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Record W2080520866 · doi:10.5539/ies.v6n9p175

Promoting Scientific Literacy by Using ICT in Science Teaching

2013· article· en· W2080520866 on OpenAlexvenueno aff
Mohammed S. Al-rsa’i

Bibliographic record

VenueInternational Education Studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologyScientific literacyContext (archaeology)Mathematics educationConstructiveConstructivist teaching methodsLiteracyInformation literacyPedagogyTeaching methodScience educationSocial constructivismSociologyProcess (computing)PsychologyComputer science

Abstract

fetched live from OpenAlex

This study aims to identify the way upon which ICT can be employed in science teaching to develop scientific literacy level.The study has conclude to design a triple learning model (PEA) based on ICT and constructive learning strategy in teaching science through a context which cares for building positive trends of searching for knowledge and increasing scientific literacy level of the students at schools and universities. The PEA model depends on the use of ICT in the teaching of science through the following three requirements:1. Using ICT in a way that is consistent with the tendencies of the students and their attitudes.2. Constructing educational content based on the constructivist theory of learning.3. Providing social learning environment.Thus, the study recommends the bodies in charge of the educational process to necessarily adopt the project of rebuilding scientific literacy through supplying classrooms and laboratories with ICT supplies as well as training teachers and faculty members in universities on science teaching skills by using ICT based on the Constructivist Theory.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
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 head0.052
GPT teacher head0.471
Teacher spread0.418 · 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

Citations19
Published2013
Admission routes1
Has abstractyes

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