The week's pick
Reseach Article
Students’ Success Prediction based on Bayes Algorithms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 178 - Number 7 |
| Year of Publication: 2017 |
| Authors: Alaa Khalaf Hamoud, Aqeel Majeed Humadi, Wid Akeel Awadh, Ali Salah Hashim |
10.5120/ijca2017915506
|
Alaa Khalaf Hamoud, Aqeel Majeed Humadi, Wid Akeel Awadh, Ali Salah Hashim . Students’ Success Prediction based on Bayes Algorithms. International Journal of Computer Applications. 178, 7 ( Nov 2017), 6-12. DOI=10.5120/ijca2017915506
Abstract
Prediction in data mining is a sophisticated task that is conducted in various disciplines. Given that the overall success of educational institutions can be measured by their students’ success, many studies are dedicated to predicting it. This paper provides a model of student’s success prediction based on Bayes algorithms and suggests the best algorithm based on performance details. Two built Bayes Algorithms (naïve Bayes and Bayes network) were used in this model with students’ questionnaire answers. The questionnaire consists of 62 questions that cover the fields affecting students’ performance the most. The questions refer to health, social activity, relationships and academic performance. The questionnaire is constructed based on a Google form and open-source applications (LimeSurvey); the total number of student answers is 161. To build this model, the tool Weka 3.8 is used. The overall model design process can be divided into two stages. The first stage is finding the most correlated questions to the final class, and the second is applying algorithms and finding the optimal algorithm. A comparison is made between these two Bayes algorithms based on performance details. Finally, the naïve Bayes algorithm is selected as an optimal choice for students’ success prediction.
References
- Shruthi P and Chaitra B P, “Student Performance Prediction in Education Sector Using Data Mining”, International Journal of Advanced Research in Computer Science and Software Engineering. Volume 6, Issue 3, March 2016.
- Agrawal Bhawana D, Gurav Bharti B,” Review on Data Mining Techniques Used For Educational System”. International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 11, November 2014).
- Kolo David Kolo, Solomon A. Adepoju, John Kolo Alhassan, “A Decision Tree Approach for Predicting Students Academic Performance”, I. J. Education and Management Engineering, 2015, 5, 12-19.
- Romero, Cristóbal, Sebastián Ventura, and Enrique García. “Data mining in course management systems: Moodle case study and tutorial.” Computers & Education 51.1 (2008): pp. 368-384.
- Friedman, Nir, Dan Geiger, and Moises Goldszmidt. “Bayesian network classifiers.” Machine Learning 29.2-3 (1997): 131-163.
- Zhang, Harry. “The optimality of naïve Bayes.” AA 1.2 (2004): 3. Israel, Glenn D., Determining Sample Size. Large databases. Proc. of ACM SIGMOD, pp. 207–216, 1993.
- R. Z. G. Humera Shaziya, “Prediction of Students Performance in Semester Exams using a Naïve Bayes Classifier,” International Journal of Innovative Research in Science, Engineering and Technology, vol. 4, no. 10, pp. 9823-9829, October 2015.
- Kaur, Gurmeet, and Williamjit Singh. “Prediction of Student Performance Using Weka Tool.” (2016).
- Jayaprakash, Sujith, E. Balamurugan, and Vibin Chandar. “Predicting Students’ Academic Performance Using Naïve Bayes Algorithm.” 8th Annual International Applied Research Conference ,2015.
- Osmanbegović, Edin, and Mirza Suljić. “Data mining approach for predicting student performance.” Economic Review 10.1 (2012): 3-12.
- George Dimitoglou, James A. Adams, and Carol M. Jim,” Comparison of the C4.5 and a Naïve Bayes Classifier for the Prediction of Lung Cancer Survivability”, Journal of Computing, Volume 4, Issue 8, 2012.
- Pandey, U. K. and Pal, S., “A Data Mining View on Class Room Teaching Language”, (IJCSI) International Journal of Computer Science Issue, Vol. 8, Issue 2, March -2011, 277-282, ISSN: 1694-0814.
- Leung, K. Ming. “Naïve Bayesian classifier.” Polytechnic University Department of Computer Science/Finance and Risk Engineering (2007).
- Witten, Ian H., et al. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2016.
- Varis, Olli. “Belief networks for modelling and assessment of environmental change.” Environmetrics 6.5 (1995): 439-444.
- Peters, Robert Henry. A critique for ecology. Cambridge University Press, 1991.
- Borsuk, Mark E., Craig A. Stow, and Kenneth H. Reckhow. “A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis.” Ecological Modelling 173.2 (2004): 219-239.
- www.educationaldatamining.org.
- Titus deLaFayette Winters, Educational Data Mining: Collection and Analysis of Score Matrices for Outcomes-based Assessment, University of California, June 2006.
- Agrawal, Rakesh, Tomasz Imieliński, and Arun Swami. “Mining association rules between sets of items in large databases.” Acm sigmod record. Vol. 22. No. 2. ACM, 1993.
- Israel, Glenn D. “Determining Sample Size. University of Florida IFAS extension.” (2009).
- Carson B. “The transformative power of action learning.” Retrieved 2017 from http://wial.org/executive-board/bea-carson-executive-board/.
- Sekaran, Uma, and Roger Bougie. Research methods for business: A skill building approach. John Wiley & Sons, 2016.
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