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Enregistrement W4412055181 · doi:10.6000/2292-2598.2025.13.02.6

Emotional Intelligence as a Predictor of Research Skills Acquisition Among University Students with Intellectual Disabilities in Calabar, Nigeria

2025· article· en· W4412055181 sur OpenAlexvenueno aff
Chidirim Esther Nwogwugwu, Henrietta Uchegbue, Sylvia Victor Ovat, Nnenna Kalu Uka, Julius Michael Egbai, Patricia Ebere Chilebe Iwuala, Anthony Pius Effiom, Helen Akpama Andong, Richard Ayuh Ojini, Mayen Ndaro Igajah, Margaret Sylvanus Umoh, Remi Modupe Omoogun, Mokutima Ekpo, Unimke Sylvester Akongi, Ogbaji Dominic Ipuole, Charles Utsu Ushie

Notice bibliographique

RevueJournal of Intellectual Disability - Diagnosis and Treatment · 2025
Typearticle
Langueen
DomainePsychology
ThématiqueEmotional Intelligence and Performance
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEmotional intelligencePsychologyIntellectual disabilityMathematics educationDevelopmental psychologyMedical educationApplied psychologyMedicinePsychiatry

Résumé

récupéré en direct d'OpenAlex

Aim: Intellectual disability is characterized by significant limitations in intellectual functioning and adaptive behavior originating before age 18 (AAIDD, 2010). In higher education, these students often require individualized support, yet inclusive practices in Nigerian universities remain underdeveloped. This study examines the predictive relationship between emotional intelligence and research skills acquisition among university students with intellectual disabilities at the University of Calabar (UNI.CAL) and the University of Cross River (UNICROSS), Cross River State, Nigeria. Five study objectives were stated to guide the research. Five research questions were formulated, and three hypotheses were stated. Literature was reviewed based on the variables under study, as research gaps were also stated. Method: The study adopted a correlational survey research design. The area of the study is Cross River State, South-South, Nigeria. The study population consists of all 20,030 final-year undergraduate students with intellectual disabilities in inclusive departments of the University of Calabar and the University of Cross River State, offering special education or support for students with disabilities. A purposive sampling technique was used to select students identified with intellectual disabilities. A sample size of 401 participants was selected based on accessibility and consent. The instrument for data collection was a questionnaire. Cronbach Alpha reliability coefficient method was used in establishing the reliability index of .82. Results of the research questions were presented using frequency counts, percentages, mean and standard deviation, and Pearson's Product Moment Correlation, Multiple linear regression, and Independent t-test were used to analyze the research question and hypotheses. Results: The results revealed that emotional intelligence significantly contributes to developing research skills in students with intellectual disabilities. Emotional competencies such as self-awareness, motivation, and interpersonal sensitivity are essential tools in enabling these students to participate fully in research activities. Hence, emotional intelligence components collectively predict research skills acquisition. There is a significant difference between male and female students with intellectual disabilities in their level of research skills acquisition. Conclusion: This research concluded that there is a statistically significant relationship between emotional intelligence and the ability to develop and perform research tasks. It affirms the critical role emotional intelligence plays not just in social functioning but also in academic productivity, especially among students who often face exclusion or limited support. Recommendation: Universities should incorporate emotional intelligence training into their special education and general academic programs to build students’ self-efficacy and research competence.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,040
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,002
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,045
Tête enseignante GPT0,384
Écart entre enseignants0,338 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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