Factors contributing to desertion among students in the Managerial Sciences Institute at UNAD-CEAD
Bibliographic record
Abstract
The investigation on academic drop at ECACEN shows the major elements at play in this problem, based mainly in the light of these factors: academic performance, family income, educational level, type of institution, and age.UNAD as a higher education institution, and therefore UNAD?s Schoolof Administrative Sciences, Accounting, Economics and Business ?ECACEN CEAD Simon Bol?var, in the city of Cartagena, is no alien tothis phenomenon ?statistics of desertion in the previous two years show the following: from the second quarter of 2007 to the first quarter 2008, the drop was 37% for terms 2008 I ? II; in 2009, the drop was 29%, and for the second quarter of 2009, it was 24%.To prevent students dropping out from the School of Administrative Sciences, Accounting, Economics and Business in the CEAD ECAC Sim?n Bol?var, in Cartagena, many strategies have been deployed, namely: visits to townships, detention sites, homes, implementing the Sponsor plan, mandatory appointments with the Counseling department, a plan for student cooperation, inter-institutional agreements for the free use of classrooms, continued availability of the computing classroom at CEAD, telephone e-mail communications available, online and web-based campus, university welfare events, motivational talks led by the counseling department andcultural events.All these strategies were put forward without the support of a formal and thorough investigation of the causes generating it.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".