Optical fog‐assisted smart learning framework to enhance students' employability in engineering education.

Sood, Sandeep K.; Singh, Kiran Deep

In the present scenario, the state of engineering education worldwide is facing a big challenge of the employability of graduates. Cloud computing technology has changed the educational landscape by allowing educators and administrators to shift to the cloud for making actionable decisions. In this paper, a smart learning framework is proposed where universities/colleges can advocate the management of employability enhancement and start skill‐set enhancement courses through e‐learning. This proposed framework monitors the academic/skill data of students to classify their employability at the early stage of graduation. In addition, an algorithm is proposed for skill‐set assessment to find various clusters of students who lack in required skill‐set. The predicted clusters of students can be offered opportunities to improve their required skill‐set through e‐learning. Further, we used the design science research methodology for conducting online courses. Moreover, the prediction of resource usage of the proposed e‐learning framework is calculated to improve its effectiveness. In our experiment, results show that our proposed hybrid classier achieves 96.45% accuracy of classification. The prediction of resource usage offers better opportunities for adaptive resource elasticity. The results depict that by introducing the proposed framework in engineering education; more effective decisions can be made to improve the employability and overall growth of students.

ENGINEERING education; EMPLOYABILITY; EDUCATION policy; SCIENTIFIC method; EDUCATIONAL change; LEARNING management system; EDUCATIONAL technology

Computer Applications in Engineering Education, 2019, Vol 27, Issue 5, p1030

1061-3773

Academic Journal

10.1002/cae.22120