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Record W2335380699 · doi:10.5539/ies.v9n4p245

Mind Maps to Modify Lack of Attention among Saudi Kindergarten Children

2016· article· en· W2335380699 on OpenAlexvenueno aff
Bulquees Ismail Abdul Majid Daghistan

Bibliographic record

VenueInternational Education Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
FundersKing Saud University
KeywordsPsychologyScale (ratio)Class (philosophy)Mathematics educationDevelopmental psychologyEarly childhood educationSample (material)ArabicGeography

Abstract

fetched live from OpenAlex

<p class="apa">This research study aims at investigating the impact of Mind Maps on modifying the lack of attention in Arabic language class among Saudi Kindergarten children. To achieve the goals of this study the researcher used an experimental design with a random sample from AlRae’d Kindergarten’s children in Riyadh -Saudi Arabia for the academic year (2014-2015). The study sample consisted of (40) children divided into two groups: (23) in the experimental and (17) in the control group. The researcher used Al-Obeidi’s (1999) Lack of Attention Scale LAS. Validity of the tool was approved through a half division to measure lack of attention (0.93) which is considered good. The scale was used before and after the implementation of the experiment on both groups. Results showed a positive change in attention concentration in favor of the experimental group. Thus, the researcher recommended the use of Mind Maps in teaching kindergarten children to avoid attention deficiency.</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 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)0.000
metaresearch head score (Gemma)0.001
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.083
GPT teacher head0.428
Teacher spread0.345 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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