Student Dropout from Foundation Program at Modern College of Business & Science, Sultanate of Oman
Bibliographic record
Abstract
Main reasons for student dropout from higher education may be low academic performance, poor socialization skills, low confidence levels, busy social life and financial issues. As students’ dropout from higher education has been rising, there is a need to understand this problem for finding suitable solutions. Research objectives for this institutional research are to explore patterns in dropout data at Foundation program, establish criteria for identifying students at-risk of dropout and identify areas of improvement for reducing dropout rate, as the dropout problem is high at Foundation level of the college. Research methodology includes application of exploratory study based on analysis of secondary data pertaining to 22 semesters, Spring 2012 to Summer-I 2017. Findings revealed that 1966 students dropped out from Foundation program during the study period with an average of 94 students per semester. Dropout rate was higher among males and was more at Levels I and IV. Though dropout happened in Foundation, academic departments would also experience major loss, as Foundation is the ‘feeder program’ for other bachelor’s programs. It is recommended to have a dropout process flow-chart not only to understand the exit journey of dropping students, but also to reverse the journey. It is recommended to set up a dropout committee, design an early warning system for creating alerts and bifurcate Foundation department into Language sub-department and Technical sub-department (Math and IT courses). It is further recommended to have an effective data management system that would enable administration to reduce dropout rates and create a ‘feel good’ environment for the students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".