Prediction of Online Students Performance by Means of Genetic Programming

References

• Adelfio, G., G. Boscaino, and V. Capursi. 2014. A new indicator for higher education student performance. Higher Education 68 (5):653–68. doi:10.1007/s10734-014-9737-x.
   Web of Science ®Google Scholar
• Agudo Peregrina, Á. F., S. Iglesias Pradas, M. Á. Conde González, and Á. Hernández García. 2014. Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior 31:542–50. doi:10.1016/j.chb.2013.05.031.
   Web of Science ®Google Scholar
• Allen, I. E., and J. Seaman. 2014. Grade Change. Tracking online education in the United States.
   Google Scholar
• Alstete, J. W., and N. J. Beutell. 2004. Performance indicators in online distance learning courses: A study of management education. Quality Assurance in Education 12 (1):6–14. doi:10.1108/09684880410517397.
   Google Scholar
• Castillo Merino, D., and E. Serradell López. 2014. An analysis of the determinants of students’ performance in e-learning. Computers in Human Behavior 30:476–84. doi:10.1016/j.chb.2013.06.020.
   Web of Science ®Google Scholar
• Chen, K.-C., and S.-J. Jang. 2010. Motivation in online learning: Testing a model of self-determination theory. Computers in Human Behavior 26:741–52. doi:10.1016/j.chb.2010.01.011.
   Web of Science ®Google Scholar
• Cheng, C. K., D. E. Paré, L.-M. Collimore, and S. Joordens. 2011. Assessing the effectiveness of a voluntary online discussion forum on improving students’ course performance. Computers and Education 56:253–61. doi:10.1016/j.compedu.2010.07.024.
   Web of Science ®Google Scholar
• Ekwunife-Orakwue, K., and T.-L. Teng. 2014, September. The impact of transactional distance dialogic interactions on student learning outcomes in online and blended environments. Computers & Education 78:414–27. doi:10.1016/j.compedu.2014.06.011.
   Web of Science ®Google Scholar
• Figini, S., and P. Giudici. 2009, July. Statistical models for e-learning data. ( Springer, Ed.). Statistical Methods and Applications 18 (2):293–304. doi:10.1007/s10260-008-0098-4.
   Google Scholar
• Giesbers, B., B. Rienties, D. Tempelaar, and W. Gijselaers. 2013, January. Investigating the relations between motivation, tool use, participation, and performance in an e-learning course using web-videoconferencing. Computers in Human Behavior 29 (1):285–92. doi:10.1016/j.chb.2012.09.005.
   Web of Science ®Google Scholar
• Guo, W. W. 2010. Incorporating statistical and neural network approaches for student course satisfaction analysis and prediction. Expert Systems with Applications 37:3358–65. doi:10.1016/j.eswa.2009.10.014.
   Web of Science ®Google Scholar
• Hachey, A. C., C. Wladis, and K. Conway (2015, October 31). Prior online course experience and G.P.A. as predictors of subsequent online STEM course outcomes. Internet and Higher Education, 25, 11–17. doi:10.1016/j.iheduc.2014.10.003
   Web of Science ®Google Scholar
• Hastie, T., R. Tibshirani, and J. Friedman. 2009. The elements of statistical learning: Data mining, inference and prediction, 2nd ed. ( Springer, Ed.) New York, USA: Springer.
   Google Scholar
• He, W., D. Gajski, G. Farkas, and M. Warschauer. 2015. Implementing flexible hybrid instruction in an electrical engineering course: The best of three worlds? Computers and Education 81:59–68. doi:10.1016/j.compedu.2014.09.005.
   Web of Science ®Google Scholar
• Hu, Y.-H., C.-L. Lo, and S.-P. Shih. 2014. Developing early warning systems to predict students’ online learning performance. Computers in Human Behavior 36:469–78. doi:10.1016/j.chb.2014.04.002.
   Web of Science ®Google Scholar
• Huang, S., and N. Fang. 2013, February. Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. ( Elsevier, Ed.) Computers & Education 61:133–45. doi:10.1016/j.compedu.2012.08.015.
   Web of Science ®Google Scholar
• Hussar, W. J., and T. M. Bailey (2013). Projection of education statistics to 2021. Institute of Education Sciences. Washington, DC: National Center for Education Statistics. Retrieved Noviembre 14, 2013, from http://nces.ed.gov/programs/projections/projections2021/index.asp
   Google Scholar
• Kalles, D., and C. Pierrakeas. 2006. Analyzing student performance in distance learning with genetic algorithms and decision trees. Applied Artificial Intelligence 20 (8):655-674. doi: 10.1080/08839510600844946.
   Web of Science ®Google Scholar
• Kardan, A. A., H. Sadeghi, S. S. Ghidary, and M. R. Fani Sani. 2013. Prediction of student course selection in online higher education institutes using neural network. ( Elsevier, Ed.) Computers & Education 65:1–11. doi:10.1016/j.compedu.2013.01.015.
