Enhancing adult learner success in higher education through decision tree models: A machine learning approach

Emily  Barnes; James  Hutson; Karriem  Perry

doi:10.59400/fes.v2i3.1415

Enhancing adult learner success in higher education through decision tree models: A machine learning approach

Emily Barnes Capitol Technology University, AI Center of Excellence (AICE), Laurel, MD 20708, USA
James Hutson Lindenwood University, Saint Charles, MO 63301, USA
Karriem Perry Capitol Technology University, Laurel, MD 20708, USA

Article ID: 1415

167 Views

DOI: https://doi.org/10.59400/fes.v2i3.1415

Keywords: adult learners; decision tree models; machine learning; higher education; predictive accuracy

Abstract

This article explores the use of machine learning, specifically Classification and Regression Trees (CART), to address the unique challenges faced by adult learners in higher education. These learners confront socio-cultural, economic, and institutional hurdles, such as stereotypes, financial constraints, and systemic inefficiencies. The study utilizes decision tree models to evaluate their effectiveness in predicting graduation outcomes, which helps in formulating tailored educational strategies. The research analyzed a comprehensive dataset spanning the academic years 2013–2014 to 2021–2022, evaluating the predictive accuracy of CART models using precision, recall, and F1 score. Findings indicate that attendance, age, and Pell Grant eligibility are key predictors of academic success, demonstrating the strong capability of the model across various educational metrics. This highlights the potential of machine learning (ML) to improve data-driven decision-making in educational settings. The results affirm the effectiveness of Decision Tree (DT) models in meeting the educational needs of adult learners and underscore the need for institutions to adapt their strategies to provide more inclusive and supportive environments. This study advocates for a shift towards nuanced, data-driven approaches in higher education, emphasizing the development of strategies that address the distinct challenges of adult learners, aiming to enhance inclusivity and support within the sector.

References

[1]Job MA, Pandey J. Academic Performance Analysis Framework for Higher Education by Applying Data Mining Techniques. In: Proceedings of the 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); 4–5 June 2020; Noida, India. pp. 1145–1149. doi: 10.1109/icrito48877.2020.9197925

[2]Caspari-Sadeghi S. Learning assessment in the age of big data: Learning analytics in higher education. Cogent Education. 2022; 10(1). doi: 10.1080/2331186x.2022.2162697

[3]Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist. 2000; 55(1): 68–78. doi: 10.1037/0003-066x.55.1.68

[4]Nieto Y, García-Díaz V, Montenegro C, et al. Supporting academic decision making at higher educational institutions using machine learning-based algorithms. Soft Computing. 2018; 23(12): 4145–4153. doi: 10.1007/s00500-018-3064-6

[5]Musso MF, Hernández CFR, Cascallar EC. Predicting key educational outcomes in academic trajectories: A machine-learning approach. Higher Education. 2020; 80(5): 875–894. doi: 10.1007/s10734-020-00520-7

[6]Teng Y, Zhang J, Sun T. Data‐driven decision‐making model based on artificial intelligence in higher education system of colleges and universities. Expert Systems. 2022; 40(4). doi: 10.1111/exsy.12820

[7]Alkhalil A, Abdallah MAE, Alogali A, et al. Applying Big Data Analytics in Higher Education. International Journal of Information and Communication Technology Education. 2021; 17(3): 29–51. doi: 10.4018/ijicte.20210701.oa3

[8]Chaurasia SS, Kodwani D, Lachhwani H, et al. Big data academic and learning analytics. International Journal of Educational Management. 2018; 32(6): 1099–1117. doi: 10.1108/ijem-08-2017-0199

[9]National Student Clearinghouse Research Center. Some college, no credential student Outcomes: Annual progress report—academic year—2021/22. Available online: https://nscresearchcenter.org/some-college-no-credential/ (accessed on 1 June 2024).

[10]Sadiq MH, Ahmed NS. Classifying and Predicting Students’ Performance using Improved Decision Tree C4.5 in Higher Education Institutes. Journal of Computer Science. 2019; 15(9): 1291–1306. doi: 10.3844/jcssp.2019.1291.1306

[11]Yağcı M. Educational data mining: prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments. 2022; 9(1). doi: 10.1186/s40561-022-00192-z

[12]Hamoud AK, Hashim AS, Awadh WA. Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis. International Journal of Interactive Multimedia and Artificial Intelligence. 2018; 5(2): 26. doi: 10.9781/ijimai.2018.02.004

[13]Marzuqi A, Laksitowening KA, Asror I. Temporal Prediction on Students’ Graduation using Naïve Bayes and K-Nearest Neighbor Algorithm. Jurnal Media Informatika Budidarma. 2021; 5(2): 682. doi: 10.30865/mib.v5i2.2919

