules across two business schools with distinct student cohorts to assess transferability; (3) evaluating learning outcomes using a mixed-methods approach to determine effects on knowledge acquisition, application, and student satisfaction; (4) examining the moderating roles of prior knowledge, learning autonomy, and digital literacy on microlearning effectiveness; and (5) generating a validated set of guidelines for scalable adoption in business education. The methodology adopts a pragmatic mixed-methods design integrating instructional design research with educational technology evaluation. The study will be conducted at three stages design, implementation, and evaluation over an 18-month period. The population comprises undergraduate and MBA students enrolled in core business courses at two universities with graduate and undergraduate programs. A total of 320 students will be recruited, with 160 assigned to the microlearning condition and 160 to traditional instruction (control) using stratified random sampling to ensure balance in prior achievement, program level, and gender. Data collection instruments include (i) validated knowledge assessments at baseline, immediately post-intervention, and at a 6-week delayed post-test; (ii) performance tasks and simulated business cases to measure applied competencies; (iii) the Intrinsic Motivation Inventory and the Digital Literacy Scale to capture attitudinal and capability-related covariates; (iv) engagement analytics from the learning management system, including time-on-task and module completion rates; (v) semi-structured interviews with a purposive subsample of 40 participants and 8 instructors to explore perceived value and implementation challenges. Validity and reliability will be established through pilot testing, Cronbach’s alpha thresholds above 0.70 for internal consistency, and inter-rater reliability checks for performance assessments (ICC > 0.75). Analytical techniques will include ANCOVA to compare post-intervention knowledge and application scores while controlling for baseline differences, multilevel modeling to account for nested data (students within classes), and regression analyses to identify predictors of learning gains. Qualitative data will be analyzed using thematic analysis guided by Braun and Clarke’s approach to derive emergent themes related to usability, engagement, and perceived efficacy. A convergent mixed-methods design will integrate quantitative outcomes with qualitative insights to triangulate findings and refine the microlearning framework. The theoretical underpinning draws on Cognitive Load Theory to optimize segment length and cognitive processing, and Activity Theory to interpret interactions among learners, tools, and institutional contexts; the framework will be augmented by the Community of Inquiry model to examine social presence and scaffolding in microlearning environments. Expected findings anticipate statistically significant improvements in knowledge retention and applied performance for the microlearning group compared with controls, particularly among students with moderate to high digital literacy and autonomous learning tendencies. Anticipated qualitative insights include enhanced perceived relevance, greater flexibility, and perceived challenges related to assessment alignment and instructor workload. The study contributes to knowledge by offering an empirically evaluated, scalable microlearning framework tailored to business education, integrating design principles with empirical performance data and practical guidelines for curriculum developers and faculty. It is anticipated that the findings will inform policy recommendations regarding microlearning adoption, accreditation alignment, and professional development for instructors. The main conclusion is that well-structured microlearning modules can meaningfully augment business curricula by improving retention and application without imposing prohibitive additional workload, provided that instructional design is grounded in cognitive load considerations, robust assessment alignment, and ongoing faculty development. Recommendations include extending the framework to inter-institutional collaborations, exploring domain-specific microlearning adaptations (e.g., marketing analytics, supply chain), and conducting longitudinal studies to assess long-term impact on graduate employability.