Emily Barnes¹, James Hutson²*, Karriem Perry³

¹Capitol Technology University, Maryland
²Lindenwood University, United States
³Capitol Technology University, Maryland

*Corresponding Author: James Hutson, Lindenwood University, United States.

Received: June 03, 2024; Published: June 24, 2024

Abstract

The integration of machine learning (ML) into higher education has been recognized as a transformative force for adult learners, a growing demographic facing unique educational challenges. This study evaluates the predictive power of three ML models—Random Forest, Gradient-Boosting Machine, and Decision Trees—in forecasting degree completion among this group. Utilizing a dataset from the academic years 2013-14 to 2021-22, which includes demographic and academic performance metrics, the study employs accuracy, precision, recall, and F1 score to assess the efficacy of these models. The results indicate that the Gradient-Boosting Machine model outperforms others in predicting degree completion, suggesting that ML can significantly enhance data-driven decision-making in educational settings. By highlighting the factors influencing adult learners' educational success, such as age and socioeconomic status, this research supports the strategic implementation of tailored educational policies and interventions, aimed at improving the retention and graduation rates of adult learners in higher education institutions.

Keywords: Machine Learning; Adult Learners; Educational Outcomes; Predictive Analytics; Degree Completion

References

1. Rueda E and Swift C. “Academic belonging in higher education”. Routledge (2023).
2. Ryan R and Deci E. “Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being”. American Psychologist 1 (2000): 68-78.
3. National Student Clearinghouse Research Center. “Some college, no credential student Outcomes: Annual progress report-academic year-2021/22 (2023).
4. Ivanov A. “Decision trees for evaluation of mathematical competencies in higher education: A case study”. Mathematics5 (2020): Article 748.
5. Salihoun “State of art data mining and learning analytics tools in higher education”. International Journal of Emerging Technologies in Learning 15.21 (2020): 58.
6. Buenaño-Fernández D., et al. “Application of machine learning in predicting performance for computer engineering students: A case study”. Sustainability10 (2019): Article 2833.
7. Korkmaz C and Correia A. “A review of research on machine learning in educational technology”. Educational Media International 3 (2019): 250-267.
8. Oliveira A., et al. “Emerging technologies as pedagogical tools for teaching and learning science: A literature review”. Human Behavior and Emerging Technologies2 (2019): 149-160.
9. Kannan R. “Advancements in machine learning techniques for educational data mining: An overview of perspectives and trends”. International Journal of Membrane Science and Technology 3 (2023): 1820-1839.
10. Hilbert S., et al. “Machine learning for the educational sciences”. Review of Education 3 (2021).
11. Son N., et al. “The applications of machine learning in education science research. VNU Journal of Science: Education Research4 (2021).
12. Pinto A., et al. “How machine learning (ML) is transforming higher education: A systematic literature review”. Journal of Information Systems Engineering and Management2 (2023): Article 21168.
13. Hessen S., et al. “Developing multiagent e-learning system-based machine learning and feature selection techniques”. Computational Intelligence and Neuroscience Article 2941840 (2022): 1-8.
14. Zhang Y and Teng Z. “Natural language processing: A machine learning perspective”. Computational Linguistics1 (2022): 233-235.
15. Teng Y., et al. “Data-driven decision-making model based on artificial intelligence in higher education system of colleges and universities”. Expert Systems (2022): 40.
16. Ashaari, et al. “Big data analytics technology capability and data-driven decision making in Malaysian higher education institutions: A conceptual framework”. IOP Conference Series Materials Science and Engineering 874.1 (2020): Article 012021.
17. Feng L. “Research on higher education evaluation and decision-making based on data mining”. Scientific Programming (2021): 1-9.
18. Hussain M., et al. “Using machine learning to predict student difficulties from learning session data”. The Artificial Intelligence Review 1 (2019): 381-407.
