| Issue |
EPJ Web Conf.
Volume 344, 2025
AI-Integrated Physics, Technology, and Engineering Conference (AIPTEC 2025)
|
|
|---|---|---|
| Article Number | 01038 | |
| Number of page(s) | 7 | |
| Section | AI-Integrated Physics, Technology, and Engineering | |
| DOI | https://doi.org/10.1051/epjconf/202534401038 | |
| Published online | 22 December 2025 | |
https://doi.org/10.1051/epjconf/202534401038
Mapping secondary school students’ botanical literacy using rasch analysis to support deep learning in science
1 Science Education Program, Universitas Trunodjoyo Madura, Bangkalan, East Java, Indonesia
2 Primary Teacher Education Program, Universitas Trunodjoyo Madura, Bangkalan, East Java, Indonesia
* Corresponding author: eneng.sururiyatul@trunojoyo.ac.id
Published online: 22 December 2025
The Merdeka Curriculum emphasizes deep learning in science education, where students connect ideas, reason scientifically, and apply concepts to real-life contexts. However, many students still demonstrate basic understanding, especially in understanding plant concepts. The contribution of this study is to provide a diagnostic profile of students’ botanical literacy abilities that can guide teachers in enhancing in-depth learning. This study aims to map secondary school students’ botanical literacy using Rasch analysis to support the implementation of deep learning in science education. A total of 253 ninth-grade students participated in a 30-item botanical literacy test covering five aspects of plant knowledge. Data were analyzed using the Rasch Model through Winsteps software. The analysis produced a Wright Map showing item difficulty ranging from basic recall (easy) to higher-order thinking (difficult). This finding indicates that the instrument effectively measures multiple botanical literacy levels. Furthermore, data obtained showed that most students had moderate botanical literacy abilities across all aspects. This suggests that students’ understanding of plants remains at a basic level. Mapping through Rasch analysis provides diagnostic insights for teachers to identify learning gaps and plan lessons that promote conceptual understanding and reasoning, which are essential components of deep learning in science.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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