Module

Research Methods and Professional Practice

  • MSc
Module cover image for the Research Methods and Professional Practice module

Introduction

This e-portfolio captures my learning across twelve units and two major projects, strengthening my understanding of how research is planned, executed, and communicated professionally. It also encourages critical reflection on my development (Satienchayakorn & Sanpatchayapong, 2025).

Reflection Approach: Gibbs’ Reflective Cycle

I used Gibbs’ Reflective Cycle (The University of Edinburgh, 2024) as my reflection framework. It allows deep, systematic reflection on each unit and accountability for completed work. This approach keeps me accountable for the work completed in each unit and provides an opportunity to elaborate further on those where more extensive work was undertaken.

For the reflection, I also applied Rolfe et al.’s (2001) “What? So what? Now what?” model, linking theory to practice. As Walland and Shaw (2020) and Majola (2025) note, e-portfolios are both process and product, capturing growth, reflection, and professional application. Majola (2025) emphasizes similar ideas saying it’s not just a final product but a process of growth.

Reflection on Each Unit

Unit 1 – Introduction to Research Methods. The Scientific Investigation and Ethics in Computing

  • Description: Introduced elements of the scientific method and the importance of ethics in research. Exploring inductive and deductive reasoning and discussed ethical issues in computing, particularly around dark UX patterns.
  • Feelings: Shockingly scoring 50 % on the reasoning quiz. A reminder that deduction and induction can be tricky to distinguish.
  • Evaluation: The readings helped me understand the foundation of scientific inquiry. The dark-patterns case study also helped me connect theory to real-world ethics.
  • Analysis: The quiz highlighted a clear knowledge gap. Ethics form the backbone of professional credibility.
  • Conclusion: Ethics and logical reasoning are the twin pillars of sound research.
  • Action Plan: Revisit inductive/deductive reasoning examples weekly until confident.
  • Collaborative discussion on Ethics
  • Unit 2 –Research Questions, the Literature Review and the Research Proposal

  • Description: I learned how to formulate clear, focused, and researchable questions, transforming general ideas into structured proposals.
  • Feelings: At first, developing precise questions felt overwhelming, but the framework provided helped simplify the process.
  • Evaluation: Early attempts were tough, yet by iterating and reviewing examples, I gained confidence in creating strong, relevant research questions.
  • Analysis: Good research begins with good questions, they guide everything from data collection to analysis.
  • Conclusion: Strong, well-crafted questions anchor all research efforts, ensuring purpose and direction.
  • Action Plan: practice reframing broad work problems into research-style questions monthly to sharpen precision before my dissertation.
  • Literature review plan - Early doors for the unit 7 project
  • Unit 3 – Methodology and Research Methods

  • Description: This unit deepened my understanding of exploratory versus descriptive research and introduced qualitative, quantitative, and mixed methods.
  • Feelings: The theory felt dense at first, but I appreciated how each approach complements different research objectives.
  • Evaluation: I recognised the strengths of each method, and how both can merge into richer mixed methods.
  • Analysis: The choice of method must always align with the research question and the type of data sought.
  • Conclusion: Understanding research design clarified how methodology transforms abstract questions into executable studies.
  • Action Plan: Before each new analysis, explicitly document whether my approach is exploratory or descriptive and justify my chosen method.
  • Researching methods, data collection and skills required for the unit 7 project
  • Unit 4 – Case Studies, Focus Groups and Observations

  • Description: I explored practical qualitative data-collection tools like case studies, focus groups, and observation, while submitting my literature-review outline for feedback.
  • Feelings: Submitting my outline was nerve-wracking but reassuring; feedback gave me clear direction.
  • Evaluation: This stage showed me how different methods produce distinct data types, each revealing unique perspectives.
  • Analysis: Balancing depth and scale is key.
  • Conclusion: Iterative feedback is essential in refining both focus and scope in research.
  • Action Plan: Apply peer and tutor feedback loops early in my dissertation process rather than waiting until submission.
  • Outline for my unit 7 project as well as feedback given
  • Unit 5 – Interviews, Survey Methods, and Questionnaire Design

  • Description: This unit focused on designing effective interviews and surveys, understanding sampling, and balancing open-ended and closed-ended questions.
  • Feelings: Designing unbiased questions was tougher than expected; it required empathy and precision.
  • Evaluation: Learning about pre and post-testing made me appreciate how much design impacts data reliability.
  • Analysis: Well-crafted instruments can make or break a study’s validity.
  • Conclusion: Quality data starts with thoughtful, ethical question design.
  • Action Plan: Create a reusable survey template with ethical disclaimers and balanced question structures for future research tasks.
  • Data collection outline for the unit 7 project
  • Unit 6 – Quantitative Methods - Descriptive and Inferential Statistics

