An Outcome-Based Educational Recommender System

• URL: http://arxiv.org/abs/2509.18186v1
• Date: Thu, 18 Sep 2025 18:18:03 GMT
• Title: An Outcome-Based Educational Recommender System
• Authors: Nursultan Askarbekuly, Timur Fayzrakhmanov, Sladjan Babarogić, Ivan Luković,
• Abstract summary: OBER-an Outcome-Based Educational Recommender embeds learning outcomes and assessment items directly into the data schema.<n>OBER uses a minimalist entity-relation model, a log-driven mastery formula, and a plug-in architecture.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Easy Data Unlearning Bench [53.1304932656586]
  We introduce a unified and benchmarking suite that simplifies the evaluation of unlearning algorithms.<n>By standardizing setup and metrics, it enables reproducible, scalable, and fair comparison across unlearning methods.
  arXiv  Detail & Related papers  (2026-02-18T12:20:32Z)
• Unlearning for Federated Online Learning to Rank: A Reproducibility Study [24.712358666002658]
  This paper reports on the effectiveness and efficiency of federated unlearning strategies within Federated Online Learning to Rank (FOLTR)<n>Our study rigorously assesses the effectiveness of unlearning strategies in managing both under-unlearning and over-unlearning scenarios.
  arXiv  Detail & Related papers  (2025-05-19T07:23:46Z)
• Top-Down vs. Bottom-Up Approaches for Automatic Educational Knowledge Graph Construction in CourseMapper [0.5937476291232802]
  This study compares Top-down and Bottom-up approaches for automatic EduKG construction.<n>Results indicate that the Bottom-up approach outperforms the Top-down approach in accurately identifying and mapping key knowledge concepts.
  arXiv  Detail & Related papers  (2025-05-15T08:11:48Z)
• Performance-bounded Online Ensemble Learning Method Based on Multi-armed bandits and Its Applications in Real-time Safety Assessment [6.0343041794631604]
  A performance-bounded online ensemble learning method based on multi-armed bandits, named PB-OEL, is proposed in this paper.<n>The proposed method outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-03-19T14:57:53Z)
• Exploring Federated Unlearning: Review, Comparison, and Insights [101.64910079905566]
  federated unlearning enables the selective removal of data from models trained in federated systems.<n>This paper examines existing federated unlearning approaches, examining their algorithmic efficiency, impact on model accuracy, and effectiveness in preserving privacy.<n>We propose the OpenFederatedUnlearning framework, a unified benchmark for evaluating federated unlearning methods.
  arXiv  Detail & Related papers  (2023-10-30T01:34:33Z)
• Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation [49.85548436111153]
  We propose a novel framework named Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation (SRC) SRC formulates the recommendation task under a set-to-sequence paradigm. We conduct extensive experiments on two real-world public datasets and one industrial dataset.
  arXiv  Detail & Related papers  (2023-06-07T08:24:44Z)
• SelfCF: A Simple Framework for Self-supervised Collaborative Filtering [72.68215241599509]
  Collaborative filtering (CF) is widely used to learn informative latent representations of users and items from observed interactions. We propose a self-supervised collaborative filtering framework (SelfCF) that is specially designed for recommender scenario with implicit feedback. We show that SelfCF can boost up the accuracy by up to 17.79% on average, compared with a self-supervised framework BUIR.
  arXiv  Detail & Related papers  (2021-07-07T05:21:12Z)
• BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism [106.43103176833371]
  Collaborative Filtering (CF) based recommendation methods have been widely studied. We propose a novel recommendation model named Balanced Collaborative Filtering Network (BCFNet) In addition, an attention mechanism is designed to better capture the hidden information within implicit feedback and strengthen the learning ability of the neural network.
  arXiv  Detail & Related papers  (2021-03-10T14:59:23Z)
• Leveraging Expert Consistency to Improve Algorithmic Decision Support [62.61153549123407]
  We explore the use of historical expert decisions as a rich source of information that can be combined with observed outcomes to narrow the construct gap. We propose an influence function-based methodology to estimate expert consistency indirectly when each case in the data is assessed by a single expert. Our empirical evaluation, using simulations in a clinical setting and real-world data from the child welfare domain, indicates that the proposed approach successfully narrows the construct gap.
  arXiv  Detail & Related papers  (2021-01-24T05:40:29Z)
• Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion [59.549664231655726]
  A case-based reasoning (CBR) system solves a new problem by retrieving cases' that are similar to the given problem. In this paper, we demonstrate that such a system is achievable for reasoning in knowledge-bases (KBs) Our approach predicts attributes for an entity by gathering reasoning paths from similar entities in the KB.
  arXiv  Detail & Related papers  (2020-10-07T17:48:12Z)
• Towards a Flexible Embedding Learning Framework [15.604564543883122]
  We propose an embedding learning framework that is flexible in terms of the relationships that can be embedded into the learned representations. A sampling mechanism is carefully designed to establish a direct connection between the input and the information captured by the output embeddings. Our empirical results demonstrate that the proposed framework, in conjunction with a set of relevant entity-relation-matrices, outperforms the existing state-of-the-art approaches in various data mining tasks.
  arXiv  Detail & Related papers  (2020-09-23T08:00:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.