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Record W3205366501 · doi:10.5539/ass.v17n11p182

Students Perception of Moral Education Textbooks Design Components and Learnability

2021· article· en· W3205366501 on OpenAlexvenueno aff
Mohamed Hilmie Mohd Mokhtar, Maizura Yasin

Bibliographic record

VenueAsian Social Science · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLearnabilityVariety (cybernetics)Mathematics educationPerceptionAffect (linguistics)Quality (philosophy)Style (visual arts)PsychologyComputer scienceMultimediaVisual artsHuman–computer interactionArtificial intelligenceArt

Abstract

fetched live from OpenAlex

Textbooks are one of the most fundamental learning and teaching tools used in schools all around the world (Nicholls, 2003). Today's technology allows students to access a variety of textbook formats, including online textbooks that they may read from anywhere. This study looks into the style and arrangement of learning textbooks in Malaysia in order to make studying more convenient. The purpose of this research is to see if the dynamics of textbook layout affect students' desire to learn. Thirty Year 6 children from a primary school in the Selangor district of Hulu Langat participated in this study. Students' views of textbook design elements, such as paper quality, printing, colour, and pedagogical aspects, were determined using a quantitative survey. Print and colour were scored higher by the students than paper quality, artwork, and images, according to the data. Furthermore, the research discovered a link between the layout of a book and its actual use.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.062
GPT teacher head0.395
Teacher spread0.333 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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