Multilevel Digital Learning Sequences in Primary Education: A Scoping Review of Learning Pace, Engagement, and Equity
DOI:
https://doi.org/10.64747/e6j8zh21Keywords:
learning pace, personalized learning, primary education, digital learning sequences, educational equityAbstract
Introduction: Primary classrooms bring together students with different starting levels, learning paces, support needs, and digital opportunities. Multilevel sequences aim to adjust difficulty, pathway, and feedback, yet the label covers teacher decisions, adaptive systems, learning analytics, and multicomponent programs that should not be treated as equivalent. Objective: To map evidence published through January 31, 2026, on differentiated, multilevel, or adaptive digital sequences and their relationships with learning, progress pace, engagement, self-regulation, and equity in primary education. Methods: A JBI-oriented scoping review was reported using PRISMA-ScR. Ten reproducible Crossref searches retrieved 600 records; 591 were unique, 74 passed a broad thematic filter, 40 reports were assessed, and 20 sources were included. Design, sample, personalization model, teacher role, outcomes, equity, and limitations were extracted. Results: The most consistent evidence favored systems that diagnosed and adjusted students’ learning levels. A meta-analysis of 16 randomized trials in low- and middle-income countries reported a mean effect of 0.18 and an effect of 0.35 for level-adaptive approaches. Early mathematics trials showed small-to-moderate effects, while programs in India and Chile yielded larger context-specific effects. However, one comparison between algorithmic recommendations and an expert-designed sequence found no significant difference. Learning analytics identified usage profiles but incompletely represented self-regulation in open tasks. Some studies suggested larger relative gains for initially lower-performing students; others found smaller benefits in disadvantaged schools, low family uptake, or Matthew effects. Conclusions: Digital sequences can support learning and differentiation when they connect diagnosis, reversible pathways, feedback, sufficient exposure, and teacher mediation. Technology does not guarantee personalization or equity. Future evaluations should separate components, use external curriculum-aligned measures, and report the distribution of progress, accessibility, fidelity, and pathway changes.
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