Learning actor ontology for a personalised recommendation in massive open online courses

Assami, Sara; Ajhoun, Rachida

doi:10.1504/ijtel.2020.110048

Cited by 4 publications

(4 citation statements)

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“…In the personalized recommendation system of learning resources, the most critical is the construction of learner model and learning resource model (Assami et al , 2020). Building an ontology as a modeling method for learners and learning resources helps to retrieve details and can be used to support personalized recommendations.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on personalized recommendation of MOOC resources based on ontology

Chen²,

Zhan³

2022

ITSE

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Purpose The purpose of this study is to analyze from multiple perspectives, so as to form an effective massive open online course (MOOC)personalized recommendation method to help learners efficiently obtain MOOC resources. Design/methodology/approach This study introduced ontology construction technology and a new semantic association algorithm to form a new MOOC resource personalized recommendation idea. On the one hand, by constructing a learner model and a MOOC resource ontology model, based on the learner’s characteristics, the learner’s MOOC resource learning preference is predicted, and a recommendation list is formed. On the other hand, the semantic association algorithm is used to calculate the correlation between the MOOC resources to be recommended and the learners’ rated resources and predict the learner’s learning preferences to form a recommendation list. Finally, the two recommendation lists were comprehensively analyzed to form the final MOOC resource personalized recommendation list. Findings The semantic association algorithm based on hierarchical correlation analysis and attribute correlation analysis introduced in this study can effectively analyze the semantic similarity between MOOC resources. The hybrid recommendation method that introduces ontology construction technology and performs semantic association analysis can effectively realize the personalized recommendation of MOOC resources. Originality/value This study has formed an effective method for personalized recommendation of MOOC resources, solved the problems existing in the personalized recommendation that is, the recommendation relies on the learner’s rating of the resource, the recommendation is specialized, and the knowledge structure of the recommended resource is static, and provides a new idea for connecting MOOC learners and resources.

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Section: Literature Reviewmentioning

confidence: 99%

Research on personalized recommendation of MOOC resources based on ontology

Chen²,

Zhan³

2022

ITSE

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“…However, this method has limitations when it is used in large-scale E-learning systems due to the complexity of computing requirements. Based on the recommendation standard of traditional MOOCs, reference [23] constructed the ontology model of learning participants for the matching process of the personalized recommendation system introduced by MOOC.…”

Section: Related Workmentioning

confidence: 99%

“…In the following, the personalized course recommendation method proposed in this paper is compared with the collaborative filtering algorithm in reference [20,21,23]. e indexes of recommendation results of different methods under the same dataset are shown in Table 3.…”

Section: Experimental Comparison and Analysismentioning

confidence: 99%

“…It can be seen from Table 3 and Figure 8 that when the same database is used, compared with the collaborative filtering algorithm in reference [20,21,23] and the traditional RNN algorithm, the precision, recall, and F1-Measure of the proposed algorithm for personalized recommendation results of different types of courses are optimal, and the maximum values are 92.1%, 89.3%, and 90.7%, respectively.…”

Section: Experimental Comparison and Analysismentioning

confidence: 99%

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Personalized Course Recommendation Method Based on Learner Interest Mining in Educational Big Data Environment

Zhang

2022

Scientific Programming

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Aiming at the problems of low accuracy and large limitations of the current personalized course recommendation method in the educational big data environment, a personalized course recommendation method based on learner interest mining in the educational big data environment is proposed. First, a corresponding online course recommendation model framework is proposed by adopting GRU, which can effectively solve the problems of gradient disappearance and gradient explosion in the process of training the RNN neural network. Then, by introducing an auto-regressive language model, XLNet (Generalized Autoregressive Pretraining for Language Understanding), the information missing problem under the Mask mechanism in the BERT model is effectively optimized, and bidirectional prediction is achieved. Finally, by introducing a temporal attention mechanism into the model, enough attention is assigned to highlight local important information on key information, which improves the quality of hidden layer feature extraction, and a high-accuracy personalized course recommendation based on learner interest mining is realized. The proposed algorithm is compared with the other three collaborative filtering algorithms and the RNN algorithm through simulation experiments. The results show that the precision, recall, and F1-measure of the proposed algorithm in the personalized course recommendation results for different types of courses under the condition of the same database are all optimal. The largest values were 92.1%, 89.3%, and 90.7%, respectively. The overall performance is better than other comparison algorithms. This method can improve the accuracy and optimization limitations of personalized courses and can fully tap the interests of learners. It is of great significance for learners to choose personalized courses in the current educational big data environment.

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