Market basket analysis of library circulation data

ACM Trans. Comput.-Hum. Interact.

Liu

2008

Web-based instruction (WBI) programs, which have been increasingly developed in educational settings, are used by diverse learners. Therefore, individual differences are key factors for the development of WBI programs. Among various dimensions of individual differences, the study presented in this article focuses on cognitive styles. More specifically, this study investigates how cognitive styles affect students' learning patterns in a WBI program with an integrated approach, utilizing both traditional statistical and data-mining techniques. The former are applied to determine whether cognitive styles significantly affected students' learning patterns. The latter use clustering and classification methods. In terms of clustering, the K-means algorithm has been employed to produce groups of students that share similar learning patterns, and subsequently the corresponding cognitive style for each group is identified. As far as classification is concerned, the students' learning patterns are analyzed using a decision tree with which eight rules are produced for the automatic identification of students' cognitive styles based on their learning patterns. The results from these techniques appear to be consistent and the overall findings suggest that cognitive styles have important effects on students' learning patterns within WBI. The findings are applied to develop a model that can support the development of WBI programs.

Section: Regressionmentioning

confidence: 99%

An Integrated Approach for Modeling Learning Patterns of Students in Web-Based Instruction

ACM Trans. Comput.-Hum. Interact.

Liu

2008

“…This is because one can find many different correlations in a large data set. Cunningham and Frank (1999) applied the association rules to the task of detecting subject categories that co-occur in transaction records of books borrowed from a university library. As shown by the aforementioned studies, data mining opens a new window for data analyses.…”

Section: Research Rationalementioning

confidence: 99%

Investigation of the Use of Navigation Tools in Web-Based Learning: A Data Mining Approach

Minetou

International Journal of Human-Computer Interaction

Liu

2008

Web-based learning is widespread in educational settings. The popularity of Web-based learning is in great measure because of its flexibility. Multiple navigation tools provided some of this flexibility. Different navigation tools offer different functions. Therefore, it is important to understand how the navigation tools are used by learners with different backgrounds, knowledge, and skills. This article presents two empirical studies in which data-mining approaches were used to analyze learners' navigation behavior. The results indicate that prior knowledge and subject content are two potential factors influencing the use of navigation tools. In addition, the lack of appropriate use of navigation tools may adversely influence learning performance. The results have been integrated into a model that can help designers develop Web-based learning programs and other Web-based applications that can be tailored to learners' needs.

“…Bollen, Nelson, Geisler, & Araujo, 2007) and electronic business (Liu & Shih, 2005;Shih & Liu, 2008). For example, Cunningham and Frank (1999) developed a recommendation system based on the transaction records of books borrowed from a university library. They found that the recommendation system is not only useful to guide users to relevant documents, but also to determine a library's physical layout.…”

Section: Related Work 21 Video Summarization and Recommendationmentioning

confidence: 99%

Investigation of learners' perceptions for video summarization and recommendation

Yang

Interactive Learning Environments

2012

Recently, multimedia-based learning is widespread in educational settings. A number of studies investigate how to develop effective techniques to manage a huge volume of video sources, such as summarization and recommendation. However, few studies examine how these techniques affect learners' perceptions in multimedia learning systems. This article aims to examine learners' perceptions for summarization and recommendation, with an emphasis on the perspective of prior experience. In this study, we developed a multimedia content summarization and recommendation system, which can automatically extract summaries from raw video sources and recommend suitable video content to learners through emails. The results demonstrate that learners' prior experience and preferences for the presentation of document types affect their perceptions, including the enhancement of interests, the ease of information acquisition and the intention for the further use of the system. Finally, the findings are applied to develop a framework that can support for the design of multimedia learning systems.