The Causal Factors Affecting the Management of Predictive Student Relationship using Business Intelligence Concept for the Retention of Undergraduate Students
Bibliographic record
Abstract
This study aims to analyze causal factors affecting predictive student relationship management for undergraduate student retention using business intelligence. Phase 1 involved identifying key factors influencing retention through document analysis, categorizing them into social, learning, teaching, and student-related factors. Social factors include student community, friendships, communication channels, and organizational culture, which promote engagement, motivation, and perseverance. Learning and teaching factors, such as supportive learning environments, scholarships, instructional design, and structured assignments, impact academic success and retention. Student-related factors, including learning abilities, academic preparedness, goals, and parental support, are essential for persistence. Data was gathered from 1,574 students at Valaya Alongkorn Rajabhat University, with 1,160 usable entries after cleansing. Exploratory Factor Analysis (EFA) grouped these variables into five components: Student Communication Channels, Academic Proficiency, Parental Guidance, Scholarships, and Organizational Culture. Confirmatory Factor Analysis (CFA) validated the model, highlighting well-clustered factors. In Phase 2, a predictive model was developed using stepwise multiple regression, identifying impactful variables, such as note-taking abilities, scholarship counseling, peer communication, and access to advisors. The final model, with an R value of 0.881 and an adjusted R² of 0.777, demonstrated 77.7% predictive accuracy, emphasizing the combined influence of academic support, communication, financial aid, and social integration on student retention. The findings suggest that institutions should prioritize these areas to foster a conducive environment for student success.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".