Data-Driven Learning Outcome Mapping in Digital Education Systems

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DOI: https://doi.org/10.63345/ijre.v14.i10.1

Dr Rambabu Kalathoti

Computer Science and Engineering

Koneru Lakshmaiah Education Foundation

Andhra Pradesh, India

Abstract
This manuscript explores the design, implementation, and evaluation of data-driven learning outcome mapping within digital education systems. By leveraging analytics and visualization techniques, educators can align curriculum content, instructional strategies, and assessment methods with predefined learning outcomes. Our proposed framework integrates an outcome ontology, automated artifact tagging, a weighted mastery scoring algorithm, and an interactive dashboard, all embedded within an open-source learning management system. We conducted a mixed-methods study with 350 undergraduate students enrolled in a semester-long “Data Structures” course, and 12 instructors (5 faculty and 7 teaching assistants). Quantitative analyses assessed mapping accuracy (Cohen’s κ), predictive validity (Pearson’s r), and system usage metrics; qualitative interviews examined instructor perceptions of transparency, usability, and instructional adaptation. Results show substantial mapping reliability (κ = 0.78), significant correlations between mapped mastery and final exam performance (r up to 0.62, p < .001), and a 12% reduction in quiz drop-outs owing to early interventions. Instructors reported enhanced clarity in alignment, more targeted pedagogy, and data-informed curriculum adjustments. Student behavior analytics revealed increased peer collaboration among those below mastery thresholds. The contributions include (1) a reusable ontology for computer science learning outcomes, (2) a scalable tagging and mapping pipeline, and (3) evidence of pedagogical impact in real-world settings. We discuss technical, pedagogical, and organizational implications, outline challenges in cross-domain generalization, and propose directions for integrating adaptive learning paths and automated tag refinement to further close the loop between data and instruction.

Keywords
Data-Driven Mapping, Learning Outcomes, Digital Education, Analytics, Curriculum Alignment

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