Research Methods & Professional Practice

A Well-performed user experience design can positively influence the user's experience by increasing browsing efficiency, ease, and joy. However, it may as well have a negative or even a deceptive effect. An example of such deception is the dark UX Pattern case published by the Association of Computer Machinery (ACM) (Acm, 2018). In this case, a software company programmer, Stewart, was asked to modify a website interface for one of the company’s clients. The modifications comprised changing buttons to arrows and using a weird color template. However, the intention was to deceive customers by leading them to purchase higher-priced items and add protection plans unknowingly.

Although not aware of the client’s intentions, Stewart reported the issues of the uncomfortable design requests to his manager. However, the manager insisted on implementing the changes.

This case brings several ethical, legal, and professional issues. First, the client's behavior is regarded as a crime according to the UK Fraud Act (Anon, 2019). In addition, a social implication is represented by the failure to make the website accessible to the visually impaired or the intentional targeting of such a vulnerable group.

In the realm of ethical and professional computing, the programmer breached the first key principle of the British Computing Society (BCS) code of conduct by acting against the public interest in accepting website design modifications that led to customers’ deception in general and abuse and deception of the visually impaired (BCS, 2021). Furthermore, although Stewart respected the manager's request and avoided escalating the issue to a conflict, by doing so, he breached the fourth key principle of the BCS code of conduct by endangering the profession's reputation in accepting and implementing suspicious design modification requests (BCS, 2021).

References

Acm (2018) Case: Dark UX Patterns. Available from: https://ethics.acm.org/code-of-ethics/using-the-code/case-dark-ux-patterns/ [Accessed 5 February 2023].

Fraud Act 2006, c.35. Available from: https://www.legislation.gov.uk/ukpga/2006/35/contents [Accessed 2023]

Bcs (2021) BCS Code of Conduct. Available from: https://www.bcs.org/membership/become-a-member/bcs-code-of-conduct/ [Accessed 31 January 2023].

It can be agreed upon that human beings are curious in nature, willing to explain encountered phenomena, and love to explore. When it comes to science, however, this curiosity is controlled with a well-known methodology called the Scientific Method (Hepburn & Andersen, 2015). One of the exciting things I learned in this unit is that one of the components of the scientific method is reasoning which can be deductive or inductive. Both are essential types of reasoning, but inductive reasoning is closer to real-world problems where curiosity or “Observation” is the first loop in the chain of reasoning (Miessler, 2020). One benefit I can take from that and relate to the field of computer science is that solutions to problems may not always be through using established theories, tools, and techniques. Instead, it may need to start from observation. In other words, thinking out of the box. Nevertheless, such solutions may need to be backed again by hypothesis testing to have scientific validity.

As expected for all fields, research is enhanced by the ethical behavior of its practitioners. This behavior can be summarized by how the research is done, how the research subjects are treated, and how the findings are interpreted and shared (Dawson, 2015).

References

Dawson, C. W. (2015) Projects in computing and information systems : a student's guide. Third edition. ed. Harlow, England: Pearson.

Available from: the Vitalsource Bookshelf [Accessed 3 February 2023].

Hepburn, B. & Andersen, H. (2015) Scientific method. Stanford Encyclopedia of Philosophy -).

Miessler, D. (2020) The Difference Between Deductive and Inductive Reasoning | Daniel Miessler. Available from: https://danielmiessler.com/blog/the-difference-between-deductive-and-inductive-reasoning/ [Accessed 31 January 2023].

* As with other sets of guiding questions in this book, select those questions that are relevant to your context, add others as appropriate, and decide the order in which you will address them to communicate effectively with your audience.

Source: Healey, M., Matthews, K., & Cook-Sather, A. (2020) Writing about learning and teaching in higher education: Creating and contributing to scholarly conversations across a range of genres. Center for Engaged Learning Open-Access Books, Elon University. 142-152.

Going back to the curiosity of human beings I touched upon in the previous reflection in unit 1, the first step in the scientific method is formulating a research question. This question should be chosen carefully as it matters more than knowing the correct answers (Phillips et al., 2007). I believe that during the process of formulating a research question, researchers get a deeper look at the way they think about a problem or observation, which helps them stay focused on what matters most.

After formulating a research question, the next step is to perform a literature review (survey) which is an iterative process that, again, as discussed for the research question, will get the researcher closer to the required focus with each iteration (Dawson, 2015).

