Affiliation:
1. School of Economics and Management, Tongji University, Shanghai 200092, China
Abstract
In the MOOCs context, learners experience information overload. Thus, it is necessary to improve personalized recommendation algorithms for learners. The current recommendation algorithm focuses mainly on the learners’ course ratings. However, the choice of courses is not only based on the learners’ interests and preferences. It is also affected by learners’ knowledge domains and learning capabilities, all of which change dynamically over time. Therefore, this study proposes a personalized hybrid recommendation algorithm combining clustering with collaborative filtering. First, data on learners’ course rating preferences, course attribute preferences, and multidimensional capabilities that match course traits are used based on multidimensional item response theory. Second, considering that learners’ preferences and multidimensional capabilities change dynamically over time, the Ebbinghaus forgetting curve is introduced by integrating memory weights to improve the accuracy and interpretation of the proposed recommendation algorithm for MOOCs. Finally, the performance of the proposed recommendation algorithm is investigated using data from Coursera, an internationally renowned MOOCs platform. The experimental results show that the proposed recommendation algorithm is superior to the baseline algorithms. Accordingly, relevant suggestions are proposed for the development of MOOCs.
Funder
Chinese National Social Science Fund “Thirteenth Five-Year Plan” education topic
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference33 articles.
1. Chi, Z.X., Zhang, S., and Shi, L. (2023). Analysis and Prediction of MOOC Learners’ Dropout Behavior. Appl. Sci., 13.
2. Iniesto, F., Rodrigo, C., and Hillaire, G. (2023). A Case Study to Explore a UDL Evaluation Framework Based on MOOCs. Appl. Sci., 13.
3. (2021, December 01). By The Numbers: MOOCs in 2021. Available online: https://www.classcentral.com/report/mooc-stats-2021/.
4. What motivates enrolment in programming MOOCs;Luik;Br. J. Educ. Technol.,2019
5. Dropout prediction in MOOCs using deep learning and machine learning;Basnet;Educ. Inf. Technol.,2022
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献