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Record W2586383993 · doi:10.5430/jct.v6n1p1

Effectiveness of using the iPad in Learning to Acquire the Mental and Performance Skills in Teaching Social Studies Curriculum

2017· article· en· W2586383993 on OpenAlexvenueno aff
Maadi Mahdi Alajmi, Hanan Abdullah Al-Hadiah

Bibliographic record

VenueJournal of Curriculum and Teaching · 2017
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumTest (biology)Control (management)PsychologySocial studiesMathematics educationComputer sciencePedagogyArtificial intelligence

Abstract

fetched live from OpenAlex

This study aims to examine the effectiveness of using the iPad in learning to acquire the mental and performanceskills in teaching the social studies. Using experimental design method, the study compared two groups: (a)experimental, taught using the iPad, and (b) control group, taught using the traditional learning without iPad. A totalof 48 (24experimental group and 24 control group) eighth grade students in state of Kuwait participated in this study.The study started on the second semester on 2015-2016 on the average of 6 weeks. After the test on the study sampleand the statistical processing, the results revolved that: (1) there are no significant differences between theexperimental and the control group in mental performance in pre-test score. (2) There are no significant differencesbetween the experimental and the control group in skills performance in pre-test score. (3) There are significantdifferences between the experimental and the control group in mental performance in post-test score in favor ofexperimental group. (4) There are significant differences between the experimental and the control group in skillsperformance in post-test score in favor of experimental group. Based on the results, the study concluded withrelevant recommendations regarding the implementation of using iPad technology in education, and suggested somefurther studies in this topic.

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)0.006
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 score0.076
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.334
Teacher spread0.321 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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