References

Dawson, C. W. (2015) Projects in computing and information systems : a student's guide. Third edition. ed. Harlow, England: Pearson.

Available from: the Vitalsource Bookshelf [Accessed 3 February 2023].

Phillips, E. M., Pugh, D. S., Bartlett, A. & Lewis, J. (2007) How to get a PhD: a handbook for students and their supervisors.

 [Accessed 17 April 2023].

Outline for a Literature Review on Deep Learning for Human Recognition in Motion

This literature review will focus on implementing deep learning for human image recognition in motion. The targeted audience is computer science professionals.

Human recognition technology has unlimited potential uses. However, to get its full potential, the speed and accuracy of recognition are logical requirements if the aim is to imitate the capabilities of human vision.

Image recognition of humans in motion is more challenging than identifying fixed objects, which has witnessed significant improvements throughout the years, due to the variable appearance of different human postures, apparel colour, and texture, the effect of the surrounding environment, and the occlusion caused by objects or humans (Nguyen et al., 2016; Chowdhury et al., 2016).

The workflow of human recognition in motion is composed of the following steps: extracting potential image parts, describing those parts, classifying them as human or not, and final processing. Human description comprises features extracted from images like shape, color, and texture (Nguyen et al., 2016).

A survey of the literature published in the last ten years will be done to identify the relevant article. The literature search will be accomplished using the University of Essex Online library and the ACM digital library. In addition, elicit.org will be used to find relevant answers to the questions from articles.

In addition to the introduction and the description of the typical workflow of human image recognition described above, this review aims to explore the state-of-the-art deep-learning tools and techniques available for image recognition and their applications, discuss the limitations, and propose the aspects that require further investigation.

References

Chowdhury, S. A., Uddin, M. N., Kowsar, M. M. S. & Deb, K. (2016) ‘Occlusion handling and human detection based on Histogram of Oriented Gradients for automatic video surveillance’.International Conference on Innovations in Science, Engineering and Technology (ICISET). International Islamic University Chittagong, Bangladesh, 28-29 October. IEEE, 1-4.

Nguyen, D. T., Li, W. & Ogunbona, P. O. (2016) Human detection from images and videos: A survey. Pattern Recognition 51(148-175.

Surveys are a beneficial research method that can quickly provide the researcher with instant data for analysis (Anon, 2022). However, it can be utilized in unethical ways, as happened in the Cambridge Analytica Scandal, where the respondents were not informed about the other hidden purpose of the survey (Confessore, 2018; Noor, 2018).

Another example of unethical use of surveys is Sugging, which is selling under the guise of research. In other words, it represents the use of surveys while hiding the real purpose or the actual entity behind them. This could be a company utilizing a third-party market research company to conduct the survey for them, often with incentives to respondents (Mazzarello et al., 2015). The actual purpose of such a case could be to improve the sales decisions of a product or direct selling (MRS, n.d).

Sugging has several ethical, legal, and social consequences. First, it is based on deception. Second, it may benefit respondents financially by utilizing data they can access without official permission from the subjects. This is the case of sugging performed on physicians (Mazzarello et al., 2015) where they share data of their patients anonymously through surveys and get incentives. In addition to the ethical issue of not obtaining permission, it may affect the trust and respect of the patients for their medical providers who received financial benefits by utilizing their data. Sugging is also considered a legal offense (Bowers, 1994).

References

After formulating a research question and performing a literature review, the researcher should now decide how to proceed. Here comes the research design, which is a blueprint for the research project that includes all the necessary details related to data gathering and analysis (BUSINESS RESEARCH METHODOLOGY, n.d). It appeared to me that terminology might differ from field to field, but it is important to have a clear plan for the sources of data and the way of processing such data to extract findings.

I learned about different research methods that act as a tool for the researcher to collect data. Research can be qualitative or quantitative. Quantitative methods use measurable variables that can be computed and statistically analyzed. Examples are experiments, questionnaires, and systematic reviews. The latter summarizes existing evidence on a topic, often including quantitative analysis.  On the other hand, qualitative methods are concerned with expanding the knowledge about a subject without the attempt to quantify its factors. Examples are action research, case studies, focus groups, observations, and qualitative surveys or interviews (Dawson, 2015; Kaplan & Maxwell, 2005; Ralph et al., 2020).

