MétaCan
Menu
Back to cohort
Record W7117686343 · doi:10.1108/jced-08-2025-0005

An exploration of generative transactive discourse patterns in structured student conversations with epistemic network analysis

2025· article· en· W7117686343 on OpenAlexaff
Robyn Ilten-Gee, Brendan Eagan, Larry Nucci

Bibliographic record

VenueJournal of Character Education · 2025
Typearticle
Languageen
FieldPsychology
TopicEducational Strategies and Epistemologies
Canadian institutionsSimon Fraser University
FundersDivision of Graduate EducationUniversity of Wisconsin-MadisonWisconsin Center for Education Research, School of Education, University of Wisconsin-MadisonNational Science Foundation
KeywordsDiscourse analysisInterpersonal communicationIntervention (counseling)Conversation analysisExploratory analysisFocus (optics)Frame (networking)Social network analysisEthnography

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0030.004
Scholarly communication0.0050.007
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.028
GPT teacher head0.397
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Character EducationSame topicEducational Strategies and EpistemologiesFrench-language works237,207