single-jc.php

JACIII Vol.28 No.3 pp. 668-678
doi: 10.20965/jaciii.2024.p0668
(2024)

Research Paper:

Student Progression and Dropout Rates Using Convolutional Neural Network: A Case Study of the Arab Open University

Mohamed Sayed ORCID Icon

Faculty of Computer Studies, Arab Open University
P.O.Box 830, Ardiya 92400, Kuwait

Received:
September 9, 2023
Accepted:
February 15, 2024
Published:
May 20, 2024
Keywords:
artificial intelligence, convolutional neural network, deep learning, dropout prediction, student learning and management systems
Abstract

Pre-trained convolutional neural network (CNN) structures are considered as one of the emerging education management tools that can help improve the quality of education by allowing decision makers to manipulate important indicators. These indicators, which are categorized as student and institution specific factors, may influence student progress, retention or dropout rates. In this paper, we develop a deep learning model of predicting students’ satisfactions and their expected outcomes and associated early failures. The model can also predict dropout rates and identify the main baseline risk factors that influence such rates. The academic data of 12,000 students enrolled from 2018 in the Arab Open University student information system are used as CNNs training dataset to ensure that all institution levels are represented. Then, the trained network provides a probabilistic model that indicates, for each student, the probability of dropout. Based on the prediction model, the study presents an early warning system framework to generate alerts and recommendations to allow early and effective institutional intervention. Experiments are achieved by using the proposed dataset and the performance of our approach is considerably better compared to the competitive models in terms of training/validation accuracy and mean square errors.

