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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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2016-08-24 12:25:15
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12831
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UniSZA
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[1] Y. Zhang, S. Oussena, T. Clark, & H. Kim, Use Data Mining to Improve Student Retention in Higher Education – A Case Study. ICEIS - 12th International Conference on Enterprise Information Systems 2010, (2010). http://dx.doi.org/10.5220/0002894101900197 [2] R. B. Sachin, & M. S. Vijay, A Survey and Future Vision of Data Mining in Educational Field, 2012 Second International Conference on Advanced Computing & Communication Technologies, (2012), 96–100. http://dx.doi.org/10.1109/acct.2012.14 [3] A. A. Aziz, N. H. Ismail, & F. Ahmad, Mining Students’ Academic Performance, Journal of Theoretical and Applied Information Technology, 53 (2013), no. 3, 485–495. [4] R. S. J. Baker, Data Mining for Education, Advantages Relative to Traditional Educational Research Paradigms, (2010). [5] C.-T. Lye, L.-N. Ng, M. D. Hassan, W.-W. Goh, C.-Y. Law, & N. Ismail, Predicting Pre-university Student’s Mathematics Achievement, Procedia - Social and Behavioral Sciences, 8 (2010), 299–306. http://dx.doi.org/10.1016/j.sbspro.2010.12.041 [6] M. F. M. Mohsin, M. H. A. Wahab, M. F. Zaiyadi, & C. F. Hibadullah, An Investigation into Influence Factor of Student Programming Grade Using Association Rule Mining, International Journal on Advances in Information Sciences and Service Sciences, 2 (2010), no. 2, 19–27. http://dx.doi.org/10.4156/aiss.vol2.issue2.3 [7] M. Wook, Y. H. Yahaya, N. Wahab, M. R. M. Isa, N. F. Awang, & H. Y. Seong, Predicting NDUM Student’s Academic Performance Using Data Mining Techniques, 2009 Second International Conference on Computer and Electrical Engineering, (2009), 357–361. http://dx.doi.org/10.1109/iccee.2009.168 [8] N. M. Norwawi, S. F. Abdusalam, C. F. Hibadullah, & B. M. Shuaibu, Classification of Student’s Performance in Computer Programming Course According to Learning Style, 2009 2nd Conference on Data Mining and Optimization, (2009), 37–41. http://dx.doi.org/10.1109/dmo.2009.5341912 [9] H. Othman, Z. M. Nopiah, I. Asshaari, N. Razali, M. H. Osman, & N. Ramli, (2009), A Comparative Study of Engineering Students on Their Pre-University Results with Their First Year Performance at Fkab, UKM. Seminar Pendidikan Kejuruteraan dan Alam Bina (PeKA’09). [10] Kabakchieva, D., Predicting Student Performance by Using Data Mining Methods for Classification, Cybernetics and Information Technologies, 13 (2013), no. 1, 61–72. http://dx.doi.org/10.2478/cait-2013-0006 [11] S. Prakash, K. S. Ramaswami, & C. A. Post, Fuzzy K- Means Cluster Validation for Institutional Quality Assessment, Communication and Computational Intelligence (INCOCCI), 2010 International Conference, (2010), 628–635. [12] S. Huang, & N. Fang, Work in Progress - Prediction of Students’ Academic Performance in an Introductory Engineering Course, In 41st ASEE/IEEE Frontiers in Education Conference, (2011), 11–13. http://dx.doi.org/10.1109/fie.2011.6142729 [13] S. Sembiring, M. Zarlis, D. Hartama, & E. Wani, Prediction of student academic performance by an application of data mining techniques, 2011 International Conference on Management and Artificial Intelligence, 6 (2011). 110–114. [14] P. Golding, L. Facey-Shaw, & V. Tennant, Effects of Peer Tutoring, Attitude and Personality on Academic Performance of First Year Introductory Programming Students, 36th ASEE/IEEE Frontiers in Education Conference, (2006), 7–12. http://dx.doi.org/10.1109/fie.2006.322662 [15] S. Parack, Z. Zahid, & F. Merchant, Application of data mining in educational databases for predicting academic trends and patterns, 2012 IEEE International Conference on Technology Enhanced Education (ICTEE), (2012), 1–4. http://dx.doi.org/10.1109/ictee.2012.6208617 [16] E. P. I. García, & P. M. Mora, Model Prediction of Academic Performance for First Year Students, 2011 10th Mexican International Conference on Artificial Intelligence, (2011), 169–174. http://dx.doi.org/10.1109/micai.2011.28 [17] A. T. Chamillard, Using student performance predictions in a computer science curriculum, ACM SIGCSE Bulletin, 38 (2006), no. 3, 260. http://dx.doi.org/10.1145/1140123.1140194 [18] D. T. Larose, Discovering Knowledge in Data: An Introduction to Data Mining, Hoboken, NJ, USA: Wiley, 2005. http://dx.doi.org/10.1002/0471687545 [19] J. Shana, & T. Venkatachalam, Identifying Key Performance Indicators and Predicting the Result from Student Data, International Journal of Computer Applications, 25 (2011), no. 9, 45–48. http://dx.doi.org/10.5120/3057-4169 [20] U. Kumar, & P. S. Pal, Data Mining: A prediction of performer or underperformer using classification, International Journal of Computer Science and Information Technologies (IJCSIT), 2 (2011), no. 2, 686–690. [21] M. Sharma, Development of Predictive Model in Education System: Using Naïve Bayes Classifier, International Conference and Workshop on Emerging Trends in Technology (ICWET 2011) – TCET, Mumbai, India, (Icwet), (2011), 185–186. http://dx.doi.org/10.1145/1980022.1980064 [22] S. Pal, Mining Educational Data Using Classification to Decrease Dropout Rate of Students, International Journal of Multidisciplinary Sciences and Engineering, 3 (2012), no. 5, 35–39. [23] E. Frank, & I. H. Witten, Generating Accurate Rule Sets without Global Optimization, In: Proc. Of The 15th Int. Conference on Machine Learning. [24] C. Romero, S. Ventura, P. G. Espejo, & C. Hervás, (2008). Data Mining Algorithms to Classify Students, in: The 1st International Conference on Educational Data Mining Montréal, Québec, Canada, (1998), 8–17.
