Applied Data Analysis: A Problem-based Learning Approach

Ann Smith

Abstract


This paper examines the transition of a conventional multivariate statistics module to a problem-based learning module, first implemented in 2021. The primary objective was to enhance students’ problem-solving skills, bridging the gap between mathematical concepts and real-world applications. The approach was implemented to instil a deeper understanding of real-world data analysis, emphasising the interpretation of domain specific problems in mathematical terms and the production of reports for industrial stakeholders.

Findings indicate that the integration of problem-based methods not only improved students’ comprehension of statistical techniques but also fostered a more profound appreciation for their practical utility in diverse professional contexts. The problem-solving cycle, a central component of the approach, guided students in critically analysing complex challenges and formulating data driven solutions. Furthermore, this study emphasises the potential for replicating the industrial study group experience within an undergraduate teaching environment.

Adopting a problem-based learning approach in the teaching of data analysis empowers students to apply their analytical skills effectively to real-world scenarios, strengthening their capacity to communicate insights and solutions to industrial stakeholders. The study underscores the value of aligning educational practices with the demands of data-driven industries, providing students with a competitive advantage in future research and the job market. The study is descriptive and reflective in nature.


Keywords


Problem-based Learning, Knowledge Exchange, Study Groups for Industry, Multivariate Statistics, Applied Data Analysis.

Full Text:

PDF

References


Barrows, H.S. (1996). Problem‐based learning in medicine and beyond: A brief overview. New Directions for Teaching and Learning, 1996(68), pp.3-12. https://doi.org/10.1002/tl.37219966804

BBC (2024). More or Less. Available at: https://www.bbc.co.uk/programmes/b006qshd [Accessed 3 April 2024].

Bond, P. (2018). The era of mathematics. UK Research and Innovation.

Brewer, G. D. (1999). The challenges of interdisciplinarity. Policy Sciences, 32(4), 327-337. https://doi.org/10.1023/A:1004706019826

Brewer, G. D. and Lövgren, K. (1999). The theory and practice of interdisciplinary work. Policy Sciences, 32(4), 315-317. https://doi.org/10.1023/A:1004789429396

Chartier, T.P. (2015). When life is linear: from computer graphics to bracketology. Mathematical Association of America.

De Graaf, E. and Kolmos, A. (2003). Characteristics of problem-based learning. International Journal of Engineering Education, 19(5), 657-662.

Felten, P. and Lambert, L.M. (2020). Relationship Rich Education: How Human Connections Drive Success in College. Johns Hopkins University Press.

Freiberger, M. and Thomas, R. (2023). A practical guide to writing about anything for anyone! Available at: https://plus.maths.org/content/PlusWritingGuide [Accessed 3 April 2024].

Government Open Data (n.d.). Available at: https://www.data.gov.uk/ [Accessed 3 April 2024].

Harford, T. (2020). How to Make the World Add Up: Ten Rules for Thinking Differently About Numbers. Hachette UK.

Krumrei-Mancuso, E.J., Haggard, M.C., LaBouff, J.P. and Rowatt, W.C. (2020). Links between intellectual humility and acquiring knowledge. The Journal of Positive Psychology, 15(2), pp.155-170. https://doi.org/10.1080/17439760.2019.1579359

Mathematics in Industry Reports (n.d.). Cambridge University Press. Available at: https://www.cambridge.org/engage/miir/ [Accessed 3 April 2024].

Murray, R. (2017). How to Write a Thesis. Maidenhead: McGraw-Hill Education.

Newman, M.J. (2005). Problem based learning: an introduction and overview of the key features of the approach. Journal of Veterinary Medical Education, 32(1), pp.12-20. https://doi.org/10.3138/jvme.32.1.12

Porter, T. and Schumann, K. (2018). Intellectual humility and openness to the opposing view. Self and Identity, 17(2), pp.139-162. https://doi.org/10.1080/15298868.2017.1361861

Shelter (2024). Legal definition of homelessness and threatened homelessness. Available at: https://england.shelter.org.uk/professional_resources/legal/homelessness_applications/homelessness_and_threatened_homelessness/legal_definition_of_homelessness_and_threatened_homelessness [Accessed 3 April 2024].

Spiegelhalter, D. (2019). The Art of Statistics: Learning from Data. Penguin UK.

V-KEMS Study Group (2022a). V-KEMS Study Group Report: Communities of the Future. Cambridge: Newton Gateway to Mathematics. Available at: https://gateway.newton.ac.uk/sites/default/files/asset/doc/2206/V_KEMS_Ageing_Society%20V1%20Issue%201%2031%20May%202022.pdf [Accessed 3 April 2024].

V-KEMS Study Group (2022b). V-KEMS Study Group Report: The Public Perception of Science. Cambridge: Newton Gateway to Mathematics. Available at: https://gateway.newton.ac.uk/sites/default/files/asset/doc/2210/The%20Public%20Perception%20of%20Science%20Virtual%20Study%20Group%20Report.pdf [Accessed 3 April 2024].

Wong, I. H. and Wong, T. T. (2021). Exploring the relationship between intellectual humility and academic performance among post-secondary students: The mediating roles of learning motivation and receptivity to feedback. Learning and Individual Differences, 88, article 102012. https://doi.org/10.1016/j.lindif.2021.102012

Wood, D. F. (2003). Problem based learning. BMJ, 326(7384), 328-330. https://doi.org/10.1136/bmj.326.7384.328

Yew, E.H. and Goh, K. (2016). Problem-Based Learning: An Overview of its Process and Impact on Learning. Health Professions Education, 2(2), pp.75-79. https://doi.org/10.1016/j.hpe.2016.01.004




DOI: https://doi.org/10.21100/msor.v22i2.1473

Refbacks

  • There are currently no refbacks.