Skip to main content
SpringerLink
Log in

What postpones degree completion? Discovering key predictors of undergraduate degree completion through explainable artificial intelligence (XAI)

  • Original Article
  • Published:
Journal of Marketing Analytics Aims and scope Submit manuscript

Abstract

The timing of degree completion for students taking post-secondary courses has been a constant source of angst for administrators wanting the best outcomes for their students. Most methods for predicting student degree completion extensions are completed by analog methods using human effort to analyze data. The majority of data analysis reporting of degree completion extension variables and impacts has, for decades, been done manually. Administrators primarily forecast the factors based on their expertise and intuition to evaluate implications and repercussions. The variables are large, varied, and situational to each individual and complex. We used machine learning (automated processes using predictive algorithms) to predict undergraduate extensions for at least 2 years beyond a standard 4 years to complete a bachelor's degree. The study builds a machine learning-based education understanding XAI model (ED-XAI) to examine students’ dependent and independent variables and accurately predict/explain degree extension. The study utilized Random Forest, Support Vector Machines, and Deep Learning Machine learning algorithms. XAI used Information Fusion, SHapley Additive exPlanations (SHAP), and Local Interpretable Model-Agnostic Explanations (LIME) models to explain the findings of the Machine Learning models. The ED-XAI model explained multiple scenarios and discovered variables influencing students’ degree completion linked to their status and funding source. The Random Forest model gave supreme predictive results with 89.1% Mean ROC, 71.6% Overall Precision, 86% Overall Recall, and 71.6% In-class Precision. The educational information system introduced in this study has significant implications for accurate variables reporting and impacts on degree extensions leading to successful degree completions minimally reported in higher education marketing analytics research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Ahmad, Z., and E. Shahzadi. 2018. Prediction of students’ academic performance using artificial neural network. Bulletin of Education and Research 40 (3): 157–164.

    Google Scholar 

  • Alaparthi, S., and M. Mishra. 2021. BERT: A sentiment analysis odyssey. Journal of Marketing Analytics 9 (2): 118–126. https://doi.org/10.1057/s41270-021-00109-8.

    Article  Google Scholar 

  • Al-Ashoor, Ahmed, and S. Abdullah. 2022. Examining techniques to solving imbalanced datasets in educational data mining systems. International Journal of Computing 21 (2): 205–213.

    Article  Google Scholar 

  • Aldowah, H., H. Al-Samarraie, and W.M. Fauzy. 2019. Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics 37: 13–49.

    Article  Google Scholar 

  • Alemany Oliver, M., and J.S. Vayre. 2015. Big data and the future of knowledge production in marketing research: Ethics, digital traces, and abductive reasoning. Journal of Marketing Analytics 3 (1): 5–13. https://doi.org/10.1057/jma.2015.1.

    Article  Google Scholar 

  • Alemany Oliver, M., J. Vayre, D. Iacobucci, M. Petrescu, A. Krishen, and M. Bendixen. 2019. The state of marketing analytics in research and practice. Journal of Marketing Analytics 7 (3): 152–181. https://doi.org/10.1057/s41270-019-00059-2S

    Article  Google Scholar 

  • Almgerbi, M., A. De Mauro, A. Kahlawi, and V. Poggioni. 2022. A systematic review of data analytics job requirements and online-courses. Journal of Computer Information Systems 62 (2): 422–434.

    Article  Google Scholar 

  • Alshanqiti, A., and A. Namoun. 2020. Predicting student performance and its influential factors using hybrid regression and multi-label classification. IEEE Access 8: 203827–203844.

    Article  Google Scholar 

  • Asamoah, D.A. 2021. Utilizing a XAI approach for data analytics pedagogy. Journal of Computer Information Systems 61 (6): 581–591.

    Article  Google Scholar 

  • Astin, A.W. 2012. Assessment for excellence: The philosophy and practice of assessment and evaluation in higher education. Lanham: Rowman & Littlefield Publishers.

    Google Scholar 

  • Arnold, L., S. Rebecchi, S. Chevallier, and H. Paugam-Moisy. 2011. An introduction to deep learning. In European symposium on artificial neural networks (ESANN).

  • Baek, C., and T. Doleck. 2022. Educational data mining: A bibliometric analysis of an emerging field. IEEE Access 10: 31289–31296.