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12831 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12831 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal image/jpeg inches 96 96 norman 773 1428 95 95 2016-08-24 12:25:15 1428x773 7138-01-FH02-FIK-16-06429.jpg UniSZA Private Access The prediction of students' academic performance using classification data mining techniques Applied Mathematical Sciences Data Mining provides powerful techniques for various fields including education. The research in the educational field is rapidly increasing due to the massive amount of students’ data which can be used to discover valuable pattern pertaining students’ learning behaviour. This paper proposes a framework for predicting students’ academic performance of first year bachelor students in Computer Science course. The data were collected from 8 year period intakes from July 2006/2007 until July 2013/2014 that contains the students’ demographics, previous academic records, and family background information. Decision Tree, Naïve Bayes, and Rule Based classification techniques are applied to the students’ data in order to produce the best students’ academic performance prediction model. The experiment result shows the Rule Based is a best model among the other techniques by receiving the highest accuracy value of 71.3%. The extracted knowledge from prediction model will be used to identify and profile the student to determine the students’ level of success in the first semester. 9 129 Hikari Ltd. Hikari Ltd. 6415-6426 [1] Y. Zhang, S. Oussena, T. Clark, & H. Kim, Use Data Mining to Improve Student Retention in Higher Education – A Case Study. ICEIS - 12th International Conference on Enterprise Information Systems 2010, (2010). http://dx.doi.org/10.5220/0002894101900197 [2] R. B. Sachin, & M. S. Vijay, A Survey and Future Vision of Data Mining in Educational Field, 2012 Second International Conference on Advanced Computing & Communication Technologies, (2012), 96–100. http://dx.doi.org/10.1109/acct.2012.14 [3] A. A. Aziz, N. H. Ismail, & F. Ahmad, Mining Students’ Academic Performance, Journal of Theoretical and Applied Information Technology, 53 (2013), no. 3, 485–495. [4] R. S. J. Baker, Data Mining for Education, Advantages Relative to Traditional Educational Research Paradigms, (2010). [5] C.-T. Lye, L.-N. Ng, M. D. Hassan, W.-W. Goh, C.-Y. Law, & N. Ismail, Predicting Pre-university Student’s Mathematics Achievement, Procedia - Social and Behavioral Sciences, 8 (2010), 299–306. http://dx.doi.org/10.1016/j.sbspro.2010.12.041 [6] M. F. M. Mohsin, M. H. A. Wahab, M. F. Zaiyadi, & C. F. Hibadullah, An Investigation into Influence Factor of Student Programming Grade Using Association Rule Mining, International Journal on Advances in Information Sciences and Service Sciences, 2 (2010), no. 2, 19–27. http://dx.doi.org/10.4156/aiss.vol2.issue2.3 [7] M. Wook, Y. H. Yahaya, N. Wahab, M. R. M. Isa, N. F. Awang, & H. Y. Seong, Predicting NDUM Student’s Academic Performance Using Data Mining Techniques, 2009 Second International Conference on Computer and Electrical Engineering, (2009), 357–361. http://dx.doi.org/10.1109/iccee.2009.168 [8] N. M. Norwawi, S. F. Abdusalam, C. F. Hibadullah, & B. M. Shuaibu, Classification of Student’s Performance in Computer Programming Course According to Learning Style, 2009 2nd Conference on Data Mining and Optimization, (2009), 37–41. http://dx.doi.org/10.1109/dmo.2009.5341912 [9] H. Othman, Z. M. Nopiah, I. Asshaari, N. Razali, M. H. Osman, & N. Ramli, (2009), A Comparative Study of Engineering Students on Their Pre-University Results with Their First Year Performance at Fkab, UKM. Seminar Pendidikan Kejuruteraan dan Alam Bina (PeKA’09). [10] Kabakchieva, D., Predicting Student Performance by Using Data Mining Methods for Classification, Cybernetics and Information Technologies, 13 (2013), no. 1, 61–72. http://dx.doi.org/10.2478/cait-2013-0006 [11] S. Prakash, K. S. Ramaswami, & C. A. Post, Fuzzy K- Means Cluster Validation for Institutional Quality Assessment, Communication and Computational Intelligence (INCOCCI), 2010 International Conference, (2010), 628–635. [12] S. Huang, & N. Fang, Work in Progress - Prediction of Students’ Academic Performance in an Introductory Engineering Course, In 41st ASEE/IEEE Frontiers in Education Conference, (2011), 11–13. http://dx.doi.org/10.1109/fie.2011.6142729 [13] S. Sembiring, M. Zarlis, D. Hartama, & E. Wani, Prediction of student academic performance by an application of data mining techniques, 2011 International Conference on Management and Artificial Intelligence, 6 (2011). 