Bibliographic record
Abstract
Current research has shown that lecturers marking electronic assignments, typically Word documents, are able to provide personalised feedback at a relevant point in a student’s piece of assessment using paper technology such as a Tablet PC. Evaluation through in-depth interview and questionnaire shows that this was important to both students and lecturers alike. Some lecturers have felt that the Tablet PC allows greater creativity in assessment than technologies such as paper and pen and PC and keyboard input device. For example the use of colour linked to learning outcomes and grammar feedback, and the ease with which the eraser can be used for re-editing. It appears that the pedagogy has been extended from the traditional ‘pen and paper’ approach to the use of ‘digital ink technology’. Students said that they liked the personal feel of the electronic hand written feedback. Reflective practice for lecturers was supported through forums and a wiki and was evaluated using virtual ethnography. Lecturers record a flow experience in assessment as either enabling or disabling their creativity in e-assessment. The potential for extending the pedagogy into graphical environments is also evident for such things as annotating graphs and diagrams, mathematical notation and scientific nomenclature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.197 | 0.117 |
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".