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As emerging e-course providers after the COVID-19 crisis, universities (UNI) policymakers in the Mekong Delta region (MDR) have faced difficulties owing to limited clues about what factors improve student retention and recruitment. This study aims to determine important factors (IF) for student satisfaction with e-course adoption (e-satisfaction) for student retention and recruitment.

Survey data collected from 850 students of the target UNI were analyzed using the DT-fuzzy DEMATEL method. Input factor dimensions included course design, technical infrastructure, interaction, teacher-related and student-related factors. Decision Trees (DT) confirmed the final factors; fuzzy decision-making trial and evaluation laboratory (DEMATEL) was used to establish the cause-effect relationships among these factors.

DT-fuzzy DEMATEL method can identify satisfied and dissatisfied students (accuracy = 94.95%) and determine IFs successfully. The most IFs included new and useful knowledge/information provided, various effective teaching methods and motivation to read provided learning materials.

Although e-satisfaction has been the focus of theories and practices, e-satisfaction in an emerging region like MDR has been studied here for the first time. Most IFs can be used as predictors for e-satisfaction and serve as a primary reference for UNIs’ policymakers. Several practical suggestions were also provided for the sustainable and long-term development of e-programs.

This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability.

The real-world samples comprised the enrolled records of 2,412 first-year students of a private university (UNI) in Taiwan. This work utilized decision trees (DT), multilayer perceptron (MLP) and logistic regression (LR) algorithms for constructing EPMs; under-sampling, random oversampling and synthetic minority over sampling technique (SMOTE) methods for solving data imbalance problems; accuracy, precision, recall, F1-score, receiver operator characteristic (ROC) curve and area under ROC curve (AUC) for evaluating constructed EPMs.

DT outperformed MLP and LR with accuracy (97.59%), precision (98%), recall (97%), F1_score (97%), and ROC-AUC (98%). The top-ranking factors comprised “student loan,” “dad occupations,” “mom educational level,” “department,” “mom occupations,” “admission type,” “school fee waiver” and “main sources of living.”

This work only used enrollment information to identify dropout students and crucial factors associated with dropout probability as soon as students enter universities. The extracted rules could be utilized to enhance student retention.

Although first-year student dropouts have gained non-stop attention from researchers in educational practices and theories worldwide, diverse previous studies utilized while-and/or post-semester factors, and/or questionnaires for predicting. These methods failed to offer universities early warning systems (EWS) and/or assist them in providing in-time assistance to dropouts, who face economic difficulties. This work provided universities with an EWS and extracted rules for early dropout prevention and intervention.