Feedback in Cumulative Coursework: Action Research in a Blended Course
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Action research calls for the solution of practical problems in the classroom as well as the expansion of theoretical knowledge. In this study, feedback in cumulative work is explored as a strategy for guiding and improving the teaching-learning process. By following a feedback loop: initial draft, feedback on first draft, final draft, and marking, 28 students in at the University Universidad Nacional Autónoma de México (UNAM) had the opportunity to receive guidance and demonstrate improvement in a blended learning, or b-learning course. The study lasted five weeks and engaged university learners as well as the educator in videoconferences focused on feed up, feedback, and feed forward.This pedagogical action research involved observation, research and planning, implementation, and reflection. Data was gathered on students’ access to technology and their perception upon effectiveness of remote learning based on their experience. Additionally, scores before and after treatment were registered and analyzed.The findings showed a general improvement after feedback sessions and learners were able to present enhanced final versions of tasks. The study’s main contributions are the confirmation of positive results on effective feedback as well as an opening to discussion, adaptation, and improvement of the practices presented.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it