The Negative Impact of the Succession of Crises and the Ineffectiveness of the E-learning System on Tertiary Education in Sudan from (2018) to Present
Notice bibliographique
Résumé
This study attempts to determine the social, economic, and psychological impacts of the 2018 temporary closure of educational institutions in Sudan amid several internal incidents and the ongoing closure due to the COVID-19 pandemic on students, teachers, and families. Most educational systems worldwide were temporarily closed and negatively affected. Nevertheless, it seemed as if the crises in Sudan extremely damaged the process of the overall educational system simply because the closure of the institutions initially began as a result of several internal incidents by the end of the academic year 2017-2018. The closure lasted until August 2019, when schools were reopened, and within almost six months; again, a decision was made in February 2020 for the entire closure of educational institutions due to the COVID-19 pandemic and continued for more than one and a half years. The impact of total closures of universities and colleges in Sudan affected students' academic achievement in different ways because the situations in Sudan were primarily different before the spread of COVID-19. Therefore, the negative implications of the long–term closure were greater not only on the students' academic achievement but also on the teachers’ sources of income, which resulted in economic issues for many families. To undertake this study, both quantitative and qualitative research methodologies were used. The researchers designed and distributed a questionnaire to a sample of 39 Sudanese university teachers to examine their attitudes towards the impact of the several internal incidents behind the closure of the entire educational institutions on overall academic achievement and online education as an alternative to face-to-face or traditional teaching. Although very few universities launched e-learning units during the last two decades, it seemed as if their purposes were very limited and mainly designed to serve a few students under certain conditions. Additionally, the researchers observed the efficient application of the e-learning educational system during the COVID-19 pandemic, represented by the Blackboard platform at both Qassim University and Prince Sattam Bin Abdulaziz University. The data analysis resulted in some significant findings, among which are the following: First, students were regularly paying the price of the poor infrastructure that contributed to preventing the application of an effective e-learning system in Sudan. Second, the long–term closure throughout 2018 has resulted in the accumulation of several student batches and generally complicated the scene. Third, the long–term closure influenced university students in different ways: academically, socially, economically, and psychologically.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,000 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».