An Interesting Profile-University Students who Take Distance Education Courses Show Weaker Motivation Than On-Campus Students
Bibliographic record
Abstract
Four models of descriptive characteristics (Demographic, Experiential, Motivational, Inhibitory) were examined using discriminant function analysis for Distance Education (DE) and On-campus students. Of 240 targeted students (120 DE and 120 On-Campus), 174 responded to a questionnaire identifying characteristics of students who enroll in DE. Using a Demographic model only 61.5% of the sample was correctly classified. Higher classification rates were obtained with an Experiential model (73.6%), a Motivational model (72.3%), and an Inhibitory model (83.9%). Significant mean differences (univariate analyses) between the two groups allowed for the construction of a profile of students who opt for DE. They are more mature, more experienced, and more likely facing barriers (situational, institutional and personal) on the one hand (predictable relationships), but less motivated on the other hand (a totally unexpected relationship). Future research directions are suggested.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".