An evaluation of rehabilitation students' learning goals in their first year: a text mining approach

Abstract

Introduction: Qualitative information in the form of written reflection reports is vital for evaluating students' progress in education. As a pilot study, we used text mining, which analyzes qualitative information with quantitative features, to investigate how rehabilitation students' goals change during their first year at university.

Methods: We recruited 109 first-year students (66 physical therapy and 43 occupational therapy students) enrolled in a university rehabilitation course. These students completed an open-ended questionnaire about their learning goals at the time of admission and at 6 and 12 months after admission to the university. Text mining was used to objectively interpret the descriptive text data from all three-time points to extract frequently occurring nouns at once. Then, hierarchical cluster analysis was performed to generate clusters. The number of students who mentioned at least one noun in each cluster was counted and the percentages of students in each cluster were compared for the three periods using Cochran's Q test.

Results: The 31 nouns that appeared 10 or more times in the 427 sentences were classified into three clusters: "Socializing," "Practical Training," and "Classroom Learning." The percentage of students in all three clusters showed significant differences across the time periods (p < 0.001 for "Socializing"; p < 0.01 for "Practical Training" and "Classroom Learning").

Conclusion: These findings suggest that the students' learning goals changed during their first year of education. This objective analytical method will enable researchers to examine transitional trends in students' reflections and capture their psychological changes, making it a useful tool in educational research.

Keywords: cluster analysis; professional education; rehabilitation; students; text mining.

Figure 1

Cluster Dendrogram Nouns extracted from the text data of students’ responses across all time periods were classified into three clusters: Socializing, Practiced Training, and Classroom Learning. English words corresponding to each Japanese noun are also shown for display purposes. Note that a few Japanese words cannot be expressed with a single English word, but require two words. The dotted vertical line represents the threshold of the agglomeration dissimilarity coefficient (approximately 1.25) that determines the number of clusters.

Figure 2

Changes in the ratio of applicable students in each cluster The bar graphs represent the percentage of students who responded with words comprising each cluster at each time point (Q_0mo, at admission; Q_6mo, 6 months later; Q_12mo, 12 months later). Note that the sum of the percentages of the three clusters in each period does not add up to 100% because it includes students belonging to more than one cluster or none of the clusters.