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Enregistrement W3003135982

Inquiry-based, Teacher directed and adaptive instruction in secondary science: A cross-national analysis of associations with science literary and interest using PISA 2015

2019· article· en· W3003135982 sur OpenAlexaboutno aff
A. McConney, Amanda Woods‐McConney

Notice bibliographique

RevueMurdoch Research Repository (Murdoch University) · 2019
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueScience Education and Pedagogy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésScience educationMathematics educationPedagogyPsychologySociology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

In science education, and STEM more generally, inquiry based teaching and learning has been a dominant pedagogical approach, particularly in Western countries, for at least half a century (e.g., Furtak, Seidel, Iverson, & Briggs, 2012; Minner, Levy, & Century, 2010). In Europe, inquiry-based teaching has become part of science education orthodoxy, receiving policy support and funding from the EU following the Rocard report (2007) recommendation that “the introduction of inquiry-based approaches in schools ... should be actively promoted” (p. 17). Recently, however, the availability of large-scale, high-quality international assessment data in science and mathematics—like Trends in International Mathematics and Science Study (TIMSS) and Programme for International Student Assessment (PISA)—has allowed scaled-up analysis of associations between various pedagogical approaches, and students’ achievement and engagement in science (e.g., Areepattamannil, 2012; Kang & Keinonen, 2017; McConney, Oliver, Woods-McConney, Schibeci, & Maor, 2014).
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\nIn this study, we report research that builds on previous investigations of inquiry-based teaching in science that used PISA 2006 (McConney, et al., 2014). Our previous study found that in Australia, Canada and New Zealand, students who reported high levels of inquiry also showed above-average levels of interest in science, but below-average levels of science literacy. We also found the corollary to be true. These findings ran counter to orthodox views about the efficacy of inquiry-based teaching in science. 
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\nIn the current study, using PISA 2015, we extend our analysis of teaching and learning in science to six countries, and ask two research questions:
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\nRQ1: To what extent is the variability observed in science literacy (as measured in PISA 2015) associated with the frequencies of three distinct approaches to teaching science, reported by students from their science classrooms? To what extent does this vary by country and/or gender?
\nRQ2: To what extent is the variability in students’ interest in science associated with the frequencies of three distinct approaches to teaching science, as reported by students? To what extent does this vary by country and/or gender?
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\nConsistent with the primary analysis of PISA 2015 as well as our previous analysis of PISA 2006, this study shows that the frequency of inquiry-based teaching is negatively associated with students’ science literacy. For students in six countries (Australia, Canada, Ireland, New Zealand, the UK and the USA), the more frequently students experienced inquiry-based activities, the weaker their literacy in science, on average. This negative relationship between science literacy and inquiry-based activities was strongest for students in the USA and Canada, but was also evident to varying degrees in all six countries. In contrast, our analysis also revealed a moderately strong positive association between inquiry and students’ interest in science. This was consistent for all six countries. With increasing focus on effective, evidence-informed practice in teacher education, our obligation is to prepare teachers based on the strongest research available regarding the efficacy of various pedagogical approaches in science. At a minimum, the findings reported in this analysis demand examination and discussion of how “inquiry” might be best conceived and applied in helping students to learn and like science.

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 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)0,004
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesÉtudes des sciences et des technologies
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,022
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0050,012
Études des sciences et des technologies0,0020,007
Communication savante0,0000,002
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,155
Tête enseignante GPT0,448
Écart entre enseignants0,293 · 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

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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