Optimizing Transdisciplinary Epistemic Knowledge Through STEAM+X-Integrated Epistemic Learning Patterns in Mathematical Comparison

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Low transdisciplinary epistemic knowledge is one of the problems in mathematical comparison learning. Therefore, this study aims to optimize this competency through the development of a teaching module based on epistemic learning patterns integrated with STEAM+X. This study used a design-based research design. The instruments used were a practical problem questionnaire, a transdisciplinary epistemic knowledge test, and a teaching module. The participants were 36 people, consisting of 15 students (aged 13–15 years) and 21 educational stakeholders (teaching experience 0–25 years). Qualitative data were analyzed using thematic analysis, while quantitative data were analyzed using the Wilcoxon Signed Rank Test. The results revealed that identity and orientation crises, lack of spatial experience, pedagogical mismatches, and ecosystem limitations were the contributing factors to low student competency. The learning principles expected to be optimized were student-centered learning, technology integration, and learning ecosystem support. ELP-STEAM+X-AI was then implemented because it aligned with the previous principles. The study concluded that the learning was able to optimize transdisciplinary epistemic knowledge (Z = -2,96; p = 0,003; and r = 0,76 with large impact) because students were facilitated in constructing mathematical comparison concepts through various STEAM+X-based epistemic activities. This finding recommends that other subjects adopt ELP-STEAM+X-AI to optimize student competency.

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