Experiential Learning for Education

After-school activities involving students’ personal interests/hobbies contribute to their holistic development, but often lack structures to attain the potentially impartable deeper learning, compounded by competing demands from formal curricula and perceived intangibility of benefits from such activities. Efficacy of a novel Experiential Learning courses, referred to as Deeper Experiential Engagement Projects (DEEP), which has been designed to address this challenge is studied.
In addition, a digital coaching environment is designed and developed for DEEP, to prototype a model of co-evolution of education with Artificial Intelligence. This is meant to complement the structural changes that our study has identified, that institutions may introduce for scaling up such pedagogical practice.​
Sze Chun ChauLEAD PI
Dr Sze Chun Chau
Director, Student Experiential Learning
Senior Lecturer
Assistant Chair (Undergraduate Education)

Email: [email protected]
Office: SSC-04-02L/ SBS-03N-38


Design and development of digital coaching environment for Deeper Experiential Engagement Projects: co-evolution of education with Artificial Intelligence.
  • Xiao, Y., H. Yaohari, Z. Zhou, C.C. Sze and D.C. Stuckey, Autoinducer-2-mediated quorum sensing partially regulates the toxic shock response of anaerobic digestion. Water Research, 2019 (in Press: https://doi.org/10.1016/j.watres.2019.04.024).
  • C.C. Sze, Stimulating Innovative Thinking Through Campus Life. Editorial by invitation, Bulletin, Association of Commonwealth Universities, 2017. 191: p4-5.
  • Xiao, Y., H. Yaohari, C. Araujo, C.C. Sze and D.C. Stuckey, Removal of selected pharmaceuticals in an anaerobic membrane bioreactor (AnMBR) with/without powdered activated carbon (PAC). Chemical Engineering Journal, 2017. 321: p. 335–345
  • Miao, H., S. Ratnasingam, C.S. Pu, M.M. Desai and C.C. Sze, Dual fluorescence system for flow cytometric analysis of Escherichia coli transcriptional response in multi-species context. Journal of Microbiological Methods, 2009. 76(2): p. 111-119