Self-Correction vs. Lecturer-Correction: Effects on Research Achievement and Alcohol Use in Intellectual Disabled Undergraduates in Federal Universities of Southern Nigeria
Notice bibliographique
Résumé
Aim: Within Nigerian universities, students with intellectual disabilities remain under-supported in terms of personalized instructional strategies that target both academic improvement and psychosocial well-being. The purpose of the study was to compare self-correction vs Lecturer-correction: Effects on research achievement and Alcohol use in Intellectual Disabled undergraduates in Federal Universities of Southern Nigeria. Method: This study adopted a quasi-experimental research design. The area of the study is Southern Nigeria. The population consists of all 3,092 students with intellectual disabilities enrolled in two selected federal universities in Southern Nigeria, specifically in Cross River and Akwa Ibom States. The sample size for this study was 120 final-year students with intellectual disabilities who depend on alcohol to cope with stress and improve self-esteem using a multi-stage sampling technique. Three instruments were used for data collection. They were the Research Achievement Test (RAT), the Interest Inventory Test (IIT), and the Alcohol Use Screening Tool (modified AUDIT). The screening criteria for alcohol dependence are that students must be 18 years and older, students must have a history of substance use, mental health conditions (depression, anxiety), and a history of use of medications contraindicated with alcohol. The study was validated by Psychology, Measurement, and Evaluation experts at the University of Calabar. The data collected were analyzed for internal consistency using the Cronbach Alpha method, which yielded a reliability index of 0.83. The test scores for the study were generated from pre-tests and post-tests using the Research Methods Achievement Test and Research Method Interest Inventory Test. Mean and standard deviation were used to answer the research questions. The pretest-posttest mean gains of each strategy of the two strategies were computed. Also, the null hypotheses formulated for the study were tested at a 0.05 level of significance using Analysis of Covariance (ANCOVA). Results: The findings revealed that student correction strategies are more effective than lecturer correction strategies in enhancing the research method achievement of students with intellectual disabilities. The self-correction strategy significantly improves student interest in research methods more than the lecturer-led correction approach. There is a significant difference in achievement between male and female students, regardless of the correction strategy used. No statistically significant difference in interest scores between male and female students taught research methods using either lecturer-correction or self-correction strategies. Male and female students differed in their alcohol use outcomes following instruction using either the self-correction or lecturers’ correction strategies. Conclusion: Based on the result of the study, it was concluded that student correction strategies are more effective than lecturer correction strategies in enhancing the research method achievement of students with intellectual disabilities. The self-correction strategy significantly improves student interest in research methods more than the lecturer-led correction approach. There is a significant difference in achievement between male and female students, regardless of the correction strategy used. No statistically significant difference in interest scores between male and female students taught research methods using either lecturer-correction or self-correction strategies. Male and female students differed in their alcohol use outcomes following instruction using either the self-correction or lecturers’ correction strategies. Recommendation: Given the superior effectiveness of student correction strategies over lecturer-led corrections in enhancing students’ achievement in research methods, it is recommended that educators integrate structured self-correction approaches into their teaching. This can be achieved through guided reflection exercises, peer review tasks, and the use of checklists or correction templates that promote independent learning and metacognitive development.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».