    Article  Google Scholar 

  • Baker, R.S. 2015. Big data and education, 2nd ed. New York: Teachers College, Columbia University.

    Google Scholar 

  • Baker, R. S., and P. S. Inventado. 2014. Educational data mining and learning analytics. In Learning analytics, pp. 61–75. New York: Springer

  • Bakhshinategh, B., O.R. Zaiane, S. ElAtia, and D. Ipperciel. 2018. Educational data mining applications and tasks: A survey of the last 10 years. Education and Information Technologies 23 (1): 537–553.

    Article  Google Scholar 

  • Botelho, A.F., R.S. Baker, and N.T. Heffernan. 2019. Machine-learned or expert-engineered features? Exploring feature engineering methods in detectors of student behavior and affect. In The twelfth international conference on educational data mining, Montréal, Canada.

  • Brownlee, J. 2016. Deep learning with Python: Develop deep learning models on Theano and TensorFlow using Keras. Machine Learning Mastery.

  • Cankaya, B., B.E. Tokgoz, A. Dag, and K.C. Santosh. 2021. Development of a machine-learning-based XAI system mechanism for predicting chemical tank cleaning activity. Journal of Modeling in Management 16 (4): 1138–1165. https://doi.org/10.1108/JM2-12-2019-0284.

    Article  Google Scholar 

  • Cankaya, B., K. Topuz, D. Delen, & A. Glassman. 2023. evidence-based managerial decision-making with machine learning: the case of Bayesian inference in aviation incidents. Omega, 102906.

  • Cano, A., and J. Leonard. 2019. Interpretable multi-view early warning system adapted to underrepresented student populations. IEEE Transactions on Learning Technologies 12: 198–211.

    Article  Google Scholar 

  • Chung, J., and S. Lee. 2019. Dropout early warning systems for high school students using machine learning. Children and Youth Services Review 96 (2019): 346–353. https://doi.org/10.1016/j.childyouth.2018.11.030.

    Article  Google Scholar 

  • Comm, C.L., and D.F.X. Mathaisel. 2018. The use of analytics to market the sustainability of “Unique” products. Journal of Marketing Analytics 6 (4): 150–156. https://doi.org/10.1057/s41270-018-0038-6.

    Article  Google Scholar 

  • Cruz-Jesus, F., M. Castelli, T. Oliveira, R. Mendes, C. Nunes, M. Sa-Velho, and A. Rosa-Louro. 2020. Using artificial intelligence methods to assess academic achievement in public high schools of a European Union country. Heliyon 6 (6): e04081.

    Article  PubMed  PubMed Central  Google Scholar 

  • de Almeida, W.M., and C.P. da Veiga. 2023. Does demand forecasting matter to retailing? Journal of Marketing Analytics 11 (2): 219–232. https://doi.org/10.1057/s41270-022-00162-x.

    Article  Google Scholar 

  • de Carvalho, W. F., and L. E. Zarate. 2019. Causality relationship among attributes applied in an educational data set. In Proceedings of the 34th ACM/SIGAPP symposium on applied computing, 1271–1277. Limassol: ACM

  • Domingos, P. 2012. A few useful things to know about machine learning. Communications of the ACM 55 (10): 78–87.

    Article  Google Scholar 

  • Elgendy, N., A. Elragal, and T. Päivärinta. 2022. DECAS: A modern data-driven decision theory for big data and analytics. Journal of Decision Systems 31 (4): 337–373. https://doi.org/10.1080/12460125.2021.1894674.

    Article  Google Scholar 

  • Esmaily, H., M. Tayefi, H. Doosti, M. Ghayour-Mobarhan, H. Nezami, and A. Amirabadizadeh. 2018. A comparison between decision tree and random forest in determining the risk factors associated with type 2 diabetes. Journal of Research in Health Sciences 18 (2): 412.

    PubMed Central  Google Scholar 

  • Farag, Y.B., and A.I. Ölçer. 2020. The development of a ship performance model in varying operating conditions based on ANN and regression techniques. Ocean Engineering 198: 106972.

    Article  Google Scholar 

  • Farkas, M., and R. Matolay. 2022. XAI system for corporate sustainability: Systems and stakeholders. Journal of Decision Systems 31: 214–225. https://doi.org/10.1080/12460125.2022.2073864.

    Article  Google Scholar 

  • Fernandes, E., M. Holanda, M. Victorino, V. Borges, R. Carvalho, and G. Van Erven. 2019. Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research 94: 335–343.

