Overview
I joined the University of Exeter in 2018 as a Lecturer in Educational Data Science. With a teaching interest in data science education and research interests in causal machine learning and algorithm aversion/appreciation, I look back on the past in order to look better into the future. This means I examine both qualitative and quantitative data generated in the past, identify patterns, derive insights, make (particularly counterfactual) predictions, so as to produce richer understandings of the past and/or inform better decision-making for the future.
Prospective PhD candidates interested in any of the following topics are welcome to get in touch:
- Digital Education
- Educational Data Science
- Evidence-Informed Policy
- Individualised Treatment Effect
- Mixed Methods Research
- Randomised Controlled Trials (RCT)
CRPL Research Methods Seminar Series
The Centre for Research in Professional Learning of the GSE is organising a research methods seminar series, which takes place on Thursday mornings twice a month during term time. We invite both Exeter and external PGR students and early career academics to present their research to our PGR and the wider research community. The seminars focus primarily on research designs and methods employed, but speakers will share some of their major findings/arguments when they are available.
Below is a list of confirmed speakers:
November 19th 2020 (Norah Alosayl, Exeter)
December 3rd 2020 (Angela Short, Exeter)
December 17th 2020 (10 AM: Kitty Parker, Exeter)
December 17th 2020 (4 PM: Joe Brassington)
January 14th 2021 (Lydia Speyer, Edinburgh)
January 28th 2021 (Ben Weidmann, Harvard)
February 11th 2021 (Vicky Yiran Zhao, Cambridge)
March 11th 2021 (Lauriane Suyin Chalmin-Pui, Sheffield)
March 25th 2021 (Alison Pearson, Exeter)
April 8th 2021 (Richard Cocks D’Souza, Exeter)
April 22nd 2021 (Janice - Hoang Huong, Exeter)
We are likely to make small adjustments as we progress, but we will keep you informed of any changes. Please get in touch with Dr ZhiMin Xiao (z.m.xiao@exeter.ac.uk) for a Zoom link to join the seminar series, or should you have any queries about the seminar series.
Career
Research Data Scientist, Durham University, 2015 - 2018
Links
Research group links
Research
Research interests
My research interests are primarily in evidence generation, synthesis, and communication for better decision-making. I collaborate with colleagues from multiple disciplines and institutions in order to solve some methodological challenges associated with the evaluation of policy interventions, for instance, evidence that is non-actionable and unintended consequences of randomisation as a result of people being randomly allocated to a treatment arm that might be sub-optimal, if not harmful, to them.
Tapping into recent development in machine learning, I have been working on predictive and personalised approaches to evidence, particularly, individualised treatment effect using experimental, observational, longitudinal, and simulated data. Understanding that what we observe right in front of our eyes is often ethnographically invisible, I employ mixed methods in my research and draw on inspirations from medical anthropology.
Research grants
- 2020 Exeter University
Machine learning to identify who benefits in policy trials, together with Professor Mihaela van der Schaar at the University of Cambridge and Dr Dmitry Kangin of IDSAI, the University of Exeter.
- 2019 Exeter University
SSIS strategic discretionary research fund to estimate individualised treatment effect for randomised controlled trials using conventional regression models and machine learning algorithms.
- 2019 Exeter University
With Katherin Barg and Lauren Stentiford: A scoping project to automatically extract data from recorded lectures at the University of Exeter.
- 2019 HEFCE
Data Science Education.
- 2019 National Institute for Health Research
With Obioha Ukoumunne and Michael Nunns from the Medical School: Cluster randomised trials and child psychiatric epidemiology studentship for three years at UK/EU level.
Links
Publications
Key publications | Publications by category | Publications by year
Key publications
Xiao Z, Higgins S (In Press). Of Young People and Internet Cafés. Frontiers in Human Dynamics
Xiao Z, Henley W, Boyle C, Gao Y, Dillon J (In Press). The Face Mask and the Embodiment of Stigma.
Abstract:
The Face Mask and the Embodiment of Stigma
The COVID-19 pandemic has spawned a rare opportunity to study some latent social structures using data science. The Chinese government and its people have been blamed for the outbreak of the virus. Face mask wearing can signal an embodied stigma and Chinese people living outside China have been subject to discrimination, assault, and other hate crimes, particularly at the early stages of the crisis. However, as we accumulate more evidence surrounding mask use, the stigma is shifting. As more scientific data become available and people leave even more information on social media during the lockdown, data science can help better understand the trajectories of the stigma. The insights generated have implications for anti-stigma interventions for future undesirable conditions and diseases.
