Predicting Achievement with Artificial Neural Networks: The Case of Anadolu University Open Education System

Yıl 2018, Cilt: 5 Sayı: 3, 474 - 490, 19.09.2018

Hilal Seda Yıldız Aybek Muhammet Recep Okur

https://doi.org/10.21449/ijate.435507

Cited By: 5

Öz

This study aims to predict the final exam scores and pass/fail rates of the students taking the Basic Information Technologies – 1 (BIL101U) course in 2014-2015 and 2015-2016 academic years in the Open Education System of Anadolu University, through Artificial Neural Networks (ANN). In this research, data about the demographics, educational background, BIL101U course mid-term, final and success scores of 626,478 students was collected and purged. Data of 195,584 students, obtained after this process was analysed through Multilayer Perception (MLP) and Radial Basis Function (RBF) models. Sixteen different networks attained through the combination of ANN parameters were used to predict the final exam scores and pass/fail rates of the students. As a result of the analyses, it was found out that networks established through MLPs make more exact predictions. In the prediction of the final exam scores, it was determined that there is a low level of correlation between the actual scores and predicted scores. In the analyses for the prediction of pass/fail rates of the students, networks established through MLPs ensured more exact prediction results. Moreover, it was determined that the variables as mid-term exam scores, university entrance scores and secondary school graduation year were of highest importance in explaining the final exam scores and pass/fail rates of the students. It was found out that in the higher institutions serving for Open and Distance Learning, pass/fail state of the students can be predicted through ANN under favour of variables of students which have been found as most the important predictors.

Anahtar Kelimeler

Prediction of Student Achievement, Achievement in the Higher Education, Open and Distance Learning, Artificial Neural Networks

Predicting Achievement with Artificial Neural Networks: The Case of Anadolu University Open Education System

Öz

This study aims to
predict the final exam scores and pass/fail rates of the students taking the
Basic Information Technologies – 1 (BIL101U) course in 2014-2015 and 2015-2016
academic years in the Open Education System of Anadolu University, through
Artificial Neural Networks (ANN). In this research, data about the demographics,
educational background, BIL101U course mid-term, final and success scores of
626,478 students was collected and purged. Data of 195,584 students, obtained
after this process was analysed through Multilayer Perception (MLP) and Radial
Basis Function (RBF) models. Sixteen different networks attained through the
combination of ANN parameters were used to predict the final exam scores and
pass/fail rates of the students. As a result of the analyses, it was found out
that networks established through MLPs make more exact predictions. In the
prediction of the final exam scores, it was determined that there is a low
level of correlation between the actual scores and predicted scores. In the
analyses for the prediction of pass/fail rates of the students, networks
established through MLPs ensured more exact prediction results. Moreover, it
was determined that the variables as mid-term exam scores, university entrance
scores and secondary school graduation year were of highest importance in
explaining the final exam scores and pass/fail rates of the students. It was
found out that in the higher institutions serving for Open and Distance
Learning, pass/fail state of the students can be predicted through ANN under
favour of variables of students which have been found as most the important
predictors.

Anahtar Kelimeler

Prediction of Student Achievement, Achievement in the Higher Education, Open and Distance Learning, Artificial Neural Networks