    Article  Google Scholar 

  • Fischer, C., Z.A. Pardos, R.S. Baker, J.J. Williams, P. Smyth, R. Yu, et al. 2020. Mining big data in education: Affordances and challenges. Review of Research in Education 44 (1): 130–160.

    Article  Google Scholar 

  • Fisher, R., Ross, B., LaFerriere, R., & Maritz, A. 2017. Flipped learning, flipped satisfaction, getting the balance right. Teaching and Learning Inquiry, 5(2), 114–127.

    Article  Google Scholar 

  • Germann, F., G.L. Lilien, and A. Rangaswamy. 2013. Performance implications of deploying marketing analytics. International Journal of Research in Marketing 30 (2): 114–128.

    Article  Google Scholar 

  • Graefe, A., J.S. Armstrong, R.J. Jones, and A.G. Cuzan. 2014. Combining forecasts: An application to elections. International Journal of Forecasting 30 (1): 43–54.

    Article  Google Scholar 

  • Gray, C.C., and D. Perkins. 2019. Utilizing early engagement and machine learning to predict student outcomes. Computers & Education 131: 22–32. https://doi.org/10.1016/j.compedu.2018.12.006.

    Article  Google Scholar 

  • Guzmán Ordóñez, A., F.J. Arroyo Cañada, E. Lasso, J.A. Sánchez-Torres, and M. Escobar-Sierra. 2023. Analytical model to measure the effectiveness of content marketing on Twitter: The case of governorates in Colombia. Journal of Marketing Analytics. https://doi.org/10.1057/s41270-023-00243-5.

    Article  Google Scholar 

  • Hawkins, DM., 2004. The Problem of Overfitting. Journal of Chemical Information and Computer Sciences 44(1) 1–12. https://doi.org/10.1021/ci0342472

    Article  CAS  PubMed  Google Scholar 

  • Hittepole, C. 2019. Nontraditional students: Supporting changing student populations. Denver: University of Denver.

    Google Scholar 

  • Iacobucci, D., M. Petrescu, A. Krishen, and M. Bendixen. 2019. The state of marketing analytics in research and practice. Journal of Marketing Analytics 7 (3): 152–181. https://doi.org/10.1057/s41270-019-00059-2.

    Article  Google Scholar 

  • Ishitani, T.T. 2006. Studying attrition and degree completion behavior among first-generation college students in the United States. The Journal of Higher Education 77 (5): 861–885.

    Article  Google Scholar 

  • Johnson, M., A. Albizri, A. Harfouche, and S. Tutun. 2023. Digital transformation to mitigate emergency situations: Increasing opioid overdose survival rates through explainable artificial intelligence. Industrial Management + Data Systems 123 (1): 324–344. https://doi.org/10.1108/IMDS-04-2021-0248.

    Article  Google Scholar 

  • Keenan, P., and C. Heavin. 2022. XAI research: A bibliometric analysis by gender. Journal of Decision Systems 31: 107–116. https://doi.org/10.1080/12460125.2022.2070953.

    Article  Google Scholar 

  • Kohavi, R. 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI 14 (2): 1137–1145.

    Google Scholar 

  • Krishen, A.S., and M. Petrescu. 2019. Data-driven decision making: Implementing analytics to transform academic culture. Journal of Marketing Analytics 7 (2): 51–53. https://doi.org/10.1057/s41270-019-00056-5.

    Article  Google Scholar 

  • Kuleto, V., M. Ilic, M. Dumangiu, M. Rankovic, O.M.D. Martins, D. Paun, and L. Mihoreanu. 2021. Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability 2021 (13): 10424.

    Article  Google Scholar 

  • Kuo C. (2019). Explain your model with the SHAP values. Towards Data Science, Sept.

  • Kuźnik, A. 2020. Information infrastructure management for XAI system in libraries of selected military universities in Poland in the light of own research. Journal of Decision Systems 29: 175–189. https://doi.org/10.1080/12460125.2020.1778252.

    Article  Google Scholar 

  • Lakkaraju, H., E. Aguiar, C. Shan, D. Miller, N. Bhanpuri, R. Ghani, and K.L. Addison. 2015. A machine learning framework to identify students at risk of adverse academic outcomes. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, 1909–1918

  • Lau, E.T., L. Sun, and Q. Yang. 2019. Modelling, prediction and classification of student academic performance using artificial neural networks. SN Applied Sciences 1: 1–10.