Convolutional neural network for student dropout model

Convolutional neural network for student dropout model

Cite this article as:
M. Sayed, “Student Progression and Dropout Rates Using Convolutional Neural Network: A Case Study of the Arab Open University,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.3, pp. 668-678, 2024.
Data files:
References
  1. [1] I. Lykourentzou et al., “Dropout prediction in e-learning courses through the combination of machine learning techniques,” Computers & Education, Vol.53, No.3, pp. 950-965, 2009. https://doi.org/10.1016/j.compedu.2009.05.010
  2. [2] A. I. T. Kiser and L. Price, “The persistence of college students from their freshman to sophomore year,” J. of College Student Retention: Research, Theory & Practice, Vol.9, No.4, pp. 421-436, 2008. https://doi.org/10.2190/CS.9.4.b
  3. [3] J.-H. Park, “Factors related to learner dropout in online learning,” Int. Research Conf. in the Americas of the Academy of Human Resource Development, 2007.
  4. [4] D. Collier and I. McMullen, “Modeling first year stop out of Kalamazoo Promise Scholars: Mapping influences of socioeconomic advantage and pre-college performance to college performance and persistence,” J. of College Student Retention: Research, Theory & Practice, Vol.25, No.4, pp. 846-870, 2024. https://doi.org/10.1177/15210251211029631
  5. [5] W. D. Mangold et al., “Who goes who stays: An assessment of the effect of a freshman mentoring and unit registration program on college persistence,” J. of College Student Retention: Research, Theory & Practice, Vol.4, No.2, pp. 95-122, 2002. https://doi.org/10.2190/CVET-TMDM-CTE4-AFE3
  6. [6] J. M. Braxton (Ed.), “Reworking the student departure puzzle,” Vanderbilt University Press, 2000.
  7. [7] V. A. Lotkowski, S. B. Robbins, and R. J. Noeth, “The role of academic and non-academic factors in improving college retention,” American College Testing, 2004.
  8. [8] A. Seidman, “Where we go from here: A retention formula for student success,” A. Seidman (Ed.), “College Student Retention: Formula for Student Success,” pp. 295-316, Praeger Publishers, 2005.
  9. [9] J. P. Campbell, P. B. DeBlois, and D. G. Oblinger, “Academic analytics: A new tool for a new era,” EDUCAUSE Review, Vol.42, No.4, pp. 40-57, 2007.
  10. [10] J.-L. Hung and K. Zhang, “Revealing online learning behaviors and activity patterns and making predictions with data mining techniques in online teaching,” MERLOT J. of Online Learning and Teaching, Vol.4, No.4, pp. 426-437, 2008.
  11. [11] L. P. Macfadyen and S. Dawson, “Mining LMS data to develop an ‘early warning system’ for educators: A proof of concept,” Computers & Education, Vol.54, No.2, pp. 588-599, 2010. https://doi.org/10.1016/j.compedu.2009.09.008
  12. [12] R. Bukralia, A. V. Deokar, and S. Sarnikar, “Using academic analytics to predict dropout risk in e-learning courses,” L. S. Iyer and D. J. Power (Eds.), “Reshaping Society Through Analytics, Collaboration, and Decision Support: Role of Business Intelligence and Social Media,” pp. 67-93, Springer, 2015. https://doi.org/10.1007/978-3-319-11575-7_6
  13. [13] M. Sayed and F. Baker, “E-learning optimization using supervised artificial neural-network,” J. of Software Engineering and Applications, Vol.8, No.1, pp. 26-34, 2015. https://doi.org/10.4236/jsea.2015.81004
  14. [14] L. Qiu et al., “Student dropout prediction in massive open online courses by convolutional neural networks,” Soft Computing, Vol.23, No.20, pp. 10287-10301, 2019. https://doi.org/10.1007/s00500-018-3581-3
  15. [15] M. M. Taye, “Theoretical understanding of convolutional neural network: Concepts, architectures, applications, future directions,” Computation, Vol.11, No.3, Article No.52, 2023. https://doi.org/10.3390/computation11030052
  16. [16] M. Sayed and F. Baker, “Thermal face authentication with convolutional neural network,” J. of Computer Science, Vol.14, No.12, pp. 1627-1637, 2018. https://doi.org/10.3844/jcssp.2018.1627.1637
  17. [17] M. Sayed, “Biometric gait recognition based on machine learning algorithms,” J. of Computer Science, Vol.14, No.7, pp. 1064-1073, 2018. https://doi.org/10.3844/jcssp.2018.1064.1073
  18. [18] M. Sayed, “Performance of convolutional neural networks for human identification by gait recognition,” J. of Artificial Intelligence, Vol.11, No.1, pp. 30-38, 2018. https://doi.org/10.3923/jai.2018.30.38
  19. [19] D. A. Shafiq et al., “Student retention using educational data mining and predictive analytics: A systematic literature review,” IEEE Access, Vol.10, pp. 72480-72503, 2022. https://doi.org/10.1109/ACCESS.2022.3188767
