Natural pedagogical conversations in high school students' internship
Bibliographic record
Abstract
Abstract Many science educators encourage student experiences of “authentic” science by means of student participation in science‐related workplaces. Little research has been done, however, to investigate how “teaching” naturally occurs in such settings, where scientists or technicians normally do not have pedagogical training and generally do not have time (or value) receiving such training. This study examines how laboratory members without a pedagogical background or experience in teaching engage high school students during their internship activities. Drawing on conversation analysis, we analyze the minute‐by‐minute transactions that occurred while high school students participated in a leading environmental science laboratory. We find that the participation trajectory was based on demonstration‐practice‐connect (D‐P‐C) phases that continually recurred in the process of “doing” science. Concerning the transactional structures, we identify two basic conversation patterns—Initiate‐Clarify‐Reply (I‐C‐R) and Initiate‐Reply‐Clarify‐Reply (I‐R‐C‐R)—that do not only differ from the well‐known Initiate‐Reply‐Evaluate (I‐R‐E) patterns previously observed in science classrooms, but also could be combined to constitute more complex patterns. With respect to the organization of natural pedagogical conversations, we find that there were not only of preferred and dispreferred modes of responding but also ambiguous dispreferred modes ; and the formulating organization not only includes self‐formulating but also other‐formulating . These natural pedagogical conversations helped, on the one hand, students to clarify their understanding and, on the other hand, technicians (or teachers) to teach toward different needs for different students in different contexts. © 2009 Wiley Periodicals, Inc. J Res Sci Teach 46: 481–505, 2009
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".