Recommendation System for Adaptive Learning

自适应学习下的推荐系统

谭淳曦

11651009

博士

数学

蒋学军

2020-07-24

2020-01-08

香港科技大学

香港

Personalized recommendation system has been widely adopted in E-learning field that is adaptive to each learners’ own learning pace. With full utilization of learning behavior data, psychometric assessment models keep track of the learners’ proficiency on knowledge points, and then a well-designed recommendation strategy selects a sequence of actions to meet the unique learning objective of individual. In this dissertation, we develop two adaptive recommendation strategies under the framework of reinforcement learning. The first strategy involved with early stopping enjoys a time-related learning mode with the aim of maximizing the learning efficiency. Secondly, we consider the element of curiosity as a critical motivate for information-seeking to propose a curiosity-driven recommendation policy, allowing for a both rewarding and enjoyable personalized learning path. Numeric analyses with the large continuous knowledge state space and concrete learning scenarios are used to further demonstrate the power of the proposed methods.

可适应每个学习者自己的学习进度的个性化推荐系统已广泛应用于电子学习领域。通过充分利用学习行为数据，统计心理测评模型可以跟踪学习者在知识点上的熟练程度，推荐策略则会根据评估结果选择一系列最优行为来实现个人的独特学习路径。本文在强化学习的框架下，提出了两种自适应推荐策略。其中引入提前停止(early stopping)机制的策略为用户定制与学习时长相关的策略，目的是最大程度地提高学习效率。其次，我们认为好奇心是寻求知识的关键动机，从而提出了以好奇心为驱动力的推荐算法，能应用在要求连续知识状态空间的学习场景。

Recommendation System Adaptive Learning

英语

联合培养

香港科技大学/南方科技大学

Tan CX. Recommendation System for Adaptive Learning[D]. 香港. 香港科技大学,2020.