[14]Widaningsih S, Muhamad W, Hendriyanto R, et al. An ID3 Decision Tree Algorithm-Based Model for Predicting Student Performance Using Comprehensive Student Selection Data at Telkom University. Ingénierie des systèmes d information. 2023; 28(5): 1205–1212. doi: 10.18280/isi.280508

[15]Quinlan JR. Induction of decision trees. Machine Learning. 1986; 1(1): 81–106. doi: 10.1007/bf00116251

[16]Gotardo MA. Using Decision Tree Algorithm to Predict Student Performance. Indian Journal of Science and Technology. 2019; 12(8): 1–8. doi: 10.17485/ijst/2019/v12i5/140987

[17]Alsariera YA, Baashar Y, Alkawsi G, et al. Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance. Computational Intelligence and Neuroscience. 2022; 2022: 1–11. doi: 10.1155/2022/4151487

[18]Martins M, Miguéis V, Fonseca D, Alves A. A data mining approach for predicting academic success: A case study. In: Rocha AC, Ferrás M (editors). Information Technology and Systems: Advances in Intelligent Systems and Computing. Springer; 2019. Volume 918. pp. 45–56. https://doi.org/10.1007/978-3-030-11890-7_5

[19]Garg A, Lilhore UK, Ghosh P, et al. Machine Learning-based Model for Prediction of Student’s Performance in Higher Education. In: Proceedings of the 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN); 26–27 August 2021; Noida, India. pp. 162–168. doi: 10.1109/spin52536.2021.9565999

[20]Ahmad Tarmizi SS, Mutalib S, Abdul Hamid NH, et al. A case study on student attrition prediction in higher education using data mining techniques. In: Berry M, Yap B, Mohamed A, Köppen M (editors). Soft Computing in Data Science, Proceedings of the 5th International Conference, SCDS 2019; 28–29 August 2019; Iizuka, Japan. Springer; 2019. pp. 181–192. doi: 10.1007/978-981-15-0399-3_15

[21]Ivanov A. Decision Trees for Evaluation of Mathematical Competencies in the Higher Education: A Case Study. Mathematics. 2020; 8(5): 748. doi: 10.3390/math8050748

[22]Crismayella Y, Satyahadewi N, Perdana H. Algoritma Adaboost pada Metode Decision Tree untuk Klasifikasi Kelulusan Mahasiswa. Jambura Journal of Mathematics. 2023; 5(2): 278–288. doi: 10.34312/jjom.v5i2.18790

[23]Nguyen MK, Huynh VT, Nguyen HD. Predicting graduation grades using Machine Learning: A case study of Can Tho University students. CTU Journal of Innovation and Sustainable Development. 2023; 15(ISDS): 83–92. doi: 10.22144/ctujoisd.2023.038

[24]Акмеше ОФ, Кьор Х, Ербей Х. Use of machine learning techniques for the forecast of student achievement in higher education. Information Technologies and Learning Tools. 2021; 82(2): 297–311. doi: 10.33407/itlt.v82i2.4178

[25]Zhou J, Ye J min. Sentiment analysis in education research: A review of journal publications. Interactive Learning Environments. 2020; 31(3): 1252–1264. doi: 10.1080/10494820.2020.1826985

[26]Oreški D, Zamuda D. Machine Learning Based Model for Predicting Student Outcomes. In: Proceedings of the International Conference on Industrial Engineering and Operations Management; 7–10 March 2022; Istanbul, Turkey. pp. 4884–4894. doi: 10.46254/an12.20220967

[27]Khor ET. A data mining approach using machine learning algorithms for early detection of low-performing students. The International Journal of Information and Learning Technology. 2022; 39(2): 122–132. doi: 10.1108/ijilt-09-2021-0144

[28]Yan J, Chi X. Analysis and Prediction of College Students’ Employment based on Decision Tree Classification Algorithm. In: Proceedings of the 2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE); 29–30 April 2023; Ballar, India. doi: 10.1109/icdcece57866.2023.10150841

[29]Meng Z. Analysis and Prediction of College Students’ Employment Based on Decision Tree Classification Algorithm. In: Proceedings of the 2023 World Conference on Communication & Computing (WCONF); 14–16 July 2023; RAIPUR, India. doi: 10.1109/wconf58270.2023.10234985

[30]Pradana MG, Rangga Pinastawa IW, Maulana N, et al. Performance Analysis of Tree-Based Algorithms in Predicting Employee Attrition. CCIT Journal. 2023; 16(2): 220–232. doi: 10.33050/ccit.v16i2.2580

[31]Yin L. The Application of Improved Decision Tree Algorithm in College Employment Management. In: Proceedings of the 2023 3rd Asian Conference on Innovation in Technology (ASIANCON); 25–27 August 2023; Ravet IN, India. doi: 10.1109/asiancon58793.2023.10270643

