An Outcome-Based Educational Recommender System
This addresses the need for educational practitioners to balance relevance and engagement against learning outcomes without extra testing overhead, though it is incremental as it builds on existing recommender methods.
The paper tackled the problem of evaluating educational recommender systems on pedagogical impact rather than just clicks or ratings, by introducing OBER, an outcome-based framework that embeds learning outcomes and assessments into the data schema. In a two-week randomized test with over 5,700 learners, collaborative filtering maximized retention, but a fixed expert path achieved the highest mastery.
Most educational recommender systems are tuned and judged on click- or rating-based relevance, leaving their true pedagogical impact unclear. We introduce OBER-an Outcome-Based Educational Recommender that embeds learning outcomes and assessment items directly into the data schema, so any algorithm can be evaluated on the mastery it fosters. OBER uses a minimalist entity-relation model, a log-driven mastery formula, and a plug-in architecture. Integrated into an e-learning system in non-formal domain, it was evaluated trough a two-week randomized split test with over 5 700 learners across three methods: fixed expert trajectory, collaborative filtering (CF), and knowledge-based (KB) filtering. CF maximized retention, but the fixed path achieved the highest mastery. Because OBER derives business, relevance, and learning metrics from the same logs, it lets practitioners weigh relevance and engagement against outcome mastery with no extra testing overhead. The framework is method-agnostic and readily extensible to future adaptive or context-aware recommenders.