Mining User-Object Interaction Data for Student Modeling in Intelligent Learning Environments

IF 0.7 4区计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Programming and Computer Software Pub Date : 2024-01-24 DOI:10.1134/s036176882308008x

J. G. Hernández-Calderón, E. Benítez-Guerrero, J. R. Rojano-Cáceres, Carmen Mezura-Godoy

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Abstract

This work seeks to contribute to the development of intelligent environments by presenting an approach oriented to the identification of On-Task and Off-Task behaviors in educational settings. This is accomplished by monitoring and analyzing the user-object interactions that users manifest while performing academic activities with a tangible-intangible hybrid system in a university intelligent environment configuration. With the proposal of a framework and the Orange Data Mining tool and the Neural Network, Random Forest, Naive Bayes, and Tree classification models, training and testing was carried out with the user-object interaction records of the 13 students (11 for training and two for testing) to identify representative sequences of behavior from user-object interaction records. The two models that had the best results, despite the small number of data, were the Neural Network and Naive Bayes. Although a more significant amount of data is necessary to perform a classification adequately, the process allowed exemplifying this process so that it can later be fully incorporated into an intelligent educational system.

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挖掘用户与物体交互数据，为智能学习环境中的学生建模

摘要这项工作旨在通过提出一种在教育环境中识别 "任务中 "和 "任务外 "行为的方法，为智能环境的开发做出贡献。该方法通过监测和分析用户在大学智能环境配置中使用有形-无形混合系统进行学术活动时所表现出的用户-对象互动来实现。通过提出一个框架和 Orange 数据挖掘工具，以及神经网络、随机森林、奈夫贝叶斯和树分类模型，对 13 名学生（11 人用于训练，2 人用于测试）的用户-对象交互记录进行了训练和测试，以便从用户-对象交互记录中找出有代表性的行为序列。尽管数据量较小，但效果最好的两个模型是神经网络和 Naive Bayes。虽然需要更多的数据量才能充分进行分类，但这一过程可以对这一过程进行示范，以便日后将其完全纳入智能教育系统。

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

Programming and Computer Software 工程技术-计算机：软件工程

CiteScore

1.60

自引率

28.60%

发文量

审稿时长

>12 weeks

期刊介绍： Programming and Computer Software is a peer reviewed journal devoted to problems in all areas of computer science: operating systems, compiler technology, software engineering, artificial intelligence, etc.