SmaEPho–Smart Photometry in Education 4.0

• Published in: Education sciences Vol. 13; no. 2; p. 136
• Main Authors: Geuer, Lena, Lauer, Frederik, Kuhn, Jochen, Wehn, Norbert, Ulber, Roland
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: Algorithms; Analysis; Data mining; Digital twins; Digitization; Education; Educational Innovation; Educational Objectives; Educational research; Educational technology; Experiments; Germany; Information management; Inquiry method; intelligent environments; Laboratories; learning analytics; Learning Processes; Machine learning; Mathematics education; Measuring instruments; Problem solving; Science education; smart classrooms; STEM education; Students; Teaching; Teaching Methods; Technology education; technology-enhanced learning; Twins; Usability; Visualization (Computers); Germany
• ISSN: 2227-7102; 2227-7102
• DOI: 10.3390/educsci13020136

Digitization offers new perspectives for educational research to identify the effects of visualizations regarding cognitive processing. In addition, new types of data can be generated, expanding the possibilities for visualizing cognitive processes and understanding human learning. Digital twins are already used in Industry 4.0, as an additional visualization to a real object, for data mining and data analysis for process optimization. The increasing integration of digital twins in the industrial sector requires the formulation of corresponding educational goals to ensure high-quality and future-oriented education. Therefore, future generations must be introduced to technologies from industry during their education. In this paper, an intelligent photometric measurement system called SmaEPho with a digital twin for science, technology, engineering, and mathematics (STEM) learning is presented. In addition to its function as a photometric measurement device, an intelligent sensor technology allows for data generation on the user’s usage behavior. The digital twin reflects and visualizes these data in real-time. This enables a variety of new didactic and methodological approaches in teaching. A first study evaluating the hardware and tracking components of SmaEPho shows that the deviation accuracy of the measurement system is sufficient for experimental applications in schools. Another study with n=52 students confirmed the excellent usability of the SmaEPho hardware platform. These research results lay the foundation for a variety of future research questions on data analysis and machine learning algorithms with the aim of increasing the quality of education. The use of intelligent digital twins as an element of digitization in educational contexts offers the extended possibility of identifying cognitive processing steps using this technology.