Language of Students: How do Students Label and Define their Class Experience?
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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