STUDENT FINAL GRADE PREDICTION BASED ON LINEAR REGRESSION

In the world of Massive Open Online Course (MOOC) and open education systems, students have flexibility to learn anything with ease as the learning content is easily available. But this facility can make student complacent. Therefore, it becomes difficult to predict student performance in advance. In this research an attempt is made to help the student to know his/her performance in advance by using univariate linear regression model. We collected the marks of internal exam components of one subject to predict the final grade in that subject. The internal marks are normalized to 100 (percentage) to have accurate results. The model provides predicted grade of final examination in particular subject. It also helps students to know how many marks in the internal examination are required to get particular grade.

[1]  Tsunenori Mine,et al.  Predicting students' grades based on free style comments data by artificial neural network , 2014, 2014 IEEE Frontiers in Education Conference (FIE) Proceedings.

[2]  Ľ Benko,et al.  Analysis of student behavior in virtual learning environment depending on student assessments , 2015, 2015 13th International Conference on Emerging eLearning Technologies and Applications (ICETA).

[3]  M. N. Vijayalakshmi,et al.  Mining of student academic evaluation records in higher education , 2012, 2012 International Conference on Recent Advances in Computing and Software Systems.

[4]  Shaobo Huang,et al.  Work in progress: Early prediction of students' academic performance in an introductory engineering course through different mathematical modeling techniques , 2012, 2012 Frontiers in Education Conference Proceedings.

[5]  S. Taruna,et al.  An empirical analysis of classification techniques for predicting academic performance , 2014, 2014 IEEE International Advance Computing Conference (IACC).

[6]  Vinayak Hegde,et al.  Prediction of students performance using Educational Data Mining , 2016, 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE).

[7]  Maria Teresa Restivo,et al.  Accuracy of self-assessment among graduate students in mechanical engineering , 2015, 2015 International Conference on Interactive Collaborative Learning (ICL).

[8]  Said Elnaffar.  Using software metrics to predict the difficulty of code writing questions , 2016, 2016 IEEE Global Engineering Education Conference (EDUCON).

[9]  Norlida Buniyamin,et al.  An overview of using academic analytics to predict and improve students' achievement: A proposed proactive intelligent intervention , 2013, 2013 IEEE 5th Conference on Engineering Education (ICEED).

[10]  Kuljit Kaur,et al.  Analyzing the effect of difficulty level of a course on students performance prediction using data mining , 2015, 2015 1st International Conference on Next Generation Computing Technologies (NGCT).

[11]  Benilda Eleonor V. Comendador,et al.  An educational model based on Knowledge Discovery in Databases (KDD) to predict learner's behavior using classification techniques , 2016, 2016 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC).

[12]  Sebastián Ventura,et al.  Predicting Academic Achievement Using Multiple Instance Genetic Programming , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[13]  Williamjeet Singh,et al.  Implementation of student SGPA Prediction System(SSPS) using optimal selection of classification algorithm , 2016, 2016 International Conference on Inventive Computation Technologies (ICICT).

[14]  Ozren Gamulin,et al.  Comparing classification models in the final exam performance prediction , 2014, 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[15]  Shakirah Mohd Taib,et al.  Determinants of student performance in advanced programming course , 2012, 2012 International Conference for Internet Technology and Secured Transactions.

[16]  Hanife Goker,et al.  Improving an Early Warning System to Prediction of Student Examination  Achievement , 2014, 2014 13th International Conference on Machine Learning and Applications.

[17]  Zhiwu Liu,et al.  Prediction and Analysis for Students' Marks Based on Decision Tree Algorithm , 2010, 2010 Third International Conference on Intelligent Networks and Intelligent Systems.

[18]  Frederick W. B. Li,et al.  Predicting Performance in an Introductory Programming Course by Logging and Analyzing Student Programming Behavior , 2013, 2013 IEEE 13th International Conference on Advanced Learning Technologies.