Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/2303
Title: An ensemble-based model for prediction of academic performance of students in undergrad professional course
Authors: Kamal P
Ahuja S.
Keywords: Student performace
Predition
Support vector machine
Educational data mining (EDM)
Educational settings
Higher educational institute (HEI).
Issue Date: 2019
Publisher: Emerald Group Publishing Ltd.
Abstract: Purpose The purpose of this paper is to develop a prediction model to study the factors affecting the academic performance of students pursuing an undergraduate professional course (BCA). For this purpose, the ensemble model of decision tree, gradient boost algorithm and Na�ve Bayes techniques is created to achieve best and accurate results. Monitoring the academic performance of students has emerged as an essential field as it plays a vital role in the accurate development and growth of students� critical and cognitive thinking. If the academic performance of students during the initial years of the graduation can be predicted, different stakeholders, i.e. government, policymakers, academicians, can be helped to make significant remedial strategies. This comprehensible practice can go a long way in shaping the ideologies of young minds, enhancing pedagogical practices and reframing of curriculum. This study aims to develop positive steps that can be taken to enhance future endeavours in the field of education. Design/methodology/approach A questionnaire was prepared specifically to find out influential factors affecting the academic performance of the students. Its specific area of investigation was demographic, social, academic and behavioural factors that influence the performance of the students. Then, an ensemble model was built using three techniques based on accuracy rate. A 10-fold cross-validation technique was applied to access the fitness of results obtained from proposed ensemble model. Findings The result obtained from ensemble model provides efficient and accurate prediction of student performance and helps identify the students that are at risk of failing or being a drop-out. The effect of previous semester�s academic performance shows a significant impact on current academic performance along with other factors (such as number of siblings and distance of university from residence). Any major mishap during past one year also affects the academic performance along with habit-based behavioural factors such as consumption of alcohol and tobacco. Research limitations/implications Though the existing model considers aspects related to a student�s family income and academic indicators, it tends to ignore major factors such as influence of peer pressure, self-study habits and time devoted to study after college hours. An attempt is made in this paper to examine the above cited factors in predicting the academic performance of the students. The need of the hour is to develop innovative models to assess and make advancements in the present educational set-up. The ensemble model is best suited to study all factors needed to accomplish a robust and reliable model. Originality\value The present model is developed using classification and regression algorithms. The model is able to achieve 99 per cent accuracy with the existing data set and is able to identify the influential factors affecting the academic performance. As early detection of at-risk students is possible with the proposed model, preventive and corrective measures can be proposed for improving the overall academic performance of the students.
URI: 10.1108/JEDT-11-2018-0204
http://hdl.handle.net/123456789/2303
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