Learning recommendations from educational event data in higher education

IF 2.3 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Intelligent Information Systems Pub Date : 2024-08-13 DOI:10.1007/s10844-024-00873-w

Gyunam Park, Lukas Liss, Wil M. P. van der Aalst

{"title":"Learning recommendations from educational event data in higher education","authors":"Gyunam Park, Lukas Liss, Wil M. P. van der Aalst","doi":"10.1007/s10844-024-00873-w","DOIUrl":null,"url":null,"abstract":"<p>This paper presents a novel approach for generating actionable recommendations from educational event data collected by Campus Management Systems (CMS) to enhance study planning in higher education. The approach unfolds in three phases: feature identification tailored to the educational context, predictive modeling employing the RuleFit algorithm, and extracting actionable recommendations. We utilize diverse features, encompassing academic histories and course sequences, to capture the multi-dimensional nature of student academic behaviors. The effectiveness of our approach is empirically validated using data from the computer science bachelor’s program at RWTH Aachen University, with the goal of predicting overall GPA and formulating recommendations to enhance academic performance. Our contributions lie in the novel adaptation of behavioral features for the educational domain and the strategic use of the RuleFit algorithm for both predictive modeling and the generation of practical recommendations, offering a data-driven foundation for informed study planning and academic decision-making.</p>","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":"16 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent Information Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10844-024-00873-w","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 0

Abstract

This paper presents a novel approach for generating actionable recommendations from educational event data collected by Campus Management Systems (CMS) to enhance study planning in higher education. The approach unfolds in three phases: feature identification tailored to the educational context, predictive modeling employing the RuleFit algorithm, and extracting actionable recommendations. We utilize diverse features, encompassing academic histories and course sequences, to capture the multi-dimensional nature of student academic behaviors. The effectiveness of our approach is empirically validated using data from the computer science bachelor’s program at RWTH Aachen University, with the goal of predicting overall GPA and formulating recommendations to enhance academic performance. Our contributions lie in the novel adaptation of behavioral features for the educational domain and the strategic use of the RuleFit algorithm for both predictive modeling and the generation of practical recommendations, offering a data-driven foundation for informed study planning and academic decision-making.

Abstract Image

查看原文本刊更多论文

从高等教育的教育事件数据中学习建议

本文介绍了一种从校园管理系统（CMS）收集的教育事件数据中生成可行建议的新方法，以加强高等教育中的学习规划。该方法分为三个阶段：针对教育背景的特征识别、采用 RuleFit 算法的预测建模以及提取可行建议。我们利用包括学业历史和课程序列在内的各种特征来捕捉学生学业行为的多维性。我们使用亚琛工业大学计算机科学本科课程的数据对我们方法的有效性进行了实证验证，目的是预测总体 GPA 并提出提高学习成绩的建议。我们的贡献在于针对教育领域对行为特征进行了新颖的调整，并战略性地将 RuleFit 算法用于预测建模和生成实用建议，为明智的学习规划和学术决策提供了数据驱动的基础。

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

Journal of Intelligent Information Systems 工程技术-计算机：人工智能

CiteScore

7.20

自引率

11.80%

发文量

审稿时长

6-12 weeks

期刊介绍： The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems. These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to: discover knowledge from large data collections, provide cooperative support to users in complex query formulation and refinement, access, retrieve, store and manage large collections of multimedia data and knowledge, integrate information from multiple heterogeneous data and knowledge sources, and reason about information under uncertain conditions. Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces. The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.