Introduction

Welcome to my e-portfolio, a comprehensive showcase of my work and learning journey throughout this module. This portfolio highlights my progress, reflections, and achievements, providing evidence of my skills and knowledge in statistical analysis and research methods within the context of Computing Science.

Objective

The primary objective of this e-portfolio is to collate all the significant work and reflective activities completed during the module. This includes showcasing artefacts from each unit, presenting statistical exercises, evaluating my literature review and research proposal submissions, and reflecting on my learning journey. Through this e-portfolio, I aim to demonstrate my ability to apply knowledge independently, critically analyse my learning process, and evaluate the impact of this module on my professional and personal development.

Table of Contents

Unit 1: Introduction to Research Methods, The Scientific Investigation, and Ethics in Computing

Summary: This unit covered the essentials of research methods, focusing on the scientific method and its application in computing. We discussed the purposes of research, the differences between deductive and inductive reasoning, and the importance of ethical considerations. Understanding these elements is fundamental to conducting rigorous and responsible research, ensuring the validity of findings and the ethical treatment of participants.

Learning Outcomes: We differentiated between inductive and deductive reasoning, understood the importance of ethics in research, and recognized how these ethical principles apply to our areas of study and professional practice. This foundational knowledge prepares us for more advanced research methods and ethical decision-making throughout our academic and professional careers.

Artifact: Reflective Activity 1 – Ethics in Computing in the age of Generative AI

Artifact Description: This piece reflects on the ethical and societal challenges of generative AI. It emphasizes the need for global consensus on AI ethics and the importance of integrating ethical considerations into AI development. The reflection concludes with a call to balance innovation with responsibility, aiming for a future where AI benefits society while mitigating its risks.

Unit 2: Research Questions, the Literature Review and the Research Proposal

Summary: This unit focused on developing a research proposal, starting with defining a research question. We explored how to formulate and revise research questions, the components of a research proposal, and the importance of a literature review. These skills enable us to survey scholarly sources, identify relevant theories and methods, and recognize gaps in existing research. We learned to present our thoughts coherently and critically evaluate literature to support our proposals.

Learning Outcomes: We examined the characteristics of suitable research topics, explored rational and creative methods for formulating research ideas, and transformed these ideas into well-crafted research questions and proposals. Additionally, we conducted literature searches, critiqued the literature, and presented comprehensive literature reviews, preparing us for more in-depth research activities.

Artifact: Literature Review and Research Proposal Outlines

Artifact Description: This artifact outlines the plan approach for a literature review aimed at assessing the effectiveness of K-means clustering in segmenting B2B ecommerce customers based on purchasing behaviors and demographic characteristics. It includes the purpose, scope, and literature search strategy, search criteria, and selection of sources.

Unit 3: Methodology and Research Methods

Summary: In Unit 3, we explored the general concept of methodology and an overview of research methods. We examined the underlying assumptions that shape research, including ontological, epistemological, and axiological perspectives. We also learned about research design, distinguishing between exploratory and conclusive research, and delved into the three main types of research methods: qualitative, quantitative, and mixed methods. Additionally, we covered primary and secondary data collection methods, providing a comprehensive understanding of how to choose and apply the appropriate research methods for our projects.

Learning Outcomes: We learned about research design, distinguishing between exploratory and conclusive research, and delved into qualitative, quantitative, and mixed methods. Additionally, we covered primary and secondary data collection methods.

Artifact: Collaborative Learning Discussion 1

Artifact Description: This entry delves into the ethical challenges faced by computing professionals, specifically focusing on dark UX patterns designed to mislead users for financial gain. The discussion, which took place over three weeks on the University of Essex forum, examines the ethical implications based on the ACM and BCS Codes of Conduct, emphasizing integrity and public interest.

Unit 4: Case Studies, Focus Groups, and Observation

Summary: This unit covered various data collection methods, including case studies, focus groups, and observations. These methods are predominantly used in qualitative research but can also be applied in quantitative research. We learned about the advantages and drawbacks of each method, which is crucial for selecting the appropriate method for a given research project.

Learning Outcomes: We gained the skills to carry out case studies, focus groups, and observations effectively. We learned to identify the suitable method for specific investigations and considered the types of data obtained from each method.

