An Imperative for the National Public Health School in Burkina Faso to Promote the Use of Information and Communication Technologies in Education During the COVID-19 Pandemic: Critical Analysis (Preprint)
Bibliographic record
Abstract
BACKGROUND Several studies have reported the positive impact of information and communication technologies (ICTs) on academic performance and outcomes. Although some equipment is available, the ICTs for education at the National Public Health School (NPHS) of Burkina Faso have many shortcomings. These shortcomings were clearly revealed during the search for responses to the crisis caused by the COVID-19 pandemic. Indeed, to curb the spread of COVID-19, some measures were taken, such as closure of educational institutions. This resulted in a 2.5-month suspension of educational activities. Despite its willingness, the NPHS was unable to use ICTs to continue teaching during the closure period of educational institutions. OBJECTIVE In this paper, we aim to propose practical solutions to promote ICT use in teaching at the NPHS by analyzing the weaknesses and challenges related to its use. METHODS We conducted a critical analysis based on information from the gray literature of NPHS. This critical analysis was preceded by a review of systematic reviews on barriers and facilitating factors to using ICTs in higher education and a systematic review of ICT use during the COVID-19 pandemic in higher education. An ICT integration model and a clustering of ICT integration factors guided the analysis. RESULTS The weaknesses and challenges identified relate to the infrastructure and equipment for the use of ICTs in pedagogical situations in face-to-face and distance learning; training of actors, namely the teachers and students; availability of qualified resource persons and adequate and specific financial resources; motivation of teachers; and stage of use of ICTs. CONCLUSIONS To promote the use of ICTs in teaching at the NPHS, actions must be performed to strengthen the infrastructure and equipment, human resources, the skills of actors and the motivation of teachers in the pedagogical use of ICTs.
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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 | 0.063 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".