   Web of Science ®Google Scholar
• Kerr, M. S., K. Rynearson, and M. C. Kerr. 2006. Student characteristics for online learning success. The Internet and Higher Education 9:91–105. doi:10.1016/j.iheduc.2006.03.002.
   Google Scholar
• Kim, J., A. Lee, and H. Ryu. 2013. Personality and its effects on learning performance: Design guidelines for and adaptive e-learning system based on a user model. International Journal of Industrial Ergonomics 43:450–61. doi:10.1016/j.ergon.2013.03.001.
   Web of Science ®Google Scholar
• Kotsiantis, S., C. Pierrakeas, and P. Pintelas. 2004. Predicting students' performance in distance learning using machine learning techniques. Applied Artificial Intelligence 18 (5):411-426. doi: 10.1080/08839510490442058.
   Web of Science ®Google Scholar
• Kotsiantis, S., K. Patriarcheas, and M. Xenos. 2010. A combinational incremental ensemble of classifiers as a technique for predicting students’ performance in distance education. ( Elsevier, Ed.) Knowledge-Based Systems 23:529–35. doi:10.1016/j.knosys.2010.03.010.
   Web of Science ®Google Scholar
• Koubek, R. J., W. K. LeBold, and G. Salvendy. 2007. Predicting performance in computer programming courses. Behaviour & Information Technology 4 (1985):113–29. doi:10.1080/01449298508901793.
   Google Scholar
• Koza, J. R. 1994, June. Genetic programming as a means for programming computers by natural selection. ( C. &. Hall, Ed.) Statistics and Computing 4 (2):87–112. doi:10.1007/BF00175355.
   Web of Science ®Google Scholar
• Koza, J. R. 1998. Genetic programming: On the programming of computers by means of natural selection. Cambridge: The MIT Press.
   Google Scholar
• Koza, J. R., and P. Riccardo (2005). Genetic programming. In J. R.Koza, R.Poli, E. K.Burke, & G.Kendall (Eds.), Search methodologies: Introductory tutorials in optimization and decision support techniques (pp. 127-164). Nottingham: Springer.
   Google Scholar
• Kuo, Y.-C., A. E. Walker, K. E. Schroder, and B. R. Belland. 2014. Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education 20:35–50. doi:10.1016/j.iheduc.2013.10.001.
   Web of Science ®Google Scholar
• Lara, J. A., D. Lizcano, M. A. Martínez, J. Pazos, and T. Riera. 2014, March. A system for knowledge discovery in e-learning environments within the European Higher Education Area. Application to student data from Open University of Madrid, UDIMA. ( Elsevier, Ed.) Computers & Education 72:23–36. doi: 10.1016/j.compedu.2013.10.009.
   Web of Science ®Google Scholar
• Lokan, C., and E. Mendes. 2009October02.Investigating the use of chronological split for software effort estimation. (IET, Ed.). IET Software 35: 422–34.doi:10.1049/iet-sen.2008.0107
   Web of Science ®Google Scholar
• Lykourentzou, I., I. Giannoukos, G. Mpardis, V. Nikolopoulos, and V. Loumos. 2009, February. Early and dynamic student achievement prediction in E-Learning courses using neural networks. ( Willey, Ed.) Journal of the American Society for Information Science and Technology 60 (2):372–80. doi: 10.1002/asi.20970.
   Web of Science ®Google Scholar
• Macfadyen, L. P., and S. Dawson. 2010. Mining LMS data to develop and “early warning system” for educators: A proof of concept. Computers and Education 54:588–99. doi:10.1016/j.compedu.2009.09.008.
   Web of Science ®Google Scholar
• Natek, S., and M. Zwilling. 2014. Student data mining solution-knowledge management system related to higher education institutions. Expert Systems with Applications 41:6400–07. doi:10.1016/j.eswa.2014.04.024.
   Web of Science ®Google Scholar
• OECD. 2013. Education at a glance 2013. OECD Indicators. Paris: OECD. doi: 10.1787/eag-2013-en.
   Google Scholar
• Poli, R., W. B. Langdon, and N. F. McPhee. 2008. A field guide to genetic programming. England: http://lulu.com. Retrieved from http://www0.cs.ucl.ac.uk/staff/w.langdon/ftp/papers/poli08_fieldguide.pdf
   Google Scholar
• Ransdell, S. 2010. Online activity, motivation, and reasoning among adult learners. Computers in Human Behavior 26:70–73. doi:10.1016/j.chb.2009.09.002.
   Web of Science ®Google Scholar
• Romero, C., M. I. López, J. M. Luna, and S. Ventura. 2013. Predicting students’ final performance from participation in online discussion forums. Computers and Education 68:458–72. doi:10.1016/j.compedu.2013.06.009.
