Listservs in the college science classroom: Evaluating participation and “richness” in computer-mediated discourse
Bibliographic record
Abstract
How do instructors motivate students to participate in computer-mediated discussion? If they do participate, how can the quality of their interactions be assessed? This study speaks to these questions by examining online participation and discourse in a science course for preservice teachers. The instructor of an introductory entomology course for preservice teachers implemented online discussions by way of a listserv that was designed to provide students with greater access to important information outside of class. Data was collected from focus groups, written questionnaires, interviews with the instructor, and 182 public listserv messages. Initial student participation was encouraged by the instructor, but participation was modest. The posting of the first mandatory assignment halfway through the course, however, corresponded to a burst period of student activity, yielding a four fold increase in the number of messages authored by students. There was also a seven fold increase in the proportion of discussions that involved at least two student participants and a 50% increase in the proportion of outside references cited within the body of students' messages. This latter finding reflected improvement in the quality of online discourse among students. This evidence suggests that instructors who are interested in listserv participation should make some of their listserv activities mandatory.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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".