Analysis of Students' Misconducts in Higher Education using Decision Tree and ANN Algorithms
A major problem that the Higher Education Institutions (HEIs) face is the misconduct of students’ behavior. The objective of this study is to decrease these misconducts by identifying the factors which cause them on college campuses. CRISP-DM Methodology has been applied to manage the process of data mining and two data mining techniques: J48 Decision Tree (DT) and Artificial Neural Networks (ANNs) have been used to build classification models and to generate rules to classify and predict students' behavior and the location of misconduct in college campuses. They take into consideration seven factors: Student Major, Student Level, Gender, GPA Cumulative, Local Address, Ethnicity, and time of misconduct by month. Both techniques were evaluated and compared. The accuracy results were high for both classification models, whereas the J48 Decision Tree gave higher accuracy.
V. Berikov and A. Litvinenko, "Methods for statistical data analysis with decision trees," Novosibirsk, Sobolev Institute of Mathematics, 2003.
C. Romero and S. Ventura, "Educational data mining: A survey from 1995 to 2005," Expert Systems with Applications, vol. 33, no. 1, pp. 135-146, Jul. 2007. DOI: https://doi.org/10.1016/j.eswa.2006.04.005
G. Durden and L. Ellis, "The Effects of Attendance on Student Learning in Principles of Economics," American Economic Review, vol. 85, no. 2, pp. 343-46, 1995.
D. Romer, "Do Students Go to Class? Should They?," Journal of Economic Perspectives, vol. 7, no. 3, pp. 167-174, Sep. 1993. DOI: https://doi.org/10.1257/jep.7.3.167
A. M. Wolaver, "Effects Of Heavy Drinking In College On Study Effort, Grade Point Average, And Major Choice," Contemporary Economic Policy, vol. 20, no. 4, pp. 415-428, 2002. DOI: https://doi.org/10.1093/cep/20.4.415
M. Didonet Del Fabro and P. Valduriez, "Towards the efficient development of model transformations using model weaving and matching transformations," Software & Systems Modeling, vol. 8, no. 3, pp. 305-324, Jul. 2009. DOI: https://doi.org/10.1007/s10270-008-0094-z
M. Alsuwaiket, A. H. Blasi, and K. Altarawneh, "Refining Student Marks based on Enrolled Modules' Assessment Methods using Data Mining Techniques," Engineering, Technology & Applied Science Research, vol. 10, no. 1, pp. 5205-5210, Feb. 20204. DOI: https://doi.org/10.48084/etasr.3284
M. Alsuwaiket, A. H. Blasi, and R. A. Al-Msie'deen, "Formulating Module Assessment for Improved Academic Performance Predictability in Higher Education," Engineering, Technology & Applied Science Research, vol. 9, no. 3, pp. 4287-4291, Jun. 2019. DOI: https://doi.org/10.48084/etasr.2794
N. Delavari, M. R. Beikzadeh, and S. Phon-Amnuaisuk, "Application of enhanced analysis model for data mining processes in higher educational system," in 2005 6th International Conference on Information Technology Based Higher Education and Training, Jul. 2005, pp. F4B/1-F4B/6.
K. Prasada Rao, M. V. P. Chandra Sekhara, and B. Ramesh, "Predicting Learning Behavior of Students using Classification Techniques," International Journal of Computer Applications, vol. 139, no. 7, pp. 15-19, Apr. 2016. DOI: https://doi.org/10.5120/ijca2016909188
K. M. Coll, "An Assessment of Drinking Patterns and Drinking Problems among Community College Students: Implications for Programming," Journal of College Student Development, vol. 40, no. 1, pp. 98-100, 1999.
C. A. Presley, P. W. Meilman, J. R. Cashin, and J. S. Leichliter, "Alcohol and Drugs on American College Campuses: Issues of Violence and Harassment," NCJRS, 187865, 1997.
F. D. Sheffield, J. Darkes, F. K. Del Boca, and M. S. Goldman, "Binge drinking and alcohol-related problems among community college students: implications for prevention policy," Journal of American college health: J of ACH, vol. 54, no. 3, pp. 137-141, Dec. 2005 DOI: https://doi.org/10.3200/JACH.54.3.137-142
A. Malathi and S. S. Baboo, "An Enhanced Algorithm to Predict a Future Crime using Data Mining," International Journal of Computer Applications, vol. 21, no. 1, pp. 1-6, May 2011. DOI: https://doi.org/10.5120/2478-3335
P. Chapman et al., CRISP-DM 1.0: Step-by-step data mining guide. SPSS, 2000.
J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Haryana, India; Burlington, MA, USA: Morgan Kaufmann, 2011.
M. Kantardzic, Data Mining: Concepts, Models, Methods, and Algorithms, 2nd ed. Hoboken, NJ, USA: Wiley-IEEE Press, 2011. DOI: https://doi.org/10.1002/9781118029145
T. Mitchell, "Decision Tree Learning," in Machine Learning, The McGraw-Hill Companies, Inc., 1997, pp. 52-78.
A. El-Halees, "Mining Students Data to Analyze Learning Behavior: A Case Study," in The 2008 international Arab Conference of Information Technology (ACIT2008), Sfax, Tunisia, Dec. 2008.
C. H. Yu, S. A. DiGangi, A. Jannasch-Pennell, W. Lo, and C. Kaprolet, "A Data-Mining Approach to Differentiate Predictors of Retention," presented at the EDUCAUSE Southwest Conference, Austin, TX, USA, Feb. 2007.
I. Witten, E. Frank, M. Hall, and C. Pal., Data Mining: Practical Machine Learning Tools and Techniques, 4th ed. Morgan Kaufmann, 2016.
A. Blasi, "Scheduling food industry system using fuzzy logic," Journal of Theoretical and Applied Information Technology, vol. 96, no. 19, pp. 6463-6473, Oct. 2018.
A. Blasi, "Performance increment of high school students using ANN model and SA algorithm," Journal of Theoretical and Applied Information Technology, vol. 95, no. 11, pp. 2417-2425, Jun. 2020.
M. A. A. Lababede, A. H. Blasi, and M. A. Alsuwaiket, "Mosques Smart Domes System using Machine Learning Algorithms," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 11, no. 3, 40/30 2020. DOI: https://doi.org/10.14569/IJACSA.2020.0110347
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