Unraveling idea development in discourse trajectories
Notice bibliographique
Résumé
With the present paper we want to shed light onto an issue that is central within the knowledge building theory but only little studied -the development of ideas in collaborative learning discourse.Starting from the construction of a network of explicit and implicit relations between ideas, we apply a scientometric method to tackle the temporality of collaborative processes based on the structure of successive ideas.The resulting discourse trajectories are shown to give a holistic and also a detailed view on how knowledge advances when their interpretation is combined with a qualitative analysis of the content of the ideas and their relations.The weighted relevance of relations between ideas enables the identification of sub-topics in the discourse, important ideas, and influence or uptake events. IntroductionHow is knowledge about the world created and advanced?Philosophers have spent enormous efforts to answer this question (e.g.Popper, 1972).Knowledge building is an approach from the learning sciences that attempts to build on contemporary answers from philosophical inquiries and research on expertise (Bereiter, 2002;Bereiter & Scardamalia, 1993;Scardamalia & Bereiter, 2006) to engage students in the kinds of knowledge work that are widely assumed important in the 21st century, including ability to collaborate, deal with novelty, and solve ill-structured problems.At the heart of knowledge building is a computermediated collaborative discourse that is oriented toward idea improvement.Following Popper's (1972) theory of objective knowledge, knowledge-building theory considers ideas as "real" objects that can be critiqued, tested and modified, much like how real objects like bicycles undergo these processes (Bereiter & Scardamalia, 2003).Ideas do not reside in the minds of participants but take on lives on their own in this discourse.Hong, Chen, Chang, Liao and Chan (2009) emphasized that the "idea-centered" educational design is enabled through the Knowledge Forum software by allowing interaction around ideas regardless of the discussion threading.Hence, the process of idea development is fundamental for understanding knowledge building.Moreover, focus on processes is widespread in the field of computer-supported collaborative learning (CSCL), reflecting the dynamic nature of discourse as an object of study.However, despite this acknowledged role, there is a dearth of analytical approaches for investigating the dynamic development of ideas in knowledge-building discourse.Therefore, the main goal of this paper is to provide an example of studying what we call a discourse trajectory, i.e. the genuine process characteristics of a discourse based on idea development over time.In order to do so, we first outline briefly previous research and then present a new methodological technique and its application to knowledge building discourse. Related ResearchA starting point to studying a discourse process is the evaluation of surface indicators of participation and communication like number, length of contributions, etc. (Strijbos, Kirschner and Martens, 2004).Such results can automatically be evaluated from the log file data of the software but they are regarded as only very basic descriptors of a collaborative process.Most studies of knowledge building have followed a content analysis approach (Chi, 1997;Gunawardena, Lowe, & Anderson, 1997;Henri, 1992), where qualitative data is segmented into idea units and these are coded for their cognitive, metacognitive, social, motivational and other aspects.The frequency of the assigned codes is then statistically
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,053 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,007 | 0,007 |
| Études des sciences et des technologies | 0,002 | 0,007 |
| Communication savante | 0,010 | 0,025 |
| Science ouverte | 0,002 | 0,008 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».