Abstract.
Xiao Z, Hauser OP, Kirkwood C, Li DZ, Jones B, Higgins S (In Press). Uncovering Individualised Treatment Effect: Evidence from Educational Trials.
Abstract:
Uncovering Individualised Treatment Effect: Evidence from Educational Trials
The use of large-scale Randomised Controlled Trials (RCTs) is fast becoming "the gold standard" of testing the causal effects of policy, social, and educational interventions. RCTs are typically evaluated — and ultimately judged — by the economic, educational, and statistical significance of the Average Treatment Effect (ATE) in the study sample. However, many interventions have heterogeneous treatment effects across different individuals, not captured by the ATE. One way to identify heterogeneous treatment effects is to conduct subgroup analyses, such as focusing on low-income Free School Meal pupils as required for projects funded by the Education Endowment Foundation (EEF) in England. These subgroup analyses, as we demonstrate in 48 EEF-funded RCTs involving over 200,000 students, are usually not standardised across studies and offer flexible degrees of freedom to researchers, potentially leading to mixed results. Here, we develop and deploy a machine-learning and regression-based framework for systematic estimation of Individualised Treatment Effect (ITE), which can show where a seemingly ineffective and uninformative intervention worked, for whom, and by how much. Our findings have implications for decision-makers in education, public health, and medical trials.
Abstract.
Parker K, Nunns M, Xiao Z, Ford T, Ukoumunne O (2021). Characteristics and practices of school-based cluster randomised controlled trials for improving health outcomes in pupils in the UK: a systematic review protocols.
BMJ Open,
11 Full text.
Uwimpuhwe G, Singh A, Higgins S, Coux M, Xiao Z, Shkedy Z, Kasim A (2020). Latent Class Evaluation in Educational Trials: What Percentage of Children Benefits from an Intervention?.
The Journal of Experimental Education, 1-15.
Full text.
Xiao Z (2019). Mobile phones as life and thought companions.
Research Papers in Education,
35(5), 511-528.
Full text.
Publications by category
Journal articles
Xiao Z, Higgins S (In Press). Of Young People and Internet Cafés. Frontiers in Human Dynamics
Xiao Z, Henley W, Boyle C, Gao Y, Dillon J (In Press). The Face Mask and the Embodiment of Stigma.
Abstract:
The Face Mask and the Embodiment of Stigma
The COVID-19 pandemic has spawned a rare opportunity to study some latent social structures using data science. The Chinese government and its people have been blamed for the outbreak of the virus. Face mask wearing can signal an embodied stigma and Chinese people living outside China have been subject to discrimination, assault, and other hate crimes, particularly at the early stages of the crisis. However, as we accumulate more evidence surrounding mask use, the stigma is shifting. As more scientific data become available and people leave even more information on social media during the lockdown, data science can help better understand the trajectories of the stigma. The insights generated have implications for anti-stigma interventions for future undesirable conditions and diseases.
Abstract.
Xiao Z, Hauser OP, Kirkwood C, Li DZ, Jones B, Higgins S (In Press). Uncovering Individualised Treatment Effect: Evidence from Educational Trials.
Abstract:
Uncovering Individualised Treatment Effect: Evidence from Educational Trials
The use of large-scale Randomised Controlled Trials (RCTs) is fast becoming "the gold standard" of testing the causal effects of policy, social, and educational interventions. RCTs are typically evaluated — and ultimately judged — by the economic, educational, and statistical significance of the Average Treatment Effect (ATE) in the study sample. However, many interventions have heterogeneous treatment effects across different individuals, not captured by the ATE. One way to identify heterogeneous treatment effects is to conduct subgroup analyses, such as focusing on low-income Free School Meal pupils as required for projects funded by the Education Endowment Foundation (EEF) in England. These subgroup analyses, as we demonstrate in 48 EEF-funded RCTs involving over 200,000 students, are usually not standardised across studies and offer flexible degrees of freedom to researchers, potentially leading to mixed results. Here, we develop and deploy a machine-learning and regression-based framework for systematic estimation of Individualised Treatment Effect (ITE), which can show where a seemingly ineffective and uninformative intervention worked, for whom, and by how much. Our findings have implications for decision-makers in education, public health, and medical trials.
Abstract.
