Professor Eric Cheng began his
professional career as a secondary school teacher before entering
higher education. He holds a Doctor of Education in Education
Management from the University of Leicester. He previously served as
Vice President (Academic) at Yew Chung College of Early Childhood
Education and as Associate Dean (Quality Assurance & Enhancement) of
the Faculty of Education and Human Development at The Education
University of Hong Kong, where he also held academic appointments
over many years.
Professor Cheng has an established record of scholarship in
AI-enhanced pedagogy, metacognitive teaching, knowledge management,
intellectual capital, and lesson and learning studies. He has
published over 100 peer-reviewed works and secured competitive
funding for research and development projects. In recognition of his
scholarly impact, he was named among the World’s Top 2% Most-Cited
Scientists by Stanford University in 2025.
Professor Cheng is a registered Specialist in the programme
accreditation area of Education at The Hong Kong Council for
Accreditation of Academic and Vocational Qualifications, a Member of
the Institute of Electrical and Electronics Engineers and a Member
of the Executive Council of the World Association of Lesson Studies.
He has also contributed to school governance as an Independent
School Manager for local schools under different sponsoring bodies.
His research, leadership, and professional service have helped
advance educational innovation and academic quality in Hong Kong and
beyond.
Research Self-Efficacy and Pedagogical Design in Robotics and STEM Education
As educational robotics and integrated STEM curricula expand rapidly, equipping future educators with rigorous educational research capabilities is essential. This study investigates the development of research competence and self-efficacy of postgraduate students. Employing a convergent mixed-methods design, the study evaluates students' learning trajectories across four core dimensions: methodological design (including Design-Based Research and action research), assessment of cognitive and affective outcomes, literature synthesis, and research ethics in technical makerspace settings. Quantitative data were collected via pre- and post-course surveys measuring Research Self-Efficacy (RSE) and rubric-based grading of core course deliverables, including a literature review and a full research proposal. Qualitative insights were gathered through focus group interviews, student proposal content analysis, and reflective reports on the transparent use of Generative Artificial Intelligence (GenAI) in the research process. Findings from paired-samples analyses and thematic coding demonstrate significant gains in students’ methodological confidence and critical evaluation skills, alongside identifying common pedagogical hurdles in designing empirical frameworks for makerspace environments. This research offers empirical implications for engineering education and educational technology curricula, providing a validated model for preparing educators to conduct structured, classroom-based STEM and robotics research.
Keywords:
Robotics Education, STEM Education, Engineering Education, Research
Methods, Research Self-Efficacy, Higher Education, Educational
Technology
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