Chen Yunxiao, Li Xiaoou, Liu Jingchen, Ying Zhiliang
Emory University, Atlanta, GA, USA.
University of Minnesota, Minneapolis, MN, USA.
Appl Psychol Meas. 2018 Jan;42(1):24-41. doi: 10.1177/0146621617697959. Epub 2017 Mar 26.
An adaptive learning system aims at providing instruction tailored to the current status of a learner, differing from the traditional classroom experience. The latest advances in technology make adaptive learning possible, which has the potential to provide students with high-quality learning benefit at a low cost. A key component of an adaptive learning system is a recommendation system, which recommends the next material (video lectures, practices, and so on, on different skills) to the learner, based on the psychometric assessment results and possibly other individual characteristics. An important question then follows: How should recommendations be made? To answer this question, a mathematical framework is proposed that characterizes the recommendation process as a Markov decision problem, for which decisions are made based on the current knowledge of the learner and that of the learning materials. In particular, two plain vanilla systems are introduced, for which the optimal recommendation at each stage can be obtained analytically.
自适应学习系统旨在提供适合学习者当前状态的指导，这与传统课堂体验不同。技术的最新进展使自适应学习成为可能，它有可能以低成本为学生提供高质量的学习益处。自适应学习系统的一个关键组件是推荐系统，该系统根据心理测量评估结果以及可能的其他个人特征，向学习者推荐下一个学习材料（关于不同技能的视频讲座、练习等）。随之而来的一个重要问题是：应该如何进行推荐？为了回答这个问题，提出了一个数学框架，该框架将推荐过程表征为一个马尔可夫决策问题，在这个问题中，决策是基于学习者对当前知识以及学习材料的了解而做出的。特别地，引入了两个简单的系统，对于这两个系统，可以通过解析方法获得每个阶段的最优推荐。
