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Record W3113050166 · doi:10.5539/elt.v14n1p13

iPads for Cognitive Skills in EFL Primary Classrooms: A Case Study in Saudi Arabia

2020· article· en· W3113050166 on OpenAlexvenueno aff
Jawza Alshammari, Ruth Reynolds, Kate Ferguson-Patrck

Bibliographic record

VenueEnglish Language Teaching · 2020
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyHigher-order thinkingContext (archaeology)Focus groupMathematics educationCognitionTeaching methodPedagogyCognitive skillSociology

Abstract

fetched live from OpenAlex

This research study was designed to clarify the effectiveness of innovative technology use in order to develop cognitive skills in Saudi Arabia with particular focus on the use of iPads in English as a Foreign Language (EFL) classes. New technology approaches are continually being implemented in educational environments but there is often lagging analysis as to the effectiveness of these approaches. In the context under review the implementation of iPads represented a significant shift from using paper and pen to using a portable touchpad and digital pen. This qualitative study comprising observations, interviews and focus groups with teachers and students in four primary EFL primary classrooms in Saudi Arabia. It aimed to investigate any links between EFL teaching approaches, revised Bloom’s Taxonomy of thinking skills and the use of iPads. The findings indicated an unevenness in the application of revised Bloom’s Taxonomy in English instruction generally and most iPad teaching practices were represented at lower order thinking levels (Remember, Understand and Apply). Also, flexible use of iPads when teaching-learning EFL represented levels of revised Bloom’s Taxonomy which aligns with specific roles of; teacher (T), teacher-student shared role (TS) and student (S) and plays a part in representing cognitive skills. These findings contribute to tablet devices use in language learning literature by highlighting the ‘how’ of EFL instruction based on revised Bloom’s Taxonomy.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.013
GPT teacher head0.292
Teacher spread0.279 · 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 designQualitative
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
Published2020
Admission routes1
Has abstractyes

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