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Record 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 on OpenAlexaboutno aff
A. McConney, Amanda Woods‐McConney

Bibliographic record

VenueMurdoch Research Repository (Murdoch University) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsScience educationMathematics educationPedagogyPsychologySociology
DOInot available

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.012
Science and technology studies0.0020.007
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.155
GPT teacher head0.448
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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