Increasing Mathematical Logical Intelligence Through RME Model Assisted by DIMSMOVE Based on Local Excellence on the North Coast of Java, Indonesia for Students with Intellectual Disabilities
Bibliographic record
Abstract
The low logical mathematical intelligence of students with mild intellectual disabilities is caused by several factors: the learning process is still classical with the lecture method; the less than optimal use of learning media and teaching materials based on the surrounding cultural environment; the unavailability of concrete and interactive teaching materials that are adjusted to the cognitive needs of students with mild intellectual disabilities; and the unavailability of Android-based applications that help students learn the material anytime and anywhere, so that they can facilitate their ability to remember and understand the material. This research was conducted at the Sunan Kudus Special Elementary School located on JL Mayor Kusmanto RT 4 RW 3 Pedawang, Bae District, Kudus Regency, Central Java Province, Indonesia. This school was chosen because of the low level of students' logical mathematical intelligence and the lack of use of Android-based learning media.The main objective of this study was to analyze the effectiveness of the Realistic Mathematics Education (RME) model assisted by the Digital Mathematics Science Module With Voice Recording (DIMSMOVE), based on local advantages on the North Coast of Java, Indonesia, in improving the logical mathematical intelligence of students with mild intellectual disabilities. The results of the study showed that the average logical mathematical intelligence score of students using the RME model supported by interactive digital modules based on local excellence exceeded the Minimum Completion Criteria (KKM), and there was a significant increase in their logical mathematical intelligence. The level of effectiveness was categorized as moderate based on the N-Gain test, which showed that the RME model and the digital media developed were effective in supporting the learning and memory of students with mild intellectual disabilities. This study contributes to the field of inclusive education by introducing an innovative learning model that integrates RME with a culturally relevant and voice-supported digital module (DIMSMOVE). The combination of local cultural content and interactive technology offers a contextual, inclusive, and adaptive learning solution for improving cognitive competence in students with intellectual disabilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".