Development of Student Worksheets Integrated with Microlearning Comics for Learning Probability
Bibliographic record
Abstract
Numerous studies have shown that students face difficulties in learning probability. This study aimed to enhance students’ understanding of the concept of probability while developing their reasoning skills by integrating comics into student worksheets. It focused on designing probability material for grade 10 using the student worksheets and microlearning (comics) that were both effective and efficient. The material was developed using Pendidikan Matematika Realistik Indonesia (PMRI) approach, incorporating a microlearning method within the context of culinary tourism, to enhance students’ understanding of probability and reasoning skills. This study employed a design research methodology in two stages, namely preliminary study and formative evaluation. The subjects of this study were 34 students of grade tenth at Senior High School in Prabumulih. Data collected through observations, tests, and interviews were analyzed descriptively. The study resulted the student worksheets on probability, which were incorporated with microlearning comics whose effectiveness and efficiency were aligned with the characteristics of the PMRI approach. Based on the findings, it can be concluded that the PMRI-based student worksheets that were incorporated with microlearning comics were efficient and effective in helping students develop their reasoning skills. The integration of comics and the PMRI approach reflects a commitment to innovation and the continuous development of effective learning designs that promote inclusive, equitable, and meaningful learning experiences.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".