Artifact: Literature Review

Artifact Description: This literature review evaluates the use of K-means clustering for customer profiling in B2B ecommerce. It analyzes various studies from academic databases to explore the algorithm’s potential and challenges in this context. The review highlights that while K-means clustering provides valuable insights for customer segmentation, its application is limited by the complexity of B2B datasets. The document calls for further research to optimize K-means clustering for B2B ecommerce, contributing both theoretically and practically to the field.

Unit 5: Interviews, Survey Methods, and Questionnaire Design

Summary: We delved into interviews, survey methods, and questionnaire design. We explored conducting in-depth interviews as a qualitative method and the importance of surveys as a quantitative data collection tool. We distinguished between surveys and questionnaires and discussed pre-testing and post-testing methods for comprehensive analysis.

Learning Outcomes: We learned to integrate interviews and surveys into research, design effective questionnaires, and analyze collected data. We also explored pre- and post-testing, enhancing our ability to evaluate the impact of new processes or systems.

Artifact: Reflective Activity 2: Case Study: Inappropriate Use of Surveys

Artifact Description: This case study explores the ethical, social, legal, and professional implications of survey misuse, focusing on the Cambridge Analytica scandal. It highlights how data from Facebook users was inappropriately used to influence political outcomes and discusses other instances of survey manipulation. The study underscores the need for ethical practices in data collection to maintain trust and integrity.

Unit 6: Quantitative Methods - Descriptive and Inferential Statistics

Summary: This unit focused on quantitative methods, emphasizing descriptive and inferential statistics. We examined how quantitative methods analyze relationships between numerically measured variables using various statistical and graphical techniques. We specifically focused on measures of location and dispersion to understand the central tendency and variability of data.

Learning Outcomes: We gained the ability to apply descriptive statistics, identify different levels of measurement, and produce measures of location and spread. This knowledge equips us to extract meaningful insights from data.

Artifact: The artifacts developed during this unit will be presented in Unit 9.

Unit 7: Inferential Statistics and Hypothesis Testing

Summary: We continued our exploration of quantitative methods, focusing on inferential statistics and hypothesis testing. We learned that inferential statistics aims to discover general patterns about a larger population by studying a smaller sample group. This unit emphasized the process of statistical inference and hypothesis testing.

Learning Outcomes: We applied inferential statistics to data analysis, identified the correct probability distributions, and performed appropriate hypothesis tests. These skills enable us to extract meaningful patterns and insights from our data.

Artifact: Collaborative Discussion 2: Case Study on Accuracy of Information

Artifact Description: This case study explores the ethical, legal, social, and professional implications of data analysis in the context of evaluating the nutritional claims of the cereal “Whizzz.” The discussion revolves around the ethical dilemma faced by a researcher, Abi, who discovers that the collected data contradicts the manufacturer’s claims. The study emphasizes the importance of presenting both positive and negative findings transparently, adhering to ethical guidelines, and mitigating potential misuse of data. The discussion, which occurred over a three-week forum at the University of Essex, highlights the complexities of maintaining integrity and accountability in research.

Unit 8: Data Analysis and Visualisation

Summary: We shifted our focus to data analysis and visualization, exploring techniques for handling and interpreting both qualitative and quantitative data. The unit highlighted the challenges of analyzing qualitative data and the process of coding to categorize such data. We also discussed the importance of data visualization and the use of dashboards in business intelligence.

Learning Outcomes: We understood different types of data analysis and their applications. We learned about various charts and graphs for presenting data and identified key elements of dashboards, enabling us to communicate our findings clearly.

Artifact: The artifacts developed during this unit will be presented in Unit 9.

Unit 9: Validity and Generalisability in Research

Summary: This unit explored the critical concepts of validity, generalisability, and reliability in research. We discussed the importance of these concepts before collecting data and examined the differences between qualitative and quantitative data, including data cleansing and validation processes.

Learning Outcomes: This unit explored the critical concepts of validity, generalisability, and reliability in research. We discussed the importance of these concepts before collecting data and examined the differences between qualitative and quantitative data, including data cleansing and validation processes.

