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Vol.12, No.2, May 2023. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Performance Evaluation of College Students’ Google Classroom Engagement Using Data Mining Techniques
Serafin C. Palmares, April Joy Abara-Palmares, Jan Carlo T. Arroyo, Allemar Jhone P. Delima
© 2023 Serafin C. Palmares, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 12, Issue 2, Pages 1023-1029, ISSN 2217-8309, DOI: 10.18421/TEM122-48, May 2023.
Received: 18 February 2023. Revised: 09 May 2023.
Abstract:
The purpose of this study is to assess how well college students use Google Classroom as a useful and informative teaching and learning tool. The survey method was utilized in the study to measure student involvement in Google Classroom. This study's sample population included 292 college students from Northern Negros State College of Science and Technology. Algorithms such as Random Forest (RF), C4.5, and Naive Bayes (NB) were utilized with three of the most crucial techniques, such as 60% split, training set, and 8-fold cross-validation, for performing analysis on the student data. After analyzing different metrics for performance (Correctly Classified Instances, FP Rate, ROC Area, F-Measure, TP Rate, Recall, Precision, Time taken to build model, Mean Absolute Error, Root Mean Squared Error, Root Relative Squared Error, Relative Absolute Error) by various algorithms for data mining, the researchers determined which algorithm performs better than others on the student dataset gathered, allowing the researchers to make a recommendation for future improvement in students' Google Classroom engagement.
Keywords – C4.5, data mining, decision tree, Google Classroom, Naive Bayes, random forest, Weka. |
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