    Article  ADS  Google Scholar 

  • Leung, K., D.Y. Mo, T. Ho, C. Wu, and G. Huang. 2020. Modelling near-real-time order arrival demand in e-commerce context: a machine learning predictive methodology. Industrial Management + Data Systems 120 (6): 1149–1174. https://doi.org/10.1108/IMDS-12-2019-0646.

    Article  Google Scholar 

  • Lopez, A., E. Guerra, B. Gonzalez, and S. Madero. 2020. Consumer sentiments toward brands: The interaction effect between brand personality and sentiments on electronic word of mouth. Journal of Marketing Analytics 8 (4): 203–223. https://doi.org/10.1057/s41270-020-00085-5.

    Article  Google Scholar 

  • Luo, Y., H.H. Tseng, S. Cui, L. Wei, R.K. Ten Haken, and I. El Naqa. 2019. Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling. BJR| Open 1 (1): 20190021.

    PubMed  PubMed Central  Google Scholar 

  • Manjarres, A.V., L.G.M. Sandoval, and M.S. Suárez. 2018. Data mining techniques applied in educational environments: Literature review. Digital Education Review 33: 235–266.

    Article  Google Scholar 

  • Marinakos, G., and S. Daskalaki. 2017. Imbalanced customer classification for bank direct marketing. Journal of Marketing Analytics 5 (1): 14–30. https://doi.org/10.1057/s41270-017-0013-7.

    Article  Google Scholar 

  • Martínez-Garmendia, J. 2023. Machine learning for product choice prediction. Journal of Marketing Analytics. https://doi.org/10.1057/s41270-023-00217-7.

    Article  Google Scholar 

  • Miguéis, V.L., A. Freitas, P.J. Garcia, and A. Silva. 2018. Early segmentation of students according to their academic performance: A predictive modelling approach. Decision Support Systems 115: 36–51.

    Article  Google Scholar 

  • Moser, C., F. den Hond, and D. Lindebaum. 2022. Morality in the age of artificially intelligent algorithms. Academy of Management Learning & Education 21 (1): 139–155.

    Article  Google Scholar 

  • Musso, M.F., C.F.R. Hernández, and E.C. Cascallar. 2020. Predicting key educational outcomes in academic trajectories: A machine-learning approach. Higher Education 80: 875–894.

    Article  Google Scholar 

  • Naimah, S.A., D. Sammon, and S. McCarthy. 2022. Understanding the characteristics of workforce transformation in a digital transformation context. Journal of Decision Systems 31: 362–383. https://doi.org/10.1080/12460125.2022.2073636.

    Article  Google Scholar 

  • Nahar, K., B.I. Shova, T. Ria, H.B. Rashid, and A.H.M. Islam. 2021. Mining educational data to predict students performance. Education and Information Technologies 26 (5): 6051–6067.

    Article  Google Scholar 

  • Ng, S.S.Y., W. Zhu, W.W.S. Tang, L.C.H. Wan, and A.Y.W. Wat. 2016. An independent study of two deep learning platforms—H2O and SINGA. IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) 2016: 1279–1283. https://doi.org/10.1109/IEEM.2016.7798084.

    Article  Google Scholar 

  • Okewu, E., P. Adewole, S. Misra, R. Maskeliunas, and R. Damasevicius. 2021. Artificial neural networks for educational data mining in higher education: A systematic literature review. Applied Artificial Intelligence 35 (13): 983–1021.

    Article  Google Scholar 

  • Ornelas, F., and C. Ordonez. 2017. Predicting student success: A naïve Bayesian application to community college data. Technology, Knowledge and Learning 22 (3): 299–315.

    Article  Google Scholar 

  • Osuna, E., R. Freund, and F. Girosi. 1997. An improved training algorithm for support vector machines. In Neural networks for signal processing VII. Proceedings of the 1997 IEEE signal processing society workshop, 276–285. IEEE. https://doi.org/10.3390/su131810424

  • Oztekin, A. 2016. A hybrid data analytic approach to predict college graduation status and its determinative factors. Industrial Management Data Systems 116 (8): 1678–1699. https://doi.org/10.1108/IMDS-09-2015-0363.

