Notice bibliographique
Résumé
This article examined self-concept, social network and study habit as predictors of students' educational success using some selected post-primary schools in Lagos metropolis, Nigeria, as a case study.Questionnaires were used to collect information from 150 students in five secondary schools in the state.Three hypotheses were formulated and tested using Pearson Product Moment Correlation Statistics.The results revealed a significant connection between social network and students' self-concept, based on the fact that r-cal (0.206) was greater than the r-crit value (0.120) @ 0.05 level of significance.Further to that, it was found that students' study habits showed a significant correlation with both students' academic performance and their self-concept using the value of r-cal (0.184) to be significantly greater than r-crit (0.024) at 0.05 level of significance and 148 degree of freedom.Lastly, the study revealed a significant relationship between students' study habits and their academic performance judging from the value of r-cal (0.139) that was greater than r-crit (0.089) at 0.05 level of significance and 148 degree of freedom.Based on these findings, recommendations were made Keywords: Self-concept, social network, study habit, students, academic performance Introduction: Wikipedia (2016) views academic performance as the outcome of education; it is the degree to which lecturers / tutors / teachers, students or institutions of learning have achieved their educational goals.Education according to Omonijo, Uche, Rotimi and Nwadialor (2014) provides humanity with understanding, knowledge, wisdom and information which could result in desired changes in their ways of life.Nonetheless, the accomplishment of these qualities rests on the innards of education, teaching methods and inclination of students to learn, coupled with positive character.However, our emphasis in this paper is on students' academic performance or accomplishments.In different institutions of learning worldwide, success of students in all level of education is determined by academic performance.Thus, the extent to which a student meets the criterion set out by his or her institution goes a long way to determine his or her success in the school environments.Examination is one of such criterions and it is being organised at a regular interval, using different expressions such as terms, semesters and sessions, depending on the level of education in question.Hence, pupils or students prove their worth via engaging in oral and written tests, presenting papers, spinning in assignments and participating in class discussion.When students must have done their parts, teachers commence their own part by marking scripts, assignments, term papers, scoring them, recording them and submitting them for scrutiny.In other words, the combination of students and teachers efforts, mostly, determines students' success in academia.This is usually observed from the elementary to the highest level of education worldwide.Previously, students' educational success was frequently determined through "ear than today" Bella, (2016, p.1).At that point in time, tutors used to engage in observation to assess students' performance.In recent times, Cumulative Grade Point Average (CGPA) calculated numerically is often used to measure students' performance (Bella, 2016), probably due to the advancement in science and
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,967 | 0,944 |
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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».