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Record W2603092674 · doi:10.6007/ijarbss/v6-i11/2458

The Principles Adopted In Designing the Webcomics to Assist Lower Secondary Students with Reading Comprehension

2016· article· en· W2603092674 on OpenAlexfundno aff
Ajurun Begum Ahamed, Raja Nor Safinas Raja Harun

Bibliographic record

VenueInternational Journal of Academic Research in Business and Social Sciences · 2016
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
FundersMcMaster UniversityUniversiti Sains Islam MalaysiaPennsylvania State UniversityWestern Carolina UniversityFlorida State University
KeywordsReading comprehensionReading (process)VocabularyComputer scienceComicsComprehensionReciprocal teachingMathematics educationLinguisticsPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

The primary objective of this study is to highlight the designing of reading instruction specifically the web comics to assist students in understanding a text read. This is because the current practice in the teaching of reading comprehension focuses towards reading texts merely from the text books provided in the form of text and inadequate vocabulary of the students' results in incapability of comprehending the particular text. Teachers need to be creative in designing reading instructions to be utilized in the reading classes to assist the average performance students to understand a text read. The webcomics are hoped to enhance the understanding of the text through the features of webcomics present in the text which represent the text including the vocabulary present. This paper presents the phases adopted in designing of webcomics to be used in reading comprehension classes.

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)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.200
GPT teacher head0.481
Teacher spread0.281 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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