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Record W3094846181 · doi:10.21083/ajote.v9i2.6270

Class Experiences with Inquiry Learning Spaces in Go-Lab in African Secondary Schools

2020· article· en· W3094846181 on OpenAlexvenueno aff
Fer Coenders, Nuno Gomes, Rola Sayegh, Isaac Kinyanjui, Aurelle Noutahi, Nissi Madu

Bibliographic record

VenueAfrican Journal of Teacher Education · 2020
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsnot available
FundersEuropean Commission
KeywordsInquiry-based learningContext (archaeology)Class (philosophy)Mathematics educationProcess (computing)Computer sciencePedagogyActive learning (machine learning)SociologyPsychologyGeographyArtificial intelligence

Abstract

fetched live from OpenAlex

Inquiry Based Learning (IBL) is a form of active learning, often used in STEM education to promote conceptual learning and to acquire scientific investigation skills. This paper reports on a study in which teachers in Kenya, Nigeria and the Republic of Benin implemented IBL embedded in online and offline Inquiry Learning Spaces (ILS) in their classes using the Go-Lab platform (https://www.golabs.eu). After a brief description of the IBL methodology, of lab work and in particular virtual labs for STEM education, of the process of preparing teachers to use IBL in class, and of the context of this study, we highlight the methodology used, and finally report our results. These show that the introduction and class enactment of a digital inquiry based learning platform such as Go-Lab in Africa (i) is possible, although challenging, (ii) does lead to student learning, (iii) for this to take place teacher training is necessary, (iv) the digital infrastructure is present in the schools though minimal and fragile, and (v) a local partner needs to provide assistance when required.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.283
Teacher spread0.262 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations3
Published2020
Admission routes1
Has abstractyes

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