Help-seeking with a computer coach in problem-based learning: Its interaction with the knowledge structure of the learning domain and the tasks’ cognitive demands
Notice bibliographique
Résumé
Help-seeking with a computer coach in problem-based learning : Its interaction with the knowledge structure of the learning domain and the tasks’ cognitive demands Julien Mercier (jmercier@cgocable.ca) Applied Cognitive Science Laboratory, McGill University 3700 McTavish St., Montreal, Canada, H3A 1Y2 Carl H. Frederiksen (carl.frederiksen@mcgill.ca) Applied Cognitive Science Laboratory, McGill University 3700 McTavish St., Montreal, Canada, H3A 1Y2 process were elaborated. Statistical analyses were also performed.. The Problem and its Context Research on tutoring has shown that the student’s interaction with the tutor heavily determines the learning outcomes. In human tutoring, the responsibility of the interaction is shared between the tutor and the student (Chi, 2001). In the case of a computer coach such as the McGill Statistics Tutor, the control of the interaction is put entirely in the hands of the learners. Learners’ ability to interact with the system productively therefore represents a critical aspect affecting the learning outcomes. This ability of help seeking (Nelson-LeGall, 1981) has not been well researched from a cognitive science point of view in the context of computer- supported learning (Aleven et al., 2003). The aims of the present work are to elaborate a cognitive model of help seeking and to examine its interaction with critical aspects of the learning situation. Two studies using discourse analysis methodology are conducted using a formal model of the learning domain. Results and Discussion Results show that a help seeking model based on information processing theory is reflected in the data. The components of the model are (1) recognize an impasse, (2) diagnose the impasse, (3) establish a specific need for help, (4) find appropriate help, (5) comprehend help, and (6) evaluate help. Help seeking interacts with the performance of the task and with the structure of the domain knowledge. Help seeking is intertwined with problem solving ; help is sought to fill gaps in students’ knowledge in order to solve the problem. However, student’s use of the computer coach is not optimal since they tend to select help at higher levels in the hierarchical knowledge structure while they tend to problem solve at lower levels. Conclusion These results have implications for the design of computer coaches and instructional situations. These results help characterize the contribution of the learners to the emergence of more or less contingent tutorial interactions. In addition, identifying key skills that students use in problem-based learning situations is a first step in training and assessing those skills. Methodology First-level Participants are 20 graduate students from a faculty of Education of a Canadian university. The seven- hour experiment involves working in pairs to solve a very challenging statistics problem (a two-way analysis of variance) for which students don’t have sufficient background. A computer coach based on human tutoring, the McGill Statistics Tutor, is available to provide help with every aspect of the task. Data consist of three complementary sources. The dialogue between the pair of participants as they work on the statistics problem using the computer coach. The interaction with the computer coach is also recorded, in two forms. First, the display of the computer is recorded using a special device. Second, the computer coach keeps a log of some characteristics of every help request made by the students. The students solutions to the problem are also integrated in the database. Data analysis consists of complementary strategies. Trace analyses of the task performance and the help seeking References Aleven, V., Stahl, E., Schworm, S., Fisher, F., & Wallace, R. (2003). Help Seeking and Help Design in Interactive Learning Environments. Review of Educational Research Chi, M.T.H., Siler, S.A., Jeong, H., Yamauchi, T., & Hausmann, R.G. (2001). Learning from Human Tutoring. Cognitive Science, 25, 471-533. Nelson – Le gall, S. (1981). Help seeking : An understudied problem-solving skill in children. Developmental Review,
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».