Online Distance Electronic Learning (ODeL) engineering tuition often brings together students with widely differing levels of prior knowledge, preparedness, and self-directed learning capability. In such contexts, one-size-fits-all instructional approaches may fail to support at-risk students effectively, resulting in weak progression, low confidence, and poor academic performance. This paper proposes and evaluates an adaptive learner-support framework designed to enhance learner progression and autonomy in ODeL engineering contexts. The framework is anchored in diagnostic pre-assessment, which establishes baseline competency and enables the classification of students into differentiated performance groups. These groups are supported through staged, adaptive interventions, including scaffolding and laddering, adaptive learning pathways, digital game-based learning, and the gradual release of responsibility, supported by continuous feedback and monitoring. A mixed-methods comparative design was employed to evaluate the framework, integrating assessment data with observations, surveys, field notes, and documentary analysis. Results indicate that the intervention reduced the proportion of struggling students, increased the number of high-performing students, and improved overall learner performance by approximately 25%. Qualitative findings further suggest enhanced learner engagement, confidence, and development of self-directed learning capabilities. The study concludes that adaptive learner-support frameworks provide an effective and scalable approach for addressing learner variability in ODeL engineering education. By aligning diagnostic assessment, differentiated instruction, and iterative feedback, the framework contributes to improved learner autonomy, retention, and academic success in mixed-ability cohorts.

Keywords

Adaptive learner progression; at-risk engineering students; Online Distance Electronic Learning (ODeL); differentiated learner support.