Faculty Perceptions of Mattering in Teaching and Learning: A Qualitative Examination of the Views, Values, and Teaching Practices of Award-Winning Professors
Bibliographic record
Abstract
We summarize qualitative research conducted on the mattering construct and then describe a qualitative investigation focused on mattering as a key aspect of the relational factors which influence the learning and development of students. Semi-structured interviews were conducted with 12 professors recognized for their teaching excellence. Specifically, we assessed professors’ attitudes towards student perceptions of mattering and awareness of mattering in terms of their own self-reported beliefs, attitudes, and teaching practices that convey to students that they matter. Thematic analysis confirmed that almost all the award-winning professors interviewed recognized students’ need to matter and found effective ways to convey to students that they matter. These professors tended to be more similar than different in their approaches and attitudes. Key themes included the need for professors to show students they care about them as students and as people, seeing and treating students as individuals who are collaborators in the learning process, and the need to avoid anti-mattering micro-practices that can result in students becoming disengaged and disillusioned. We discuss these findings in terms of how an explicit focus on mattering promotion is warranted as a central attribute of effective teaching and learning, how the current findings enhance understanding of the mattering construct and how it should be assessed.
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 | 0.026 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| 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".