Exploring the Impact of CPS-Based Robot-Assisted Teaching in STEM Education: Enhancing Knowledge, Skills, and Attitudes

摘要

AbstractCyber-physical systems (CPS), along with information technology devices such as portable devices and robots, materials including knowledge contents, and Internet resources, can be integrated into curricula to guide learners in observing and experiencing STEM (science, technology, engineering, and mathematics) knowledge and skills. This integration provides opportunities for learners to cultivate their abilities to design creative works. The objective of this study was to investigate the impact of a robot-assisted teaching and learning system in STEM education on learners' academic knowledge, skills, and attitudes. To achieve this objective, an experimental design was employed, with 30 college students assigned to learn and create a Drawdio electronic sound synthesizer using robot teachers and tablet computers. Pre- and post-tests were administered to measure students' academic achievement in STEM, and a survey was conducted to assess their attitudes toward STEM. The findings of the study indicate that the combination of robot teachers and tablet computers served as a supplementary learning resource within the learning environment. It accelerated knowledge acquisition, expanded hands-on experiences, and improved engagement and motivation among the learners. These results suggest that robot-assisted teaching and learning systems have the potential to enhance learning outcomes and attitudes among students. However, it is important to note that human teachers still outperformed robot teachers in certain aspects, particularly in higher levels of knowledge acquisition according to Bloom's taxonomy. Therefore, this study suggests the adoption of a collaborative model involving both robot and human teachers to effectively promote STEM education in the classroom.Keywords: Cyber-physical system (CPS)human-robot interaction (HRI)multimodalnarrative-based knowledge translationrobot-assisted learning (RAL) AcknowledgmentsThis research received partial support from the National Science and Technology Council, Taiwan, under project number NSTC 112-2410-H-036-002, as well as from the Ministry of Education (MOE) in Taiwan under project numbers PBM1120352 and 1111N0059.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsChih-Chien HuChih-Chien Hu is an Assistant Professor in the Department of Information Management at Tatung University, Taiwan. He worked as a researcher at ITRI from 2009 to 2019. His current interests are centered around technology-enhanced learning, context-aware systems, and multi-criteria decision-making methods.