Engineerings curriculum quality management using artificial intelligence

Abstract

This study investigated alignment of industrial engineering curriculum at the University of South Africa, to the skill and knowledge needs of industry. The study developed a curriculum management system that utilises artificial intelligence to interpret the skill/knowledge requirements of the industry from job advertisements, and then takes the approach of decomposed (atomic) curriculum management to align curriculum to industry. The concept of ’atomising’ curriculum is characterised by decomposing program courses or modules into distinct micro-curriculum elements which although highly complex to manipulate, provide unmatched robustness in curriculum alignment to industry. The study contributed a blend of reforms and improvements to the industrial engineering curriculum in the UNISA context and developed an intelligent management system to manipulate curriculum to better align to the needs of industry. The main objective of the study was to develop a system to support decision making in bridging the gap between higher education and industry needs, in view of the graduate engineer. The problem is misalignment of curriculum to industry needs. One reason for misalignment is that in some cases, teachers interpret and accept curriculum in different ways, according to the unique individual strengths, weaknesses, experience, personality and background of the teachers. Regardless, the result is curriculum misalignment, which was shown to ultimately contribute to problems such as graduate youth unemployment, skill underutilisation and low innovation. In this study therefore, the intention is to develop an intelligent and automated system which can support the management of curriculum for improved alignment to industry needs. The methodology begins with a survey carried out to map the needs of industry in terms of skill and knowledge requirement, vi from a graduate industrial engineer perspective. The curriculum management system is designed to align curriculum to industry, based on the needs presented by both the employment avenue and the entrepreneurship avenue. Control data is obtained from on-line job advertisement platforms, which after the necessary preprocessing, is fed into the management system. The curriculum management system maps industry needs to curriculum specifications by interpreting the qualitative job advertisement information into ranked quantitative curriculum elements by first converting job functions from the advertisements into some curriculum molecules then from the molecules into curriculum atoms (curriculum elements). These final curriculum elements become the means to curriculum adjustments, allowing curriculum manipulation across the entire program course. The complexity of the mapping process is proportional to the volume of control data. Results show that artificial intelligence (artificial neural network) sufficiently delivers satisfactory control. Results show that atomic curriculum manipulation, compared to the conventional molecular-type manipulation, presents a more meticulous and holistic approach to aligning engineering curriculum to industry. It was concluded from the study, that atomic curriculum manipulation not only improves the effectiveness of curriculum alignment, but also promotes curriculum integrity. Atomic curriculum manipulation, as proposed in this study, decentralises curriculum management to the teacher level, rather than the institutional level. This encourages improved teacher-student and teacher-teacher interaction. Further studies will adapt the proposed solution to any particular program of study.