Mu-Sheng Chen
Papers
2
Total Citations
31
H-Index
2
About
Mu-Sheng Chen is a leading researcher at the intersection of computational thinking (CT), artificial intelligence (AI) education, and game-based learning. Their work focuses on designing innovative pedagogical approaches that integrate emerging technologies—such as personal audio classifiers and board games—to foster students’ engagement, creativity, and self-regulated learning. Chen’s most-cited study (2022, 18 citations) explores how students learning to control robot cars with a personal audio classifier within a CT board game enhances their creative thinking and learning achievement. A more recent influential work (2024, 13 citations) compares experiential learning cycles with self-regulated learning in the context of AI applications, demonstrating how game-based environments can effectively cultivate both CT and AI literacy. By bridging hands-on robotics, audio classification, and structured learning frameworks, Chen has made notable contributions to K-12 STEM education, offering practical tools for teachers to make abstract computing concepts tangible and engaging. Their research is widely cited for its empirical rigor and innovative integration of AI into classroom practice, positioning Chen as a key voice in modern computational education.
Research Focus
Key Achievements
Top Papers
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