    Article  Google Scholar 

  • Petrescu, M., and A.S. Krishen. 2023. Hybrid intelligence: Human–AI collaboration in marketing analytics. Journal of Marketing Analytics 11 (3): 263–274. https://doi.org/10.1057/s41270-023-00245-3.

    Article  Google Scholar 

  • Poudel, S., R. Paudyal, B. Cankaya, N. Sterlingsdottir, M. Murphy, S. Pandey, J. Vargas, and K. Poudel. 2023. Cryptocurrency price and volatility predictions with machine learning. Journal of Marketing Analytics. https://doi.org/10.1057/s41270-023-00239-1.

    Article  Google Scholar 

  • Qazdar, A., B. Er-Raha, C. Cherkaoui, and D. Mammass. 2019. A machine learning algorithm framework for predicting students performance: A case study of baccalaureate students in Morocco. Education and Information Technologies 24 (6): 3577–3589.

    Article  Google Scholar 

  • Ribeiro, M.T., S. Singh, and C. Guestrin. 2016. Why should I trust you? Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 1135–1144

  • Romero, C., and S. Ventura. 2013. Data mining in education. Wires: Data Mining and Knowledge Discovery 3 (1): 12–27.

    Google Scholar 

  • Romero, C., and S. Ventura. 2017. Educational data science in massive open online courses. Wires Data Mining and Knowledge Discovery 7 (1): e1187.

    Article  Google Scholar 

  • Romero, C., and S. Ventura. 2020. Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10 (3): e1355.

    Google Scholar 

  • Saeed, K., A. Sidorova, and A. Vasanthan. 2022. The bundling of business intelligence and analytics. Journal of Computer Information Systems 63: 1–12.

    Google Scholar 

  • Saltelli, A. 2002. Sensitivity analysis for importance assessment. Risk Analysis 22 (3): 579–590.

    Article  PubMed  Google Scholar 

  • Schmidt, P., F. Biessmann, and T. Teubner. 2020. Transparency and trust in artificial intelligence systems. Journal of Decision Systems 29 (4): 260–278. https://doi.org/10.1080/12460125.2020.1819094.

    Article  Google Scholar 

  • Sevim, C., A. Oztekin, O. Bali, S. Gumus, and E. Guresen. 2014. Developing an early warning system to predict currency crises. European Journal of Operational Research 237 (3): 1095–1104.

    Article  Google Scholar 

  • Sghir, N., A. Adadi, Z. A. El Mouden, and M. Lahmer. 2022. Using learning analytics to improve students' enrollments in higher education. In 2022 2nd international conference on innovative research in applied science, engineering and technology (IRASET), Meknes, Morocco, 1–5. https://doi.org/10.1109/IRASET52964.2022.9737993

  • Shankaranarayanan, G., A. Even, and P.D. Berger. 2015. A decision-analysis approach to optimize marketing information-system configurations under uncertainty. Journal of Marketing Analytics 3 (1): 14–37. https://doi.org/10.1057/jma.2015.3.

    Article  Google Scholar 

  • Shapiro, D., M. Ryu, F. Huie, Q. Liu, and Y. Zheng. 2019. Completing college 2019 national report (Signature Report 18). Herndon: National Student Clearinghouse Research Center

  • Shilbayeh, S.A., A.A. Abonamah, and A. Dhabi. 2021. Predicting student enrolments and attrition patterns in higher educational institutions using machine learning. The International Arab Journal of Information Technology 18 (4): 562–567.

    Article  Google Scholar 

  • Simões, D., and J. Nogueira. 2022. Learning about the customer for improving customer retention proposal of an analytical framework. Journal of Marketing Analytics 10 (1): 50–63. https://doi.org/10.1057/s41270-021-00126-7.

    Article  Google Scholar 

  • Sloan, J. 2022. Military enlisted rank structure. Military Times. Retrieved at https://www.military.com/hiring-veterans/resources/military-enlisted-rank-structure.html

  • Sonderlund, A., E. Hughes, and J.R. Smith. 2018. The efficacy of learning analytics interventions in higher education: A systematic review. British Journal of Educational Technology 50 (5): 2594–2618.

    Article  Google Scholar 

  • Tarka, P., and E. Jędrych. 2023. Toward an exploratory framework of determinants of marketing research effectiveness in business organizations. Journal of Marketing Analytics 11 (3): 503–522. https://doi.org/10.1057/s41270-022-00182-7.

