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Record W4399028746 · doi:10.5430/wje.v14n2p24

The Development of an Instructional Model Based on Rogers’ Theory to Enhance the Adversity Quotient for Guangxi International Business Vocational College

2024· article· en· W4399028746 on OpenAlexvenueno aff
Qin Sun, Bung-on Sereerat, Saifon Songsiengchai, Penporn Thongkumsuk

Bibliographic record

VenueWorld Journal of Education · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEducational Reforms and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsVocational educationPsychologyMathematics educationPedagogy

Abstract

fetched live from OpenAlex

This study aimed to investigate the current status of adversity quotient (AQ) development, develop an instructional model based on Rogers’ theory, and assess the impact of the model on students' AQ. The study involved 33 first-year students from infant care services and management majors at Guangxi International Business Vocational College. Research tools included questionnaires, interviews, lesson plans, the Adversity Quotient Scale, observation forms, and interview forms. The study comprised three steps: studying the current situation of AQ development, developing the instructional model, and implementing and improving the model. Data were analyzed using statistical and qualitative methods. Results showed four aspects of existing problems in college students' AQ: students, teachers, learning processes, and environment. The instructional model comprised four components: Principle, Objective, Learning Process, and Result. After implementing the model, students' AQ significantly improved compared to pre-class levels (p < 0.01). This study provides insights into addressing challenges in college students' AQ development and demonstrates the effectiveness of an instructional model grounded in Rogers’ theory for enhancing students' AQ. Enhancing college students' adversity quotient is of significant importance for effectively coping with adversity in their daily learning life.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.294
Teacher spread0.283 · 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 designNot applicable
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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