Abstract
Industry readiness of Engineering students community is a big challenge in the recent campus recruitments. 21st century skills are completely mapped with the technical and non – technical knowledge background of the engineering graduates. In this paper the work narrated the process of identifying the parameters for skill assessment of the candidates and derived a learner model using deep learning framework. Further the model can be used to predict the employability readiness of candidates.
Highlights
Campus placements are the vital key indicator for the students to choose their professional course of study
Abstract:Industry readiness of Engineering students community is a big challenge in the recent campus recruitments. 21st century skills are completely mapped with the technical and non – technical knowledge background of the engineering graduates
In this paper the work narrated the process of identifying the parameters for skill assessment of the candidates and derived a learner model using deep learning framework
Summary
Campus placements are the vital key indicator for the students to choose their professional course of study. During their pre final and final year courses of study, their complete starvation sticks to placement oriented programs. Their preparation comprises of both technical as well as non technical domains. Our work is ready to concentrate with 20% of students exiting campus without placements From their second year onwards, 21st century skill based parameters must be explained and train them using the outcome of the model derived. A parameterized framework can be designed using the convolutional neural network and analyze the outcome of the students in the regular intervals This will enhance the placement opportunity for the identified community. Allison et al[3] illustrated personal
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