An exploration of generative transactive discourse patterns in structured student conversations with epistemic network analysis
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».