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
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".