Abstract
In MOOCs, generally speaking, curriculum designing, course selection, and knowledge concept recommendation are the three major steps that systematically instruct users to learn. This paper focuses on the knowledge concept recommendation in MOOCs, which recommends related topics to users to facilitate their online study. The existing approaches only consider the historical behaviors of users, but ignore various kinds of auxiliary information, which are also critical for user embedding. In addition, traditional recommendation models only consider the immediate user response to the recommended items, and do not explicitly consider the long-term interests of users. To deal with the above issues, this paper proposes AGMKRec, a novel reinforced concept recommendation model with a heterogeneous information network. We first clarify the concept recommendation in MOOCs as a reinforcement learning problem to offer a personalized and dynamic knowledge concept label list to users. To consider more auxiliary information of users, we construct a heterogeneous information network among users, courses, and concepts, and use a meta-path-based method which can automatically identify useful meta-paths and multi-hop connections to learn a new graph structure for learning effective node representations on a graph. Comprehensive experiments and analyses on a real-world dataset collected from XuetangX show that our proposed model outperforms some state-of-the-art methods.
Highlights
Massive Open Online Courses (MOOCs), aimed at unlimited participation and open access via the web, are rapidly becoming an established online and distant education method
In MOOCs, We use course concepts to refer to the knowledge concepts taught in course videos and help users better to understand the related topics videos
We model XuetangX MOOCs as a Heterogeneous Information Network (HIN) and propose a concept recommendation framework with reinforcement learning (RL) to offer a personalized and dynamic knowledge concept label list to users for getting a course certificate
Summary
Massive Open Online Courses (MOOCs), aimed at unlimited participation and open access via the web, are rapidly becoming an established online and distant education method. Pan et al leverage the demographics and course prerequisite relation to better reveal users’ potential choices, they overlook the MOOCs system as a dynamic learning environment, unable to model the current reward and future reward of users’ choices [2] This results in that its approach cannot provide personalized candidate concepts. We model XuetangX MOOCs as a Heterogeneous Information Network (HIN) and propose a concept recommendation framework with reinforcement learning (RL) to offer a personalized and dynamic knowledge concept label list to users for getting a course certificate.
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