Year: 2026
Type: article
Abstract: Distance learning creates a structural retention problem because student disengagement is often invisible until it has already become severe.Unlike face-toface classrooms, online environments do not naturally provide instructors with social or physical cues that a learner is drifting away from course participation.This paper proposes a practical Tiered Engagement Support (TES) framework for the MIDS Multilingual Co-Learning platform [1].Rather than claiming a fully automated diagnosis of a learner's cognitive or motivational state, the framework focuses on a more feasible objective: early detection of observable disengagement patterns and structured coordination of timely support.The framework uses an Engagement Health Score derived from measurable platform signals such as login recency, video completion, quiz participation, and use of support features.Students are then grouped into actionable engagement states -Active, At-Risk, Disengaged, and Recovered -and are served through escalating support tiers.Tier 1 delivers low-risk automated support using existing Co-Learning resources, including review prompts, chapter summaries, vocabulary aids, and quiz retry opportunities.Tier 2 prepares concise instructorfacing reports that support timely and consistent outreach.Tier 3 reserves direct instructor or academic support intervention for persistent cases.By shifting from fully automated remediation to semi-automated engagement monitoring and support orchestration, the proposed framework offers a more realistic and institutionally deployable model for multilingual online learning.The result is a scalable retention architecture grounded in a clear educational commitment: no student in a distance learning environment should be left behind because disengagement was not detected early enough for support.
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7151691529
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