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Record W3081786720 · doi:10.5539/ies.v13n9p10

Investigation of Three-Tier Diagnostic and Multiple Choice Tests on Chemistry Concepts with Response Change Behaviour

2020· article· en· W3081786720 on OpenAlexvenueno aff
Suat Türkoğuz

Bibliographic record

VenueInternational Education Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsTest (biology)Mathematics educationDescriptive statisticsDisadvantagePsychologyChemistryMathematicsStatisticsComputer science

Abstract

fetched live from OpenAlex

This study aims to investigate the test scores of the three-tier diagnostic chemistry test (TDCT) and multiple choice chemistry test (MCCT) by response change behaviour (RCB). The study is a descriptive research study aiming to investigate the item response efforts of TDCT and MCCT in a computerized testing environment (Quizzer test program, QTP). In both TDCT and MCCT, QTP maintains a continuous record for each tier of the test. Participants in the study are students in the Science Education Department at the state university in the Aegean region of Turkey (n=115). The study was conducted in two groups: there were 58 students in Group 1 and 57 students in Group 2. In Group 1, a TDCT was used; in Group 2, an MCCT test was applied. Tests were distributed by random sampling between Group 1 and Group 2. The data were collected by adding a confirmation tier to the TDCT involving 44 items. The TDCT was applied to 115 pre-service teachers; the reliability coefficient of the test was found to be 0.72. SPSS and MS Excel programs were used to analyse the data. Data were analysed using descriptive statistical methods. Considering the results obtained from the study, the rate of completing the test with RCB of test items for both tests is approximately 7–12 per cent. Another important consequence is that RCB does not provide an advantage or disadvantage in terms of scoring.

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)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.014
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 head0.269
GPT teacher head0.471
Teacher spread0.202 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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