Learning management systems, learning analytics, and student outcomes in engineering and computing higher education: A systematic review

Learning Management Systems (LMS) have transformed higher education pedagogical practices, yet optimal configurations for maximizing learning outcomes remain unclear, with technologies frequently deployed in isolation from pedagogically sound strategies. This systematic review evaluated empirical relationships between educational technology implementations, learning analytics practices, and student outcomes in engineering and computing higher education, examining the mediating role of educational modalities and LMS platforms. Following PRISMA 2020 guidelines, 21 empirical studies (2020-2025) were synthesized across four objectives: modality-technology-outcomes integration, engagement-performance correlations, platform-implementation-success relationships, and methodological configurations. Convergent mixed methods synthesis with explicit robustness stratification was employed. Findings revealed field-wide convergence on 50-50 blended learning models (66.67%) and Moodle platform dominance (41.67%), though AI-specific effects demonstrated low robustness. Critical engagement thresholds emerged: >3 logins per week associated with 20% higher grades (7 studies), and video-watching combined with self-assessment showed strongest correlation with performance (r = 0.84). Platform implementations were associated with 15-20% grade differences, though 90% were single-institution studies. Critically, among the 21 studies reporting sampling methods, 85.7% employed convenience sampling, with 47.6% exhibiting problematic convenience + quasi-experimental configurations compromising causal validity. TPACK framework appeared in 84% of studies as design principle rather than measured construct. This synthesis advances understanding of LMS as socio-technical ecosystems while revealing systematic methodological fragility. Future research requires randomized controlled designs, AI-specific dismantling studies, engagement measurement standardization, and multi-institution replication to establish causal mechanisms beyond descriptive documentation.