MétaCan
Menu
Back to cohort
Record W1964390686 · doi:10.5539/ies.v3n1p96

The Effectiveness of Conflict Maps and the V-Shape Teaching Method in Science Conceptual Change among Eighth-Grade Students in Jordan

2010· article· en· W1964390686 on OpenAlexvenueno aff
Ali Khalid Ali Bawaneh, Ahmad Nurulazam Md Zain, Munirah Ghazali

Bibliographic record

VenueInternational Education Studies · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsConceptual changeMathematics educationTeaching methodSample (material)CurriculumConceptual frameworkPsychologyTest (biology)Energy (signal processing)MathematicsPedagogySociologySocial scienceStatisticsPhysicsEcology

Abstract

fetched live from OpenAlex

The purpose of the present study is to investigate the effectiveness of Conflict Maps and the V-Shape method as teaching methods in bringing about conceptual change in science among primary eighth-grade students in Jordan. A randomly selected sample (N=63) from the Bani Kenana region North of Jordan was randomly assigned to the two teaching methods (Conflict Maps, N=31; V-Shape, N=32). A conceptual multiple-choice test was developed to measure misconceptions regarding electric energy commonly held by eighth-grade students. The data gathered were statistically processed using SPSS software. The results indicated that both methods, Conflict Maps and the V-Shape method, were successful in bringing about conceptual changes in the students. At the same time, the results did not prove either method to be superior in facilitating conceptual shifts regarding electric energy in eighth-grade students. In light of earlier results, this study recommends that curriculum developers take into account teaching methods designed according to conceptual change models and organize training workshops for teachers to learn how to integrate Conflict Maps and the V-Shape method into their teaching. Furthermore, this study recommends that similar studies be conducted with various subjects.

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)0.015
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
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 head0.124
GPT teacher head0.525
Teacher spread0.401 · 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 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

Citations10
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueInternational Education StudiesSame topicScience Education and PedagogyFrench-language works237,207