Discourse Patterns at Laboratory Practices and the Co-Construction of Knowledge by Applying SDIS-GSEQ
Bibliographic record
Abstract
The purpose of this study is to analyze the discourse through IRE (Intervention-Response-Evaluation) in the co-construction of knowledge of Biology students during laboratory practices by applying the SDIS-GSEQ software to assess IRE discourse patterns developed during the same. The study group consisted of second semester students of the Bachelor’s Degree in Biology from the Facultad de Estudios Superiores Iztacala, UNAM. This process included audiovisual records of the practice, the creation of an instrument where a categories system and verbal sub-systems are put together with sub-categories to be defined based on the discourse and IRE structure; then this audiovisual records and the obtained category pattern were used to apply the SDIS-GSEQ software which was in charge of establishing the category sequences created in the interaction between teachers and students during the practice. The obtained results show IRE discourse patterns demonstrating that students prefer to use reproducible and dependent practice manual structures, instead of thoughtful and non-cognitive structures where their knowledge about the practice content is involved; the study also demonstrates that the SDIS-GSEQ software is a useful tool for the research of these patterns. Therefore, we propose to modify the IRE structure in order to create better conditions in the construction of knowledge between students and teachers by using a IRF (Initiation-Response-Feedback) pattern leading to feedback, negotiation and co-construction of knowledge so as to improve the Teaching-Learning process during laboratory practices.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".