Effectiveness of The Feedback-Dialogue in Hybrid University Courses
Bibliographic record
Abstract
Feedback is one of the key factors in students’ success (Hattie and Timperley, 2007). It is essential in order for them to maintain or increase their level of competence (Brookhart, 2010). But do students understand the feedback provided by their teachers? Does feedback allow students to learn and to improve their work? For the purpose of augmenting feedback effectiveness in our hybrid writing courses at the university, we created the feedback-dialogue, a method consisting in interacting with the student within its text, using the comment function of the word processor. Unlike traditional feedback, the feedback-dialogue is bi-directional, i.e. the student must not only revise its text but also respond to the teacher’s comments. To measure the effectiveness of the feedback-dialogue, we designed a two-step methodology. First, students’ perceptions of the effectiveness of feedback-dialogue were collected in a self-reported questionnaire. The items in the questionnaire were formulated from an analysis of different typologies in relevant studies (Anson, 2015; Grigoryan, 2017; Mauri et al., 2016). Second, the students’ responses to their teacher’s comments were analyzed, as well as the modifications they made during the revision of their texts.
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 | 0.017 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".