Understanding teachers’ usage of YouTube as a pedagogical tool: A qualitative case study of basic school teachers in Ghana
Bibliographic record
Abstract
YouTube has been widely considered as a pedagogical tool over the last few decades. Recent findings from research portray YouTube videos as an instructional part of learning that contributes to best practices in teaching. Much of the studies have focused on usage by teachers and students at the tertiary level without much attention given to basic school teachers. Using an exploratory qualitative case study design and the ICT Pedagogical Beliefs Classification framework, we explored teachers’ usage of YouTube as a pedagogical tool. We drew on the experiences of 18 teachers in 3 private schools in Ghana to find out how Youtube was used in instruction. Four dominant ways of usage were identified. YouTube was used as a teaching tool, a means of enhancing specific topics, a means of learning new and varied ways of teaching, and as a means of developing teachers’ professional competence. Findings showed that whilst some of the ways of usage align with constructivist methods of teaching others still fall within traditional and teacher centred approaches to teaching. We argue that for teachers to enact meaningful pedagogies with technology through a more student-centred model, their knowledge and skills need to be developed alongside the reshaping of their motives and reasons and subsequently the ways in which they use technology for teaching. We recommend the planning and reinforcement of innovative instructional design of technological integration for teachers through informed policy and the use of training interventions to build new skills geared towards constructive and creative teaching suited to developing a net generation of students.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".