计算机科学
适应性学习
钥匙(锁)
计算机化自适应测验
过程(计算)
质量(理念)
组分(热力学)
马尔可夫决策过程
推荐系统
人工智能
机器学习
马尔可夫过程
多媒体
心理学
心理测量学
数学
计算机安全
哲学
临床心理学
物理
操作系统
认识论
统计
热力学
作者
Yunxiao Chen,Xiaoou Li,Jingchen Liu,Zhiliang Ying
标识
DOI:10.1177/0146621617697959
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI