On Learning Path Planning Algorithm Based on Collaborative Analysis of Learning Behavior

Abstract

Studying learning path planning can help find useful implicit learning behavior patterns from learners' online learning behavior data, which is conducive to helping beginners or learners with low participation to reasonably arrange the learning sequence of online knowledge points. This paper proposes a learning path planning algorithm based on collaborative analysis of learning behavior through collaborative data analysis of online learning behaviors. The algorithm, based on the learner's online learning behavior data set, first establishes the concept interaction degree model of knowledge points and the directed learning path network, and proposes a local structure similarity measurement method between the knowledge nodes of the directed learning path network. Second, based on the learner's Kullback-Leibler divergence (KLD) matrix, a learning behavior similarity calculation method on the basis of eigenvector matrix similarity is proposed, which is used to perform cluster analysis on learners with similar learning behaviors and to analyze the personalized optimal learning path of each kind of learners. Finally, the clustering algorithm and the evaluation index of the directed complex network have both verified the advantages of the algorithm. This paper employs the online behavior data set and online test data set obtained from the e-learning platform to conduct an empirical analysis of the learning path planning algorithm proposed hereof. The results show that the learner's learning effect has been improved, verifying the validity and reliability of the algorithm. INDEX TERMS local structural similarity on the knowledge node of the directed learning path network, similarity measure on learning behavior, concept interaction degree of knowledge points, individualized optimal learning path

Learn Smarter, Not Harder – Exploring the Development of Learning Analytics Use Cases to Create Tailor-Made Online Learning Experiences

“…Brinton et al [44] also recommend including data on the learner's browsing behavior and clickstreams. Furthermore, to assess the knowledge state of the learner, the user's data on prior knowledge, test data, and the learner's historical log information must be included [45].…”

“…Interventions. To increase the learners' enthusiasm and improve the learning effect, LA applications suggest a personalized learning path to the user [40,45]. Raman and Nedungadi [43] mention that the adaption can comprise the format of learning content, the order of content, and the navigation flow of the user.…”

“…Application. The use case includes the generation of individualized learning paths based on learning behavior [45,46] or learning content [40,43,5]. In general, some authors execute the individualization of learning paths on the web environment, whereas others focus on describing the algorithms used for the individualization.…”

“…These different methods are not exclusive, but can also be combined in the use cases [44]. One approach of clustering is an analysis based on a learning behavior similarity calculation method (e.g., Kull-back-Leibler divergence matrix) with comparable learning types [45]. Recommender systems calculate content similar to user's preferences and then recommend an appropriate content list [42,5,44].…”

A Dynamic Knowledge Diagnosis Approach Integrating Cognitive Features

“…in the exercise interaction process. Specifically, the results of diagnosis can help students discover areas of knowledge weakness and then obtain personalized services, such as targeted knowledge training [3], dynamic planning of learning paths [5], and personalized resource recommendations [6]. Fig.…”