[32]Muzumdar P, Basyal GP, Vyas P. An Empirical Comparison of Machine Learning Models for Student’s Mental Health Illness Assessment. Asian Journal of Computer and Information Systems. 2022; 10(1). doi: 10.24203/ajcis.v10i1.6882

[33]Battista K, Diao L, Patte KA, et al. Examining the use of decision trees in population health surveillance research: An application to youth mental health survey data in the COMPASS study. Health Promotion and Chronic Disease Prevention in Canada. 2023; 43(2): 73–86. doi: 10.24095/hpcdp.43.2.03

[34]Li H. Analysis of mental health influencing factors and students’ psychological education countermeasures based on ID3 algorithm. Applied Mathematics and Nonlinear Sciences. 2023; 9(1). doi: 10.2478/amns.2023.2.01377

[35]Xiaocheng H. Application of Decision Tree Algorithm in College Students’ Mental Health Evaluation. In: Proceedings of the 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS); 24–25 February 2023; Raichur, India. doi: 10.1109/icicacs57338.2023.10099654

[36]Krishnan R, Kumari S, Badi AA, et al. Predictive machine learning model for mental health issues in higher education students due to COVID-19 using HADS assessment. Arab Gulf Journal of Scientific Research. 2023. doi: 10.1108/agjsr-01-2023-0034

[37]Rueda E, Lowe Swift C. Academic Belonging in Higher Education. Routledge; 2023. doi: 10.4324/9781003443735

[38]Salihoun M. State of Art of Data Mining and Learning Analytics Tools in Higher Education. International Journal of Emerging Technologies in Learning (IJET). 2020; 15(21): 58. doi: 10.3991/ijet.v15i21.16435

[39]Tahiru F, Parbanath S, Agbesi S. Machine Learning-based Predictive Systems in Higher Education: A Bibliometric Analysis. Journal of Scientometric Research. 2023; 12(2): 436–447. doi: 10.5530/jscires.12.2.040

[40]Mao H, Fang Z, Xia Z. Model of the influence of students’ participation on learning effects using educational artificial intelligence. In: Proceedings of the 2023 8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA); 26-28 April 2023; Chengdu, China. pp. 512–515. doi: 10.1109/icccbda56900.2023.10154663

[41]Pinto AS, Abreu A, Costa E, et al. How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review. Journal of Information Systems Engineering and Management. 2023; 8(2). doi: 10.55267/iadt.07.13227

[42]Feng L. Research on Higher Education Evaluation and Decision-Making Based on Data Mining. Scientific Programming. 2021; 2021: 1–9. doi: 10.1155/2021/6195067

[43]Jacob D, Henriques R. Educational Data Mining to Predict Bachelors Students’ Success. Emerging Science Journal. 2023; 7: 159–171. doi: 10.28991/esj-2023-sied2-013

[44]Attiya WM, Shams MB. Predicting Student Retention in Higher Education Using Data Mining Techniques: A Literature Review. In: Proceedings of the 2023 International Conference On Cyber Management and Engineering (CyMaEn); 26–27 January 2023; Bangkok, Thailand. pp. 171–177. doi: 10.1109/cymaen57228.2023.10051056

[45]Gafarov MF, Okishev KY, Makovetskiy AN, et al. Construction of Models for Predicting the Microstructure of Steels after Heat Treatment Using Machine Learning Methods. Steel in Translation. 2023; 53(11): 1120-1129. doi: 10.3103/S0967091223110104

[46]Chen R. Research on the development path of higher education model innovation based on quadratic planning algorithm. Applied Mathematics and Nonlinear Sciences. 2023; 9(1). doi: 10.2478/amns.2023.2.01100

Published

2024-07-09

How to Cite

Barnes, E., Hutson, J., & Perry, K. (2024). Enhancing adult learner success in higher education through decision tree models: A machine learning approach. Forum for Education Studies, 2(3), 1415. https://doi.org/10.59400/fes.v2i3.1415

Download Citation

Issue

Vol. 2 No. 3 (2024)

Section

Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

Editor-in-Chief

Dr. Surapati Pramanik

Nandalal Ghosh B.T. College, India

eISSN

3029-2956

Publication Frequency

Quarterly

About the Publisher

Academic Publishing insists on taking academic exchange and publication as the main line, carrying out comprehensive management based on science and technology, and fully exploring excellent international publishing resources. Within 5 years, it will form a strategic framework and scale with science (S), technology (T), medicine (M), education (E), and humanities and arts (H) as the main publishing fields. Academic Publishing is headquartered in Singapore and based in Malaysia, with the United States and China providing the main scientific and academic resources. At the same time, it has established long-term good cooperative relations with other publishing companies, scientific research communities, and academic organizations in more than a dozen countries and regions. Academic Publishing uses English and Chinese as its main publishing languages, mainly publishing books, journals, and conference papers in print and online. The vast majority of publications follow the international open access policy, providing stable and long-term quality and professional publications. With the joint efforts of the expert team and our professional editorial team, our publications will gradually be indexed by international databases in stages to provide convenient and professional retrieval for various scholars. At the same time, manuscripts we accept will be subject to the peer review principle, and cutting-edge and innovative research articles will be preferentially accepted for peer reference and discussion. All kinds of our publications are welcome for peer to contribute, access, and download.