110–114. [14] P. Golding, L. Facey-Shaw, & V. Tennant, Effects of Peer Tutoring, Attitude and Personality on Academic Performance of First Year Introductory Programming Students, 36th ASEE/IEEE Frontiers in Education Conference, (2006), 7–12. http://dx.doi.org/10.1109/fie.2006.322662 [15] S. Parack, Z. Zahid, & F. Merchant, Application of data mining in educational databases for predicting academic trends and patterns, 2012 IEEE International Conference on Technology Enhanced Education (ICTEE), (2012), 1–4. http://dx.doi.org/10.1109/ictee.2012.6208617 [16] E. P. I. García, & P. M. Mora, Model Prediction of Academic Performance for First Year Students, 2011 10th Mexican International Conference on Artificial Intelligence, (2011), 169–174. http://dx.doi.org/10.1109/micai.2011.28 [17] A. T. Chamillard, Using student performance predictions in a computer science curriculum, ACM SIGCSE Bulletin, 38 (2006), no. 3, 260. http://dx.doi.org/10.1145/1140123.1140194 [18] D. T. Larose, Discovering Knowledge in Data: An Introduction to Data Mining, Hoboken, NJ, USA: Wiley, 2005. http://dx.doi.org/10.1002/0471687545 [19] J. Shana, & T. Venkatachalam, Identifying Key Performance Indicators and Predicting the Result from Student Data, International Journal of Computer Applications, 25 (2011), no. 9, 45–48. http://dx.doi.org/10.5120/3057-4169 [20] U. Kumar, & P. S. Pal, Data Mining: A prediction of performer or underperformer using classification, International Journal of Computer Science and Information Technologies (IJCSIT), 2 (2011), no. 2, 686–690. [21] M. Sharma, Development of Predictive Model in Education System: Using Naïve Bayes Classifier, International Conference and Workshop on Emerging Trends in Technology (ICWET 2011) – TCET, Mumbai, India, (Icwet), (2011), 185–186. http://dx.doi.org/10.1145/1980022.1980064 [22] S. Pal, Mining Educational Data Using Classification to Decrease Dropout Rate of Students, International Journal of Multidisciplinary Sciences and Engineering, 3 (2012), no. 5, 35–39. [23] E. Frank, & I. H. Witten, Generating Accurate Rule Sets without Global Optimization, In: Proc. Of The 15th Int. Conference on Machine Learning. [24] C. Romero, S. Ventura, P. G. Espejo, & C. Hervás, (2008). Data Mining Algorithms to Classify Students, in: The 1st International Conference on Educational Data Mining Montréal, Québec, Canada, (1998), 8–17.
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| spellingShingle |
The prediction of students' academic performance using classification data mining techniques
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| summary |
Data Mining provides powerful techniques for various fields including education. The research in the educational field is rapidly increasing due to the massive amount of students’ data which can be used to discover valuable pattern pertaining students’ learning behaviour. This paper proposes a framework for predicting students’ academic performance of first year bachelor students in Computer Science course. The data were collected from 8 year period intakes from July 2006/2007 until July 2013/2014 that contains the students’ demographics, previous academic records, and family background information. Decision Tree, Naïve Bayes, and Rule Based classification techniques are applied to the students’ data in order to produce the best students’ academic performance prediction model. The experiment result shows the Rule Based is a best model among the other techniques by receiving the highest accuracy value of 71.3%. The extracted knowledge from prediction model will be used to identify and profile the student to determine the students’ level of success in the first semester.
|
| title |
The prediction of students' academic performance using classification data mining techniques
|
| title_full |
The prediction of students' academic performance using classification data mining techniques
|
| title_fullStr |
The prediction of students' academic performance using classification data mining techniques
|
| title_full_unstemmed |
The prediction of students' academic performance using classification data mining techniques
|
| title_short |
The prediction of students' academic performance using classification data mining techniques
|
| title_sort |
prediction of students' academic performance using classification data mining techniques
|