Students' Conceptions of Tutor and Automated Feedback in Professional Writing
Bibliographic record
Abstract
B ackground Professional writing is an essential outcome for engineering graduates and hence a vital part of engineering education. To provide a successful learning experience for students engaged in writing activities, timely feedback is necessary. Providing this feedback to increasing numbers of students poses a major challenge for instructors. New automated systems work towards providing both timely and appropriate writing feedback, but students' views on automated feedback, and feedback in general, are not well understood. P urpose (H ypothesis ) To contribute to a deeper understanding of students' conceptions of feedback from tutors and an automated system called Glosser, and how these conceptions are related to achievement. D esign /M ethod Students in an engineering course worked in pairs to write an engineering report on e‐business. The design of the study involves in‐depth interviews and the analysis employs an approach in which student conceptions of automated feedback are investigated in relation to related feedback from their tutor, perceptions of automated feedback in general, and their academic achievement. R esults Students' conceptions of feedback vary and can be grouped into cohesive and fragmented, which is consistent with other theoretical models. Close associations were found between more cohesive conceptions of feedback and better academic performance. C onclusions A student's conception of traditional and automated feedback is similar, being either cohesive or fragmented. Changing one may change the other. Deep learners see feedback as a way of learning about the topic whereas shallow learners see them as a way to improve the communication aspects of writing. Design considerations based on these results 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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".