Unit 1 - Introduction to Research Methods
Critical Reflection on Learning and Development in Research Methods and Methodology
This reflection offers a critical analysis of my academic and professional development over the course of the Research Methods and Methodology module. Drawing upon the reflective e-Portfolio activities, statistical exercises, major summative submissions (the Literature Review and Research Proposal), and associated feedback, I explore the evolution of my skills, conceptual understanding, and future trajectory. The process was both enlightening and at times challenging, requiring continual engagement with complex academic expectations and applied methodological rigour.
Literature Review: Insights and Limitations
My literature review explored the role of AI-based assistive technologies in supporting physically disabled and elderly populations. Feedback highlighted that while my written expression and structural coherence were strong, there was insufficient synthesis of contrasting views and limited diversity of sources. As noted in the summative feedback, my approach compartmentalised strengths and limitations into separate sections rather than embedding a more integrated critical discussion within each thematic area.
In hindsight, this structure limited my ability to engage in the kind of comparative evaluation that would elevate the criticality of my work. While I relied heavily on a small number of methodologically rigorous studies, this narrow base restricted broader contextual debate. Furthermore, the feedback underscored the importance of applying more regular and varied citation, which I now recognise as a symptom of insufficient source mapping during the early stages of review writing.
In response, I revisited my literature management strategy and refined my synthesis technique to focus more sharply on methodological critique and epistemological tension within the reviewed studies. This has informed my future writing by embedding citation planning and critical comparison earlier in the review process.
Research Proposal: Design, Differentiation, and Evaluation
My research proposal extended the themes from the literature review but shifted to a distinct context: the integration of demand forecasting and personalised recommendation systems in nutraceutical e-commerce using machine learning. Formative feedback encouraged me to ensure the project built upon, rather than overlapped with, the literature review. I responded by adopting a Design Science Research (DSR) methodology and clearly defining my research question, technical methods, and ethical considerations within a commercial rather than clinical setting.
This proposal demonstrated stronger methodological alignment than my literature review. The technical detail, particularly the integration of time-series forecasting (ARIMA, LSTM) and neural collaborative filtering, reflected a clearer grasp of applied research design. I also took care to address ethical considerations such as GDPR compliance, bias mitigation, and user validation through informed consent, marking significant progress in applying ethical frameworks to AI development.
One notable limitation was the broad scope of the proposal. While the dual focus on forecasting and personalisation was ambitious, it risked stretching feasibility within the MSc timeline. Although I clearly outlined deliverables and a structured plan, future implementation would likely require tighter focus and phased development to maintain depth and manageability.
Statistical Analysis Skills: Progress and Pitfalls
The statistical exercises in Units 8 and 9 presented a steep learning curve. Prior to the module, my understanding of statistical tests and tools like SPSS was limited. Working through the exercises, particularly those involving t-tests, normality testing, and Pearson correlation, allowed me to build foundational competence. I became more confident interpreting statistical outputs and presenting results in academically appropriate ways.
However, one key area for critical reflection was my early overreliance on p-values and the binary logic of statistical significance. I now understand this approach can be reductive, neglecting important considerations such as effect size, statistical power, and confidence intervals. Although I developed practical skill in executing basic tests, I recognise the need to further develop statistical literacy to apply and critique more complex models in future research.
Reflecting on the Research Process and Methodological Development
The module’s progression through philosophical foundations, research design, ethical frameworks, and statistical application provided a comprehensive structure for developing research competence. Early reflective tasks prompted me to examine my own position as a researcher, particularly the tension between my professional background in AI and the ethical responsibilities of applied research in healthcare and commerce.
One of the most instructive stages was engaging with ethical questions in my proposal. I was required to critically consider algorithmic fairness, data bias, and the risks of exclusion for marginalised users. This moved my thinking beyond technical implementation and into the broader social implications of research design and digital innovation.
Nevertheless, the module gave limited attention to critical research traditions such as post-positivism or participatory inquiry. I found myself seeking external readings to supplement this, particularly around co-design methods and data ethics in applied AI. Integrating these perspectives earlier would enhance the inclusiveness and critical depth of the research training offered.
Professional Development: SWOT and Skills Matrix Reflection
Completing the SWOT analysis and Professional Skills Matrix encouraged structured reflection on my capabilities and growth areas. I identified strengths in written communication, conceptual synthesis, and planning—reinforced by positive feedback on both the literature review and research proposal. At the same time, I acknowledged areas for improvement including statistical interpretation, source diversity, and methodological transparency.
To address these, I committed to three key actions: expanding my use of academic databases and citation tools, improving statistical fluency through SPSS and Python-based practice, and enhancing integration between theory and method in my project work. These priorities are now informing my dissertation planning and shaping my contributions to research-related tasks in professional contexts.
Conclusion
The Research Methods and Methodology module has been a catalyst for both academic and professional development. I have made clear progress in research design, critical writing, and data analysis, while also identifying key areas for continued growth. Through reflective practice, methodological critique, and technical skill-building, I have gained not only a deeper understanding of the research process but also a stronger foundation for ethical and impactful scholarship in AI and data-driven disciplines.