How to cite this paper
Aleid, M., Morsi, S., Shishakly, R., Aldhyani, T & Almaiah, M. (2024). Modelling and predicting student flexibility in online learning using artificial intelligence approaches.International Journal of Data and Network Science, 8(4), 2255-2266.
Refrences
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Bergamin, P. B., Ziska, S., Werlen, E., & Siegenthaler, E. (2012). The relationship between flexible and self-regulated learning in open and distance universities. The International Review of Research in Open and Distributed Learning, 13(2), 101–123. doi: https://doi.org/10.19173/irrodl.v13i2.1124
Boer, W. D., & Collis, B. (2005). Becoming more systematic about flexible learning: beyond time and distance. ALT-J, 13(1), 33-48.
Brahim, G. B. (2022). Predicting student performance from online engagement activities using novel statistical fea-tures. Arabian Journal for Science and Engineering, 47(8), 10225-10243.
Buenaño-Fernández, D., Gil, D., & Luján-Mora, S. (2019). Application of machine learning in predicting performance for computer engineering students: A case study. Sustainability, 11(10), 2833.
Burgos, D. (2019). Background similarities as a way to predict students’ behaviour. Sustainability, 11(24), 6883.
Collis, B., & Moonen, J. (2001). Flexible learning in a digital world: Experiences and expectations. London: Routledge. doi: https://doi.org/10.4324/9780203046098
Collis, B., & Moonen, J. (2002). Flexible learning in a digital world.Open Learning 17(3), 217–230.doi: https://doi.org/10.1080/0268051022000048228
Collis, B., Vingerhoets, J., & Moonen, J. (1997). Flexibility as a key construct in European training: Experiences from the TeleScopia Project. British Journal of Educational Technology, 28(3),199–217. doi: https://doi.org/10.1111/1467-8535.00026
Cornelius, S., & Gordon, C. (2008). Universalists, butterflies and changelings: Learners’ roles and strategies for using flexible online resources. Proceedings of EdMedia: World Conference on Educational Media and Technology, 2008 (Vol. 1), 4052–4057.
Cornelius, S., Gordon, C., & Ackland, A. (2011). Towards flexible learning for adult learners in professional contexts: An activity-focused course design. Interactive Learning Environments, 19(4), 381–393. doi: https://doi.org/10.1080/10494820903298258.
Dabbagh, N. (2007). The Online Learner: Characteristics and Pedagogical Implications. Contemporary Issues in Technol-ogy and Teacher Education, 7(3), 217-226.
Daghestani, L. F., Ibrahim, L. F., Al‐Towirgi, R. S., & Salman, H. A. (2020). Adapting gamified learning systems using educational data mining techniques. Computer Applications in Engineering Education, 28(3), 568-589.
Dietz-Uhler, B., & Hurn, J. E. (2013). Using learning analytics to predict (and improve) student success: A faculty per-spective. Journal of interactive online learning, 12(1), 17-26.
Gardner, J., & Brooks, C. (2018). Student success prediction in MOOCs. User Modeling and User-Adapted Interac-tion, 28, 127-203.
Gillingham, M., & Molinari, C. (2012). Online courses: Student preferences survey. Journal of Online Learning Research and Practice, 1(1).
Hamim, T., Benabbou, F., & Sael, N. (2021). Survey of machine learning techniques for student profile model-ing. International Journal of Emerging Technologies in Learning (iJET), 16(4), 136-151.
Hussain, M., Zhu, W., Zhang, W., & Abidi, S. M. R. (2018). Student engagement predictions in an e-learning system and their impact on student course assessment scores. Computational Intelligence & Neuroscience.
Kagklis, V., Lionarakis, A., Marketos, G., Panagiotakopoulos, G. T., Stavropoulos, E. C., & Verykios, V. S. (2017). Stu-dent admission data analytics for open and distance education in Greece. Ανοικτή Εκπαίδευση: το περιοδικό για την Ανοικτή και εξ Αποστάσεως Εκπαίδευση και την Εκπαιδευτική Τεχνολογία, 13(2), 6-16.
Kim, H. J., Hong, A. J., & Song, H. D. (2019). The roles of academic engagement and digital readiness in students’ achievements in university e-learning environments. International Journal of Educational Technology in Higher Edu-cation, 16(1), 1-18.
