This module introduces advanced quantitative and qualitative methods in educational research. It develops students’ knowledge and skills in research design, data collection, data management, analysis, and modelling through the use of educational and related social science datasets. A key component of the module is training in relevant analytical software and digital tools, including critical and ethical consideration of appropriate AI-enabled options that may support research design, data analysis, interpretation, and dissemination. The module is designed particularly for students who are interested in further study (e.g. PhD, EdD) and/or professional roles requiring advanced research capability across diverse educational and related professional contexts.
A Critically understand the principles of quantitative and qualitative research, data analysis, and statistical reasoning in educational research. B Apply appropriate methods and digital tools, including relevant analytical software and, where appropriate, AI-enabled tools, to analyse quantitative and qualitative data in education and related social science contexts. C Critically reflect on issues relating to validity, reliability, trustworthiness, rigour, and the ethical use of digital and AI-supported tools in the collection, analysis, and interpretation of research data. D Critically analyse and evaluate research reports, findings, and interpretations in relation to methodological quality, evidence use, and relevance to educational research and practice.
The module is delivered through a combination of lectures and tutorials/workshops. Lectures provide key conceptual, methodological, and analytical foundations in advanced educational research methods, while tutorials and workshops provide structured opportunities for students to apply these concepts in practice. Teaching and learning activities are designed to support practice-based and research-informed learning. Students engage in collaborative and individual tasks involving research design, data handling, and data analysis using relevant analytical software and digital tools. These activities include critical consideration of appropriate AI-supported options and their ethical, rigorous, and transparent use in educational research. Where feasible, students will work with authentic or live datasets drawn from educational research, partner-related contexts, or other relevant professional settings. The module also includes opportunities for critical reflection, discussion, oral presentation, and methodological justification, enabling students to respond to questions from both the instructor and fellow students, connect theoretical principles with real-world research problems, and develop the advanced research capabilities required for further study and professional practice.