  • Description: I revisited descriptive statistics, data levels, and measures of spread. Familiar ground as a data scientist.
  • Feelings: Comfortable and confident; it felt like home territory.
  • Evaluation: While the content was easy, I reflected that computing results isn’t enough as interpretation for the audience is what matters.
  • Analysis: Statistical literacy is only impactful when paired with communication skills.
  • Conclusion: EDA and descriptive statistics should always serve storytelling, not just accuracy.
  • Action Plan: For every quantitative output at work, include one visual and one plain-language explanation to reinforce comprehension.
  • Unit 7 – Inferential Statistics and Hypothesis Testing

  • Description: This unit explored probability, distributions, and hypothesis testing, the transition from description to inference.
  • Feelings: I felt confident but aware that true rigor demands precise sampling and data collection.
  • Evaluation: It reinforced the relationship between data quality and inference reliability.
  • Analysis: Hypothesis testing reminded me that research is about evidence, not proof.
  • Conclusion: Inferential statistics ground predictions in evidence, promoting disciplined interpretation.
  • Action Plan: Apply formal hypothesis framing in my next predictive-model project before drawing conclusions.
  • Hypothesis testing
  • Summary measures
  • Project 1

  • Description: This project examined academic and industry literature on churn-prediction models using machine learning, applying academic rigor to a real corporate context.
  • Feelings: Although comfortable with the technical domain, writing on a company you work for can be daunting. I had to research to make sure no assumptions were made.
  • Evaluation: The review revealed a major academic gap: most churn studies emphasise accuracy rather than interpretability.
  • Analysis: By evaluating methodologies, I learned how academic rigor differs from business analytics. i received a grade of 72%, lacking in further research into the domain and depth of the project.
  • Conclusion: This project turned a familiar work problem into a formal research investigation, deepening my ethical and analytical maturity.
  • Action Plan: No matter the domain expertise, do further research and ask other domain experts for review.
  • Unit 7 project with feedback
  • Unit 8 – Data Analysis and Visualisation

  • Description: The unit discussed analysis and presentation techniques for qualitative and quantitative results, with a focus on dashboards and storytelling.
  • Feelings: I enjoyed this section, it aligned perfectly with my professional skills and interest in data storytelling.
  • Evaluation: I realised that visualisation in academic research demands clarity and context, not just aesthetic dashboards.
  • Analysis: Good visuals bridge understanding between technical and non-technical audiences, something I practice daily at work.
  • Conclusion: Effective visualisation is equal parts communication and ethics, data must never mislead.
  • Action Plan: Adopt a “clarity-first” design rule for all future dashboards and research figures.
  • Research proposal outline for the unit 10 project
  • Unit 9 – Validity and Generalisability in Research

  • Description: I studied validity, reliability, and threats such as bias and confounding. I also completed a statistical-charts activity in Excel.
  • Feelings: The theory was intriguing but repetitive; I wanted to ensure I truly grasped its real-world implications.
  • Evaluation: Understanding validity refined my approach to evaluating data sources and methodology credibility.
  • Analysis: Reliability ensures consistency, while validity ensures truth. Both are non-negotiable in any research claiming credibility.
  • Conclusion: Quality research stands on rigorous validity checks and transparent methodology.
  • Action Plan: Build a validity checklist to review all analyses and future academic projects.
  • Charts in excel
  • Unit 10 – Research Writing

  • Description: This unit guided me through structuring a dissertation and preparing for research reporting.
  • Feelings: Initially nervous, I ended up enjoying it. Merging my professional and personal passion for F1.
  • Evaluation: Conducting extensive literature review taught me how depth creates credibility.
  • Analysis: Research writing is persuasion: clarity, evidence, and coherence convince readers more than jargon.
  • Conclusion: Strong research writing turns analysis into impact; it communicates rather than intimidates.
  • Action Plan: Before my dissertation, practice summarising complex results into one-page executive briefs to sharpen clarity.
  • Project 2

  • Description: My second artefact involved designing and presenting a predictive-modelling research proposal in an unfamiliar domain — Formula 1 racing.
  • Feelings: I was initially anxious about presenting to non-technical peers but excited to merge my analytical expertise with personal interest.
  • Evaluation: The project strengthened my ability to communicate complex ideas simply. With a mark of 75% i believe i am mastering it now.
  • Analysis: Working outside my professional field revealed the universality of research design principles. I learned how transferable data-science logic can be and how domain context shapes variables, assumptions, and storytelling.
  • Conclusion: The proposal boosted my presentation confidence and refined how I frame and justify methodological decisions. Feedback suggests i should work on methodology depth in presentations.
  • Action Plan: practice 10-minute concise presentations monthly, focusing on explaining research frameworks to mixed audiences.
  • Presentation on the research proposal
  • Research proposal presentation transcript
  • Excel Exercises - Found in unit 9 and 7

