Community of practice: converting IT graduate students into specialists via professional knowledge sharing

Abstract

Purpose

The paper aims to highlight how an applied learning framework or “community of practice” (CoP) combined with a traditional theoretical course of study enables the identification of teaching-learning processes which facilitate knowledge transfer from practitioners to graduate information technology (IT) students for quicker integration in the labour market.

Design/methodology/approach

CoPs are identified based on cluster analysis according to Kolb’s Learning Style Inventory (1984), with data obtained through a survey. Empirical research is applied to the CoP developed within a non-formal learning framework, principal new actors being IT specialists linked to graduate IT students and teachers on a traditional university course. Graduate IT students can gain knowledge of the ideal employee and the social and emotional skills needed to integrate with the IT labour market.

Findings

The K-Means algorithm helps to identify clusters of graduate IT students displaying necessary knowledge acceptance behaviour to convert them into specialists. The results of the cluster analysis show different learning styles of the labour force, providing an overview of candidate selection methods and the knowledge, skills and attitudes expected by users.

Research limitations/implications

Although the research adds value to the existing literature on learning styles and the knowledge and core skills needed by IT specialists, it was limited to an emerging market.

Originality/value

The study provides a preliminary overview of graduate IT students’ attitudes from an emerging market to the re-engineering of academic learning contexts to facilitate professional knowledge transfer, converting them into IT practitioners and integrating them in the labour market of an emerging economy.

Citation

Stanca, L., Dabija, D.-C. and Păcurar, E. (2022), "Community of practice: converting IT graduate students into specialists via professional knowledge sharing", Kybernetes, Vol. 51 No. 2, pp. 557-581. https://doi.org/10.1108/K-10-2020-0711