An exploration of generative transactive discourse patterns in structured student conversations with epistemic network analysis
Bibliographic record
Abstract
Purpose This analysis takes a quantitative ethnographic approach (Shaffer, 2017) to examining middle school students' peer discourse about moral dilemmas in their history curriculum. Building on previous research in domain-based moral education (Midgette et al., 2017; Nucci & Ilten-Gee, 2021; Nucci et al., 2015), this analysis examines co-occurrences of operational and representational transacts(Berkowitz & Gibbs, 1983) as students discussed dilemmas relating to issues of fairness, loyalty, justice and welfare embedded in history lessons. Design/methodology/approach Using epistemic network analysis (ENA: Shaffer et al., 2016) and microanalytic techniques, we examined the connections between transacts and the type of discourse protocols provided by teachers, to identify how students achieved sustained patterns of sophisticated moral reasoning. Findings Our findings demonstrate that ENA is an effective way to isolate portions of discourse transcripts that indicate sophisticated reasoning. We discovered ways that students departed from their assigned discourse protocols to move from interpersonal thinking to systems thinking, which aligns with moral developmental findings for Grade 7 students. Research limitations/implications ENA helped us change the focus of our analysis and see new patterns. Without the temporal structure of ENA, identifying these patterns would have been much more difficult. This study was an exploratory application with a limited sample size. Additionally, the initial intervention (Midgette et al., 2017) involved only two research lessons per class, and so it was not possible to assess the impact of those discussions on development. Another limitation of this analysis was that the student discourse was only coded for types of speech acts (as per the transactive discourse coding scheme). Therefore, we were limited in what connections we could make using ENA. Practical implications This study supports the 2-for-1 moral and character education approach that has been elaborated by Nucci (2024) in which thoughtful dilemmas and questions are embedded into academic subject areas, as opposed to scheduling separate moral and character activities. The existing sentence starters led students into generating representational transacts. Providing more complicated sentence starters might lead students to generate operational transacts, such as critiques, or contradictions, which may help students contribute to the group in a critical way. Social implications We imagine the deep learning that could occur if students themselves became co-researchers and conversation analysts. How might it change students' peer interactions if they reviewed transcripts of their own conversations and identified moments of responsive engagement? Students might identify moments where they dominated the conversation, successfully persuaded someone, or revised their own thinking. Pedagogy of this sort connects to recent efforts by the National Academy of Education to articulate a research agenda toward civic reasoning and discourse (Lee et al., 2021). Our study offers conceptual and methodological tools for deepening our understanding of the mechanics of civic and moral discourse. Originality/value This study presents a novel approach to studying moral reasoning through discourse. Using ENA to locate generative patterns of transactive discourse activity, and then microanalytic techniques to situate these transacts within the context of middle school history dilemmas provided insight into how educators might facilitate moral reasoning in their classrooms.
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 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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".