19. Albreiki, et al. “A systematic literature review of student’ performance prediction using machine learning techniques”. Education Sciences 11.9 (2021): Article 552.
20. Chen F and Cui Y. “Utilizing student time series behaviour in learning management systems for early prediction of course performance”. Journal of Learning Analytics 2 (2020): 1-17.
21. Dake D and Buabeng-Andoh C. “Using machine learning techniques to predict learner drop-out rate in higher educational institutions. Mobile Information Systems (2022): Article 2670562.
22. Mandinach E and Schildkamp K. “Misconceptions about data-based decision making in education: An exploration of literature. Studies in Educational Evaluation 69 (2020): Article 100842.
23. Oyedeji A., et al. “Analysis and prediction of student academic performance using machine learning”. Journal of Information Technology and Computer Engineering1 (2020): 10-15.
24. Roberts L., et al. “Student attitudes toward learning analytics in higher education: The Fitbit version of the learning world”. Frontiers in Psychology 7 (2016).
25. Sadiq M and Ahmed N. “Classifying and predicting students’ performance using improved Decision Tree C4.5 in higher education institutes”. Journal of Computer Science9 (2019): 1291-1306.
26. Gotardo M. “Using Decision Tree algorithm to predict student performance”. Indian Journal of Science and Technology 12 (2019): 1-8.
27. Kim J., et al. “Online higher education for nontraditional adult students: Best cases for public universities. In K. Junghwan, H. Shin, K. Smith and J. Hwang (Eds.), Research Anthology on Developing Effective Online Learning Courses (2021): 265-286.
28. Saidani O., et al. “Predicting student employability through the internship context using gradient boosting models”. IEEE Access 10 (2022): 46472-46489.
29. Akmeshe, et al. “Use of machine learning techniques for the forecast of student achievement in higher education”. Information Technologies and Learning Tools 82 (2021): 297-311.
30. Hasan R., et al. “Predicting student performance in higher educational institutions using video learning analytics and data mining techniques”. Applied Sciences 11 (2020): Article 3894.
31. Alsariera, et al. “Assessment and evaluation of different machine learning algorithms for predicting student performance”. Computational Intelligence and Neuroscience (2020): 1-11.
32. Beaulac C and Rosenthal J. “Predicting university students’ academic success and major using Random Forests”. Research in Higher Education 7 (2019): 1048-1064.
33. Cardona T., et al. “Data mining and machine learning retention models in higher education”. Journal of College Student Retention: Research, Theory and Practice 1 (2020): 51-75.
34. Kamal P and Ahuja S. “An ensemble-based model for prediction of academic performance of students in undergrad professional course”. Journal of Engineering, Design and Technology 4 (2019): 769-781.
35. Charilaou P and Battat R. “Machine learning models and over-fitting considerations”. World Journal of Gastroenterology 5 (2022): 605-607.
36. Jalota C and Agrawal R. “Feature selection algorithms and student academic performance: A study”. In D. Gupta, A. Khanna, S. Bhattacharyya, A. Hassanien, S. Anand, & A. Jaiswal, (Eds.), International Conference on Innovative Computing and Communications: Vol. 1165. Advances in Intelligent Systems and Computing (2021): 317-328.
37. Islam M., et al. “A comprehensive survey on the process, methods, evaluation, and challenges of feature selection”. IEEE Access 10 (2022): 99595-99632.
38. Kaneko H. “Interpretation of machine learning models for data sets with many features using feature importance”. Acs Omega25 (2023): 23218-23225.
39. Marcinkowski F., et al. “Implications of AI (un)fairness in higher education admissions: The effects of perceived AI (un-)fairness on exit, voice, and organizational reputation”. In ACM Digital Library (Eds.), Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery (2020).
40. Palacios C., et al. “Knowledge discovery for higher education student retention based on data mining: Machine learning algorithms and case study in Chile”. Entropy 4 (2021): Article 485.
41. Arqawi, et al. “Predicting university student retention using artificial intelligence”. International Journal of Advanced Computer Science and Applications 13.9 (2022).
42. Brdesee, et al. “Predictive model using a machine learning approach for enhancing the retention rate of students at-risk”. International Journal of Semantic Web Information Systems 18 (2022): 1-21.
