Understanding I/We positions in a blended university course: Polyphony and chronotopes as dialogical features
Bibliographic record
Abstract
This paper uses Dialogical Self Theory to explore university students’ I/We positions before and after participating in a blended course with both individual and collaborative learning activities. Two focus group discussions were held; one at the beginning and the other one at the end (18 students in total; 3 M, 15F; average age 24 years old). The focus groups were analyzed through discursive analysis by referring to the Bakhtinian concepts of chronotope and polyphony, as dialogical features of positioning. Results show that at the end of the course the polyphony became richer, including also technology. This was initially “suppressed” and became later a voice supporting both We-position and collaborative learning. A shift from initial I-positions rooted in a broad chronotope (including past, present and future) toward We-positions placed in the specific and situated chronotope of the course occurred. This result poses the question of sustainability and transferability of innovation.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".