A Survey on Acceptance and Readiness to Use Robot Teaching Technology Among Primary School Science Teachers
Bibliographic record
Abstract
Interest in educational robotics has grown in recent years, and many efforts have been undertaken across the globe to include robots into school instruction from kindergarten to high school, mostly in science and technology subjects. The current study is to determine teachers' technological acceptance and readiness to implement robotic technology in the teaching and learning process. A descriptive research design was employed which utilized a survey method. This survey was conducted among primary school teachers of Science, Mathematics, Design and Technology, and Information and Communication Technology (ICT) in Malaysia. According to the findings, teachers' acceptance of robot technology in the classroom is at a modest 3.77 (SD = 0.598) while the readiness score is 3.67 (SD = 0.611). The findings indicated that school teachers are only moderately prepared to employ robotic technology in classrooms. Respondents also argued that the high cost of robotic technology is a significant barrier to incorporate robotic technology into teaching and learning. The practicality of this paper is the provision of insights for exploring adoption possibilities and barriers in auguring robots into primary school classrooms. This indicates that the higher the level of teachers’ acceptance, the higher teachers’ readiness in robotic technology. Respondents argued that the high cost of robotic technology is a significant barrier to incorporating robotic technology into teaching and learning.
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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 | 0.003 | 0.009 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".