Language of Students: How do Students Label and Define their Class Experience?
Bibliographic record
Abstract
Every day hours are spent in classrooms with professors teaching and students learning - or so we think. As professors, we are expected to engage students in the learning process (Kuh, 2003), keep them entertained (Delaney et al., 2010), impart wisdom, etc. However, what professors see as effective class experiences may be very different from how and why students experience the class as they do. This qualitative study, as the first part of a multiphase research project, sought to identify the language students use to label and describe their perceptions of individual classes. The study involved semi-structured interviews with 24 students, ranging from first to fifth year. Developing an understanding of the labels and definitions students use to articulate their classroom experience may provide insight for both faculty and students in that they may be able to better communicate, or at minimum faculty may better understand how students describe class experiences. Findings may provide both students and faculty ideas into how to create a more effective learning experience.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".