Promoting academic writing/referencing skills: Outcome of an undergraduate e‐learning pilot project
Bibliographic record
Abstract
Abstract Future health care professionals will require self‐directed learning skills. e‐Learning is a tool to assist in this process and therefore there is a need to develop the capacity and readiness to utilise e‐learning within educational programmes. The aim of this study was to determine if extra‐curricular online referencing and anti‐plagiarism lectures would be utilised and would ultimately improve 1st‐year undergraduate health sciences students’ performance in written assessments. A series of six online archived multimedia lectures (asynchronous) were offered. Adult learning theory principles guided the resource design. Pre‐ and post‐testing of knowledge, attitudes and computer skills was carried out. In‐person tutorials and online email support were also offered. Less than 36% (self‐report) of students accessed the online resources. The poor uptake revealed in this study is consistent with a number of other studies. These findings indicate the need for more careful scrutiny of the learning theory applied in e‐learning design. Prochaska's transtheoretical model is suggested as a framework with strong potential for e‐learning initiatives.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".