more

Volume Arrangement

2025

2024

2023

Featured Articles

Diachronic analysis of Spanish scientific production on teaching methodologies in Primary Education: A scientometric and conceptual perspective (2000–2023)

The evaluation of science is essential to ensuring the quality, validity, and reliability of scientific results. Science needs to undergo a review process to ensure the rigorousness of scientific output. This evaluation provides a solid basis for political and economic decisions related to the design and execution of research projects, the establishment of new lines of research, or the identification of areas of specialization. This paper analyses diachronically the Spanish scientific production related to the implementation and development of teaching methodologies in primary education and indexed in the Scopus and Web of Science databases during the period 2000–2023. This analysis is carried out on the one hand, from a scientometric perspective, based on the analysis of indicators such as diachronic production, the journals with the highest scientific productivity, and the most productive institutions, and, on the other hand, from a conceptual perspective, trying to define its relationship with other areas of education. The general results of this study reveal two clear stages: the first, up to 2010, with little scientific production; and the second, from 2011 onwards, characterised by a general growth. The relationship between this field and others such as initial teacher training, ICT, and didactics is also evident.

The relationship between teachers’ self-efficacy and classroom management practices in secondary schools

This study explores the intricate relationship between teacher self-efficacy and classroom management practices in secondary schools in the Mansehra district of Pakistan. Teacher self-efficacy, defined as the belief in one’s ability to manage and influence classroom environments effectively, has been identified as a critical factor influencing both teaching performance and student outcomes. The research employed a mixed-method approach, gathering data from 62 teachers and 310 students using both online surveys (via Google Forms) and physical questionnaires to ensure a diverse and inclusive participant pool. Data analysis was conducted using two complementary tools: SPSS and Smart PLS. SPSS was used for descriptive statistics and inferential analyses, such as t-tests, chi-square tests, and measures of central tendency, to offer an overview of group differences and relationships between variables. Meanwhile, Smart PLS was employed for Partial Least Squares Structural Equation Modeling (PLS-SEM), a technique suited for complex models and smaller sample sizes. This method allowed for the analysis of both direct and indirect relationships between the study variables—teacher self-efficacy, teaching practices, and classroom management. The findings reveal a significant positive correlation between teacher self-efficacy and classroom management practices. Additionally, teaching practices were found to mediate this relationship, indicating that higher levels of self-efficacy not only directly improve classroom management but also enhance teaching performance, which in turn contributes to better-managed classrooms. These results suggest that interventions aimed at enhancing teacher self-efficacy can have far-reaching effects on educational outcomes. The study highlights the need for focused teacher development programs that foster self-efficacy, thereby improving classroom management, student engagement, and overall academic success.

Artificial Intelligence in education: A comprehensive study

This comprehensive study delves into the multifaceted role of AI in education, exploring its applications, benefits, challenges, and future implications. The purpose of the study is to show how AI in education helps educators identify gaps in student knowledge and provide targeted feedback to improve learning outcomes. As a methodology, the library method and the study and review of various documents have been used in this research. The study examines the diverse range of AI technologies employed in educational settings, including intelligent tutoring systems, personalized learning platforms, educational chatbots, and virtual reality simulations. Furthermore, the study delves into the numerous benefits that AI brings to education. It highlights how AI-powered analytics and data-driven insights enable educators to gain deeper insights into student learning patterns, identify areas for improvement, and tailor instructional strategies accordingly. Additionally, AI-driven tools promote inclusivity by providing personalized support to learners with diverse needs and learning styles. Despite its transformative potential, the study also acknowledges the challenges and ethical considerations associated with integrating AI into education. Data privacy, algorithmic bias, and the digital divide are examined in detail, emphasizing the importance of responsible AI deployment and ethical guidelines. Looking ahead, the study explores the future implications of AI in education and the evolving role of educators in AI-enabled classrooms. It discusses how AI technologies will continue to evolve, offering new opportunities for collaborative learning, skill development, and lifelong education. In conclusion, this comprehensive study underscores the profound impact of AI on education and the need for thoughtful implementation strategies that prioritize equity, inclusivity, and ethical considerations. By harnessing the potential of AI, education systems can better prepare learners for the challenges and opportunities of the future.

Enhancing adult learner success in higher education through decision tree models: A machine learning approach