Management of Mathematics Learning Based on Interactive Digital Worksheets to Improve Students' Critical Thinking Ability
Bibliographic record
Abstract
The use of interactive digital technology in learning is an alternative strategy to create joyful learning activities. Therefore, interactive digital worksheet requires development by considering the critical thinking skills of elementary school students. This research determined the interactive digital media worksheet necessity based on critical thinking skills, compiling design, feasibility identification, and effectiveness test of the interactive digital media worksheet based on critical thinking skills for elementary school students. This research and development applied seven steps: (1) potential and problem identification; (2) data collection; (3) product design; (4) design validation; (5) design revision; (6) product test; and (7) product revision. The researchers collected the data with interviews, observations, questionnaires, and tests. The research results found the necessity of interactive digital student worksheets in managing mathematics learning to improve students' critical thinking skills. The interactive digital LKS design was based on critical thinking skills. The development considered the three components, such as (1) Planning; (2) implementing; and (3) evaluating. Planning consists of (1) the analysis of student characteristics; (2) the analysis of learning achievements (CP); (3) the determination of the learning objectives (TP) and the learning objective flow (ATP) and the assessment; (4) the selections of media, learning methods, and teaching materials. The implementation of the interactive digital student worksheets in learning was based on the problem-based learning model and formative and summative tests for the evaluation. The expert validation found the interactive digital worksheet was valid and reliable. The teacher responses reached the percentage of 87.5% while the student with 90%. The product test obtained an average of N-gain for the control group was 0.31 while the experimental group was 0.50, indicating an increment of students' critical thinking skills in mathematics. Thus, interactive digital worksheet implementation is useful in managing learning. The implementation was also useful for further development to manage various challenging activities and to realize meaningful 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 | 0.002 | 0.008 |
| 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 | 0.000 |
| 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".