A Hybrid Framework for Digital Experimental Reports: Integrating Chaoxing Platform in Experimental Education
The traditional approach to experimental teaching often involves cumbersome, paper-based reporting, which can be time-consuming, repetitive, and lacks the interactive qualities necessary for engaging students effectively. This paper presents a novel approach to digitalizing university physics experimental reports using the Chaoxing Learning Platform. By integrating digital components into the experimental reporting process, this study aims to streamline experimental documentation, improve student engagement, and enhance the overall quality of experimental education. The proposed design includes a three-part structure comprising a Pre-Experiment Report, an Experiment Record Page, and a Post-Experiment Summary Report. The methodology is based on constructivist learning theory, Outcome-Based Education (OBE), and the BOPPPS model (Bridge-in, Objective, Pre-assessment, Participatory Learning, Post-assessment, Summary) to provide a comprehensive framework that supports student-centered learning. The results demonstrate the potential benefits of this approach in improving learning outcomes, enhancing digital literacy, and fostering active participation. This study concludes by discussing the broader implications of digital integration in experimental education and proposing future research directions, including the potential scalability of the approach to other experimental sciences.
Authors
- Chowwalit Chookhampaeng
- Jiraporn Chano (ORCID: https://orcid.org/0000-0002-2527-4038)
- Dingnan Deng (ORCID: https://orcid.org/0000-0003-4528-1064)
- Jingming Luo
- Weizhi Yang (ORCID: https://orcid.org/0009-0009-8191-3891)
- Hai-Ning Zhang (ORCID: https://orcid.org/0000-0002-4573-6746)
- Qing‐Hu Zhong
- Ruijie Xiao (ORCID: https://orcid.org/0000-0001-7213-3433)
Institutions
- Mahasarakham University (TH)
- Jiaying University (CN)
Publication Details
- Published
- 2024-12-27
- DOI
- https://doi.org/10.1109/iceet65156.2024.10913948
- Primary Topic
- Scientific Computing and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00