Utilizing Relevant Academic and Personality Features from Big Unstructured Data to Identify Good and Bad Fit Students

Education is the backbone for the success of every economy and industry. It is of vital importance to understand it through lens of academic and real world data for our students and job candidates. Such analytics and knowledge can help to improve the system towards making right academic and career choices for fresh individuals. Traditionally, very little data is used by individual to make decision and most of the time; he or she is at mercy of trend, family inclination, finance and inner motivation. However, chances of success or failures are absolute unknown to them at that time. In this paper, we discuss the potential of using personality data from social networking such as Facebook, Twitter, and LinkedIn and correlating it with academic and career data to improve prediction and classify good fit and bad students accordingly. We present framework to utilize huge data with relevant features of personality of individual to reveal the useful analytics and insights. This work is in conjunction with our main research using Stochastic Probability Distribution, Bayesian Networks and Excel data tools. We provide relevant study and conclude with future work with recommendation to produce more Good fit students in our school and colleges.