  • Description: I completed a series of Excel exercises focusing on descriptive and inferential statistics. These included summary measures, hypothesis testing, and visualising relationships through charts.
  • Feelings: Initially, I found the tasks repetitive compared with Python or SQL analysis, but soon realised they forced me to slow down and verify each mathematical step manually.
  • Evaluation: Working in Excel helped reinforce conceptual understanding of what my automated functions actually do — from calculating variance to interpreting p-values.
  • Analysis: Manually recreating analyses deepened my appreciation for the logic behind data-science libraries.
  • Conclusion: These exercises bridged the gap between theoretical statistics and applied analytics.
  • Action Plan: Make sure the fundamentals are understood before just applying data-science libraries. Don't loose the fundamentals by getting comfortable.
  • Unit 11 – Going Forward: Professional Development and Your e-Portfolio

  • Description: Focused on reflective practice, professional skills, and constructing the e-portfolio narrative.
  • Feelings: I appreciated the chance to consolidate learning and assess progress through the Professional Skills Matrix.
  • Evaluation: I could trace clear growth — stronger analysis, ethics awareness, and storytelling — compared with earlier modules.
  • Analysis: Reflection has become a habit, not an assignment. Regular self-review is now part of how I work professionally.
  • Conclusion: Continuous reflection transforms experience into expertise.
  • Action Plan: Schedule quarterly professional reflections tied to KPIs to maintain ongoing growth.
  • Unit 12 – Project Management and Managing Risk

  • Description: The final unit introduced project-management principles, risk identification, and change-management strategies.
  • Feelings: It was a satisfying conclusion, both practical and relevant to both work and research. Excited for feedback on my e portfolio.
  • Evaluation: Understanding risk reinforced how uncertainty must be managed proactively, especially in long-term research projects.
  • Analysis: Risk management in research parallels data-science production: identify dependencies early, plan contingencies, and communicate transparently.
  • Conclusion: Effective project management balances structure with adaptability — essential for complex, data-driven studies.
  • Action Plan: Develop a standard risk-register template for my dissertation and personal analytics projects.
  • Reflection Piece

    What? — My Learning Journey

    Over twelve units, I learned to slow down and think like a researcher rather than just a data scientist. Research is not only about producing results but also about understanding and justifying every step that leads there. My Cartrack literature review taught me how to synthesise complex sources and identify academic gaps. My Formula 1 proposal allowed me to design a research process from start to finish, combining methodology, ethics, and storytelling.

    Each unit contributed a distinct layer of competence. The early units built my foundation in ethics and reasoning. Mid-module, I developed methodological fluency and statistical awareness. The final units shifted my attention to professional reflection, project management, and risk mitigation. Together, they formed a comprehensive journey from curiosity to execution, and now, evaluation.

    So What? — Growth, Emotions, and Turning Points

    Emotionally, this module was different. It was slower and more reflective than the technical modules I’m used to. That initially frustrated me. I wanted to “do” rather than “think.” But reflection revealed that strong research starts with thinking.

    The first turning point was in Unit 1-3, where the ethics discussion on dark UX patterns connected academic principles with my daily professional reality. It reminded me that even subtle design decisions can have ethical consequences.

    The second turning point came during Unit 5, when I designed a questionnaire and realised how delicate wording can influence data integrity. It changed the way I interpret survey results; no question is neutral.

    The third was Unit 7, submitting my first full literature review and receiving 72% (Which 70%+ is distinction). It was validation and direction in one. My tutor’s comments on adding academic depth pushed me to seek more peer-reviewed evidence and not rely solely on technical white papers.

    Finally, Unit 10 reignited my love of applied analytics through the Formula 1 project. Translating data-driven predictions into a structured academic proposal was exciting, humbling, and satisfying — especially achieving 74%. Though it was all theory, it sparked drive for the thesis i will complete in the next module.

    Beyond content, the module improved how I think:

    • Criticality: I now question assumptions and test logic before coding or analysing.
    • Ethics: I include a short “ethical risk” note in my scripts to flag potential biases.
    • Communication: I practice explaining models in plain language, anticipating the reader’s background.
    • Resilience: Balancing workload, study, and research taught me to plan smarter, not longer.

    Now What? — Applying the Learning

    The skills and awareness gained will directly feed into my dissertation and my professional practice.