Parker K, Nunns M, Xiao Z, Ford T, Ukoumunne O (2021). Characteristics and practices of school-based cluster randomised controlled trials for improving health outcomes in pupils in the UK: a systematic review protocols.
BMJ Open,
11 Full text.
Parker K, Eddy S, Nunns M, Xiao Z, Ford T, Eldridge S, Ukoumunne O (2020). Characteristics of school-based feasibility studies with a cluster randomised trial design: a systematic review. PROSPERO: International prospective register of systematic reviews
Uwimpuhwe G, Singh A, Higgins S, Coux M, Xiao Z, Shkedy Z, Kasim A (2020). Latent Class Evaluation in Educational Trials: What Percentage of Children Benefits from an Intervention?.
The Journal of Experimental Education, 1-15.
Full text.
Parker K, Nunns M, Xiao Z, Ford T, Ukoumunne OC (2020). Systematic review of the characteristics, design and analysis methods of school-based cluster randomised controlled trials for improving health outcomes in pupils in the UK. PROSPERO
Xiao ZM (2020). ‘You Are Too Out!’: a mixed methods study of the ways in which digital divides articulate status and power in China.
Information Development,
36(2), 257-270.
Abstract:
‘You Are Too Out!’: a mixed methods study of the ways in which digital divides articulate status and power in China
© the Author(s) 2019. This study investigates the differences in adolescent engagement with Information and Communication Technologies (ICT), such as computers, the Internet, and mobile phones. Involving 698 second-year high school students from urban, rural, and ethnic Tibetan regions of China, it finds that patterns of access and use indicate status and power, and the meanings teenagers pour into the technologies articulate social and educational differences. On average, Tibetans are disadvantaged in access, and the return on parental education is greater for the mainstream Han than it is for Tibetans. However, state ‘preferential policies’ have mitigated Tibetans’ plight in use, which makes the least privileged to be Han students with parents having no more than six years of education.
Abstract.
Full text.
Xiao Z (2019). Mobile phones as life and thought companions.
Research Papers in Education,
35(5), 511-528.
Full text.
Xiao Z, Higgins S (2018). The power of noise and the art of prediction.
International Journal of Educational Research,
87, 36-46.
Full text.
Xiao Z, Higgins S, Kasim A (2017). An Empirical Unraveling of Lord's Paradox.
The Journal of Experimental Education,
87(1), 17-32.
Full text.
Xiao Z, Kasim A, Higgins S (2016). Same difference? Understanding variation in the estimation of effect sizes from educational trials.
International Journal of Educational Research,
77, 1-14.
Abstract:
Same difference? Understanding variation in the estimation of effect sizes from educational trials
By applying four analytic models with comparable outcomes and covariates to a dataset of 20 outcomes from 17 educational trials, we found results closely matching in well-powered studies without serious implementation problems. The interventions and evaluations were all funded by the Education Endowment Foundation and independently evaluated. We demonstrated that when an analysis takes little account of research design, or where there were difficulties with implementation and data collection, point estimates of effect differ and estimates of precision vary. This adds to the challenge of understanding the comparative impact of interventions and deciding which are worth scaling up.
Abstract.
Full text.
Chapters
Xiao Z, Higgins S (2014). When English Meets Chinese in Tibetan Schools: Towards an Understanding of Multilingual Education in Tibet. In Feng A, Adamson B (Eds.)
Trilingualism in Education in China: Models and Challenges, Dordrecht: Springer, 117-140.
Abstract:
When English Meets Chinese in Tibetan Schools: Towards an Understanding of Multilingual Education in Tibet
Abstract.
Author URL.
Reports
Uwimpuhwe G, Singh A, Higgings S, Xiao Z, De Troyer E, Kasim A (2020).
eefAnalytics: Robust Analytical Methods for Evaluating Educational Interventions using Randomised Controlled Trials Designs., CRAN.
Abstract:
eefAnalytics: Robust Analytical Methods for Evaluating Educational Interventions using Randomised Controlled Trials Designs
Abstract.
Author URL.
Full text.
Hutton C, Lukes S, Abdulkader MS, Choudhury R, Gulaid A, Sadia S, Xiao Z, Zere A (2015).
Trusting the dice: immigration advice in Tower Hamlets., Toynbee Hall.
Author URL.
Higgins S, Xiao Z, Katsipataki M (2012).
The impact of digital technology on learning: a summary for the Education Endowment Foundation., Education Endowment Foundation.
