Bibliographic record
Abstract
Higher education can be seen as a “gateway” to entering professional careers (Rubenson, 2010), yet the teaching and learning practices used in the classroom may not always prepare students for their futures the way instructors intended, one example is the use of student participation to increase interaction and learning in the classroom. Although verbalizing thoughts can help students learn, students in this study felt they were more often rewarded for frequency of their contribution instead of quality of their contribution which challenges its intended use. They called it “verbal diarrhea” and explained how prominent it was in their university learning experiences making the learning environment not only disengaging and a practice they dreaded but also unrealistic to the real world setting. However, in the Active Learning Classroom (ALC), students noticed verbal diarrhea was significantly reduced and for the most of the time non-existent, and made their learning more authentic (Herrington, Reeves, & Oliver, 2014). This paper presents case studies of students’ lived experiences in their undergraduate degree, and their “prescriptions” and recommendations to instructors and other students on avoiding verbal diarrhea and encouraging meaningful discussions facilitated by the learning environment.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".