Overview
One sentence introduction:
I am a Lecturer in Educational Data Science, meaning I look back on the past in order to look into the future.
Slightly longer:
Motivated by social problems such as unequal access to education and inequality in health and wellbeing, I am interested in educational programmes that unbar the gate without lowering the bar and, research projects that hold promise to help solve or re-solve the problems through improved data analysis and effective storytelling.
Even longer:
I worked in Durham University as a Research Data Scientist before joining the Graduate School of Education in Exeter. In Durham, I conducted follow-up analyses of increasingly big trial data from educational interventions funded by the Education Endowment Foundation (EEF) in England. All EEF projects are independently evaluated by research teams from universities and research organisations. The data from the interventions are then deposited in a data archive which aims to accumulate research findings and help answer research questions surrounding scientific evidence. As the EEF intends to award as much as £200 million by 2025, the archive may also help us better track longer-term impact of the interventions as results from national tests become available where this is possible.
Prospective PhD candidates interested in any of the following topics are welcome to get in touch:
- Causal Inference
- Data Science Education
- Digital Education
- Educational Data Science
- Evidence-Informed Policy
- Individualised Treatment Effect
- Mixed Methods Research
- Randomised Controlled Trials (RCT)
Qualifications
PhD (Dunelm), MA, MPhil (Cantab), MSc, MSc (Oxon)
I am also a part-time, distance, and lifelong learner with Harvard University Extension School.
Career
Research Data Scientist, Durham University, 2015 - 2018
Links
Research group links
Research
Research interests
Introduction
As an educational data scientist with an interest in data science education, I am primarily interested in data science for causal inference, evidence generation, synthesis, and communication. In particular, I am interested in counterfactual prediction using causal machine learning techniques, the reproducibility of research findings, and if and how the results researchers reported are sensitive to analytical choices.
I collaborate with colleagues from multiple disciplines and institutions in order to solve some methodological challenges associated with social and educational interventions, for instance, the unintended consequences of randomisation in educational interventions as a result of pupils 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.
Policy Implications
A few months ago, when I presented our research on individualised evidence for education, some colleagues might understandably question its value or relevance to society. Today, the current COVID-19 global pandemic, at the time of writing, has led more than 150 countries to close their schools, affecting more than 1.5 billion children worldwide (source: The World Bank). Learning has never been so personalised, and individualised evidence has never been so important!
By showing where an intervention worked, for whom, and by how much, our research findings have implications for policy-makers in diverse areas of science and policy, such as education, public health, tax auditing, and medical trials. Beyond education, one area of particular relevance of our individualised approach is public health where policies that encourage certain behaviours for the public good are critical. Take, for example, the COVID-19 crisis: encouraging social distancing may take different forms and policy-makers would be well-advised to understand how different subgroups (e.g., the elderly, young people, and key workers) might respond to different messaging.
Our approach could go even further, in areas of rapid development and testing of drugs or vaccines, in order to better understand who benefits and by how much, even a vaccine that only works for a relevant subgroup (e.g., patients with underlying health conditions) would be a much-needed advancement to battle this deadly disease.
Nevertheless, we should not ignore that fact that a third of the world’s population relies heavily on traditional medicine and that folklores played significant roles in the advancement of science and medicine, e.g., cow fleas to eradicate smallpox, mosquitos that caused human blood to boil, and iodine against goitre. We survived and thrived by drawing on inspirations from nature and tales from the outposts. We can, again, today!
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. (approximately £10,000)
- 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. (£3,500)
- 2019 Exeter University
With Katherin Barg and Lauren Stentiford: A scoping project to automatically extract data from recorded lectures at the University of Exeter. (£2,660)
- 2019 HEFCE
Data Science Education. (£30,000)
- 2019 Exeter University
Data and Science with R. (£2,000)
- 2019 Exeter University
With Alexandra Allan, Karen Knapp, Susan McAnulla, and Matthew Newcombe: Education Incubator Project on flexible blended learning for mature students in the Graduate School of Education and Exeter Medical School. (£9,638)
- 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. (approximately £58,227)
Links
Publications
Key publications | Publications by category | Publications by year
Key publications
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.
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 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.
Publications by category
Journal articles
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.
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.
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
Kasim A, Xiao Z, Higgings S, De Troyer E (2017).
eefAnalytics: Analysing Education Trials., CRAN.
Abstract:
eefAnalytics: Analysing Education Trials
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, 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.
2020
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.
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.
Kasim A, Xiao Z, Higgings S, De Troyer E (2017).
eefAnalytics: Analysing Education Trials., CRAN.
Abstract:
eefAnalytics: Analysing Education Trials
Abstract.
Author URL.
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.
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 2020-09-19 05:14:58
Refresh publications
External Engagement and Impact
Committee/panel activities
Emerging Applications Section of the Royal Statistical Society: Committee member.
Invited lectures
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.
A tale of three gaps: Individualised treatment effect for better evidence in education. Jesus College & Faculty of Educaton, University of Cambridge, March 12th, 2020.
Latest working paper presented at the Health Statistics Group, College of Medicine and Health, University of Exeter, January 7th, 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
Teaching
I contribute to teaching and supervision of the following modules:
2019 - 2020:
(EFPM308) Preparing for Educational Research and Dissertation.
(EFPM913) Debating the Big Questions in Education.
(SSI2001) Learning from Work Experience (Undergraduate).
(EFPM005Z1, EFPM005Z3, EFPM005Z5 SU20A) Preparing for Educational Enquiry.
2018 - 2019:
(EFPM308) Preparing for Educational Research and Dissertation.
(ERPM005) Designing and Communicating Research.
(SSI2001) Learning from Work Experience (Undergraduate).
(EFPM329a) Preparing for TESOL Enquiry and Dissertation.
(ERPM100) MSc Programme Dissertation Supervision.
Data and Science with R.
Supervision / Group
Research Fellows
- Caitlin Nickson (Summer Intern)
Postdoctoral researchers
Postgraduate researchers
- Amal Alammar
- Abdallah Alharbi
- Thana Aljumaah
- Norah Alosayl
- Tanya Day Clark
- Yue Ma
- Kitty Parker (Health Statistics, College of Medicine and Health)
- Angela Short
- Julio Cesar Torres Rocha
- Yiwen Wang
