Open AccessStudents Competency level Prediction
Author(s) -
Sonali Kadam
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.35510
Subject(s) - competence (human resources) , core competency , psychology , task (project management) , process (computing) , computer science , knowledge management , quality (philosophy) , psychological resilience , medical education , engineering , management , medicine , social psychology , philosophy , epistemology , economics , operating system , systems engineering
Companies always thrive to achieve best whether that be in the quality they offer or the freshers they recruit. Organizations recruit students who are effective and efficient in work and they have different techniques to determine this. This ability of being effective and efficient is known as competency. Various researchers have understood its importance and defined Competence from time to time. According to [1]Chan and her team (the University of Hong Kong)(2019) has defined competency as- the holistic competency is an umbrella term inclusive of different types of generic skills (e.g. critical thinking, problem-solving skills), positive values and attitudes (e.g. resilience, appreciation for others) which is essential for a student's life long learning and whole person development. Knowing one's Competency level is not an easy task. It needs a 360 degree view to understand it. Till now there is not a specific way of determining it. And graduates don’t know their competency level until they face the recruitment process. Knowledge of competency level at an early stage is necessary for students to improve and invest their time to become more competent so as to get a job in their core field. To overcome this problem we are designing a system which would predict the competency level of the computer science engineer and related graduates. To build this system we are using unsupervised machine learning algorithms. The predicted competency level could be used by students to understand how hard they need to work so as to get a job in their core field.