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Record W2512784226 · doi:10.5539/ies.v9n9p199

Comparison of Digital Technology Competencies among Mexican and Spanish Secondary Education Students

2016· article· en· W2512784226 on OpenAlexvenueno aff
Omar Cuevas Salazar, Joel Angulo Armenta, Imelda García-López, Lizzeth Navarro-Ibarra

Bibliographic record

VenueInternational Education Studies · 2016
Typearticle
Languageen
FieldComputer Science
TopicDigital literacy in education
Canadian institutionsnot available
FundersInstituto Tecnológico de Sonora
KeywordsCompetence (human resources)Information and Communications TechnologyPsychologyMathematics educationTechnological literacyPedagogyMedical educationTeaching methodComputer scienceMedicineSocial psychology

Abstract

fetched live from OpenAlex

<p class="apa">Information and Communication Technologies (ICT) are tools to be used to support educational processes and students have access to them more and more every day. However this does not assure the appropriate use of these tools. That is why the objective of the present study is to identify the level of competency in the use of ICT of students in secondary schools in Obregon City, Sonora, Mexico, in the opinions of the students themselves and of their teachers and to compare this with the level of digital competencies of students in some regions of Spain, using an investigation carried out earlier. Two questionnaires with 51 questions were used, one for 949 students and the other for 49 teachers. The results show that students claim to have between moderately and very high levels of competence in ICT skills while their teachers say the students have high levels of competence in some technological skills such as producing a written documents with a word processor. In the study in Spain, students and teachers voiced similar averages in the highest level of competencies, but differed in the competencies with the lowest averages.</p>

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 score0.305
Threshold uncertainty score0.469

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 communication0.0000.002
Open science0.0010.000
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 head0.028
GPT teacher head0.391
Teacher spread0.363 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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