MétaCan
Menu
Back to cohort
Record W2896949477 · doi:10.5539/hes.v8n4p77

Water: Turkish Prospective Biology Teachers' Conceptual Structures and Semantic Attitudes towards Water

2018· article· en· W2896949477 on OpenAlexvenueno aff
Hakan Kurt

Bibliographic record

VenueHigher Education Studies · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsAdjectiveSemantic differentialTurkishMathematics educationPsychologyCognitionDevelopmental psychologyComputer scienceLinguisticsNatural language processingNoun

Abstract

fetched live from OpenAlex

This study was prepared to investigate prospective biology teachers' cognitive structures related to "water". As the research design of the study, the case study was applied. The data were collected from 44 prospective biology teachers. The free word-association test, the drawing-writing technique and the semantic differential attitude scale were used as data collection instruments. The data were subject to content analysis and divided into categories through coding. In the analysis, the categories were formed and determined through the results of word-association test and drawing-writing test which were completed by the prospective biology teachers. With the help of these categories, the cognitive structures of prospective biology teachers were explained. These categories were determined as “the place and importance of water in life, the definition and chemical properties of water and water and metabolism”. It was determined that prospective biology teachers' semantic attitudes towards water were at a positive level in terms of mean scores of all adjectives considered; However based on each and every adjective, they mostly perceive water as compulsory, valuable and necessary. Moreover, the data collected through three data collection instruments indicated that prospective biology teachers had misconceptions about water considering the categories determined.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0050.000

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 head0.122
GPT teacher head0.380
Teacher spread0.258 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueHigher Education StudiesSame topicEducation Practices and ChallengesFrench-language works237,207