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Data Mining User Activity in Free and Open Source Software (FOSS)/ Open Learning Management Systems

Data Mining User Activity in Free and Open Source Software (FOSS)/ Open Learning Management Systems

Owen McGrath
Copyright: © 2011 |Pages: 12
ISBN13: 9781615209170|ISBN10: 1615209174|EISBN13: 9781615209187
DOI: 10.4018/978-1-61520-917-0.ch008
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MLA

McGrath, Owen. "Data Mining User Activity in Free and Open Source Software (FOSS)/ Open Learning Management Systems." Free and Open Source Software for E-Learning: Issues, Successes and Challenges, edited by Betul Özkan Czerkawski, IGI Global, 2011, pp. 120-131. https://doi.org/10.4018/978-1-61520-917-0.ch008

APA

McGrath, O. (2011). Data Mining User Activity in Free and Open Source Software (FOSS)/ Open Learning Management Systems. In B. Czerkawski (Ed.), Free and Open Source Software for E-Learning: Issues, Successes and Challenges (pp. 120-131). IGI Global. https://doi.org/10.4018/978-1-61520-917-0.ch008

Chicago

McGrath, Owen. "Data Mining User Activity in Free and Open Source Software (FOSS)/ Open Learning Management Systems." In Free and Open Source Software for E-Learning: Issues, Successes and Challenges, edited by Betul Özkan Czerkawski, 120-131. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-61520-917-0.ch008

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Abstract

Free and Open Source Software (FOSS)/Open Educational Systems development projects abound in higher education today. Many universities worldwide have adopted open source software like ATutor and Moodle as an alternative to commercial or homegrown systems. The move to open source learning management systems entails many special considerations, including usage analysis facilities. The tracking of users and their activities poses major technical and analytical challenges within web-based systems. This paper examines how user activity tracking challenges are met with data mining techniques, particularly web usage mining methods, in four different open learning management systems: ATutor, LON-CAPA, Moodle, and Sakai. As examples of data mining technologies adapted within widely used systems, they represent important first steps for moving educational data mining outside the research laboratory. Moreover, as examples of different open source development contexts, exemplify the potential for programmatic integration of data mining technology processes in the future. As open systems mature in the use of educational data mining, they move closer to the long-sought goal of achieving more interactive, personalized, adaptive learning environments online on a broad scale.

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