Curriculum Delivery Through Learning Technologies in Online Classrooms: Challenges and Prospects in Higher Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Higher education institutions in most developing African nations have been classroom-based. This practice has been in place for decades in African countries, with many benefits for stakeholders. Lessons from the COVID-19 pandemic experiences in the global space brought a new approach to curriculum delivery. Universities in most developed countries have expanded, using various digital technologies for teaching and learning. However, the case was a severe challenge in Africa, where many were cut off from teaching and learning activities for months. The study explored the transition from conventional classroom curriculum delivery to online learning as the only alternative approach during the pandemic. Although online learning encourages self-directed learning in students, the study explains the self-determination theory as it underpins online learning. A content analysis of various literature sources on the phenomenon was employed for this systematic review. Findings revealed that many universities in South Africa encountered severe challenges in fully adopting online classes for curriculum delivery. Teaching and learning activities were grounded for months until the Department of Higher Education and Training compelled all to embrace learning technologies to salvage the academic calendar. Rural-based students were reported to be significantly challenged in accessing online learning activities. affirmExtant literature sources affirmed that higher education institutions were unprepared for the sudden transition from conventional classrooms to online learning. Hence, they needed help to take rapid measures to integrate online learning into the system. Many challenges have been encountered in this technological transformation of the teaching and learning approach; the study, therefore, recommends, among others, adequate provision of learning technologies, provision of intense technical know-how support for lecturers for effective use of online learning.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it