Recommending Learning Objects with Arguments and Explanations
Recommending Learning Objects with Arguments and Explanations
复制标题
DOI：
10.3390/app10103341
复制
发表时间：
2020-05
期刊：
影响因子：
--
通讯作者：
S. Heras;Javier Palanca;Paula Rodríguez;N. Duque-Méndez;V. Julián
中科院分区：
文献类型：
--
作者：
S. Heras;Javier Palanca;Paula Rodríguez;N. Duque-Méndez;V. Julián
大量的在线学习资源导致许多学生获得的信息超出了他们有效利用的范围。因此，学生并不总能找到适合自己需求和偏好的适应性学习材料。在本文中，我们提出了一种对话式教育推荐系统（C-ERS），它可以帮助学生根据他们的学习目标和个人资料找到更合适的学习资源。推荐过程基于基于论证的方法，该方法选择允许生成更多参数来证明其适用性的学习对象。我们的系统包括一个简单直观的与用户的通信界面，为任何建议提供解释。这允许用户与系统交互并接受或拒绝建议，并提供此类行为的原因。这样，用户可以检查系统的运行情况并了解推荐，同时系统也可以得出用户的实际偏好。该系统已经在哥伦比亚国立大学的一组真实本科生中进行了在线测试，显示出可喜的结果。
The massive presence of online learning resources leads many students to have more information than they can consume efficiently. Therefore, students do not always find adaptive learning material for their needs and preferences. In this paper, we present a Conversational Educational Recommender System (C-ERS), which helps students in the process of finding the more appropriated learning resources considering their learning objectives and profile. The recommendation process is based on an argumentation-based approach that selects the learning objects that allow a greater number of arguments to be generated to justify their suitability. Our system includes a simple and intuitive communication interface with the user that provides an explanation to any recommendation. This allows the user to interact with the system and accept or reject the recommendations, providing reasons for such behavior. In this way, the user is able to inspect the system’s operation and understand the recommendations, while the system is able to elicit the actual preferences of the user. The system has been tested online with a real group of undergraduate students in the Universidad Nacional de Colombia, showing promising results.