Personalized Learning Path Recommendation Based on Weak Concept Mining

Abstract

Discovering valuable learning path patterns from learner online learning data can provide follow-up learners with effective learning path reference and improve their learning experience and learning effects. In this paper, a personalized learning path recommendation method based on weak concept mining is proposed. Firstly, according to the degree of concept mastery of historical learners, concept maps of different types of learners are generated by clustering and association rule mining algorithms. A set of weak concept learning paths are then automatically generated through topological sorting algorithm. Secondly, the long short-term memory neural network based on the attention mechanism (LSTM+attention) is trained to predict the learning effect of the weak concept learning path. Finally, the personalized weak concept path that meets the expected learning effect is selected from the path prediction results. In the experiment, the proposed method is not only compared with the traditional recommendation method, but also, a comparative experiment on the impact of different learning effect prediction models is carried out. The experiment results show that our proposed method has obvious advantages in recommendation performance.

Learning Path Optimization based on Multi-Attribute Matching and Variable Length Continuous Representation

“…The sequence of learning materials is constrained by the relation between the materials. The relation can be captured by a knowledge model, such as knowledge map [13,14], knowledge graph [15,16], or concept map [17], etc. The model can either be built from the experts' experience [18] or via educational data mining technology [19].…”

“…The representative methods include machine learning [17,20] and evolutionary algorithms [21,22]. In [17], a set of learning paths are generated through topological sorting algorithm and the long short-term memory neural network is trained to predict the learning effect of the learning path. In [23], a learning path recommendation model is developed based on a knowledge graph.…”

“…Many methods are explored to construct a learning path that satisfies the learner's needs and the constraints of knowledge model. The representative methods include machine learning [17,20] and evolutionary algorithms [21,22]. In [17], a set of learning paths are generated through topological sorting algorithm and the long short-term memory neural network is trained to predict the learning effect of the learning path.…”

A New Learning Resource Recommendation Method for Improving the Efficiency of Students' Online Independent Learning

“…Based on the laws and characteristics of online learning, in order to obtain better learning efficiency, it is necessary to guide college students to study consciously and actively, and further realize student-centered high-quality education [9][10][11][12][13][14][15]. The development of data mining, learning analysis and other technologies makes it possible to realize the auxiliary service of college students' online independent learning [16][17][18][19][20][21][22]. The learning resource recommendation method considering the efficiency improvement of college students' online independent learning not only saves college students' learning energy and improves their learning quality, but also promotes such learning attitudes as active participation, willingness to explore and frequent practices.…”

Retrieval and Recommendation of English Learning Resources Based on Knowledge Correlation

“…Thus, in order to improve the accuracy of recommendation results, Qiu and Cheng [18] designed a personalized recommendation system of network resources based on the collaborative filtering algorithm; in the hardware design, the authors devised the overall circuit module, configured the bus sequence, and accelerated the running speed of system hardware; in the software design, the authors calculated the time weight function, established an implicit scoring matrix of users, calculated the measured scores and actual scores of each scoring item, and constructed the said model. Discovering valuable learning paths and patterns from learners' online learning data can provide useful references for subsequent learners and improve their learning experience and learning effects, Diao et al [19] put forward a personalized learning path recommendation method based on weak concept mining, which can use clustering and association rule mining algorithms to generate concept maps for different type learners according to their history mastery degree of the concepts.…”