PERKC: Personalized kNN With CPT for Course Recommendations in Higher Education

IF 2.9 3区教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Learning Technologies Pub Date : 2024-01-10 DOI:10.1109/TLT.2023.3346645

Gina George;Anisha M. Lal

引用次数: 0

Abstract

E-learning is increasingly being used by students in the higher education level for their university credit purpose and some for improving their knowledge. E-learning is also used for skill enhancement purpose by organizations. Due to the availability of wide-ranging options, recommender systems that provide personalized suggestions are much needed. The proposed methodology takes advantage of compact prediction tree (CPT), a popular sequence prediction algorithm. In this article, a new prediction model based on applying CPT over similar students which is found in a novel manner is proposed. The aim of the work is to recommend courses to students at university level. The methodology was evaluated in terms of accuracy and results show the proposed work performs better than applying only CPT, when applying fuzzy C-means with CPT, and when applying k nearest neighbors with CPT.

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PERKC：采用 CPT 的个性化 kNN 用于高等教育课程推荐

高等教育阶段的学生越来越多地使用电子学习来获得大学学分，还有一些学生使用电子学习来提高自己的知识水平。组织机构也利用网络学习来提高技能。由于存在多种选择，因此非常需要能提供个性化建议的推荐系统。所提出的方法利用了紧凑型预测树（CPT）这一流行的序列预测算法。本文提出了一种新的预测模型，该模型基于对相似学生应用 CPT 的新方法。这项工作的目的是向大学阶段的学生推荐课程。对该方法的准确性进行了评估，结果表明，与仅应用 CPT、应用模糊 C-means 和 CPT 以及应用 k 近邻和 CPT 相比，所提出的方法表现更好。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Learning Technologies COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-

CiteScore

7.50

自引率

5.40%

发文量

审稿时长

>12 weeks

期刊介绍： The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.