MétaCan
Menu
Back to cohort
Record W4413920992 · doi:10.15517/r1m8dy28

Computer or Paper-Based Delivery Mode: An Analysis for Testing English Reading Strategies

2025· article· en· W4413920992 on OpenAlexaff
Alejandro Fallas Godinez, Walter Araya Garita

Bibliographic record

VenueRevista de Lenguas Modernas · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsComputer scienceReading (process)Mode (computer interface)LinguisticsHuman–computer interaction

Abstract

fetched live from OpenAlex

This study aims to examine the comparability of scores on the Reading Comprehension Exam (EDI for its acronym in Spanish) with institutional multiple-choice items in two administration modes, namely paper-based tests and computer-based tests, taken by first-year Costa Rican students from different majors at the University of Costa Rica, Costa Rica. The analysis considered both types of administration by comparing test per- formance and conducting item differential analysis (DIF) among 1145 students during February 2023. Findings revealed that differences between both administration modes are minimal and weak. Furthermore, when differences, they indicate a slight advan- tage of computer-based over paper-based administration. This study exemplifies how to leverage computer-based testing without adversely affecting candidates by adapting a measurement instrument from paper to digital format.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.125
GPT teacher head0.404
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueRevista de Lenguas ModernasSame topicTechnology Adoption and User BehaviourFrench-language works237,207