Analysis of Research Trends in Process Data using Text Mining
Jinsu Choi, Hyewon Chung · Journal of Curriculum and Evaluation · 2024
The purpose in this study is to analysis the research trends for process data in the field of pedagogy. To do this, these were subjects of the study that a total of 60 Korean and foreign research papers. And topic modeling based on the Latent Dirichlet Allocation(LDA) algorithm and semantic network analysis(SNA) methods were used for this study. The results are as follows. First, the research on process data were divided into each four topics: Korean research-‘learning analysis’, ‘teaching and learning type analysis’, ‘AI learning’, ‘academic achievement’, foreign research-‘evaluation process (student attitude)’, ‘inquiry-based learning behavior analysis’, ‘problem-solving process’, and ‘learning type analysis using LMS’. Second, as a result of analysis the trend of the topics in time, ‘academic achievement’ topic was a hot topic in Korea, it is hot in foreign, ‘evaluation process (student attitude)’ and ‘inquiry-based learning behavior analysis’. Third, the topics of process data research were linked to the keywords: Korean research-learning, data, time, foreign research- student, process, performance. Specially, ‘learning’ was found to be an important keyword connecting the most topics in Korea, while it is ‘student’ in foreign. Based on these results, we can use them as basic data for future research direction and policy establishment.