Solutions and trends of recommendation systems for massive open online courses

IF 12.9 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change Pub Date : 2025-05-02 DOI:10.1016/j.techfore.2025.124118

Rodrigo Campos , Rodrigo Pereira dos Santos , Jonice Oliveira

{"title":"Solutions and trends of recommendation systems for massive open online courses","authors":"Rodrigo Campos , Rodrigo Pereira dos Santos , Jonice Oliveira","doi":"10.1016/j.techfore.2025.124118","DOIUrl":null,"url":null,"abstract":"<div><div>Massive Open Online Courses (MOOCs) have been widely disseminated due to the arrival of Web 2.0. In recent years, recommendation systems have been applied to support MOOCs users in choosing suitable learning materials (e.g., courses and videos) in this modality. However, implementing such systems remains a challenge since several recommendation aspects should be considered. In this work, we identify and analyze such aspects (e.g., inputs, approaches, and outputs), investigating several implementation possibilities based on the literature. Results show that collaborative filtering and content-based are the most used approaches. More than 60 techniques are used to support recommendations in MOOCs, and most of the studies focus on recommending courses. The main contributions are: (1) a better understanding of the aspects, benefits, and limitations of MOOCs recommendation; and (2) the identification of open issues and research trends, providing an analysis that can support the implementation of emerging recommendation systems for MOOCs.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"217 ","pages":"Article 124118"},"PeriodicalIF":12.9000,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technological Forecasting and Social Change","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0040162525001490","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}

引用次数: 0

Abstract

Massive Open Online Courses (MOOCs) have been widely disseminated due to the arrival of Web 2.0. In recent years, recommendation systems have been applied to support MOOCs users in choosing suitable learning materials (e.g., courses and videos) in this modality. However, implementing such systems remains a challenge since several recommendation aspects should be considered. In this work, we identify and analyze such aspects (e.g., inputs, approaches, and outputs), investigating several implementation possibilities based on the literature. Results show that collaborative filtering and content-based are the most used approaches. More than 60 techniques are used to support recommendations in MOOCs, and most of the studies focus on recommending courses. The main contributions are: (1) a better understanding of the aspects, benefits, and limitations of MOOCs recommendation; and (2) the identification of open issues and research trends, providing an analysis that can support the implementation of emerging recommendation systems for MOOCs.

查看原文本刊更多论文

大规模网络公开课程推荐系统的解决方案及发展趋势

由于Web 2.0的到来，大规模在线开放课程（MOOCs）得到了广泛的传播。近年来，推荐系统被应用于支持mooc用户以这种方式选择合适的学习材料（如课程和视频）。但是，执行这些系统仍然是一项挑战，因为应考虑几个建议方面。在这项工作中，我们识别和分析了这些方面（例如，输入，方法和输出），并根据文献调查了几种实施可能性。结果表明，协同过滤和基于内容的过滤是最常用的过滤方法。在mooc中，有60多种技术用于支持推荐，大多数研究都集中在推荐课程上。主要贡献有：(1)更好地理解mooc推荐的方面、好处和局限性；(2)识别开放问题和研究趋势，为mooc新兴推荐系统的实施提供分析支持。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Technological Forecasting and Social Change Multiple-

CiteScore

21.30

自引率

10.80%

发文量

813

期刊介绍： Technological Forecasting and Social Change is a prominent platform for individuals engaged in the methodology and application of technological forecasting and future studies as planning tools, exploring the interconnectedness of social, environmental, and technological factors. In addition to serving as a key forum for these discussions, we offer numerous benefits for authors, including complimentary PDFs, a generous copyright policy, exclusive discounts on Elsevier publications, and more.