用学习成果评估推荐系统，让教育推荐真正有效。
An Outcome-Based Educational Recommender System
- 将学习结果直接嵌入数据模型，用真实掌握度评估推荐效果
- 协同过滤提升留存率，固定路径实现最高掌握度（5700+学员）
- 无需额外测试即可权衡互动与学习成效，适合教育平台优化
多数教育推荐系统以点击或评分作为评价标准，难以反映实际教学效果。本文提出基于学习成果的推荐系统OBER，将学习目标和测评项目直接融入数据结构，使任何算法都能基于掌握程度进行评估。OBER采用极简实体关系模型、日志驱动的掌握公式和插件架构。在非正式教育平台中，通过为期两周的随机对照实验，对三种方法（固定专家路径、协同过滤、知识库过滤）在超过5700名学习者中进行评估：协同过滤提升了留存率，但固定路径实现了最高掌握度。由于OBER从同一日志中提取业务、相关性与学习指标，实践者可无额外成本权衡相关性与参与度，同时兼顾最终掌握效果。该框架不依赖具体方法，可轻松扩展至未来自适应或上下文感知推荐系统。
原文摘要 · Abstract (English)
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.
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