Quantification of Knowledge Exchange Within Classrooms: An AI-based Approach

Abstract

Knowledge management improves efficiency and productivity of a company. A typical knowledge transfer pipeline, an enabler of knowledge management, consists of academics, higher education institutions, research funding bodies and companies. While the knowledge exchange mainstream sheds light on research collaborations, the evaluation of in-classroom knowledge exchange is often omitted, underestimating the impact this would have on the student employability. Current work on knowledge exchange at higher education institutions primarily focuses on: (i) collaborations with external parties, and (ii) identifying factors that affect knowledge sharing behaviours. This paper extends knowledge exchange to classroom teaching through: (i) formulating a framework among undergraduate Engineering students, and (ii) proposing an Artificial Intelligence based approach for evaluating the knowledge exchange process. The framework comprises of two group coursework with an intermediate handover event emulating an industrial workplace scenario in which knowledge exchange plays a key role. Then, an artificial intelligence-based visualisation technique processes data from two coursework-based surveys, completed before and after the abrupt handover event, to assess the change in the student intellectual backgrounds using two-dimensional maps embedding students as datapoints. The results interestingly reveal correlations between standard student evaluation metrics (for example grades, peer review and survey scores) and the formation of datapoint clusters. It is argued in the paper that the proposed artificial intelligence tool lends educators with tools to better understand the individual student performance in ways that are not captured by conventional academic assessments.