Analysis of Course Data for Curriculum Review and Improvement

Youngseok Lee, Jungwon Cho

Abstract


Analyzing class data collected by educational institutions is imperative for deriving measures to improve the curriculum. Data analysis can identify students' grades and learning behaviors; on this basis, students' learning effectiveness and satisfaction can be improved by promoting curriculum improvement. In this study, it was possible to derive students' learning patterns, key factors, and process improvement plans necessary for the composition and development of the curriculum. The analysis results according to the type of course taken showed that class interest was highly related to learning content understanding, and class understanding had a weak relationship. In addition, it was found that interest in the class and understanding of the contents were important when learners took the course, and the difficulty of the assignment was found to have no relationship with other factors except for the number of assignments. Cluster analysis based on cross-analysis of the subject showed that the subject's difficulty, importance, and association all play an important role, and actual academic grades do not affect it. Interest in the subject and understanding of the contents of the class were crucial factors, and the number and difficulty of tasks did not have a significant impact. Based on these analysis results, providing a quantitative basis for expanding learners' participation in curriculum improvement is possible. These analyses and improvements must be carried out continuously, and the introduction of new data and technologies enables more effective curriculum improvement by performing more detailed and accurate analyses.

Keywords


Course data analysis; curriculum improvement; cluster analysis; intergroup mean analysis; learning factors; academic achievement

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References


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DOI: http://dx.doi.org/10.18517/ijaseit.13.3.18462

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