   Web of Science ®Google Scholar
• Romero, C., P. González, S. Ventura, M. J. Del Jesús, and F. Herrera. 2009. Evolutionary algorithms for subgroup discovery in e-learning: A practical application using Moodle data. ( Elsevier, Ed.) Expert Systems with Applications 36:1632–44. doi:10.1016/j.eswa.2007.11.026.
   Web of Science ®Google Scholar
• Romero, C., P. G. Espejo, A. Zafra, J. R. Romero, and S. Ventura. 2010. Web usage mining for predicting final marks of students that use moodle courses. Computer Applications in Engineering Education 21 (1):135-146. doi: 10.1002/cae.v21.1.
   Google Scholar
• Romero, C., S. Ventura, and P. De-Bra. 2004, December. Knowledge discovery with genetic programming for providing feedback to courseware authors. ( Springer, Ed.) User Modeling and User-Adapted Interaction 14:425–64. doi:10.1007/s11257-004-7961-2.
   Web of Science ®Google Scholar
• Ross, S. M. 2004. Introduction to Probability and Statistics for Engineers and Scientists. Burlington, MA: Elsevier Press.
   Google Scholar
• Schoor, C., and M. Bannert. 2011. Motivation in a computer-supported collaborative learning scenario and its impact on learning activities and knowledge acquisition. Learning and Instruction 21:560–73. doi:10.1016/j.learninstruc.2010.11.002.
   Web of Science ®Google Scholar
• Sette, S., and L. Boullart. 2001, December. Genetic programming: Principles and applications. ( Elsevier, Ed.) Engineering Applications of Artificial Intelligence 14 (6):727–36. doi:10.1016/S0952-1976(02)00013-1.
   Web of Science ®Google Scholar
• Solimeno, A., M. E. Mebane, M. Tomai, and D. Francescato. 2008. The influence of students and teachers characteristics on the efficacy of face-to-face and computer supported collaborative learning. Computers and Education 51:109–28. doi:10.1016/j.compedu.2007.04.003.
   Web of Science ®Google Scholar
• Taylan, O., and B. Karagözoglu. 2009. An adaptive neuro-fuzzy model for prediction of student’s academic performance. ( Elsevier, Ed.) Computers & Industrial Engineering 57:732–41. doi:10.1016/j.cie.2009.01.019.
   Web of Science ®Google Scholar
• Tempelaar, D. T., A. Niculescu, B. Rienties, W. H. Gijselaers, and B. Giesbers. 2012. How achievement emotions impact students’ decisions for online learning, and what precedes those emotions. The Internet and Higher Education 15 (3):161–69. doi:10.1016/j.iheduc.2011.10.003.
   Web of Science ®Google Scholar
• Ulloa-Cazarez, R. L., and C. Lopez-Martin. 2013. A fuzzy logic model for predicting the number of course needed from the number of students enrolled. FECS’12 211–15. WorldComp, Las Vegas.
   Google Scholar
• Varela, O. E., J. J. Cater III, and N. Michel. 2012. Online learning in management education: An empirical study of the role personality traits. Journal of Computing in Higher Education 24 (3):209–55. doi:10.1007/s12528-012-9059-x.
   Web of Science ®Google Scholar
• Wang, Y.-H., and H.-C. Liao. 2011. Data mining for adaptive learning in a TESTL-based e-learning system. ( Elsevier, Ed.) Expert Systems with Applications 38:6480–85. doi:10.1016/j.eswa.2010.11.098.
   Web of Science ®Google Scholar
• Wanli, X., G. Rui, P. Eva, and G. Sean. 2015. Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory. Computers in Human Behavior 47:168–81. doi:10.1016/j.chb.2014.09.034.
   Web of Science ®Google Scholar
• Xu, D., and S. Smith Jaggars. 2013, December. The impact of online learning on students’ course outcomes: Evidence from a large community and technical college system. ( Elsevier, Ed.) Economics of Education Review 37:46–57. doi:10.1016/j.econedurev.2013.08.001.
   Web of Science ®Google Scholar
• You, J. W. 2015. Examining the effect of academic procrastination on achievement using LMS data in e-learning. Educational Technology & Society 18 (3):64–74. Accessed October 04, 2015.
   Web of Science ®Google Scholar
• Zafra, A., C. Romero, and S. Ventura. 2013. DRAL: A tool for discovering relevant e-activities for learners. ( S. Link, Ed.) Knowledge and Information Systems 36(1):211–50. doi:10.1007/s10115-012-0531-8.
   Web of Science ®Google Scholar
• Zafra, A., and S. Ventura. 2012. Multi-instance genetic programming for predicting student performance in web based educational environments. ( Elsevier, Ed.) Applied Soft Computing 12:2693–706. doi:10.1016/j.asoc.2012.03.054.
   Web of Science ®Google Scholar