    • 1. Integrating Academic Rigor into Work: I will embed research design principles into every project. Defining questions, identifying variables, and selecting methods before writing any code. This ensures each project can stand academic scrutiny.
    • 2. Ethical & Professional Conduct: Inspired by Satienchayakorn and Sanpatchayapong (2025), I’ll advocate for structured reflection within our analytics team. Introducing guided prompts during project reviews could promote ethical awareness and accountability.
    • 3. Enhanced Communication: My goal is to create concise, stakeholder-friendly summaries for every technical project, explaining why choices were made. This aligns with Rolfe et al.’s emphasis on action-oriented reflection.
    • 4. Research Continuity: The Formula 1 proposal opened a curiosity about predictive modelling in sport. I plan to explore publishing a small paper comparing sport prediction models versus telematics forecasting models.
    • 5. Personal Growth: Reflection has helped me manage imposter syndrome. By documenting progress, I can see growth in real terms rather than relying on memory or emotion. Being relatively early in my career, imposter syndrome plays a large factor in projects, goals and growth.

    In short, this module taught me how to think like a researcher, act ethically like a professional, and communicate like a leader. It bridged academia and practice in a way that will influence my dissertation and my long-term career trajectory.

    Professional Skills Matrix & Action Plan

    Skill / Competency How Developed in Module Evidence / Artefact Next Steps / Action Plan
    Critical Thinking & AnalysisEvaluated literature, critiqued methods, assessed validity and reliability.Unit 7 Literature Review.Continue analysing at least one peer-reviewed paper weekly to strengthen critical evaluation skills.
    Ethical AwarenessApplied ethical reasoning to UX and data handling.Unit 1 Discussion; LR Ethics Section.Formalise an ethical-impact checklist for my analytics team.
    Research DesignCreated questions, selected methods, planned sampling.Units 2–5 artefacts; F1 Proposal.Use what i learnt this module and feedback from lecturer to improve research
    Statistical Analysispracticed descriptive and inferential statistics, hypothesis testing.Units 6–7 exercises.Explore Bayesian methods and confidence-interval reasoning for deeper inference.
    Communication & StorytellingPresented proposal and explained methods clearly to non-technical audiences.Unit 10 Presentation + Transcript.Conduct monthly internal presentations at work to refine narrative delivery.
    Project & Risk ManagementApplied risk-identification techniques to academic and business projects.Unit 12 Risk Plan.Maintain a living risk register for all major research and analytics tasks.
    Reflective PracticeUsed Gibbs & Rolfe frameworks to document growth.This e-Portfolio.Continue quarterly reflections aligned with performance metrics.

    Conclusion

    This module has been transformative. I began it expecting a theory-heavy hurdle but leave it with practical, transferable frameworks that shape everything I do. My understanding of ethics, validity, and research design has deepened, my confidence in writing and presentation has grown, and reflection has become a habit rather than an academic exercise.

    Both my Cartrack literature review and Formula 1 proposal show how research methods extend beyond academia — they make industry work more disciplined, ethical, and impactful. This e-portfolio captures that evolution: from practitioner to reflective professional, from coder to researcher.

    References

    Satienchayakorn, N., & Sanpatchayapong, U. (2025). Reflection and English Learning in Pre-Medical Students’ E-Portfolio. THAITESOL Journal, 38(1), 22–43. Available from: https://so05.tci-thaijo.org/index.php/thaitesoljournal/article/view/271725 [Accessed 16 October 2025]

    The University of Edinburgh (2024) Reflection Toolkit. Gibbs' Reflective Cycle. Available from: https://reflection.ed.ac.uk/reflectors-toolkit/reflecting-on-experience/gibbs-reflective-cycle [Accessed 17 October 2025]

    Rolfe, G., Freshwater, D., & Jasper, M. (2001). Critical Reflection for Nursing and the Helping Professions: A User's Guide. Available at: https://books.google.co.za/books?id=kcAPGwAACAAJ&printsec=front_cover&redir_esc=y [Accessed 17 October 2025]

    Walland, E., Shaw, S. (2020) E-portfolios in teaching, learning and assessment: tensions in theory and praxis. Available from: https://www.tandfonline.com/doi/full/10.1080/1475939X.2022.2074087#abstract [Accessed 17 October 2025]

    Majola, M.X. (2025) A Proposed Framework For E-portfolio Use to Enhance Teaching and Learning: Process E-portfolio. Available from: https://www.ijem.com/a-proposed-framework-for-e-portfolio-use-to-enhance-teaching-and-learning-process-e-portfolio [Accessed 17 October 2025]

    Artefacts from this module

    Downloadable work produced for Research Methods and Professional Practice. Each file opens in a new tab.