Chapter 4 - Computational intelligence in human-computer interaction–Case study on employability in higher education

Abstract

Modern intelligent systems nowadays make significant use of human–computer interaction (HCI). Questions of human perception, human intelligence, human cognitive capabilities, human decision-making capabilities, and interactive techniques of visualization are dealt with by HCI. Whereas, the questions of machine intelligence and data mining is dealt with by knowledge discovery in databases (KDD). HCI mainly emphasizes supervised methods. Automatic computational methods can be worked upon by combining the capabilities of both HCI and KDD. The development of algorithms that are scalable for discovering earlier unknown associations in the data thus are based on automatic computational methods by combining the capabilities of both. The first HCI happens in the primitive years of a child. The digital era we are living in and all the more pandemics have made our education system adapt to the newest technology with open hands. Challenging times in the field of education have been conquered by the fast adaptation of the latest technology and trends. It is not going to stop here and will become an integral part of our education system. The latest technologies like artificial intelligence and data mining are known for their predictive capabilities and can play a significant role in the field of education, specifically in the higher education system. Every year a substantial number of graduates strive in the job market for decent employment opportunities. On the contrary, the economy of the world is not generating adequate employment opportunities. The pandemic has made the situation even worse with lots of job cutdowns. To increase the possibility of obtaining a decent job, institutes need to improve their academic qualification. They should also equip students with essential employability skills. Accurate prediction of graduates' employability at early stages such as in the very first year of their course enrollment can be of great help. This chapter projects a proposal for building a system that applies the KDD–HCI integrated approach by showcasing a case study for prediction of employability in higher education by using data generated and stored after HCI in various tests. The framework proposed in this chapter provides a generalized solution to students' employability prediction by integrating supervised and unsupervised techniques of artificial intelligence. These skills can be tested first with HCI by conducting various tests of students at the entry level. The scores obtained then go through the process of employability prediction with the help of the proposed framework, which can help in deciding the skills to be improved in an individual student before they compete, thus improving the chance of selection. Therefore, the proposed framework provides a customized solution to every student.