Assessment-Driven Learning Analytics
A Paradigm Shift for Equitable K–12 STEM Education
DOI:
https://doi.org/10.33830/ijrse.v8i1.14355Keywords:
STEM, Learning Analytics, Paradigm shift, Psychometric assessment, K–12Abstract
Learning analytics (LA) has transformed educational practice through data-driven personalization, yet its dependence on continuous digital trace data systematically excludes learners in low-technology K–12 environments. This paper proposes Assessment-Driven Learning Analytics (ADLA)—a paradigm shift that repositions psychometric assessments as primary analytic signals rather than supplementary data. Grounded in sociocognitive theory and computational psychometrics, ADLA operationalizes three-dimensional learner modeling (cognitive, affective, behavioral) that functions without digital infrastructure. We articulate theoretical foundations distinguishing ADLA from clickstream-based approaches, specify detailed operational frameworks with validated instruments, outline implementation architecture, propose comprehensive research validation agenda, and examine policy implications for educational equity and SDG 4. ADLA demonstrates that meaningful learning analytics need not depend on technological abundance, offering methodological pathways toward inclusive, evidence-based STEM education accessible to all students regardless of infrastructural constraints.
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