Student Success in Face-To-Face and Distance Teleclass Environments: A matter of contact?
Bibliographic record
Abstract
Learning from a distance continues to gain popularity. An influx of traditional, and even on-campus students attest to its flexibility, but are they equipped to succeed in a low-contact distance environment versus a face-to-face, on-campus environment? This research explored whether several variables including background, preparedness and self-perceptions assessed within the first week of class contributed differently to the success of students completing one-way distance teleclasses (n = 35) versus students completing the same classes in face-to-face, on-campus environments (n = 64). The distance students were less successful than face-to-face students when exam grades were examined (A, B, C versus D, F, drop). For distance learners, higher reading comprehension and scholastic competence were indicative of exam grade success. Student-initiated contact with the instructor was marginally related to distance student success. For face-to-face learners, reading comprehension, reading rate and lower athletic competence was indicative of exam grade success. Suggestions to help students decide whether distance learning is right for them and ways to support distance learners in low-contact environments are discussed.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".