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.

中文翻译：

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挖掘用户-对象交互数据以在智能学习环境中进行学生建模

摘要

这项工作旨在通过提出一种识别教育环境中任务内和任务外行为的方法，为智能环境的发展做出贡献。这是通过监视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表现出的用户与对象交互来实现的。通过提出框架和 Orange 数据挖掘工具以及神经网络、随机森林、朴素贝叶斯和树分类模型，对 13 名学生（11 名用于训练和 11 名学生）的用户-对象交互记录进行了训练和测试。两个用于测试）从用户-对象交互记录中识别代表性的行为序列。尽管数据量很少，但效果最好的两个模型是神经网络和朴素贝叶斯。尽管需要更大量的数据才能充分执行分类，但该过程可以举例说明该过程，以便以后可以将其完全纳入智能教育系统中。