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Enregistrement W1930496101 · doi:10.1111/j.1467-8535.2011.01262.x

Can verbalisers learn as well as visualisers in simulation‐based CAL with predominantly visual representations? Preliminary evidence from a pilot study

2011· article· en· W1930496101 sur OpenAlex

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.

Notice bibliographique

RevueBritish Journal of Educational Technology · 2011
Typearticle
Langueen
DomainePsychology
ThématiqueLearning Styles and Cognitive Differences
Établissements canadiensAthabasca University
Organismes subventionnairesNational Science Council
Mots-clésThink aloud protocolMathematics educationPsychologyReading (process)MainstreamRepresentation (politics)Educational technologyComputer scienceMultimediaHuman–computer interactionLinguistics

Résumé

récupéré en direct d'OpenAlex

Abstract Simulation‐based computer‐assisted learning (CAL) is emerging as new technologies are finding a place in mainstream education. Dynamically linked multiple representations (DLMRs) is at the core of simulation‐based CAL. DLMRs includes multiple visual representations, and it enables students to manipulate one representation and to immediately receive feedback from others. An interesting and important research question is whether verbalisers, who prefer to process verbal material, have similar learning performance and learning features as visualisers, who prefer to process visual material. To answer this question, 28 undergraduate students were selected as participants from the 855 undergraduate students who were initially tested with the style of processing scale (SOP). They were representative of either visualisers or verbalisers (students who scored upper 10% and lower 10% on the SOP). A study was conducted using an experimental design that included pre‐ and posttest and thinking‐aloud methods. Simulation‐Assisted Learning Statistics (SALS) was adopted as the learning environment for both groups. The analysis results are based on the data of 25 participants because three participants had trouble thinking aloud while using SALS. The results indicated that the visualisers and verbalisers did not differ significantly in their learning performance, but they did exhibit significantly different learning features in their use of DLMRs, their methods of reading learning guides and their learning strategies. Additionally, the learning features of the verbalisers explained why their learning performance was similar to that of the visualisers. Finally, this study provides recommendations for future applications and studies of simulation‐based CAL. Practitioner Notes What is already known about this topic Simulation‐based computer‐assisted learning (CAL) is useful for conceptual learning and is increasingly being applied in many educational fields. Visual‐verbal is one important dimension of cognitive styles. A number of studies have examined the learning performance of visualisers and verbalisers using learning materials that emphasise either visual or verbal representations; however, the results are mixed. What this paper adds Investigating the differences between the learning effects of visualisers and verbalisers after learning with simulation‐based CAL. Investigating the learning process features of visualisers and verbalisers when learning with simulation‐based CAL. Investigating the differences between visualisers' and verbalisers' learning features when learning with simulation‐based CAL. Implications for practice and/or policy Practitioners could use simulation‐based CAL in teaching statistical concepts. Practitioners should consider the learning features of visualisers and verbalisers when they are learning with simulation‐based CAL. Practitioners should try to develop and use targeted instruction that is developed based on the learning strategies to enhance visualisers' and verbalisers' learning effects.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,021
Score d'incertitude au seuil0,996

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

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.

Tête enseignante Opus0,048
Tête enseignante GPT0,376
Écart entre enseignants0,327 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle