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Record W2943350688 · doi:10.5539/ies.v12n5p109

The Impact of Electronic Tests on Students’ Performance Assessment

2019· article· en· W2943350688 on OpenAlexvenueno aff
Dalia Alyahya, Nada Almutairi

Bibliographic record

VenueInternational Education Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsHandwritingMathematics educationReading comprehensionTest (biology)PsychologyReading (process)Achievement testComprehensionStandardized testComputer scienceLinguisticsArtificial intelligence

Abstract

fetched live from OpenAlex

This study has aimed to measure the effect of electronic tests on the academic achievements of middle school students in Arabic course. The sample has been divided into two groups; the experimental group and the non-experimental group after using the mixed experimental method. Statistical measurements had been used before, and after, the experiment for both groups; whereas, study tools were consisted of achievements test and focus group. The results have assured the existence of statistical differences between the experimental group and non-experimental group in the (language classification) category marks. The results have shown no statistical differences on the audio comprehension, reading comprehension, writing, handwriting skills, language style, grammatical function and writing expression categories marks, which give preferences to use the electronic test rather than the traditional (pen and paper) test. The study has concluded that teachers must be encouraged to perform continuous evaluation throughout the academic semester by applying electronic tests. They must emphasize on the importance of grounding rules and regulations to apply electronic tests in the educational institutions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.184

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 studies0.0000.000
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 head0.032
GPT teacher head0.509
Teacher spread0.477 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

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