43. Zeineddine, et al. “Enhancing prediction of student success: Automated machine learning approach”. Computers and Electrical Engineering 89.1 (2021): Article 106903.
44. Gaftandzhieva, et al. “Exploring online activities to predict the final grade of student”. Mathematics 10.20 (2022): Article 3758.
45. Oqaidi, et al. “Towards a students’ dropout prediction model in higher education institutions using machine learning algorithms”. International Journal of Emerging Technologies in Learning 17.18 (2022): 103-117.
46. Jusslin, et al. “Post-approaches to education and the arts: putting theories to work in arts educational practices”. Journal for Research in Arts and Sports Education 6.3 (2022): 1-10.
47. Nauman M., et al. “Guaranteeing correctness of machine learning based decision making at higher educational institutions”. IEEE Access 9 (20221): 92864-92880.
48. Allen L., et al. “Natural language processing as a tool for learning analytics-towards a multi-dimensional view of the learning process. In C. Lang, A. Friend Wise, A. Merceron, D. Gaševic, and G. Siemens (Eds.). Handbook of learning analytics. (2^nd) (2022).
49. Nieto Y., et al. “Supporting academic decision making at higher educational institutions using machine learning-based algorithms”. Soft Computing 23 (2019): 4145-4153.
50. Tarmizi, et al. “A review on student attrition in higher education using big data analytics and data mining techniques”. International Journal of Modern Education and Computer Science 11.8 (2019): 1-14.
51. Yağcı “Educational data mining: Prediction of students’ academic performance using machine learning algorithms”. Smart Learning Environments 9.1 (2022).
52. Martins M., et al. “A data mining approach for predicting academic success: A case study. In A. Rocha, C. Ferrás, M. Paredes (Eds.), Information Technology and Systems: 918”. Advances in Intelligent Systems and Computing (2019): 45-56.
53. Jun-on N., et al. “Prediction of students’ performance in English using machine learning algorithms”. English Language Teaching 4 (2023): 24.
54. Davari M., et al. “Predictive analytics of student performance determinants in education”. World Academy of Science, Engineering and Technology, Open Science Index 191, International Journal of Educational and Pedagogical Sciences 11 (2022): 716-721.
55. Liu J., et al. “Fair Compass: Operationalising fairness in machine learning. IEEE Transactions on Artificial Intelligence 99 (2023): 1-10.
56. Saltz J., et al. “Integrating ethics within machine learning courses”. ACM Transactions on Computing Education 19 (2019): 1-26.
57. Toms A and Whitworth S. “Ethical considerations in the use of machine learning for research and statistics”. International Journal for Population Data Science 3 (2022).
58. Musso M., et al. “Predicting key educational outcomes in academic trajectories: A machine-learning approach”. Higher Education 5 (2020): 875-894.
59. Pande P., et al. “Long-term effectiveness of immersive VR simulations in undergraduate science learning: Lessons from a media-comparison study”. Research in Learning Technology 29 (2021): 1-24.
60. Maravé-Vivas M., et al. “A longitudinal study of the effects of service-learning on physical education teacher education students”. Frontiers in Psychology 13 (2022): 787346.
61. Holenstein, et al. “Transfer effects of mathematical literacy: An integrative longitudinal study”. European Journal of Psychology of Education 36 (2020): 799-825.
62. Carless D. “Longitudinal perspectives on students’ experiences of feedback: A need for teacher-student partnerships”. Higher Education Research and Development 39 (2020): 425-438.
63. Friedman J. “Greedy function approximation: A gradient boosting machine”. The Annals of Statistics 5 (2001): 1189-1232.
64. Quinlan J. “Induction of Decision Trees”. Machine Learning 1 (1986): 81-106.
65. Huynh-Cam T., et al. “Using Decision Trees and Random Forest algorithms to predict and determine factors contributing to first-year university students’ learning performance”. Algorithms 14 (2021): 318.
66. Raschka S and Mirjalli V. “Python machine learning with PyTorch and Scikit-Learn”. Packt Publishing (2022).
67. Breiman “Random forests”. Machine Learning 45 (2001): 5-32.

Citation

Citation: James Hutson., et al. “Predictive Power of Machine Learning Models on Degree Completion Among Adult Learners".Acta Scientific Computer Sciences 6.7 (2024): 79-96.

Copyright

Copyright: © 2024 James Hutson., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.