Kaynakça

• Akbilgiç, O. (2011). Hybrid radial basis function networks and variable selection and estimation: an application for securities investment decisions. (Doctoral dissertation). Istanbul University. Istanbul.
• Amro, H. J., Mundy, M., & Kupczynski, L. (2015). The effects of age and gender on student achievement in face-to-face and online college algebra classes. Research in Higher Education Journal, 27.
• Astin, A. W. (1991). Assessment for excellence: The philosophy and practice of assessment and evaluation in higher education. New York: Macmillian.
• Aydın, S. (2007). Data mining and an application in anadolu university open education system. (Doctoral dissertation). Anadolu University Graduate School of Social Sciences. Eskisehir.
• Basheer, I. A., & Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of microbiological methods, 43(1), 3-31.
• Bower, B. L., & Hardy, K. P. (2004). From correspondence to cyberspace: Changes and challenges in distance education. New Directions for Community Colleges, 2004(128), 5-12.
• Brown, S. J., White, S., Sharma, B., Wakeling, L., Naiker, M., Chandra, S., & Bilimoria, V. (2015). Attitude to the study of chemistry and its relationship with achievement in an introductory undergraduate course. Journal of the Scholarship of Teaching and Learning, 15(2), 33-41.
• Bullinaria, J. A. (2015) Biological neurons and neural networks, artificial neurons: neural computation: Lecture 2. Retrieved April 20, 2016, from http://www.cs.bham.ac.uk/~jxb/INC/l2.pdf
• Burnett, C. M. (2006, December 27). Artificial neural network. Retrieved April 20, 2016, from https://commons.wikimedia.org/wiki/File:Artificial_neural_network.svg#/media/Fi
• Collins, C. W., McLeod, J., & Kenway, J. (2000). Factors influencing the educational performance of males and females in school and their initial destinations after leaving school. Canberra: Department of Education, Training and Youth Affairs.
• da Silva, E. T., de Fátima Nunes, M., Santos, L. B., Queiroz, M. G., & Leles, C. R. (2012). Identifying student profiles and their impact on academic performance in a Brazilian undergraduate student sample. European Journal of Dental Education, 16(1), 27-32.
• Finger, S., & Tansey, T. (1994). Origins of neuroscience: a history of explorations into brain function. Trends in Neurosciences, 17(7), 310-310.
• Guillery, R. W. (2005). Observations of synaptic structures: origins of the neuron doctrine and its current status. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 360(1458), 1281-1307.
• Hattie, J.C. (2009). Visible learning. a synthesis of over 800 meta-anaylses relating to achievement. New York: Routledge.
• Herzog, S. (2006). Estimating student retention and degree‐completion time: Decision trees and neural networks vis‐à‐vis regression. New Directions for Institutional Research, 2006(131), 17-33.
• Huang, S., & Fang, N. (2013). Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. Computers & Education, 61, 133-145.
• Ibrahim, Z., & Rusli, D. (2007). Predicting students’ academic performance: comparing artificial neural network, decision tree and linear regression. In 21st Annual SAS Malaysia Forum, 5th September.
• IBM Knowledge Center. (n.d.). Preparing the Data for Analysis. Retrieved April 21, 2016, from https://www.ibm.com/support/knowledgecenter/en/SSLVMB_24.0.0/spss/tutorials/mlp_bankloan_dataprep.html
• Jurdak, M. (2014). Socio-economic and cultural mediators of mathematics achievement and between-school equity in mathematics education at the global level. Zdm, (7), 1025.
• Kandel, E. R., Schwartz, J. H., & Jessell, T. M. (Eds.). (2000). Principles of neural science, (4), 1227-1246. New York: McGraw-hill.
• Khalaila, R. (2015). The relationship between academic self-concept, intrinsic motivation, test anxiety, and academic achievement among nursing students: Mediating and moderating effects. Nurse Education Today, (3), 432. doi:10.1016/j.nedt.2014.11.001
• Koca, Z. (2006). Speed control with artificial neural networks with vector control of three phase asynchronous motors (Master’s thesis). Kahramanmaras Surcu Imam University Graduate School of Natural and Applied Sciences. Kahramanmaras.
• Kose, U., & Arslan, A. (2017). Optimization of self‐learning in Computer Engineering courses: An intelligent software system supported by Artificial Neural Network and Vortex Optimization Algorithm. Computer Applications in Engineering Education, 25(1), 142-156.
• Kotsiantis, S., Pierrakeas, C., & Pintelas, P. (2003). Preventing student dropout in distance learning systems using machine learning techniques. In Knowledge -Based Intelligent Information and Engineering Systems, (pp. 267–274).
• Lippmann, R. (1987). An introduction to computing with neural nets. IEEE ASSP Magazine IEEE ASSP Mag., 4-22.
• Lykourentzou, I., Giannoukos, I., Mpardis, G., Nikolopoulos, V., & Loumos, V. (2009). Early and dynamic student achievement prediction in e‐learning courses using neural networks. Journal of the American Society for Information Science and Technology, 60(2), 372-380.
