Differentiated Learning Innovation Assisted by Wordwall Media in Mathematics Learning
Bibliographic record
Abstract
This study aims to examine differentiated learning innovation assisted by Wordwall media in mathematics learning in grade VI of state elementary school Banyumanik 03. The main problem identified is the low motivation and participation of students in mathematics learning materials. To overcome this, a differentiated learning approach was developed that adapts to the learning styles and needs of students, with the support of Wordwall as an interactive medium to increase engagement and enthusiasm for learning. This study used a qualitative method with a case study approach involving 28 sixth-grade students. Data were collected through observation, interviews, and analysis of learning outcomes. The results showed that the implementation of differentiated learning assisted by Wordwall significantly increased student motivation and participation in learning, as indicated by an increase in the average learning outcome score from 56 to 90. In addition, the use of Wordwall also helped teachers in classroom management and provided more effective feedback. Inovasi Pembelajaran Berdiferensiasi Berbantuan Media Wordwall dalam Pembelajaran Matematika Penelitian ini bertujuan untuk mengkaji inovasi pembelajaran berdiferensiasi berbantuan media Wordwall dalam pembelajaran matematika di kelas VI SDN Banyumanik 03. Permasalahan utama yang diidentifikasi adalah rendahnya motivasi dan partisipasi peserta didik terhadap materi pembelajaran matematika. Untuk mengatasinya, dikembangkan pendekatan pembelajaran berdiferensiasi yang menyesuaikan gaya dan kebutuhan belajar peserta didik, dengan dukungan Wordwall sebagai media interaktif guna meningkatkan keterlibatan dan semangat belajar. Penelitian ini menggunakan metode kualitatif dengan pendekatan studi kasus yang melibatkan 28 peserta didik kelas VI. Data dikumpulkan melalui observasi, wawancara, dan analisis hasil belajar. Hasil penelitian menunjukkan bahwa penerapan pembelajaran berdiferensiasi berbantuan Wordwall dapat meningkatkan motivasi dan partisipasi belajar peserta didik secara signifikan, yang ditunjukkan oleh peningkatan nilai rata-rata hasil belajar dari 56 menjadi 90. Selain itu, penggunaan Wordwall juga membantu guru dalam pengelolaan kelas dan pemberian umpan balik yang lebih efektif.
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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 | 0.001 | 0.002 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".