Speakers

Prof. Zehui Zhan, South China Normal University, China

詹泽慧教授，华南师范大学，中国

Research Area:  Learning science, STEAM, innovation and entrepreneurship education

研究领域：学习科学，STEAM，创新创业教育

Title: C-STEAM Education and Technology-supported Collaborative Innovation

Abtract: 

C-STEAM is a typical kind of transdisciplinary education, with the goals of inheriting outstanding traditional culture and fostering learners’ STEAM competency, which mainly has three potential core values: (1) the educational value of cultivating students’key competences; (2) the carrier value of inheriting traditional culture; (3) the social value of booming regional culture.  In this presentation, the C-STEAM concept model and the related cases applied in primary and middle schools would be introduced (e.g., the Wooden Arch Bridge C-STEAM case, the Cantonese Slang C-STEAM case, the Dragon Boat C-STEAM case, the Ceremic Lights C-STEAM case, the P-CAR model, the Cultural Guangzhou C-STEAM case, etc.). Besides, technology also supports the cross-regional C-STEAM collaboration.

Prof. Xiaopeng Shao, Xidian University, China

邵晓鹏教授，西安电子科技大学，中国

Research Area:  Computational Imaging

研究领域：计算成像

Title: Development status and future trend of Computational Imaging

Abstract: 

Traditional photoelectric imaging technology was developed in the industrial age and plays an important role in many detection and measurement app lications. However, it is difficult for traditional imaging technologies to achieve further detection distances and higher resolution. Computational imaging technology (CIT) integrates optics, mathematics, and information technology, breaking through the limits of physical information through optical encoding and decoding. CIT changes the traditional linear optical information transmission theory, makes full use of information channels, and improves information dimension.

The report deeply analyzes the development status of CIT, and summarizes its connotation. And a nonlinearity and complex field transformation imaging model is proposed. Furthermore, the fundamental commonality problems and key technologies to be solved such as longer distance, higher resolution, larger field of view, stronger environmental adaptability, and smaller size of the imaging system are analyzed. The corresponding key issues of super-large aperture imaging system, Sub-diffraction limit imaging, bionic optics, interpretation of light field information, computational optical system design and computational detector design are expounded. The corresponding ideas and thoughts are also put forward in the report.

Prof. Yonghong Wang, Hefei University of Technology, China

王永红教授，合肥工业大学，中国

Research Area:  Optical precision measurement, Machine vision and Image processing 

研究领域：光学精密测量，机器视觉与图像处理

Title: Developments and Application of Digital Speckle Technique

Abstract: 

With the rapid development of equipment and manufacturing industry, accurate measurement of mechanical properties of materials and structures has increasingly become an urgent need in such important fields as aerospace, advanced manufacturing, national defense, automobile and ship. Three dimensional deformation/strain/equivalent mechanical properties are key parameters for material performance testing and structural reliability analysis. Digital speckle measurement technology has the advantages of non-contact, high-precision, full field measurement, etc. It is an ideal tool for three-dimensional displacement/deformation/strain measurement. This speech reports the research work and technical progress of the full field speckle light measurement laboratory in digital speckle measurement technology and its deformation/defect detection.

Prof. Paul Prinsloo, University of South Africa, The Republic of South Africa

Paul Prinsloo教授，南非大学，南非共和国

Research Area: Distance education, Student success in open and distributed learning environments, The collection, analysis and use of student data, Learning analytics, Postgraduate supervision

研究领域：远程教育，开放和分布式学习环境下的学生成功，学生数据的收集、分析和使用，学习分析，研究生监督

Title:  The ethics of (not) using student data to improve teaching and learning

Abstract: 

Higher education has always collected, analysed and used student data - whether to assess students' progress, to identify students who may be at risk of failing or in need of additional support, and/or for planning and operational purposes. As higher education is increasingly digitised and datafied, institutions have access to not only more student data, but also a greater variety and nuance and velocity of data, often in real-time. Having access to students' learning data creates wonderful opportunities to adjust our pedagogical and student support strategies to deepen students' learning experiences and improve our teaching. While the collection, analysis and use of student data raise some important ethical issues that we should not ignore, we should equally consider the ethical implications of not using their learning data. This presentation will map the potential and some of the ethical issues in the collection, analysis and use of student data.