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As someone who works in work-integrated learning or cooperative education, the theoretical framework I have gravitated toward is the Experiential Learning theory, first developed by David Kolb (1984). I will explore Kolb’s model as a framework for my applied research project this term since it relates to how artificial intelligence tools can aid in improving learning outcomes for learners in an experiential learning program (Co-op). Essentially, the theory is split into the experiential learning cycle and the four distinct learning styles students can take towards learning. The life cycle can be broken down into four components: concrete experience, reflective observation, abstract conceptualization and active experimentation, or as others have called it, feeling, watching, thinking and doing (Virtanen, 2021).<\/p>\n\n\n\n
The active component of this learning cycle is how it identifies how learners have a preferential learning style(s) and how they move along the experiential process. These styles are again broken down into four components: Diverging, Assimilating, Converging and Accommodating (Kolb, 1984). Working in an engineering co-op program, I see snippets of how students learn in the classroom by experimentation, observation, or applying previous educational or work experiences to solve problems. For example, those who prefer a more hands-on approach, which is most commonly found within cooperative education (e.g. biomedical engineering students working within a prosthetics company), suggest that the learner is following the accommodating learning style.<\/p>\n\n\n\n
Even though Experiential Learning theory has been around for many years, it has been studied and applied to various STEM-related fields (Harb et al., 1993; Konak et al., 2014; Muscat & Mollicone, 2012). One of the main questions I want to understand better with Kolb’s model is to determine which of the particular learning styles Kolb describes and how specifically I can apply one (or all) to my applied research project. I am also curious to explore the Kolb model’s limitations and whether more modern theoretical frameworks have been proposed.<\/p>\n\n\n\n
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References<\/p>\n\n\n\n
Harb, J. N., Durrant, S. O., & Terry, R. E. (1993). Use of the Kolb Learning Cycle and the 4MAT System in Engineering Education. Journal of Engineering Education, 82(2), 70\u201377. https:\/\/doi.org\/10.1002\/j.2168-9830.1993.tb00079.x<\/p>\n\n\n\n
Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development<\/em> (Vol. 1). Englewood Cliffs, NJ: Prentice-Hall. https:\/\/www.researchgate.net\/publication\/235701029<\/a><\/p>\n\n\n\n Konak, A., Clark, T., & Nasereddin, M. (2014). Using Kolb’s Experiential Learning Cycle to improve student learning in virtual computer laboratories. Computers & Education, 72, 11\u201322. https:\/\/doi.org\/10.1016\/j.compedu.2013.10.013<\/p>\n\n\n\n Muscat, M., & Mollicone, P. (2012). Using Kolb’s Learning Cycle to Enhance the Teaching and Learning of Mechanics of Materials. International Journal of Mechanical Engineering Education, 40(1), 66\u201378. https:\/\/doi.org\/10.7227\/IJMEE.40.1.10<\/p>\n\n\n\n