Factors affecting successful student completion in a career oriented program.
Bibliographic record
Abstract
Research has been conducted over the past several decades to review conceptual models, assessment instruments and other methods to discover factors and find solutions to high rates of lack of completion in distance education programs. This thesis explores unusually high successful completion rates in a Career Practitioner Certificate Program operated by the Open Learning Agency in British Columbia. The research was conducted to investigate the following problem statement: What are student perceptions of the factors that contribute to successful completion of courses within the OLA Career Practitioner Certificate Program? Five themes were developed that formed a set of questions which served as discussions items during the participant interviews: alignment of the course objectives to career goals; course design and delivery features; personal characteristics and supports; support from instructors and effectiveness of administration systems and processes. Fourteen students were interviewed using in a qualitative study involving exploration and discussion of the five themes. These participants identified student personal characteristics and the most important success factor in their course completion(s). Course content and support from instructors were also identified as important factors. The study concluded that, when combined with program design and course content that had a direct relevance to the students’ career goals, personal characteristics and supports appeared to be the most substantive component of the high successful completion rates in this program.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".