Relationships between instructional language and primary students' learning.
Notice bibliographique
Résumé
Causal modeling was used to examine how primary students’ language ability interacted with pace and redundancy of instructional language during an expository science lesson to explain students’ attention and learning. Language ability and pace of the instructional language, mediated by students’ attention to the lesson, accounted for significant variance in learning outcomes. Higher language ability related to greater learning. Overall, slow-paced instructional language was positively related to learning, but students attended less to it than to fast-paced talk. Students with special needs attended significantly less and learned less, whereas students not so identified attended slightly less but learned more. Teachers’ talk matters, but because instructional language impacts differentially on students, heuristics for modifying it are not straightforward. In communicative settings involving large groups, most individuals do more listening than talking. This is particularly so for students in many elementary classrooms, where the teacher does up to 80% of the talking, and the remaining 20% of the talking time is shared by all of the students (Cazden, 1988). Despite recent changes in educational practices (Lapadat 2000a, 2000b; Pappas, Kiefer, & Levstik, 1999), this nonreciprocity of talking time persists across educational levels. In such classrooms, students’ receptive language knowledge and the listening strategies they use have a close relationship with their academic learning outcomes. However, even in classrooms where talk is distributed more equitably than in typical whole-class expository teaching, such as in constructivist, small group, and cooperative learning models (Lauritzen & Jaeger, 1997), students’ ability to engage, listen, and respond are still important determinants of both communicative and academic success. Roth and Spekman (1989) called for more research focusing on students’ knowledge of and ability to understand and use language, pointing out that this is crucial in coming to understand how students understand, participate in, and learn from the interactions that make up instruction. Even during expository instruction, talk is constructed interactively (O’Connor & Michaels, 1996; Pappas et al., 1999). Just as young primary school students have the task of attending to and understanding the teacher’s talk (and peers’ talk) in order to learn in school, so too does the teacher need to talk in ways that students understand in order to teach effectively (Derwing, 1991). If students have insufficient linguistic knowledge or do not engage, or if teachers fail to adjust their instructional language, academic learning will be affected. As expository instruction is so frequently used in schools, it is all the more important to do it well. In classrooms in which talk is distributed differently than in wholeclass expository teaching, teachers’ effective use of instructional language in small groups or with individuals, as well as in segments of whole-class instruction, remains a critical component of effective teaching (Lindsay, 1996; Merritt, 1982; O’Connor & Michaels, 1996). This is a challenging task, as students bring diverse understandings to learning, based on their prior knowledge, language skills, cognitive processing abilities, cultural perspectives, motivations, and interests.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».