Method for Clustering Comments in Class Evaluation Questionnaires using Keyword Feature Scores

In Japanese universities, improving classes is a task that must be continually implemented. Broadly speaking, a university class’s purpose is threefold: (1) have students understood the content of the class, (2) have they achieved their goals for the class, and (3) were they satisfied with the class? Universities must embrace these three points. For this reason, class evaluation questionnaires administered at many universities nearly always include questions on “comprehension,” “achievement,” and “satisfaction.” However, it is not possible to collect sufficient information by simply establishing questions and rating the responses. Therefore, research analyzing free descriptions in class evaluation questionnaires is increasing. This study developed a system to classify free descriptions provided by students into “comprehension,” “achievement,” and “satisfaction.” In addition, it proposed a method of knowledge construction for such classification. One of its benefits was that it could be implemented within a short period of time with little effort. The proposed method’s accuracy was evaluated by comparing the results of the classification by means of the proposed method with the results of the classification of the class evaluation questionnaire by an analyst. The two classification results matched with a probability of 92.67% to 93.67%, confirming that the method was sufficiently practical.

Authors

Institutions

Publication Details

Journal
International Journal of Education
Published
2024-12-11
DOI
https://doi.org/10.5296/ije.v16i4.22097
Primary Topic
Educational Technology and Assessment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Method for Clustering Comments in Class Evaluation Questionnaires using Keyword Feature Scores

Maya Iwano, Kazuhiko Tsuda
International Journal of Education
Educational Technology and Assessment
article

Method for Clustering Comments in Class Evaluation Questionnaires using Keyword Feature Scores

Maya Iwano, Kazuhiko Tsuda
article en

Abstract

In Japanese universities, improving classes is a task that must be continually implemented. Broadly speaking, a university class’s purpose is threefold: (1) have students understood the content of the class, (2) have they achieved their goals for the class, and (3) were they satisfied with the class? Universities must embrace these three points. For this reason, class evaluation questionnaires administered at many universities nearly always include questions on “comprehension,” “achievement,” and “satisfaction.” However, it is not possible to collect sufficient information by simply establishing questions and rating the responses. Therefore, research analyzing free descriptions in class evaluation questionnaires is increasing. This study developed a system to classify free descriptions provided by students into “comprehension,” “achievement,” and “satisfaction.” In addition, it proposed a method of knowledge construction for such classification. One of its benefits was that it could be implemented within a short period of time with little effort. The proposed method’s accuracy was evaluated by comparing the results of the classification by means of the proposed method with the results of the classification of the class evaluation questionnaire by an analyst. The two classification results matched with a probability of 92.67% to 93.67%, confirming that the method was sufficiently practical.

International Journal of EducationVol. 16(4)
Yamaguchi University (JP)
Quality Education
Openalex Percentile: Top 64%
Educational Technology and Assessment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.