Artifact: Statistical Worksheet Submissions

Artifact Description: This artifact consolidates the statistical analyses conducted in Units 6, 7, 8, and 9. It includes detailed results and interpretations from various exercises, focusing on descriptive and inferential statistics, hypothesis testing, and data visualization. The worksheets provide insights into the effectiveness of different weight loss diets, brand preferences across demographics, and the significance of mean differences in various contexts. Each section presents the findings clearly, with downloadable links to the relevant Excel files for further review and validation.

Unit 10: Research Writing

Summary: This unit focused on the essential skill of research writing, crucial for communicating technical knowledge effectively in tech-related businesses. We emphasized the importance of structuring and writing a dissertation, preparing research proposals, and publishing research papers in peer-reviewed journals.

Learning Outcomes: We gained skills to structure a dissertation and prepare for the writing process, involving identifying a research area, conducting a literature review, and selecting appropriate research methods. This culminated in creating a project proposal for our final dissertation.

Artifacts:

Artifacts Description: The artifacts include a video presentation and a transcript that outline the research proposal for evaluating K-means clustering in segmenting B2B ecommerce customers. The presentation highlights the project’s significance, methodology, ethical considerations, and the artefacts to be created, providing a detailed overview of the proposed research.

Unit 11: Going Forward: Professional Development and Your e-Portfolio

Summary: This unit emphasized model selection and evaluation methodologies, highlighting the importance of rigorously assessing model performance and selecting suitable models for specific tasks.

Learning Outcomes: We gained proficiency in evaluating model performance and selecting appropriate models based on established criteria, facilitating informed decision-making in machine learning projects.

Artifact: Professional Development

Artifact Description: This piece includes a comprehensive Professional Skills Matrix and SWOT Analysis, crucial components of my professional development plan. The Professional Skills Matrix details the competencies I have acquired and m proficiency levels. The SWOT Analysis identifies strengths, weaknesses, opportunities, and threats, informing a strategic action plan to enhance my skills and career trajectory. These tools provide a structured approach to evaluating and advancing my professional capabilities in data science and analytics.

Unit 12: Project Management and Managing Risk

Summary: This unit covered project management and the crucial aspect of managing risk within a project. We explored project life cycles and methodologies, emphasizing the importance of selecting the right metrics to measure project performance and assessing potential risks. Additionally, we discussed managing inevitable changes during a project and the need for a robust change control process to minimize impacts on cost, time frames, and quality.

Learning Outcomes: We gained a comprehensive understanding of project management concepts and methodologies, appreciated project life cycles, and recognized the technologies needed for remote collaboration. We learned how projects are impacted by risk and uncertainty and developed effective risk and change management plans.

Artifact: NA

Final Reflection

Introduction

This reflective piece aims to provide a detailed account of my experiences and learnings from the Research Methods and Professional Practice module at the University of Essex. The reflection focuses on the development of essential skills and understanding critical concepts in statistical analysis and research methods within the field of Computing Science. It will cover the description, emotional response, analysis, learning outcomes, and changed actions resulting from this project.

What?

Throughout this module, I engaged in various activities designed to enhance my understanding of research methods, ethical considerations, and professional practices in computing. These activities included conducting statistical analyses, participating in discussions on ethical dilemmas, developing literature reviews, and crafting research proposals. Each task presented unique challenges and learning opportunities that contributed to my overall development.

One significant experience was the case study on the Cambridge Analytica scandal. This exercise involved critically analysing the misuse of data from Facebook users for political purposes, highlighting the ethical, social, and legal implications of such actions. Additionally, the discussion on dark UX patterns focused on how certain design practices deceive users for financial gain, prompting a deep reflection on the ethical responsibilities of computing professionals.

Another central experience was developing a literature review and a research proposal on the effectiveness of K-means clustering in segmenting B2B ecommerce customers. This task required meticulous research, critical analysis, and coherent presentation of findings, which refined my academic writing and research skills.

So What?

The implications of these experiences are profound and multifaceted. The ethical case studies, such as the Cambridge Analytica scandal and dark UX patterns, underscored the critical importance of ethical considerations in all aspects of computing. These cases reinforced the principles outlined in the ACM and BCS Codes of Conduct, which emphasize honesty, transparency, and prioritizing the public interest. These experiences demonstrated that ethical lapses could have far-reaching consequences, including loss of public trust, legal repercussions, and harm to individuals and society.

