Correspondence between KOLBAS experiential learning and rigor-relevance framework

Autor: Vlad Jinga, Doru Ursutiu, Gomel Samoila
Rok vydání: 2017
Předmět:
Zdroj: EDUCON
DOI: 10.1109/educon.2017.7942923
Popis: Today, many theories of learning work in parallel. Each of these theories contains valuables notions useful in knowledge transfer but, unification efforts are weaker than efforts to create new concepts of learning. Recently, the powerful involving of technology (Internet, remote experiment, learning platforms, etc.) in teaching and learning led to a regrouping of the concepts regarding the transfer and acquisition of knowledge. One point of view regarding this process is presented in this paper. It is speaking about an independent combination between Bloom's taxonomy and applications and possible correlations with Kolb s learning model. Each theory contains irrefutable truths and simultaneously neglects important issues of the process. An attempt to build a correspondence between these two models shows that: • Teaching is a combination of theory and practical experiments, what usually are called courses for “knowledge transfer”. Today is generally accepted that the sum of courses contained in a curriculum adequately prepares the student for the future career; • Charts Rigor-Relevance however shows that the stage of “applications across disciplines” brings closer the student to the real life; • The same diagram shows that in teaching environment the applications located in unpredictable environment lead to the formation of skills closer to the real career problems; The question is whether the Kolbs experiential learning cycle has the same conclusions as Rigor-Relevance, especially since it considers learning starting from experiment not from knowledge. The paper reconsiders the cycle of Kolb theory content and shows that each of the four stages, which this cycle passes, contains obviously both elements from the taxonomy of Bloom and the possibility to organize experiments passing successively from those that are required for a single discipline towards the experiments across disciplines under predictable and unpredictable conditions.
Databáze: OpenAIRE