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Record W3046277993 · doi:10.5539/ies.v13n8p103

A Formative Assessment Example: Word Association Test

2020· article· en· W3046277993 on OpenAlexvenueno aff
Fatih Çetin Çetinkaya, Muhammet SÖNMEZ, Abdurrahman Baki Topçam

Bibliographic record

VenueInternational Education Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Assessment and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsFormative assessmentWilcoxon signed-rank testPsychologyTest (biology)Mathematics educationAssociation (psychology)PedagogyCurriculum

Abstract

fetched live from OpenAlex

This research was carried out to determine the effectiveness and functionality of the word association test (WAT), which is a formative assessment tool that is frequently emphasized on today’s modern education systems. The study group consisted of 60 students in a public school in Kocaeli in the school year 2018-2019. Participants were identified using convenience sampling technique. The data of the study were obtained by using pre-test and post-test quasi-experimental design with no control group. The data were categorized by subjecting to content analysis. The findings were tabulated using the cut-off technique and analyzed using the Wilcoxon signed-rank test. When the results of the study were examined, it was concluded that conceptual change and development occurred in participants’ minds and there was a significant difference in the results of the Wilcoxon test performed before and after the implementation. It was observed that the students wrote 1669 words before the implementation, and the number increased to 2193 after it. This shows that the students associate the key concept of “migration” with more words after the implementation and thus there is a wider connotation related to migration in their minds. In addition, the results of this research reveal that the WAT is suitable for formative assessment and can be used in educational studies.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.808
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.190
GPT teacher head0.520
Teacher spread0.331 · 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 designNot applicable
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

Citations4
Published2020
Admission routes1
Has abstractyes

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