With the rise of cyber-infrastructure in higher education research and teaching, new challenges surface when it comes to understanding users and usage. How, where, and when user activity gets captured and analyzed in academic online systems is particularly critical in internet-based systems. The flexibility that these open systems allow for in promoting easy integration of different technologies (e.g., applications layer, presentation layer, middleware, and data sources) has repercussions for usage analysis: round the clock access, unseen users, distributed logs, and huge volumes of cryptic data. This paper demonstrates how knowledge discovery solutionsparticularly web usage mining methods -have been taken up to address these challenges in one higher education setting involving the Sakai collaboration and learning environment. The goals of this paper include: 1) providing some definitions and explications by example of specific data mining processes as they are actually being used; 2) describing the issues and challenges that motivate the use of data mining and 3) showing how data mining integrates with established project management best practices.
Established best practices in software development tend to assume that a product's intended stakeholders (i.e., users, customers, and clients) are fairly well known and generally accessible. This paper outlines specific issues faced by those who conduct requirements analysis in the context of open source projects in which the user communities are widely distributed. The examples described are drawn from the experience of managing tool development within the Sakai Project [1], a higher education effort to build and share a community source framework for supporting on-line collaboration in academic courses and projects. With a far-flung community of users and developers, this project requires new approaches to eliciting, analyzing, and prioritizing user needs. The issues outlined in this paper are currently being met by a preliminary set of solutions that makes use of web-based project management technologies. These technologies along with some planning and communication strategies help improve the decision-making process involved in deciding whether and how to choose among proposed constraints, use cases, and feature requests.
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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