Formulation and Enhancement of User Adaptive Access to the Learning Resources in E-Learning Using Fuzzy Inference Engine

Kumaran, V. Senthil; Periakaruppan, Rm.

doi:10.1007/978-981-10-0251-9_30

Cited by 1 publication

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References 7 publications

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“…Currently, such learning resources are student-oriented, highlighting students' learning engagement and learning enthusiasm [1][2][3][4][5]. During the development of online course learning resources, putting resource diversity over difficulty makes it impossible to grasp the overall learning situation of students [6][7][8][9][10][11][12][13][14]. Learning needs and learning preferences are not static.…”

Section: Introductionmentioning

confidence: 99%

Online Course Learning Resource Recommendation Based on Difficulty Matching

Jia¹

2022

Int. J. Emerg. Technol. Learn.

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The current methods for online course learning resource recommendation tend to continuously push similar items to students. To make students more satisfied with the overall recommendation service, it is necessary to explore the recommendation methods for online course learning resources based on difficulty matching. Therefore, this paper devises an online course learning resource recommendation method based on difficulty matching. Firstly, an online course learning resource recommendation approach was developed based on the autoencoder, in the light of difficulty matching. The flowchart of learning resource recommendation was presented, and the proposed algorithm was described in details. Then, the authors depicted the flow of the online course learning resource recommendation model, which considers difficulty matching, and proposed to capture the hierarchy of resources by assigning difficulty labels to texts, such that the proposed recommendation model outputs interpretable recommendation results. Finally, the authors designed a model that can discover the influence of the difficulty of learned resources on the difficulty of unlearned learning resources, and recommended online course learning resources considering difficulty matching. The effectiveness of the proposed model is verified through experiments.

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Section: Introductionmentioning

confidence: 99%

Online Course Learning Resource Recommendation Based on Difficulty Matching

Jia¹

2022

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

show abstract

Formulation and Enhancement of User Adaptive Access to the Learning Resources in E-Learning Using Fuzzy Inference Engine

Cited by 1 publication

References 7 publications

Online Course Learning Resource Recommendation Based on Difficulty Matching

Online Course Learning Resource Recommendation Based on Difficulty Matching

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