Dr. Mei-Shiu Chiu 

Professor, Department of Education

National Chengchi University

64, Zhinan Rd. Sec. 2

Taipei 11605, Taiwan ROC

E-mail: chium@nccu.edu.tw; chium@g.nccu.edu.tw; meishiuchiu@gmail.com

ORCID: http://orcid.org/0000-0002-2929-5151

Website: https://sites.google.com/view/mei-shiu-chiu/home

Primary interest: Affective education: On the Wellbeing, Happiness, and Ambience in Learning 情意教育：學習的幸福、快樂與氛圍(品味) (see "lab" for more information)
Linkedin; ResearchGate  

Short Profile

Dr. Mei-Shiu Chiu is a professor of education at National Chengchi University, Taiwan. She received her B.A. and M.A. degrees in Education from National Taiwan Normal University and completed her doctoral study at the Faculty of Education, Cambridge University, U.K. Her primary research interest is affective education: she is most interested in understanding well-being, happiness, and ambiance in learning. Her research topics focus on the design, implementation, and effectiveness evaluation of learning, teaching, and assessment in a variety of areas of knowledge (e.g., mathematics, science, and energy); interactions between emotions, cognition, and culture; and multiple research methods and data analysis methods (including educational and data science methods). She has developed several research-based educational theories, assessment tools, as well as school and teacher development courses for educational and research practices. 

邱美秀獲得國立臺灣師範大學教育學士學位和碩士學位，並在英國劍橋大學教育學院完成博士學業。她的主要興趣是情意教育，研究主題集中在各種知識領域的（例如數學，科學和能源）的學習、教學、評量的設計、實施和效果評估；情緒、認知和文化之間的相互作用；以及多元研究方法和數據分析方法（包括教育和數據科學方法）。除了基於研究的學習和教學理論外，還為教育和研究實踐開發了若干相關的評估工具、學校課程和教師發展課程。

Long Profile

Research experiences. Dr. Mei-Shiu Chiu's major expertise is in educational psychology, which is applied to diverse domains of knowledge, mainly mathematics, energy, and STEM. With a primary interest in affective education, her PhD thesis title is ‘children’s emotional responses to mathematical problem-solving: The role of teaching methods and problem types.’ In 2020, she served as a Fulbright scholar at the University of Pennsylvania Graduate School of Education, USA, working for facilitating dialogues between educational and data sciences for STEM education, with Dr. Ryan Baker's laboratory. 

Research topics. With the primary interest in affective education, her research topics focus on interactions between emotion/affect, cognition, and culture for diverse knowledge domains (mainly mathematics, science, and energy) in relation to teaching, assessment, and large-scale databases. She uses diverse quantitative and qualitative methodologies and diverse data analysis techniques to address her research interests. The aim of her research is to improve educational practices, mainly in the aspects of learning and instruction. Example research topics include identifying effective educational provisions for diverse (especially disadvantaged) learners, developing effective teaching strategies, and evaluating education programs. She also studies contextual factors in relation to learning and instruction such as educational policies, curricula, cultures, and parents. 

Research skills. She uses diverse methodologies to conduct research. Her PhD thesis used both qualitative and quantitative methodology. Later her research skills extended to open, large-scale, and complex data analysis using diverse data analysis methods, e.g., learning analytics, psychometrics, and longitudinal data analysis from both micro and macro (social-cultural and ecological) perspectives. She has a strong learning ability to learn and apply new data analysis methods to research diverse important topics in education, mainly in the STEM fields. In addition to traditional software packages well-used in social sciences such as SPSS, HLM, and LISREL, she has self-learned Mplus, R, Python, and machine learning or data science skills to analyze data from diverse sources of educational data. For example, she has used machine learning, text mining, time series analysis, web scraping programming skills to analyze data from Facebook, qualitative data from teaching, and quantitative log-file data from computer-assisted learning systems using R and Python.  She joined the ASSISTments Longitudinal Data Mining Competition and obtained the rank 9th position (https://sites.google.com/view/assistmentsdatamining/data-mining-competition-2017). For teaching purposes, some of my codings are shared in my Kaggle.

Contribution to research. She is currently an editor for educational psychology and mathematics education journals. Her research papers have been published in journals on mathematics, science, and technology education (e.g., Research in Mathematics Education; International Journal of Science and Mathematics; Research in Science Education; International Journal of Science Education, Part B; Educational Technology Research and Development), educational psychology (e.g., Journal of Educational Psychology; Thinking Skills and Creativity), and general education (e.g., Asia-Pacific Education Researcher; Educational Studies; International Journal of Educational Development). She has developed an intervention program to reduce gender gaps in attitudes towards learning science, especially the value of learning science (published in Asia-Pacific Education Researcher). Her research also demonstrates that gender gaps in mathematics and science attitudes and achievements exist in Taiwan society). She has developed several research-based educational theories, relevant assessment tools, as well as school and teacher development courses for educational and research practices, by collaborating with her graduate students and scholars worldwide.