Assessment of Female University Students’ Digital Competence: Potential Implications for Higher Education in Africa
Bibliographic record
Abstract
This study assessed the digital skills of female university students and the implications for higher education in Africa. A descriptive survey was used to sample 100 female university students from four African countries (Nigeria, Rwanda, South Africa, and Uganda). The instrument used was the digital competence survey. Two research questions and two hypotheses were postulated and tested. According to the study's findings, most female university students in Nigeria and South Africa have expert and advanced levels of information and digital literacy, communication and collaboration, digital content creation, and safety.On the other hand, Uganda was mainly found at the basic or no levels, whereas Rwanda was mostly found at the intermediate levels. The chi-square analysis reveals a significant difference between the ages of female university students and their DC levels (χ2 =.000; p < 0.05). A significant difference exists between female university students’ program of study and their levels of DC (χ2 = .000; p < 0.05). Students also faced challenges such as a lack of ICT tools, insufficient knowledge and skills, data issues, and poor internet connectivity. The implications of these findings for African higher education institutions suggest that female students, particularly in Rwanda and Uganda, require training to be digitally competent and compete globally with their peers. As a result, we recommend that students from different programs of study with less demand in technology be allowed to take compulsory electives in technology courses while older female students are given adequate support.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".