Analyzing Navigational Data and Predicting Student Grades Using Support Vector Machine
The advent of Learning Management System (LMS) has unfolded a unique opportunity to predict student grades well in advance which benefits both students and educational institutions. The objective of this study is to investigate student access patterns and navigational data of Blackboard (Bb), a form of LMS, to forecast final grades. This research study consists of students who are pursuing a Networking course in Information Science and Technology Department (IST) at George Mason University (GMU). The gathered data consists of a wide variety of attributes, such as the amount of time spent on lecture slides and other learning materials, number of times course contents are accessed, time and days of the week study material is reviewed, and student grades in various assessments. By analyzing these predictors using Support Vector Machine, one of the most efficient classification algorithms available, we are able to project final grades of students and identify those individuals who are at risk for failing the course so that they can receive proper guidance from instructors. After comparing actual grades with predicted grades, it is concluded that our developed model is able to accurately predict grades of 70% of the students. This study stands unique as it is the first to employ solely online LMS data to successfully deduce academic outcomes of students.
Kuhn, Max, and Kjell Johnson. “Applied Predictive Modeling” (2013). doi:10.1007/978-1-4614-6849-3..
Rastrollo-Guerrero, Juan L., Juan A. Gómez-Pulido, and Arturo Durán-Domínguez. “Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review.” Applied Sciences 10, no. 3 (February 4, 2020): 1042. doi:10.3390/app10031042.
Alyahyan, Eyman, and Dilek Düştegör. “Predicting Academic Success in Higher Education: Literature Review and Best Practices.” International Journal of Educational Technology in Higher Education 17, no. 1 (February 10, 2020). doi:10.1186/s41239-020-0177-7.
Burger, Andri, and Luzelle Naudé. “Predictors of Academic Success in the Entry and Integration Stages of Students’ Academic Careers.” Social Psychology of Education 22, no. 3 (May 25, 2019): 743–755. doi:10.1007/s11218-019-09497-3.
Cardona, Tatiana A., and Elizabeth a. Cudney. “Predicting Student Retention Using Support Vector Machines.” Procedia Manufacturing 39 (2019): 1827–1833. doi:10.1016/j.promfg.2020.01.256.
Shahiri, Amirah Mohamed, and Wahidah Husain. "A Review on Predicting Student's Performance Using Data Mining Techniques." Procedia Computer Science 72 (2015): 414-422. doi: 10.1016/j.procs.2015.12.157.
Buenaño-Fernández, Diego, David Gil, and Sergio Luján-Mora. “Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study.” Sustainability 11, no. 10 (May 17, 2019): 2833. doi:10.3390/su11102833.
Oloruntoba, S. A., and J. L. Akinode. "Student academic performance prediction using support vector machine." International Journal of Engineering Sciences and Research Technology 6, no. 12 (2017): 588-597. doi:10.5281/zenodo.1130905.
Minaei-Bidgoli, B., D.A. Kashy, G. Kortemeyer, and W.F. Punch. “Predicting Student Performance: An Application of Data Mining Methods with an Educational Web-Based System.” 33rd Annual Frontiers in Education, 2003. FIE. (November 2003). doi:10.1109/fie.2003.1263284.
Burman, Iti, and Subhranil Som. “Predicting Students Academic Performance Using Support Vector Machine.” 2019 Amity International Conference on Artificial Intelligence (AICAI) (February 2019). doi:10.1109/aicai.2019.8701260.
Kotsiantis, Sotiris, Christos Pierrakeas, and Panagiotis Pintelas. "Predicting Students' performance in Distance Learning Using Machine Learning Techniques." Applied Artificial Intelligence 18, no. 5 (2004): 411-426. doi:10.1080/08839510490442058.
Kabra, R. R., and R. S. Bichkar. "Performance prediction of engineering students using decision trees." International Journal of computer applications 36, no. 11 (2011): 8-12.
Devasia, Tismy, Vinushree T P, and Vinayak Hegde. “Prediction of Students Performance Using Educational Data Mining.” 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE) (March 2016): 91-95. doi:10.1109/sapience.2016.7684167.
Sorour, Shaymaa E., Jingyi Luo, Kazumasa Goda, and Tsunenori Mine. “Correlation of Grade Prediction Performance with Characteristics of Lesson Subject.” 2015 IEEE 15th International Conference on Advanced Learning Technologies (July 2015): 247-249. doi:10.1109/icalt.2015.24.
Ashenafi, Michael Mogessie, Giuseppe Riccardi, and Marco Ronchetti. "Predicting students' final exam scores from their course activities." In 2015 IEEE Frontiers in Education Conference (FIE) (October 2015): 1-9. doi:10.1109/FIE.2015.7344081.
Brodic, D., A. Amelio, and R. Jankovic. “Comparison of Different Classification Techniques in Predicting a University Course Final Grade.” 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (May 2018): 1382-1387. doi:10.23919/mipro.2018.8400249.
Kadambande, A., Thakur, S., Mohol, A., and Ingole, A. M. “Predict Student Performance by Utilizing Data Mining Technique and Support Vector Machine.” International Research Journal of Engineering and Technology 4, no. 5 (May 2017): 2818- 2821.
Damuluri, Ms SriUdaya, and Pouyan Ahmadi. "A Study of Several Classification Algorithms to Predict Students’ Learning Performance." 126th American Society for Engineering Education (2019).
Buuren, S. V. “Flexible Imputation of Missing Data, Second Edition” (July 20, 2018). doi: 10.1201/b11826.
- There are currently no refbacks.
Copyright (c) 2020 SriUdaya Damuluri, Khondkar Islam, Pouyan Ahmadi