MétaCan
Menu
Back to cohort
Record W2565521042 · doi:10.5539/ies.v10n1p122

Determining the Cognitive Structures of Geography Teacher Candidates on “Earthquake”

2016· article· en· W2565521042 on OpenAlexvenueno aff
Baştürk Kaya, Caner Aladağ

Bibliographic record

VenueInternational Education Studies · 2016
Typearticle
Languageen
FieldComputer Science
TopicSeismology and Earthquake Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDamagesNatural disasterMathematics educationLandformSample (material)GeographyPsychologyCartographyPolitical science

Abstract

fetched live from OpenAlex

The objective of this study is to determine the cognitive structures of the students of geography teaching department by identifying their conceptual frameworks about the concept of earthquake. A case study design from qualitative research approaches was used in this research. Sample group of the study constitutes 155 students from the Department of Geography Teaching who took the course of natural disasters. Free Word Association Test was used to collect the data. The data were evaluated according to the content analysis, categories were formed according to the results of this evaluation and frequencies and percentages of the response words in each category were calculated. A total of 9 categories were created according to their semantic associations. Some of them are; “concepts about earthquake, damages of the earthquake, a category of defining earthquake, types and causes of earthquakes, landforms caused by earthquakes and other effects” and they form the dominant categories. The frequencies and percentages of some categories are low such as; “The things that earthquake makes feel, regions where earthquakes happened before or there is a possibility to happen, people, institutions and organizations about earthquakes, helping to the victims of the earthquakes, factors affecting the safety of life and property in the earthquake”. In addition, this study revealed that students have some alternative concepts about earthquake.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.205

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.033
GPT teacher head0.341
Teacher spread0.308 · 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 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

Citations13
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueInternational Education StudiesSame topicSeismology and Earthquake StudiesFrench-language works237,207