Dr Jinxin ZHU is an Assistant Professor at the Department of Curriculum and Instruction at The Education University of Hong Kong (EdUHK). He received a bachelor’s degree from Beijing Normal University majoring in Mathematics and Applied Mathematics and a Ph.D. from EdUHK majoring in Educational and Psychological Measurement. Before re-joining EdUHK as an Assistant Professor, he worked as a Research Assistant Professor at the Department of Education Studies at Hong Kong Baptist University, a manager at the Assessment Technology & Research Division in the Hong Kong Examinations and Assessment Authority, a post-doctoral fellow at Assessment Research Centre at EdUHK, and a mathematics teacher at Fuyong Secondary School in Shenzhen, Guangdong, Mainland China.

His research focuses on digital assessment and learning, aiming to reduce teachers’ workload with digital tools allowing them to have more time and energy to help students to take an active role in the assessment and feedback process and become self-directed learners and thereby lifelong learners. Meanwhile, he applies rigorous data analytic methods (e.g., structural equation modeling, Rasch model, and multilevel modeling) to educational studies.

Research Interests:

• Digital Assessment and Learning
• Data Analytics in Education
• Mathematics/STEM Education
• Self-regulated Learning
• Civic Education
• Special Education

Accept PhD/EdD students:

• Please get in touch with Dr Zhu via email (jxzhu@eduhk.hk) with

1. a cover letter stating your understanding of educational research and long-term career plan,
2. a research proposal,
3. a CV (please specify your research experience, English level; specify the language of the publication, if any), and
4. other supporting documents such as transcripts of bachelor's and master’s degrees, certificates, published articles, awards, and other documents that might be helpful.

An informal test and an informal interview may be arranged before the formal application.

• Preferred research areas include but not limited to Digital/Online Assessment and Learning and Mathematics/STEM Education.
• Willingnessto learn or readiness to apply advanced quantitative research methods using Mplus or R and qualitative analysis using NVivo is desirable.
• Responsible, fast learning, self-motivated, and meticulous are expected.