Author URL.
Publications by year
In Press
Xiao Z, Higgins S (In Press). Of Young People and Internet Cafés. Frontiers in Human Dynamics
Xiao Z, Henley W, Boyle C, Gao Y, Dillon J (In Press). The Face Mask and the Embodiment of Stigma.
Abstract:
The Face Mask and the Embodiment of Stigma
The COVID-19 pandemic has spawned a rare opportunity to study some latent social structures using data science. The Chinese government and its people have been blamed for the outbreak of the virus. Face mask wearing can signal an embodied stigma and Chinese people living outside China have been subject to discrimination, assault, and other hate crimes, particularly at the early stages of the crisis. However, as we accumulate more evidence surrounding mask use, the stigma is shifting. As more scientific data become available and people leave even more information on social media during the lockdown, data science can help better understand the trajectories of the stigma. The insights generated have implications for anti-stigma interventions for future undesirable conditions and diseases.
Abstract.
Xiao Z, Hauser OP, Kirkwood C, Li DZ, Jones B, Higgins S (In Press). Uncovering Individualised Treatment Effect: Evidence from Educational Trials.
Abstract:
Uncovering Individualised Treatment Effect: Evidence from Educational Trials
The use of large-scale Randomised Controlled Trials (RCTs) is fast becoming "the gold standard" of testing the causal effects of policy, social, and educational interventions. RCTs are typically evaluated — and ultimately judged — by the economic, educational, and statistical significance of the Average Treatment Effect (ATE) in the study sample. However, many interventions have heterogeneous treatment effects across different individuals, not captured by the ATE. One way to identify heterogeneous treatment effects is to conduct subgroup analyses, such as focusing on low-income Free School Meal pupils as required for projects funded by the Education Endowment Foundation (EEF) in England. These subgroup analyses, as we demonstrate in 48 EEF-funded RCTs involving over 200,000 students, are usually not standardised across studies and offer flexible degrees of freedom to researchers, potentially leading to mixed results. Here, we develop and deploy a machine-learning and regression-based framework for systematic estimation of Individualised Treatment Effect (ITE), which can show where a seemingly ineffective and uninformative intervention worked, for whom, and by how much. Our findings have implications for decision-makers in education, public health, and medical trials.
Abstract.
2021
Parker K, Nunns M, Xiao Z, Ford T, Ukoumunne O (2021). Characteristics and practices of school-based cluster randomised controlled trials for improving health outcomes in pupils in the UK: a systematic review protocols.
BMJ Open,
11 Full text.
2020
Parker K, Eddy S, Nunns M, Xiao Z, Ford T, Eldridge S, Ukoumunne O (2020). Characteristics of school-based feasibility studies with a cluster randomised trial design: a systematic review. PROSPERO: International prospective register of systematic reviews
Uwimpuhwe G, Singh A, Higgins S, Coux M, Xiao Z, Shkedy Z, Kasim A (2020). Latent Class Evaluation in Educational Trials: What Percentage of Children Benefits from an Intervention?.
The Journal of Experimental Education, 1-15.
Full text.
Parker K, Nunns M, Xiao Z, Ford T, Ukoumunne OC (2020). Systematic review of the characteristics, design and analysis methods of school-based cluster randomised controlled trials for improving health outcomes in pupils in the UK. PROSPERO
Uwimpuhwe G, Singh A, Higgings S, Xiao Z, De Troyer E, Kasim A (2020).
eefAnalytics: Robust Analytical Methods for Evaluating Educational Interventions using Randomised Controlled Trials Designs., CRAN.
Abstract:
eefAnalytics: Robust Analytical Methods for Evaluating Educational Interventions using Randomised Controlled Trials Designs
Abstract.
Author URL.
Full text.
Xiao ZM (2020). ‘You Are Too Out!’: a mixed methods study of the ways in which digital divides articulate status and power in China.
Information Development,
36(2), 257-270.
Abstract:
‘You Are Too Out!’: a mixed methods study of the ways in which digital divides articulate status and power in China
© the Author(s) 2019. This study investigates the differences in adolescent engagement with Information and Communication Technologies (ICT), such as computers, the Internet, and mobile phones. Involving 698 second-year high school students from urban, rural, and ethnic Tibetan regions of China, it finds that patterns of access and use indicate status and power, and the meanings teenagers pour into the technologies articulate social and educational differences. On average, Tibetans are disadvantaged in access, and the return on parental education is greater for the mainstream Han than it is for Tibetans. However, state ‘preferential policies’ have mitigated Tibetans’ plight in use, which makes the least privileged to be Han students with parents having no more than six years of education.