    Article  Google Scholar 

  • Thaler, R.H. 2018. Nudge, not sludge. Science 361 (6401): 431–431.

    Article  ADS  CAS  PubMed  Google Scholar 

  • Today's Military, Paying for college. Paying for College (n.d.). https://www.todaysmilitary.com/education-training/paying-college#:~:text=Tuition%20Assistance%20pays%20for%20up,members%20in%20all%20Military%20Services. Accessed 30, 2022

  • Tomasevic, N., N. Gvozdenovic, and S. Vranes. 2020. An overview and comparison of supervised data mining techniques for student exam performance prediction. Computers & Education 143: 103676.

    Article  Google Scholar 

  • Unhelkar, B. 2017. Big data strategies for agile business, 1st ed. Boca Raton: Auerbach Publications.

    Google Scholar 

  • Valluri, C., S. Raju, and V.H. Patil. 2022. Customer determinants of used auto loan churn: Comparing predictive performance using machine learning techniques. Journal of Marketing Analytics 10 (3): 279–296. https://doi.org/10.1057/s41270-021-00135-6.

    Article  Google Scholar 

  • Vollrath, M.D., and S.G. Villegas. 2022. Avoiding digital marketing analytics myopia: Revisiting the customer decision journey as a strategic marketing framework. Journal of Marketing Analytics 10 (2): 106–113. https://doi.org/10.1057/s41270-020-00098-0.

    Article  Google Scholar 

  • Vriens, M., N. Bosch, C. Vidden, and J. Talwar. 2022. Prediction and profitability in market segmentation typing tools. Journal of Marketing Analytics 10 (4): 360–389. https://doi.org/10.1057/s41270-021-00145-4.

    Article  Google Scholar 

  • Wang, Y., and S.H. Chung. 2022. Artificial intelligence in safety-critical systems: A systematic review. Industrial Management + Data Systems 122 (2): 442–470. https://doi.org/10.1108/IMDS-07-2021-0419.

    Article  Google Scholar 

  • Weinberg, B.D., L. Davis, and P.D. Berger. 2013. Perspectives on big data. Journal of Marketing Analytics 1 (4): 187–201. https://doi.org/10.1057/jma.2013.20.

    Article  Google Scholar 

  • Wedel, M., and P.K. Kannan. 2016. Marketing analytics for data-rich environments. Journal of Marketing 80 (6): 97–121.

    Article  Google Scholar 

  • Xing, W., R. Guo, E. Petakovic, and S. Goggins. 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: 168–181.

    Article  Google Scholar 

  • Yağcı, M. 2022. Educational data mining: Prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments 9 (1): 1–19.

    Article  MathSciNet  Google Scholar 

  • Yasin, H., R.E. Caraka, and A. Hoyyi. 2016. Prediction of crude oil prices using support vector regression (SVR) with grid search-cross validation algorithm. Global Journal of Pure and Applied Mathematics 12 (4): 3009–3020.

    Google Scholar 

  • Zabriskie, C., J. Yang, S. DeVore, and J. Stewart. 2019. Using machine learning to predict physics course outcomes. Physical Review Physics Education Research 15 (2): 020120.

    Article  ADS  Google Scholar 

  • Zeineddine, H., U. Braendle, and A. Farah. 2021. Enhancing prediction of student success: Automated machine learning approach. Computers & Electrical Engineering 89: 106903.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Worldwide, Department of Management and Technology, Embry-Riddle Aeronautical University, 1 Aerospace Blvd., Daytona Beach, FL, 32114, USA

    Burak Cankaya, Robin Roberts, Stephanie Douglas & Rachel Vigness

  2. Department of Operations and Information Systems, Manning School of Business, University of Massachusetts Lowell, Lowell, MA, 01854, USA

    Asil Oztekin

Authors
  1. Burak Cankaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Robin Roberts
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stephanie Douglas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rachel Vigness
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Asil Oztekin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Burak Cankaya.

Ethics declarations

Conflict of interest

The author has no financial or personal relationship with a third party whose interests could be positively or negatively influenced by the article’s content.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cankaya, B., Roberts, R., Douglas, S. et al. What postpones degree completion? Discovering key predictors of undergraduate degree completion through explainable artificial intelligence (XAI). J Market Anal (2024). https://doi.org/10.1057/s41270-024-00290-6

Download citation

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1057/s41270-024-00290-6

Keywords

Search

Navigation