Developing Contextual Mathematical Thinking Learning Model to Enhance Higher-Order Thinking Ability for Middle School Students
Bibliographic record
Abstract
The purpose of this research is to develop contextual mathematical thinking learning model which is valid, practical and effective based on the theoretical reviews and its support to enhance higher-order thinking ability. This study is a research and development (R & D) with three main phases: investigation, development, and implementation. The experiment consisted of 78 Junior High School students who were divided into two groups, namely experimental group and control group. The model development phase results the syntax of contextual mathematical thinking learning model which are as follows: (1) presentation of the contextual problems; (2) asking the critical and analytical questions; (3) individual and group investigation; (4) presentation and discussion; (5) reflection; and (6) higher-order thinking test. The implementation phase concludes the contextual mathematical thinking learning model which can be applied effectively to enhance the students’ higher-order thinking ability. This model is able to intensify higher-order thinking ability at high category. The observation of learning activities was seen in the main elements of learning model which are syntax, social system, reaction principle, support system, instructional impact, and accompanist impact. The three main elements were observed by the observer and showed an average in the good category: syntax has an average of 3.5, social system has an average of 3.52, and reaction principle has an average of 3.47. This model is recommended for mathematics learning activities in the classroom to support the improvement of higher-order thinking ability. Contextual problems can be presented to the local cultural context that allows students to learn mathematics in a real context.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".