Questionnaires are an interesting research method type because they can provide quantitative and qualitative data in a reasonably short time (Anon, 2022). However, I realized that it could be utilized in unethical ways. One example is called Sugging which is the topic I prepared for one of the reflective activities.

References

Anon (2022) Interviews and Survey Design [Lecturecast]. RMPP PCOM7E January 2023 Research Methods and Professional Practice January 2023. University of Essex Online.

Business Research Methodology (n.d) Research Design - Research Methodology. Available from: https://research-methodology.net/research-methodology/research-design/#_ftn2 [Accessed 7 February 2023].

Dawson, C. W. (2015) Projects in computing and information systems : a student's guide. Third edition. ed. Harlow, England: Pearson.

Available from: the Vitalsource Bookshelf [Accessed 3 February 2023].

Kaplan, B. & Maxwell, J. A. (2005) Qualitative Research Methods for Evaluating Computer Information Systems. 2nd ed. New York, USA: Springer.

Available from: https://ebookcentral.proquest.com/lib/universityofessex-ebooks/detail.action?docID=264813.

Ralph, P., et al. (2020) ACM SIGSOFT Empirical Standards. Available  [Accessed 23 February 2023].

This case study is about Abi, a statistical programmer, who was in a position to analyze data for the manufacturer of a new cereal product, Whizz. Abi found, through his analysis, that the nutritional value of the cereal is not as claimed. Also, he found that a different statistical analysis could be manipulated to support the product in a positive way.

If Abi manipulated the statistical analysis to hide the real negative results, Abi would breach the first item of the BCS code of conduct by acting against the public interest (BCS, 2021). This is because he would present false information to the public and ignore the fact that the cereal is harmful to satisfy the manufacturer.

Also, by presenting false information, Abi would have breached the fourth item of the BCS code of conduct, Duty to the Profession, by not acting professionally to maintain the profession's reputation.

Regarding the other questions, I think Abi is obligated to present the positive and negative results of the analyses to the manufacturer. Although he can’t be responsible for the way that data is used, he could report any misuse of the results to his supervisor at work and to relevant authorities.

References

Bcs (2021) BCS Code of Conduct. Available from: https://www.bcs.org/membership/become-a-member/bcs-code-of-conduct/ [Accessed 31 January 2023].

Research is unreliable if it can’t be replicated by other researchers, and its findings can be of limited use if they can’t be applied to similar situations  (Anon, 2022). It is fascinating to realize such facts. I can think of reliability as distributed proof of the validity of a particular research endeavor. i.e., when a research result methods and results are replicable by other researchers, this indicates that the methodology and implementation of the original idea were valid, not tampered with, and can be applied to different situations.

During this part of the module, I also learned that validity could be enhanced in several ways. First, by collecting representative data that fully describe the situation observed. Second, by the effort to resolve ambiguity through further data collection or modification of the protocol. Third, by using several data sources and collection strategies. Fourth, by getting feedback. Finally, by reporting unfavorable data in addition to favorable one (Kaplan & Maxwell, 2005).

References

Anon (2022) Validity and Generalisability [Lecturecast]. RMPP PCOM7E January 2023 Research Methods and Professional Practice January 2023. University of Essex Online.

Kaplan, B. & Maxwell, J. A. (2005) Qualitative Research Methods for Evaluating Computer Information Systems. 2nd ed. New York, USA: Springer.

Available from: https://ebookcentral.proquest.com/lib/universityofessex-ebooks/detail.action?docID=264813.

In this unit, I had applicable knowledge with practical exercises that helped formulate a deeper understanding of the inferential statistics topic. I had to opportunity to explore the DCOVA framework for defining (D), collecting (C), organizing (O), visualizing (V), and analyzing (A) data to get valuable insights (Berenson et al., 2015). The cited book also helped me appreciate the sampling distribution and how it can be used for variables that are not normally distributed using the central limit theorem.

Another piece of information that I found interesting is the fact that accepting a null hypothesis doesn't mean its absolute correctness but the absence of an adequate amount of evidence to warrant its rejection. (Berenson et al., 2015). This, in my opinion, brings us back to the importance of reliable and valid research to accumulate the necessary evidence to answer important questions.