Abstract.
Full text.
2019
Xiao Z (2019). Mobile phones as life and thought companions.
Research Papers in Education,
35(5), 511-528.
Full text.
2018
Xiao Z, Higgins S (2018). The power of noise and the art of prediction.
International Journal of Educational Research,
87, 36-46.
Full text.
2017
Xiao Z, Higgins S, Kasim A (2017). An Empirical Unraveling of Lord's Paradox.
The Journal of Experimental Education,
87(1), 17-32.
Full text.
2016
Xiao Z, Kasim A, Higgins S (2016). Same difference? Understanding variation in the estimation of effect sizes from educational trials.
International Journal of Educational Research,
77, 1-14.
Abstract:
Same difference? Understanding variation in the estimation of effect sizes from educational trials
By applying four analytic models with comparable outcomes and covariates to a dataset of 20 outcomes from 17 educational trials, we found results closely matching in well-powered studies without serious implementation problems. The interventions and evaluations were all funded by the Education Endowment Foundation and independently evaluated. We demonstrated that when an analysis takes little account of research design, or where there were difficulties with implementation and data collection, point estimates of effect differ and estimates of precision vary. This adds to the challenge of understanding the comparative impact of interventions and deciding which are worth scaling up.
Abstract.
Full text.
2015
Hutton C, Lukes S, Abdulkader MS, Choudhury R, Gulaid A, Sadia S, Xiao Z, Zere A (2015).
Trusting the dice: immigration advice in Tower Hamlets., Toynbee Hall.
Author URL.
2014
Xiao Z, Higgins S (2014). When English Meets Chinese in Tibetan Schools: Towards an Understanding of Multilingual Education in Tibet. In Feng A, Adamson B (Eds.)
Trilingualism in Education in China: Models and Challenges, Dordrecht: Springer, 117-140.
Abstract:
When English Meets Chinese in Tibetan Schools: Towards an Understanding of Multilingual Education in Tibet
Abstract.
Author URL.
2012
Higgins S, Xiao Z, Katsipataki M (2012).
The impact of digital technology on learning: a summary for the Education Endowment Foundation., Education Endowment Foundation.
Author URL.
zhimin_xiao Details from cache as at 2021-03-08 03:41:25
Refresh publications
External Engagement and Impact
Committee/panel activities
Emerging Applications Section of the Royal Statistical Society: Committee member.
Invited lectures
Causal Inference: A Quest for Better Evidence in Education. University of Glasgow, November 12th, 2020.
Non-Intervention Research Designs and Analysis, Educational Psychology, University of Exeter, November 5th, 2020.
Causal Inference in Education: Average or Individualised Treatment Effect? Data Crunch Meeting, Exeter Centre for the Study of the Life Sciences (EGENIS), May 18th, 2020.
Uncovering Individualised Treatment Effect: Evidence from Educational Trials, Department of Computer Science, University of Bath, April 28th, 2020.
The case against perfection in the mean: Why it is time for an individualised approach to evidence for education (Q-Step Centre, University of Exeter), November 8th, 2019.
The Case Against Perfection in the Mean: From Average to Individualised Treatment Effect in Randomised Controlled Trials for Education (Royal Statistical Society), September 23rd, 2019.
Pupil Advantage Index as an Alternative to Subgroup Analysis in RCTs for Education (Royal Statistical Society South West Local Group), February 13th, 2019.
Journal and book series Editorships and Editorial board membership
Associate Editor for the Digital Impacts Section of Frontiers in Human Dynamics
Reviewer for the Education Endowment Foundation
Reviewed for the following journals:
Journal of Child Psychology and Psychiatry
International Journal of Environmental Research and Public Health
Healthcare
Studies in Science Education
Research Papers in Education
International Journal of Science Education
Research in Science & Technological Education
Supervision / Group
Postgraduate researchers
- Amal Alammar
- Abdallah Alharbi
- Thana Aljumaah
- Layla Mansour M Alsughayyer
- Lyndsey Carmichael
- Xiaoming Jiang
- Yue Ma
- Kitty Parker (Health Statistics, College of Medicine and Health)
- Angela Short
- Julio Cesar Torres Rocha
- Halime Tosun
- Yiwen Wang
