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A Recommender System for Students Based on Social Knowledge and Assessment Data of Competences

机译：基于社会知识和能力评估数据的学生推荐系统

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摘要

TEL Recommender systems have been used to improve experiences of students or teachers. Many such systems use information about students, such as interests, preferences, and demographic data. They also use resource metadata and ratings. The authors of this paper think that recommender systems are also valuable when implemented in online or blended courses using competence-based assessment since these systems can take advantage of social knowledge about competence development, and students' performance. By using collaborative filtering and knowledge-based techniques, it is possible to obtain recommendations from social knowledge and adapt the former to each student's performance. In this paper, the authors propose a system to recommend activities and resources that help students in achieving competence levels throughout an online or blended course. This recommender system takes into consideration experiences previously stored and ranked by former students. In order to offer successful learning advice, this recommender system analyzes the student's current competence levels against similar former students' performances. Functional test results indicate that the proposed technical approach is accurate. Moreover, these results seem to reflect that social knowledge and students' qualifications are sources of valuable recommendations for online and blended courses.
机译：TEL Recommender系统已用于改善学生或教师的体验。许多这样的系统使用有关学生的信息，例如兴趣，偏好和人口统计数据。他们还使用资源元数据和评级。本文的作者认为，推荐器系统在基于能力评估的在线或混合课程中实施时也很有价值，因为这些系统可以利用有关能力发展和学生表现的社会知识。通过使用协作过滤和基于知识的技术，可以从社会知识中获得推荐，并使前者适应每个学生的表现。在本文中，作者提出了一个系统来推荐活动和资源，以帮助学生在整个在线或混合课程中提高能力水平。该推荐器系统考虑了以前由以前的学生存储和排名的经验。为了提供成功的学习建议，此推荐器系统将学生的当前能力水平与以前的类似学生的表现进行分析。功能测试结果表明，所提出的技术方法是准确的。此外，这些结果似乎反映出社会知识和学生资格是在线课程和混合课程的宝贵建议的来源。

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