# An Outcome-Based Educational Recommender System

arXiv: https://arxiv.org/abs/2509.18186 · HTML: https://mlsanity.com/paper/2509.18186/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Builds on (most cited first, 2 of 2)

- [Reinforcement learning based recommender systems: A survey](https://mlsanity.com/paper/2101.06286/) (2021)
- [Learning Outcomes, Assessment, and Evaluation in Educational Recommender Systems: A Systematic Review](https://mlsanity.com/paper/2407.09500/) (2024)

## Built on by (most cited first, 1 of 1)

- [Who Are We Recommending To? Recommender Systems in the Agentic Web](https://mlsanity.com/paper/2609.11945/) (2026)

## Abstract (arXiv)

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.