Clustering Approach on Concept Diagram of Statistics Learning
Abstract:Statistics is an important course for university students because it is the foundation of quantitative research. The purpose of this study is to analyze the concept diagram of statistical concepts for university students and clustering based on concept proficiency. Methodology in this study is CAISM (concept advanced interpretive structural modeling). This method can not only present the personal concept structure by hierarchical diagram, but also calculate the magnitude of mastery on each concept. Besides, fuzzy clustering on concept proficiency expresses the cognitive characteristics. Empirical data is the paper-and-pencil assessment of statistics course. The results show that all students could be classified into two clusters. Proficiency and characteristics of concepts between these two clusters are quite different. Moreover, task-takers of same total score with different response pattern have distinct concept diagram. According to the results, it shows CAISM can provide useful information for cognition diagnosis. Finally, some suggestions and recommendations for future investigation are discussed.
Document Type: Research Article
Publication date: July 1, 2012
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