ICT Teachers’ Acceptance of “Scratch” as Algorithm Visualization Software
Bibliographic record
Abstract
This study aims to investigate the acceptance of ICT teachers pertaining to the use of Scratch as an Algorithm Visualization (AV) software in terms of perceived ease of use and perceived usefulness. An embedded mixed method research design was used in the study, in which qualitative data were embedded in quantitative ones and used to explain the results. The data were collected from 214 pre-service ICT teachers studying in four large public universities. Data was gathered through a questionnaire adapted from David’s Technology Acceptance Survey (1989) and through open-ended questions. T-test and Pearson correlation, as well as descriptive statistics, were used to analyze quantitative data and constant analysis techniques were used to analyze qualitative data. Both kinds of data were mixed and are presented in the results section. The results show that pre-service ICT teachers mainly have positive and similar Scratch acceptance scores in terms of usefulness and ease of use. The factors explaining participants’ perceived usefulness are identified as visual interface (37%), pedagogy(36%), and computational thinking (27%). The majority of the participants also found Scratch to be easy to use. Pre-service ICT teachers explained that what makes AV software easy to use is color separation (40%), drag and drop (30%), and familiar interface (30%). Additionally, no significant difference between the acceptance scores of the participants was found in terms of gender, years of programming experience, programming background, and the high school they graduated from as indicators of programming experience. Results congruent with previous studies regarding Scratch were found by the current study.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".