Moore, M. G., & Kearsley, G. (2012). Distance education: A systems view of online learning.
Nagori, R., & Aghila, G. (2011, April). LDA based integrated document recommendation model for e-learning systems. In 2011 international conference on emerging trends in networks and computer communications (ETNCC) (pp. 230-233). IEEE.
Prenkaj, B., Velardi, P., Stilo, G., Distante, D., & Faralli, S. (2020). A survey of machine learning approaches for student dropout prediction in online courses. ACM Computing Surveys (CSUR), 53(3), 1-34.
Romero, C., & Ventura, S. (2010). Educational data mining: a review of the state of the art. IEEE Transactions on Sys-tems, Man, and Cybernetics, Part C (applications and reviews), 40(6), 601-618.
Samigulina, G., & Samigulina, Z. (2016). Intelligent system of distance education of engineers, based on modern innova-tive technologies. Procedia-Social and Behavioral Sciences, 228, 229-236.
Sekeroglu, B., Dimililer, K., & Tuncal, K. (2019, March). Student performance prediction and classification using ma-chine learning algorithms. In Proceedings of the 2019 8th international conference on educational and information technology (pp. 7-11).
Shearer, R. L., & Park, E. (2018). Theory to practice in instructional design. In M. G. Moore & W. C. Diehl (Eds.), Hand-book of Distance Education (4th ed.), (pp. 260 -280). New York: Routledge.
Tomasevic, N., Gvozdenovic, N., & Vranes, S. (2020). An overview and comparison of supervised data mining techniques for student exam performance prediction. Computers & education, 143, 103676.
Van Rooyen, A. (2015). Distance education accounting students’ perceptions of social media integration. Procedia-Social and Behavioral Sciences, 176, 444-450.
Yan, X., Guo, J., Lan, Y., & Cheng, X. (2013, May). A biterm topic model for short texts. In Proceedings of the 22nd in-ternational conference on World Wide Web (pp. 1445-1456).
Zhong, J., Zhang, S. F., Guo, W. L., & Li, X. (2018). TFLA: A quality analysis framework for user generated con-tents. Tien Tzu Hsueh Pao/Acta Electron. Sin, 46, 2201-2206.
Baker, R. S., Martin, T., & Rossi, L. M. (2016). Educational data mining and learning analytics. The Wiley handbook of cognition and assessment: Frameworks, methodologies, and applications, 379-396.
Bates, T. (2001). National strategies for e-learning in post-secondary education and training (Vol. 132). Paris: Unesco.
Bergamin, P. B., Ziska, S., Werlen, E., & Siegenthaler, E. (2012). The relationship between flexible and self-regulated learning in open and distance universities. The International Review of Research in Open and Distributed Learning, 13(2), 101–123. doi: https://doi.org/10.19173/irrodl.v13i2.1124
Boer, W. D., & Collis, B. (2005). Becoming more systematic about flexible learning: beyond time and distance. ALT-J, 13(1), 33-48.
Brahim, G. B. (2022). Predicting student performance from online engagement activities using novel statistical fea-tures. Arabian Journal for Science and Engineering, 47(8), 10225-10243.
Buenaño-Fernández, D., Gil, D., & Luján-Mora, S. (2019). Application of machine learning in predicting performance for computer engineering students: A case study. Sustainability, 11(10), 2833.
Burgos, D. (2019). Background similarities as a way to predict students’ behaviour. Sustainability, 11(24), 6883.
Collis, B., & Moonen, J. (2001). Flexible learning in a digital world: Experiences and expectations. London: Routledge. doi: https://doi.org/10.4324/9780203046098
Collis, B., & Moonen, J. (2002). Flexible learning in a digital world.Open Learning 17(3), 217–230.doi: https://doi.org/10.1080/0268051022000048228
Collis, B., Vingerhoets, J., & Moonen, J. (1997). Flexibility as a key construct in European training: Experiences from the TeleScopia Project. British Journal of Educational Technology, 28(3),199–217. doi: https://doi.org/10.1111/1467-8535.00026
Cornelius, S., & Gordon, C. (2008). Universalists, butterflies and changelings: Learners’ roles and strategies for using flexible online resources. Proceedings of EdMedia: World Conference on Educational Media and Technology, 2008 (Vol. 1), 4052–4057.
