Assessing the Use of YouTube Videos and Interactive Activities as a Critical Thinking Stimulator for Tertiary Students: An Action Research
Bibliographic record
Abstract
The purpose of this action research was to investigate the use of YouTube videos and interactive activities in stimulating critical thinking among students from a public university in Malaysia. There were 50 students of mixed background, comprised of local and foreign students who participated in this study which lasted for one semester. Data was collected using a few approaches which included video recording of the lessons, students’ and researcher’s reflections and role play. In this paper, we specifically focus on the students’ reflections of their experience while using YouTube videos. Thematic analysis was conducted to examine the themes that emerged in their reflections. Using Lewin’s Action research model supported by Constructivism Theory, a-four stage action research consisted of planning, acting, observing and reflecting were conducted. We found that YouTube Videos were fun and interesting, increased students’ participation and engagement and enhanced their critical thinking skills. The students were able to participate actively and demonstrated strong interest in the learning process as they were able to understand lectures better by visualizing the content and relating it to real workplace. Our study revealed the potential of YouTube video as a tool for stimulating students’ learning and enhancing their critical thinking.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".