• Macher, D., Paechter, M., Papousek, I., Ruggeri, K., Freudenthaler, H. H., & Arendasy, M. (2013). Statistics anxiety, state anxiety during an examination, and academic achievement. British Journal of Educational Psychology, 83(4), 535-549.
• Mangels, J. (2003). Cells of the Nervous System. Retrieved May 3, 2016, from http://www.columbia.edu/cu/psychology/courses/1010/mangels/neuro/neurocells/neurocells.html.
• Mastin, L. (2010). Neurons & Synapses. Retrieved from May, 4, 2016, from http://www.human-memory.net/brain_neurons. html.
• Matignon, R. (2005). Neural network modeling using SAS enterprise miner. AuthorHouse.
• Mlambo, V. (2012). An analysis of some factors affecting student academic performance in an introductory biochemistry course at the University of the West Indies. The Caribbean Teaching Scholar, 1(2).
• Moore, M. G., & Kearsley, G. (2011). Distance education: A systems view of online learning. Cengage Learning.
• Musso, M. F., Kyndt, E., Cascallar, E. C., & Dochy, F. (2013). Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks. Frontline Learning Research, 1(1), 42-71.
• Naik, B., & Ragothaman, S. (2004). Using neural networks to predict MBA student success. College Student Journal, 38(1), 143.
• Odom, A. L., & Bell, C. V. (2015). Associations of middle school student science achievement and attitudes about science with student-reported frequency of teacher lecture demonstrations and student-centered learning. International Journal of Environmental and Science Education, 10(1), 87-97.
• Özkan, C., & Erbek, F. S. (2003). The comparison of activation functions for multispectral Landsat TM image classification. Photogrammetric Engineering & Remote Sensing, 69(11), 1225-1234.
• Öztemel, E. (2012). Yapay Sinir Ağları [Artificial Neural Networks]. İstanbul: Papatya Yayıncılık.
• Pike, G. R., Schroeder, C. C., & Berry, T. R. (1997). Enhancing the educational impact of residence halls: The relationship between residential learning communities and first-year college experiences and persistence. Journal of College Student Development, 38(6), 609-621.
• Power, C., Robertson, F., & Baker, M. (1987). Success in higher education (No. 94). National Institute of Labour Studies.
• Rojas, R. (2013). Neural Networks: A Systematic Introduction. Springer Science & Business Media.
• Rusli, N. M., Ibrahim, Z., & Janor, R. M. (2008, August). Predicting students’ academic achievement: Comparison between logistic regression, artificial neural network, and Neuro-fuzzy. In Information Technology, 2008. ITSim 2008. International Symposium on (1), 1-6. IEEE.
• Scheiber, C., Reynolds, M. R., Hajovsky, D. B., & Kaufman, A. S. (2015). Gender differences in achievement in a large, nationally representative sample of children and adolescents. Psychology in the Schools, 52(4), 335-348.
• Schumacher, P., Olinsky, A., Quinn, J., & Smith, R. (2010). A comparison of logistic regression, neural networks, and classification trees predicting success of actuarial students. Journal of Education for Business, 85(5), 258-263.
• Shalev-Shwartz, S. (2011). Online learning and online convex optimization. Foundations and Trends in Machine Learning, 4(2), 107-194.
• Siegelbaum, S. A., & Hudspeth, A. J. (2000). Principles of neural science (Vol. 4, pp. 1227- 1246). E. R. Kandel, J. H. Schwartz, & T. M. Jessell (Eds.). New York: McGraw- hill.
• Simonson, M., Smaldino, S., Albright, M. & Zvacek, S. (2003). Teaching and Learing at a Distance. Ohio: Columbus.
• Snyder, C. R., Shorey, H. S., Cheavens, J., Pulvers, K. M., Adams, V. H. III, & Wiklund, C. (2002). Hope and academic success in college. Journal of Educational Psychology, 94(4), 820-826.
• Strayhorn, T. L. (2006). Factors influencing the academic achievement of first-generation college students. NASPA Journal, 43(4), 82-111.
• Suphi, N., & Yaratan, H. (2012). Effects of learning approaches, locus of control, socio- economic status and self-efficacy on academic achievement: a Turkish perspective. Educational Studies, 38(4), 419-431.
• Şen, B., Uçar, E., & Delen, D. (2012). Predicting and analyzing secondary education placement-test scores: A data mining approach. Expert Systems with Applications, 39(10), 9468-9476.
• Turhan, K., Kurt, B., & Engin, Y. Z. (2013). Estimation of Student Success with Artificial Neural Networks. Education and Science, 38(170).
• Valentine, J. C., DuBois, D. L., & Cooper, H. (2004). The relation between self-beliefs and academic achievement: A meta-analytic review. Educational Psychologist, 39(2), 111-133.
• Ventura, P. R. (2005). Identifying predictors of success for an objects-first CS1. Computer Science Education, 15(3).
• Yu, H., Xie, T., Paszczynski, S., & Wilamowski, B. M. (2011). Advantages of radial basis function networks for dynamic system design. Industrial Electronics, IEEE Transactions on, 58(12), 5438-5450.
• Yukselturk, E., Ozekes, S., & Türel, Y. K. (2014). Predicting dropout student: an application of data mining methods in an online education program. European Journal of Open, Distance and e-Learning, 17(1), 118-133.
• Zheng, J. L., Saunders, K. P., Shelley, I. I., Mack, C., & Whalen, D. F. (2002). Predictors of academic success for freshmen residence hall students. Journal of College Student Development, 43(2), 267.