Can Relational Feed-Forward Enhance Students’ Cognitive and Affective Responses to Assessment?
Bibliographic record
Abstract
Assessment feedback should be an integral part of learning in higher education, but students can find this process emotionally and cognitively challenging. Instructors need to consider how to manage students’ responses to feedback so that students feel capable of improving their work and maintaining their wellbeing. In this paper, we examine the role of instructor-student relational feed-forward, enacted as a dialogue relating to ongoing assessment, in dissipating student anxiety, enabling productive learning attitudes and behaviours, and supporting wellbeing. We undertook qualitative data collection within two undergraduate teaching units that were adopting a relational feed-forward intervention over the 2019–2020 academic year. Student responses were elicited via small group, semi-structured interviews and personal reflective diaries, and were analysed inductively using thematic analysis. The results demonstrate that relational feed-forward promotes many elements of student feedback literacy, such as appreciating the purpose and value of feedback, judging work against a rubric, exercising volition and agency to act, and managing affect. Students were keen for instructors to help them manage their emotions related to assessment, believing this would promote their wellbeing. We conclude by exploring academic strategies and pedagogies that position relational instructor feed-forward as an act of care, and we summarize the key characteristics of emotionally resonant relational feed-forward meetings.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".