The Development of Students’ Reading Comprehension Through Creative-Based Learning with Canva Application
Bibliographic record
Abstract
The aims of this study were 1) to investigate the effect of using CBL with Canva application on the reading comprehension performance 2) to determine the satisfaction level of students towards learning through CBL with Canva application and 3) to explore the experiences that the students have obtained with regard to their reading comprehension tasks using CBL with Canva application. The study involved 32 purposively selected students who studied at the third-year Vocational Certificate in Electronics at Kalasin Technical College, Kalasin, Thailand. Furthermore, this study employed a mixed-methods approach. For the qualitative part, the following research instruments were pre-test and post-test, a questionnaire on students’ satisfaction levels with learning through CBL with Canva application. For the qualitative part, it utilized the semi-structured interview. The data statistically analyzed and interpreted using the mean, standard deviation, and t-test. The data from the semi-structured interviews were analyzed using a thematic analysis. Results from the study revealed that there was a significant difference between the pre-test and post-test at 0.001 level of significance after using CBL with Canva Application was implemented. In addition, the percentage level of satisfaction with the intervention was high. Moreover, the interview revealed that the students consistently expressed their appreciation for the interactive, collaborative, and visually stimulating aspects of CBL with the Canva application. These results confirm that using CBL with the Canva application not only to enhance students’ reading comprehension performance but also increase experiences and promote positive attitudes towards learning English.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".