DESIGN THINKING IN MATHEMATICS EDUCATION FOR PRIMARY SCHOOL: A SYSTEMATIC LITERATURE REVIEW
Bibliographic record
Abstract
Design Thinking is a knowledge as a thriving innovation practice and an approach to creative problem solving. The main purpose of this study is to review existing studies which are related to the Design Thinking in Mathematics Education for primary school. The search terms were used by inserting the suitable keywords based on the main topic such as "design thinking", "mathematics education", "mathematics", "primary school" and "elementary school". Systematic Literature Review (SLR) was conducted to gain information for better understanding regarding our topic. This SLR was performed through two search engines which were SpringerLink and Scopus. In reporting this study, the PRISMA guidelines were followed. We identified and screened 1123 articles published between 2017 until 2021 in SpringerLink and Scopus. After elimination of duplicates and non-relevant topics, there were 23 articles remained based on the inclusion and exclusion criteria. The limited number of studies on the main topic as design thinking for primary school caused the small numbers of articles were selected. This is because there are many articles about teachers and post-graduate. Our findings indicated that the year of 2021 was mostly research conducted, followed by the year of 2020, 2019 and 2017 meanwhile there is no research conducted in 2018. Concerning the geographical distribution of the authors, the findings indicated that the predominant authors that developing the design thinking were in Germany, Australia, USA, Singapore and Switzerland, and only a few research conducted in Hong Kong, Sweden, Cyprus, Israel, Turkey, UK, Spain, Canada, Netherlands, Malaysia, and mixed countries. From the results, we can see that this design thinking should be practiced by teachers in their teaching regardless of whether it is mathematics or any other subject. In conclusion, design thinking among the students, especially primary school students nowadays, is indispensable to ensure that our country is always moving forward in the era of globalization.
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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 | 0.018 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.027 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".