The discussions and analyses of ethical dilemmas highlighted the complexities of maintaining ethical standards in a rapidly evolving field. For example, the debate around dark UX patterns illustrated how subtle design choices could significantly impact user behaviour and trust. It emphasized the necessity for computing professionals to be vigilant and proactive in identifying and mitigating unethical practices.

Developing a literature review and research proposal emphasized the importance of thorough research and critical analysis in producing high-quality academic work. This task required me to sift through numerous scholarly articles, identify relevant studies, and synthesize information coherently. It reinforced the value of a systematic approach to research, where each step builds on the previous one to create a robust and comprehensive study.

Emotionally, these experiences were both challenging and rewarding. Analysing the ethical breaches in the Cambridge Analytica case invoked a sense of responsibility and urgency to uphold ethical standards in my future work. The complexity of the literature review and research proposal was initially daunting, but successfully completing these tasks brought a sense of accomplishment and confidence in my abilities.

Reflecting on my prior learning, these experiences have reinforced and expanded my existing knowledge. For instance, my understanding of the importance of ethical considerations in research and professional practice was deepened by the case studies and discussions. Similarly, my skills in critical analysis and academic writing were honed through the literature review and research proposal tasks.

What Next?

Looking ahead, I plan to apply the lessons learned from this module in several ways. First and foremost, I will maintain a high ethical standard in all my professional activities. This includes ensuring transparency in data reporting and considering the broader social and legal implications of my work. I intend to stay updated with the latest ethical guidelines and industry standards to navigate the complexities of the tech industry responsibly.

Continuous learning and development are essential for professional growth. To this end, I have outlined several goals in my professional development action plan. One of my primary goals is to enhance my communication, persuasion, and resilience skills. I plan to enrol in professional development courses focused on these areas and seek feedback from peers and mentors to gauge my progress. Improved communication and resilience will enable me to articulate my ideas better, navigate challenges, and collaborate effectively with diverse teams.

Another key area for improvement is my technical skills. While I have a strong foundation in tools like SQL, Python, and statistical analysis, I aim to deepen my knowledge in emerging technologies and tools. This includes gaining proficiency in Java and NoSQL, areas where I currently have only a basic understanding. I plan to complete online courses in these subjects and apply my learning to real-world projects to solidify my skills.

Additionally, I plan to increase my participation in community activities and professional organizations. Engaging in community-driven projects and supporting corporate social responsibility (CSR) initiatives will enhance my cultural awareness and provide opportunities to apply my skills meaningfully. This aligns with the principles of social responsibility and community engagement emphasized in the module.

In future projects, I will apply the knowledge and skills gained from this module to ensure rigorous and ethical research practices. This includes conducting thorough literature reviews, designing robust research methodologies, and critically evaluating findings to contribute valuable insights to the field of computing. I will continue to refine my data analysis and visualization skills to communicate research findings effectively and support informed decision-making.

Finally, I plan to document my progress and reflect on my learning journey regularly. Maintaining a detailed record of completed courses, new skills acquired, and their application in projects will help me track my development and identify areas for further improvement. Seeking feedback from peers and mentors will provide additional perspectives and help me adjust my plans as necessary.

Conclusion

The Research Methods and Professional Practice module has been a transformative experience, providing me with a solid foundation in research methods, ethical considerations, and professional practices. By applying the lessons learned and continuously seeking to improve, I am confident that I will be well-equipped to navigate the challenges and opportunities in the field of computing. This reflective piece documents my achievements and serves as a roadmap for my ongoing professional and personal development.

References

Chapman, R. (2019) Exploring the Value of Risk Management for Projects: Improving Capability Through the Deployment of a Maturity Model. IEEE Engineering Management Review 47(1): 126-143.

Correa, N. et al. (2023) Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance.

Project Management Institute. (2020) A Guide to the Project Management Body of Knowledge PMBOK Guide, 7th Edition. Newtown Square, PA: Project Management Institute.

Ring, G., Waugaman, C. & Brackett, B. (2017) The Value of Career ePortfolios on Job Applicant Performance: Using Data to Determine Effectiveness. International Journal of ePortfolio 7(2): 225-236.

Weber, K. (2018) Employer perceptions of an engineering student’s electronic portfolio._ International Journal of ePortoflio_ 8(1): 57–71.