Student Enrollment and Dropout: An Evaluation Study of Diploma in Computer Science and Application Program at Bangladesh Open University
Bibliographic record
Abstract
<h2><span style="font-size: x-large;">Abstract</span><span style="font-size: x-large;">: </span></h2><p><span style="font-family: Times New Roman;"><span style="font-size: medium;">The aim of this study is to investigate the present status of DCSA program focusing on student enrollment, dropout and completion trends. </span><span style="font-size: medium;"> </span><span style="font-size: medium;">The study tries to explore the factors that attract or pull students to enroll in the program and push them to dropout from the program. Secondary data analysis and interview are used to generate data. Quantitative analysis for the secondary data is used to explore students’ enrollment, dropout and completion trends. Qualitative approach is used to analyze the information generated from key participants’ interviews. The findings of the study reveal that students’ enrollment and completion trends are not at satisfactory level. The push factors identified from the study are mostly extrinsic or institution related. The factors that need to improve are current instructional strategy, timely delivery of learning materials and provide course related information, strengthen the activities of Regional Resource Centers (RRC) and Sub Regional Resource Centers (SRRC). The findings have some policy implications implying that the policy makers of BOU should take into account to improve </span><span style="font-size: medium;">the quality of DCSA program offered by BOU through the delivery mode of ODL.</span></span></p><p> </p>
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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 | 0.025 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".