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Record W2802967058 · doi:10.5539/ies.v11n5p25

The Process of Facilitating Knowledge Acquisition and Retention: An Inquiry into Magnetic Poles with Challenging Questions

2018· article· en· W2802967058 on OpenAlexvenueno aff
Hasan Zühtü Okulu, Ayşe Oğuz Ünver

Bibliographic record

VenueInternational Education Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsTest (biology)Process (computing)MagnetKnowledge acquisitionPsychologyMathematics educationComputer scienceArtificial intelligenceEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

The current research is to give an example to the inquiry-based science teaching implementations for facilitating knowledge acquisition and retention in a short period of time. Thus, the aim of the research is to transfer of acquired knowledge into different situations using sequential inquiry activities, which have challenging questions for inquiry about what the magnetic pole is and how to discover it. The research was designed as a pre-experimental, one-group pre-test/post-test (N=65) with a retention-test. Sequential inquiry-based science activities were applied to provide a series of developmentally appropriate experiences and discussions, which concretely scaffold participant’s ideas of magnetism. According to the results, the participants interpreted the magnets and magnetic poles regarding their functions. The common view of the participants was that a magnetic pole should be at the ends. This view is associated with upper-lower or internal-external surfaces for a ring magnet. Finally, with a sphere magnet, both upper-lower or internal-external surfaces have lost their functions and the inquiry begins with the question “How to find the poles of a magnet?” In that process, students get to engage and feel that they do not know something that they should know.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.128
GPT teacher head0.508
Teacher spread0.380 · 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.

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

Citations5
Published2018
Admission routes1
Has abstractyes

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