  20. [20] G. C. Deka, “Big data predictive and prescriptive analytics,” P. Raj and G. C. Deka (Eds.), “Handbook of Research on Cloud Infrastructures for Big Data Analytics,” pp. 370-391, IGI Global, 2014.
  21. [21] A. Siri, “Predicting students’ dropout at university using artificial neural networks,” Italian J. of Sociology of Education, Vol.7, No.2, pp. 225-247, 2015. https://doi.org/10.14658/PUPJ-IJSE-2015-2-9
  22. [22] B. K. Yousafzai et al., “Student-performulator: Student academic performance using hybrid deep neural network,” Sustainability, Vol.13, No.17, Article No.9775, 2021. https://doi.org/10.3390/su13179775
  23. [23] O. F. Porter, “Undergraduate completion and persistence at four-year colleges and Universities: Completers, persisters, stopouts, and dropouts,” National Institute of Independent Colleges and Universities, 1989.
  24. [24] S. Carr, “As distance education comes of age, the challenge is keeping the students,” The Chronicle of Higher Education, Vol.46, No.23, pp. 41-57, 2000.
  25. [25] D. P. Diaz, “Comparison of student characteristics, and evaluation of student success, in an online health education course,” Ph.D. thesis, Nova Southeastern University, 2000.
  26. [26] M. M. Lynch, “Effective student preparation for online learning,” The Technology Source, Vol.2001, No.6, 2001.
  27. [27] S. Hussain and M. Q. Khan, “Student-performulator: Predicting students’ academic performance at secondary and intermediate level using machine learning,” Annals of Data Science, Vol.10, No.3, pp. 637-655, 2023. https://doi.org/10.1007/s40745-021-00341-0
  28. [28] P. Long and G. Siemens, “Penetrating the fog: Analytics in learning and education,” EDUCAUSE Review, Vol.46, No.5, pp. 31-40, 2011.
  29. [29] E. Wagner and P. Ice, “Data changes everything: Delivering on the promise of learning analytics in higher education,” EDUCAUSE Review, Vol.47, No.4, pp. 33-42, 2012.
  30. [30] B. Daniel, “Big data and analytics in higher education: Opportunities and challenges,” British J. of Educational Technology, Vol.46, No.5, pp. 904-920, 2015. https://doi.org/10.1111/bjet.12230
  31. [31] C. Romero and S. Ventura, “Educational data mining: A review of the state of the art,” IEEE Trans. on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol.40, No.6, pp. 601-618, 2010. https://doi.org/10.1109/TSMCC.2010.2053532
  32. [32] R. S. J. D. Baker, “Learning, schooling, and data analytics,” M. Murphy, S. Redding, and J. Twyman (Eds.), “Handbook on Innovations in Learning,” pp. 179-190, Center on Innovations in Learning, Temple University, 2013.
  33. [33] B. K. Daniel and R. Butson, “Technology enhanced analytics (TEA) in higher education,” Int. Conf. on Educational Technologies 2013, pp. 89-96, 2013.
  34. [34] J. Bichsel, “Analytics in higher education: Benefits, barriers, progress, and recommendations,” EDUCAUSE Center for Applied Research, 2012.
  35. [35] P. Vashisht and V. Gupta, “Big data analytics techniques: A Survey,” 2015 Int. Conf. on Green Computing and Internet of Things (ICGCIoT), pp. 264-269, 2015. https://doi.org/10.1109/ICGCIoT.2015.7380470
  36. [36] J. Rajni and D. B. Malaya, “Predictive analytics in a higher education context,” IT Professional, Vol.17, No.4, pp. 24-33, 2015. https://doi.org/10.1109/MITP.2015.68
  37. [37] R. S. Baker and P. S. Inventado, “Educational data mining and learning analytics,” J. A. Larusson and B. White (Eds.), “Learning Analytics: From Research to Practice,” pp. 61-75, Springer, 2014. https://doi.org/10.1007/978-1-4614-3305-7_4
  38. [38] T. S. Ashwin and R. M. Guddeti, “Automatic detection of students’ affective states in classroom environment using hybrid convolutional neural networks,” Education and Information Technologies, Vol.25, No.2, pp. 1387-1415, 2020. https://doi.org/10.1007/s10639-019-10004-6
  39. [39] L. Sweeney, “A predictive model of student satisfaction,” Irish J. of Academic Practice, Vol.5, No.1, Article No.8, 2016. https://doi.org/10.21427/D7MH80
  40. [40] T. A. Etchells et al., “Learning what is important: Feature selection and rule extraction in a virtual course,” Proc. of the 14th European Symp. on Artificial Neural Networks, 2006.
  41. [41] S. Herzog, “Estimating student retention and degree-completion time: Decision trees and neural networks vis-à-vis regression,” New Directions for Institutional Research, Vol.2006, No.131, pp. 17-33, 2006. https://doi.org/10.1002/ir.185
  42. [42] J. D. Campbell, “Analysis of institutional data in predicting student retention utilizing knowledge discovery and statistical techniques,” Ph.D. thesis, Northern Arizona University, 2008.
  43. [43] L. Breiman et al., “Classification and Regression Trees,” Chapman and Hall/CRC, 1998.