References

Berenson, M. L., Levine, D. M. & Szabat, K. A. (2015) Basic Business Statistics: Concepts and Applications. 13th global edition ed. Harlow, UK: Pearson Education.

Available from: https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=1419545 [Accessed 27 March 2023].

Visualizing data is part of the process of extracting meaningful insights. It enhances the comprehension and communication of results (Embarak, 2018). In this unit, I had a chance to practice constructing bar charts and histograms through a charts worksheet which increased my understanding of the topic and my appreciation of the importance of data visualization.

Another topic that was discussed in this unit is the analysis of qualitative data, which takes a totally different route without statistical analysis. This process is focused on extracting themes from data and synthesizing meaning with the help of standard software packages like word processors or more specialized software made for that purpose (Learning for Action, n.d). It was my first time exploring how to analyze qualitative data systematically. This helped me appreciate this kind of research I may consider in the future.

References

Embarak, O. (2018) The Importance of Data Visualization in Business Intelligence. United States: Apress L. P.

 [Accessed 17 April 2023].

Learning for Action (n.d) Analyzing Qualitative Data. Available from: http://learningforaction.com/analyzing-qualitative-data [Accessed 27 March 2023].

Research consumes time in its planning and implementation. Also, it is meaningless if it can’t be shared with others (Dawson, 2015). I realized in this unit the importance of acquiring the skills needed for researchers to publish their work. Those include good planning, time management, and project management skills, which are not easy to master but require practice and repetition. In this unit, I had the opportunity to watch a TED Talk about the mind of procrastinators (Urban, 2016). This talk helped me to think about the goal of implementing a project rather than focusing on small steps that can be boring and less motivating.

References

Dawson, C. W. (2015) Projects in computing and information systems : a student's guide. Third edition. ed. Harlow, England: Pearson.

Available from: the Vitalsource Bookshelf [Accessed 3 February 2023].

Urban, T. (2016) Inside the Mind of a Master Procrastinator. Available from: https://www.ted.com/talks/tim_urban_inside_the_mind_of_a_master_procrastinator?language=en [Accessed 17 April 2023].

Although an ePortfolio can be a daunting task to start with, it becomes easier if done gradually. I was not able to upload materials regularly during the modules. However, I documented my artifacts and the notes I took during the course. This proved to be handy.

I also learned that ePortfolios could serve several purposes, including learning, self-assessment, and employment (Miller & Morgaine, 2009; Moon, 2004; Weber, 2018). In addition, I believe that the process of ePortfolio teaches students discipline and time management skills too.

References

Miller, R. & Morgaine, W. (2009) The benefits of e-portfolios for students and faculty in their own words. Peer review : emerging trends and key debates in undergraduate education 11(1): 8.

Moon, J. A. (2004) A Handbook of Reflective and Experiential Learning Theory and Practice. United Kingdom: Taylor & Francis Group.

Weber, K. (2018) Employer Perceptions of an Engineering Student’s Electronic Portfolio. International Journal of ePortfolio 8(1): 57-71.

In this unit, I learned that projects might have different levels of complexity. With increased complexity comes the need for more structured project management. Learning about the “Hedgehog Syndrome” was interesting, where mistakes are repeated without an effort to reflect on previous mistakes (Maylor, 2010). I am planning to start my own software project in the next few months, for which I am eager to apply the knowledge I learned about project management. However, it will also be challenging as I must balance between being realistic and motivated.

My project will utilize cloud computing technology; hence I am planning to include security risk management in the early stages of the planning of my project as recommended in the literature (Marichetty, 2017), which also make complete sense as the project success might be affected if customers can’t trust the security of the system (CAST, n.d). My plan is to use an agile method for project management since the development of the feature will be incremental and frequent feedback will be needed from future software system users.

References

Cast (n.d) Risk Management in Software Development and Software Engineering Projects. Available from: https://www.castsoftware.com/glossary/software-risk-management [Accessed 10 April 2023].

Marichetty, U. K. (2017) The Use of Effective Risk Management in Cloud Computing Projects. Harrisburg University of Science and Technology.Available from: https://digitalcommons.harrisburgu.edu/pmgt_dandt/23/.

Maylor, H. (2010) Project Management. 4th ed. Harlow, UK: Pearson Education.

Available from: https://ebookcentral.proquest.com/lib/universityofessex-ebooks/detail.action?docID=5173571 [Accessed 16 April 2023].