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Record W4392159303 · doi:10.5430/jct.v13n1p333

A Study on Developmental Strategies for Improving the Quality of Secondary Special Education for Students with Disabilities

2024· article· en· W4392159303 on OpenAlexvenueno aff
Yung Keun Park

Bibliographic record

VenueJournal of Curriculum and Teaching · 2024
Typearticle
Languageen
FieldComputer Science
TopicEducation and Learning Interventions
Canadian institutionsnot available
FundersJoongbu University
KeywordsSpecial educationQuality (philosophy)PsychologyMathematics educationMedical educationMedicine

Abstract

fetched live from OpenAlex

In the era of the fourth industrial revolution, addressing the multifaceted learning needs of students with special needs has become increasingly pivotal. This study aims to explore and enhance the quality of secondary special education, adapting to new educational methodologies in tandem with technological advancements while fostering the development of teachers and educational stakeholders. To identify the key areas necessitating development in secondary special education, interviews were conducted with special education teachers. These interviews informed the creation of a questionnaire, which was subsequently disseminated through a Google survey to special education teachers in various special schools and classes nationwide. The survey responses were analyzed using descriptive statistics to gain comprehensive insights. This study's findings integrate the perspectives from the interviews and survey data, leading to the formulation of strategies for the advancement of secondary special education. The analysis categorized the developmental needs into four primary domains: teacher development, curriculum enhancement, school infrastructure, and external factors related to the school environment. The paper concludes with a discussion that synthesizes these findings, offering a pathway for future improvements in the quality of secondary special education, and highlighting the importance of adapting to the changing educational landscape in the era of technological progress.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.040
GPT teacher head0.397
Teacher spread0.357 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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