Personalized process–type learning path recommendation based on process mining and deep knowledge tracing

Abstract

Personalized learning path recommendation considers learning goals, learning abilities, and other personalized characteristics of learners to generate a suitable learning path. Existing approaches include global optimal and local iterative path recommendation, which recommend a sequence of learning objects. Consequently, the learner can only learn in the order specified by the learning path, which provides limited flexibility for the learner. In addition, existing studies cannot both present the complete path and handle changes in the learner's knowledge state while learning along the path. This study proposes a process-type learning path model and its recommendation approach, which presents a learning path in the form of a flowchart and dynamically recommends path branches according to the knowledge states of the learner during the learning process. Specifically, deep knowledge tracing is used to annotate the knowledge states of learners in historical logs, and process mining is used to generate a personalized process–type learning path that contains sequences, parallel relationships, and selection relationships between learning objects. In addition, the correlation between the knowledge state and the selection of different branches of a learning path in historical logs can be obtained via decision mining. Thus, a branch recommendation model is trained and used to recommend a path branch in a process-type path with the highest probability of mastering the target learning object of the learner based on the learner's knowledge state. The experimental results demonstrate that the learning effectiveness and efficiency of the proposed approach are better than those of the existing approaches.