  44. [44] M. Cocea and S. Weibelzahl, “Cross-system validation of engagement prediction from log files,” Proc. of the 2nd European Conf. on Technology Enhanced Learning (EC-TEL 2007), pp. 14-25, 2007. https://doi.org/10.1007/978-3-540-75195-3_2
  45. [45] M. Mühlenbrock, “Automatic action analysis in an interactive learning environment,” Proc. of the Workshop on Usage Analysis in Learning Systems at the 12th Int. Conf. on Artificial Intelligence in Education (AIED 2005), pp. 73-80, 2005.
  46. [46] P. E. Ramírez and E. E. Grandón, “Prediction of student dropout in a Chilean public university through classification based on decision trees with optimized parameters,” Formación universitaria, Vol.11, No.3, pp. 3-10, 2018 (in Spanish). https://doi.org/10.4067/S0718-50062018000300003
  47. [47] S. C. Kumar et al., “M5P model tree in predicting student performance: A case study,” 2016 IEEE Int. Conf. on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp. 1103-1107, 2016. https://doi.org/10.1109/RTEICT.2016.7808002
  48. [48] L. Najdi and B. Er-Raha, “Implementing cluster analysis tool for the identification of students typologies,” 2016 4th IEEE Int. Colloquium on Information Science and Technology (CiSt), pp. 575-580, 2016. https://doi.org/10.1109/CIST.2016.7804852
  49. [49] J. L. C. Ramos et al., “A comparative study between clustering methods in educational data mining,” IEEE Latin America Trans., Vol.14, No.8, pp. 3755-3761, 2016. https://doi.org/10.1109/TLA.2016.7786360
  50. [50] C. H. Yu et al., “A data-mining approach to differentiate predictors of retention,” EDUCAUSE Southwest Conf., 2007.
  51. [51] Z. Zdravev et al., “Analytics and report plugins in moodle,” Proc. of the 8th Int. Scientific Conf. on Computer Science 2018, pp. 163-168, 2018.
  52. [52] A. Zafar et al., “A comparison of pooling methods for convolutional neural networks,” Applied Sciences, Vol.12, No.17, Article No.8643, 2022. https://doi.org/10.3390/app12178643
  53. [53] L. Yann, K. Kavukcuoglu, and C. Farabet, “Convolutional networks and applications in vision,” Proc. of 2010 IEEE Int. Symp. on Circuits and Systems, pp. 253-256, 2010. https://doi.org/10.1109/ISCAS.2010.5537907
  54. [54] G. E. Hinton et al., “Improving neural networks by preventing co-adaptation of feature detectors,” arXiv:1207.0580, 2012. https://doi.org/10.48550/arXiv.1207.0580
  55. [55] N. Srivastava et al., “Dropout: A simple way to prevent neural networks from overfitting,” J. of Machine Learning Research, Vol.15, No.56, pp. 1929-1958, 2014.
  56. [56] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Proc. of the 25th Int. Conf. on Neural Information Processing Systems (NIPS’12), Vol.1, pp. 1097-1105, 2012.
  57. [57] A. Kandpal, “Machine Learning Part-2,” 2017. https://codeburst.io/machine-learning-day-1-60bd231d0660 [Accessed October 24, 2018]
  58. [58] S. L. Smith and Q. V. Le, “A Bayesian perspective on generalization and stochastic gradient descent,” 6th Int. Conf. on Learning Representations (ICLR), 2018.
  59. [59] C.-W. Zhang et al., “Pedestrian detection based on improved LeNet-5 convolutional neural network,” J. of Algorithms & Computational Technology, Vol.13, 2019. https://doi.org/10.1177/1748302619873601
  60. [60] C. Szegedy et al., “Going deeper with convolutions,” 2015 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2015. https://doi.org/10.1109/CVPR.2015.7298594
  61. [61] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv:1409.1556, 2014. https://doi.org/10.48550/arXiv.1409.1556
  62. [62] C. Szegedy et al., “Inception-v4, Inception-ResNet and the impact of residual connections on learning,” Proc. of the 31st AAAI Conf. on Artificial Intelligence (AAAI’17), pp. 4278-4284, 2017.
  63. [63] K. He et al., “Deep residual learning for image recognition,” 2016 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016. https://doi.org/10.1109/CVPR.2016.90
  64. [64] G. Huang et al., “Densely connected convolutional networks,” 2017 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 2261-2269, 2017. https://doi.org/10.1109/CVPR.2017.243
  65. [65] Y. Baashar et al., “Predicting student’s performance using machine learning methods: A systematic literature review,” 2021 Int. Conf. on Computer & Information Sciences (ICCOINS), pp. 357-362, 2021. https://doi.org/10.1109/ICCOINS49721.2021.9497185
  66. [66] W. Villegas-Ch, J. Govea, and S. Revelo-Tapia, “Improving student retention in institutions of higher education through machine learning: A sustainable approach,” Sustainability, Vol.15, No.19, Article No.14512, 2023. https://doi.org/10.3390/su151914512

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Oct. 11, 2024