Quantification of Knowledge Exchange Within Classrooms: An AI-based Approach
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The industry is increasingly becoming a highly dynamic environment with competence and turnover indicators as prevailing characteristics, where only those who can both acquire and pass knowledge effectively can thrive. Little attention is paid to the value of incorporating Knowledge Exchange (KE) in classroom teaching, leaving students out of the current KE pipeline. Current strategies fostering KE stem from research corridors with the aim of building internal and external collaborations, and growing industrial arms of academia. Communities involved in the current knowledge exchange pipeline are typically the academics, Higher Education Institutes, research funding bodies and industries. Embracing KE in academic courses is likely to produce competent graduates ready to work in various industries. STEM courses are found effective in cultivating task-specific technical skills. However, graduates tend to exhibit a slow learning curve after taking over an on-going project, and poorly respond to abrupt changes in the hierarchy or workflow. This paper attempts to quantify the KE process in which teacher-to-student and peer interactions play a major role. Without the knowledge of students, a coursework was designed such that two checkpoints reflect a before-and-after abrupt change scenario which is common in industrial environments. A survey-based approach was used to measure students’ knowledge at each checkpoint. With the aid of an Artificial Intelligence based visualisation technique, we are able to extract insights from a low-dimensional map, supported with standard metrics, to gauge the individual’s knowledge and how they are positioned within the entire population of students.
Omar Elnaggar, University of Liverpool, United Kingdom
Roselina Arelhi, University of Sheffield, United Kingdom
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