Assessing employability of students using data mining techniques

Graduate employability is an increasingly major concern for academic institutions and assessing student employability provides a way of linking student skills and employer business requirements. Enhancing student assessment methods for employability can improve their understanding about companies in order to get suitable company for them. So, enhanced employability prediction of student can help them to match their desirability with company requirements and fill the gap between the two in order to fit the job profile of company for which they are looking for. Data mining with intelligent learning algorithms is used to enrich the current assessment process. The data for training classifier model is collected from survey by making students appear for assessment test to measure their skills. This data can be valuable to find their proficiency in various skill sets such as soft skills, problem-solving skills, technical skills etc. as well as their weaknesses that they need to overcome for being employable in various companies. Student skillset data can also be helpful to map against the companies criteria in order to suggest student with the list of companies having requirements matching with their skills.

I. Introduction

Nowadays students employability is a major concern for the institutions and predicting their employability beforehand can help in taking timely actions in order to increase institutional placement ratio. To know weakness before appearing for interview of any company can help students to work in areas that they need to improve in order to best match the skillset required by company. Data mining technique such as classification is best suited for predicting the employability of students. The application of data mining in student employability is to search for significant relationships such as patterns, association and changes among variables in datasets. It provides classification methods to predict the level of employability for students [2]. Predicting student employability can help identify the students who are at risk of unemployment and thus management can intervene timely and take essential steps to train the students to improve their performance.