Cognitive Machine Learning Framework to Evaluate Competency during the Course Endorsement for Computer Science Postgraduates

Abstract

The approach of content designing along with content delivery by the facilitator changes in accordance to the environment undergoing a dramatic shift. Career-oriented learners look forward for the endorsement in the course which persuades them in career advancement. In India looking at the current scenario having more than 789 universities and 20,000 colleges offering postgraduate courses, the challenging task is to find better course endorsement options. The research framework is based on neuro-education, the consequence of using brain imaging methods is to serve as analytical tools for measuring the educational interferences of the learner for shaping a better arrangement for increase learning ability. The neuro signals collected through the EEG device are analyzed with the machine learning algorithms resulting to a cognitive machine learning model to foresee the competency of student in picking a course labeling a output to Attention Span endeavor to choose the course. The research work will be organized in four phases. Phase 1 is discretize cognitive skill of a learner. Phase 2 is procuring neuro signals of a learner. Phase 3 is applying active learning methods to facilitate the content at the time of the course endorsement. Phase 4 is developing cognitive machine learning model 34to generate neuro feedback. The neuro feedback is generated, beneficial to facilitator as well as the learner. The learner can endorse the course to uplift knowledge domain. In this study, a sample size of 60 undergraduate program and postgraduate program learners are taken into consideration while choosing elective course for the higher semester.