E-Learning & decision making system for automate students assessment using remote laboratory and machine learning

Abstract

This paper describes an implementation of a remote laboratory system for the practical works (PW) of electronics, this system make available to target and analysis the gaps, weaknesses and lack of scientific knowledge of students in the context of electric engineering through data mining algorithms and students’ study behavior. Experimental work has traditionally been developed in laboratories. However, the increase in the number of higher education students in the last decades has put pressure on the physical structures and resources of laboratories. To overcome this, computational simulations and remote laboratories have been developed enabling the expansion of educational boundaries. this paper provides new opportunities to enhance the student’s learning process. The results are presented and discussed according to two levels. The first is development a complete system of remote laboratory E@Slab and compare it with the related work. second level, we present an algorithms of Intelligence Artificial that automate evaluation and classify students in different groups attending to an assessment rubric. After this classification we compare the obtained results from algorithms of Intelligence Artificial with the levels obtained from interviews with the students and from the practical work review for to be a validation of sorts. Finally we compare the two results and we remark that algorithm classifies correctly the students with an accuracy of more than 90%.