Dr Xie completed her PhD in the University of Hong Kong and her MA (with Distinction) in the University of Warwick, UK. Her research focused on language assessment and evaluation, especially the relationships between assessment and curriculum. She has published in the related areas of assessment and evaluation, focusing on assessment impact, test validity and reliability, test-taker perceptions, and assessing writing on international peer-reviewed journals such as Language Testing, Language Assessment Quarterly, Assessing Writing and International Journal of Testing. She is interested in Structural Equation Modelling, Rasch measurements, Cognitive Diagnostic Modelling, including their applications in language teaching, learning, and assessment.

Dr. Xie and her research team have recently completed a Teaching Development Project supported by the University's TDG grant (T0178). With the support of the grant, she and her team have constructed a searchable online database (http://assesslang.eduhk.hk) which contains more than three hundreds of English language assessment tasks designed to assess school children's English skills; these assessment tasks were designed by cohorts of undergraduate and postgraduate students enrolled in the courses of Language Assessment and Curriculum & Assessment Course taught by Dr. Xie. The project team has polished the assessment tasks and coded them according to their target test-taker groups, target language skills (and subskills), as well as key features of assessment tasks (e.g. item type, input type, response format). Potential users of the database are pre-service and in-service teachers of English language in primary and secondary schools. Users of the database can search language tasks according to school types (local, mainland), target student grades, target English skills as well as other features of tasks.

Research Interest: Language testing and assessment, Curriculum Innovation, Second/Foreign Language Learner motivation and learning strategies, Structural Equation Modelling, Rasch Modelling