Clustering of Mindset towards Self-Regulated Learning of Undergraduate Students at the University of Phayao

摘要

The effects of Covid-19 severely affected the Thai higher education model. Therefore, there are three significant objectives in this research: (1) to cluster the mindsets and attitudes toward self-regulated learning styles of undergraduate students at the University of Phayao. (2) to construct a predictive model for recommending an appropriate student learning clusters. (3) to evaluate the predictive model that has been constructed. Samples collected a compilation of 472 student satisfaction with questionnaires from three schools, with seven disciplines at the University of Phayao, Thailand. Research tools consisted of statistical and machine learning techniques as follows: frequency, percentage, average, standard deviation, k-means clustering, decision tree techniques, cross-validation methods, confusion matrix performance, accuracy, precision, and recall measurement. Researcher found that the k-means model with the highest accuracy is the decision tree model that was classified into three clusters by dividing the model testing into the leave-one-out cross-validation method with a depth of seven levels of the decision tree model and an accuracy of 98.73%. From the results and studies, it can be concluded that the developed model is effective and reasonable to be further developed as an application for further organizational development.