Cornelius, S., Gordon, C., & Ackland, A. (2011). Towards flexible learning for adult learners in professional contexts: An activity-focused course design. Interactive Learning Environments, 19(4), 381–393. doi: https://doi.org/10.1080/10494820903298258.
Dabbagh, N. (2007). The Online Learner: Characteristics and Pedagogical Implications. Contemporary Issues in Technol-ogy and Teacher Education, 7(3), 217-226.
Daghestani, L. F., Ibrahim, L. F., Al‐Towirgi, R. S., & Salman, H. A. (2020). Adapting gamified learning systems using educational data mining techniques. Computer Applications in Engineering Education, 28(3), 568-589.
Dietz-Uhler, B., & Hurn, J. E. (2013). Using learning analytics to predict (and improve) student success: A faculty per-spective. Journal of interactive online learning, 12(1), 17-26.
Gardner, J., & Brooks, C. (2018). Student success prediction in MOOCs. User Modeling and User-Adapted Interac-tion, 28, 127-203.
Gillingham, M., & Molinari, C. (2012). Online courses: Student preferences survey. Journal of Online Learning Research and Practice, 1(1).
Hamim, T., Benabbou, F., & Sael, N. (2021). Survey of machine learning techniques for student profile model-ing. International Journal of Emerging Technologies in Learning (iJET), 16(4), 136-151.
Hussain, M., Zhu, W., Zhang, W., & Abidi, S. M. R. (2018). Student engagement predictions in an e-learning system and their impact on student course assessment scores. Computational Intelligence & Neuroscience.
Kagklis, V., Lionarakis, A., Marketos, G., Panagiotakopoulos, G. T., Stavropoulos, E. C., & Verykios, V. S. (2017). Stu-dent admission data analytics for open and distance education in Greece. Ανοικτή Εκπαίδευση: το περιοδικό για την Ανοικτή και εξ Αποστάσεως Εκπαίδευση και την Εκπαιδευτική Τεχνολογία, 13(2), 6-16.
Kim, H. J., Hong, A. J., & Song, H. D. (2019). The roles of academic engagement and digital readiness in students’ achievements in university e-learning environments. International Journal of Educational Technology in Higher Edu-cation, 16(1), 1-18.
Moore, M. G., & Kearsley, G. (2012). Distance education: A systems view of online learning.
Nagori, R., & Aghila, G. (2011, April). LDA based integrated document recommendation model for e-learning systems. In 2011 international conference on emerging trends in networks and computer communications (ETNCC) (pp. 230-233). IEEE.
Prenkaj, B., Velardi, P., Stilo, G., Distante, D., & Faralli, S. (2020). A survey of machine learning approaches for student dropout prediction in online courses. ACM Computing Surveys (CSUR), 53(3), 1-34.
Romero, C., & Ventura, S. (2010). Educational data mining: a review of the state of the art. IEEE Transactions on Sys-tems, Man, and Cybernetics, Part C (applications and reviews), 40(6), 601-618.
Samigulina, G., & Samigulina, Z. (2016). Intelligent system of distance education of engineers, based on modern innova-tive technologies. Procedia-Social and Behavioral Sciences, 228, 229-236.
Sekeroglu, B., Dimililer, K., & Tuncal, K. (2019, March). Student performance prediction and classification using ma-chine learning algorithms. In Proceedings of the 2019 8th international conference on educational and information technology (pp. 7-11).
Shearer, R. L., & Park, E. (2018). Theory to practice in instructional design. In M. G. Moore & W. C. Diehl (Eds.), Hand-book of Distance Education (4th ed.), (pp. 260 -280). New York: Routledge.
Tomasevic, N., Gvozdenovic, N., & Vranes, S. (2020). An overview and comparison of supervised data mining techniques for student exam performance prediction. Computers & education, 143, 103676.
Van Rooyen, A. (2015). Distance education accounting students’ perceptions of social media integration. Procedia-Social and Behavioral Sciences, 176, 444-450.
Yan, X., Guo, J., Lan, Y., & Cheng, X. (2013, May). A biterm topic model for short texts. In Proceedings of the 22nd in-ternational conference on World Wide Web (pp. 1445-1456).
Zhong, J., Zhang, S. F., Guo, W. L., & Li, X. (2018). TFLA: A quality analysis framework for user generated con-tents. Tien Tzu Hsueh Pao/Acta Electron. Sin, 46, 2201-2206.