Author(s)

WenYang Cao, Nhu Tam Mai^*

Affiliation(s)

University of Southern California, Los Angeles 90089, CA, USA.

Corresponding Author

Nhu Tam Mai

ABSTRACT

Predictive analytics has emerged as a transformative approach in educational technology, leveraging artificial intelligence and machine learning algorithms to identify at-risk students, predict academic outcomes, and recommend targeted interventions before failure occurs. This comprehensive review examines the current state of predictive analytics applications in education, analyzing methodologies, effectiveness, and implementation challenges across diverse educational contexts. Through systematic analysis of literature from 2019 to 2025, this study explores the technological foundations of early warning systems, including data mining techniques, feature engineering approaches, and predictive modeling frameworks. The review synthesizes empirical evidence from over studies demonstrating the effectiveness of predictive analytics in reducing dropout rates, improving retention, and enhancing overall student success outcomes. Key findings indicate that machine learning models can achieve prediction accuracies of 85-95% for identifying at-risk students, with ensemble methods and deep learning approaches showing superior performance compared to traditional statistical methods. Random forest and gradient boosting algorithms demonstrate particular effectiveness, achieving AUC scores of 0.92-0.96 in dropout prediction tasks. However, significant challenges persist in areas including data quality and integration, model interpretability, ethical considerations surrounding algorithmic decision-making, and the translation of predictions into effective interventions. The paper identifies emerging trends such as real-time analytics platforms, multimodal data integration, explainable AI frameworks, and automated intervention recommendation systems. Future research directions include the development of causal inference methods for intervention effectiveness, federated learning approaches for multi-institutional collaboration, and ethical frameworks for responsible deployment of predictive systems in educational contexts. This review contributes to understanding how AI-powered predictive analytics can transform educational support systems while highlighting critical considerations for implementation, scalability, and ethical use in diverse learning environments.

KEYWORDS

Predictive analytics; Student success; Early warning systems; Educational data mining; Machine learning; Dropout prediction; Academic risk identification; Intervention strategies

CITE THIS PAPER

WenYang Cao, Nhu Tam Mai. Predictive analytics for student success: AI-driven early warning systems and intervention strategies for educational risk management. Educational Research and Human Development. 2025, 2(2): 36-48. DOI: https://doi.org/10.61784/erhd3042.

REFERENCES

[1] Yagci M. Educational data mining: prediction of students' academic performance using machine learning algorithms. Smart Learning Environments, 2022, 9(1), 1-11.

[2] Khan, M. Applying Cognitive-Behavioral Techniques in Elementary Education: A Comprehensive Approach to Enhancing Teacher Efficacy in Supporting Students With Anxiety. Alliant International University ProQuest Dissertations & Theses, 2025, 31844052.

[3] Chui K T, Fung D C L, Lytras M D, et al. Predicting at-risk university students in a virtual learning environment via a machine learning algorithm. Computers in Human Behavior, 2020, 107, 105584.

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

[5] Fernandes E, Holanda M, Victorino M, et al. Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research, 2019, 94, 335-343.

[6] Alghamdi S, Soh B, Li A. A Comprehensive Review of Dropout Prediction Methods Based on Multivariate Analysed Features of MOOC Platforms. Multimodal Technologies and Interaction, 2025, 9(1), 3.

[7] Tsai Y S, Perrotta C, Gasevic D. Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics. Assessment & Evaluation in Higher Education, 2020, 45(4), 554-567.

[8] Malesevic A. Perspective Chapter: Digitization of Traditional Higher Education Touchpoints. E-Service Digital Innovation, 2022. DOI: 10.5772/intechopen.105926.

[9] Adepoju A H, Austin-Gabriel B L E S S I N G, Hamza O L A D I M E J I, et al. Advancing monitoring and alert systems: A proactive approach to improving reliability in complex data ecosystems. IRE Journals, 2022, 5(11), 281-282.

[10] Liz-Domínguez M, Caeiro-Rodríguez M, Llamas-Nistal M, et al. Systematic literature review of predictive analysis tools in higher education. Applied Sciences, 2019, 9(24), 5569.

[11] Gray C C, Perkins D. Utilizing early engagement and machine learning to predict student outcomes. Computers & Education, 2019, 131, 22-32.

[12] Hooshyar D, Pedaste M, Yang Y. Mining educational data to predict students' performance through procrastination behavior. Entropy, 2019, 22(1), 12.

[13] Williamson B, Eynon R, Potter J. Pandemic politics, pedagogies and practices: Digital technologies and distance education during the coronavirus emergency. Learning, Media and Technology, 2020, 45(2), 107-114.

[14] Aristovnik A, Kerzic D, Ravselj D, et al. Impacts of the COVID-19 pandemic on life of higher education students: A global perspective. Sustainability, 2020, 12(20), 8438.

[15] Williamson B. Policy networks, performance metrics and platform markets: Charting the expanding data infrastructure of higher education. British Journal of Educational Technology, 2019, 50(6), 2794-2809.

[16] Olayinka O H. Leveraging predictive analytics and machine learning for strategic business decision-making and competitive advantage. International Journal of Computer Applications Technology and Research, 2019, 8(12), 473-486.

[17] Yuan C, Xiao N, Pei Y, et al. Enhancing Student Learning Outcomes through AI-Driven Educational Interventions: A Comprehensive Study of Classroom Behavior and Machine Learning Integration. International Theory and Practice in Humanities and Social Sciences, 2025, 2(2), 197-215.

[18] Sghir N, Adadi A, Lahmer M. Recent advances in predictive learning analytics: A decade systematic review (2012–2022). Education and Information Technologies, 2023, 28(7), 8299-8333.

[19] Ramnani, S. Exploring Ethical Considerations of Artificial Intelligence in Educational Settings: An Examination of Bias, Privacy, and Accountability. International Journal of Novel Research and Development (IJNRD), 2024, 9(2), 2456-4184.

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

[21] Asselman A, Khaldi M, Aammou S. Enhancing the prediction of student performance based on the machine learning XGBoost algorithm. Interactive Learning Environments, 2023, 31(3), 1-20.

[22] Batool S, Rashid J, Nisar M W,et al. Educational data mining to predict students' academic performance: A survey study. Education and Information Technologies, 2023, 28(1), 905-971.

[23] Baek C, Doleck T. Reviewing the differences between learning analytics and educational data mining: Towards educational data science. Computers in Human Behavior, 2024, 154, 108137.

[24] Trakunphutthirak R, Lee V C S. Application of educational data mining approach for student academic performance prediction using progressive temporal data. Journal of Educational Computing Research, 2022, 60(3), 742-776.

[25] Ahmed M, Faisal M, Alotaibi F, et al. Student performance prediction using machine learning algorithms. Applied Computational Intelligence and Soft Computing, 2024, 4067721.

[26] Al-Emran M, Al-Nuaimi M N, Arpaci I. Using machine learning to predict factors affecting academic performance: the case of college students on academic probation. Education and Information Technologies, 2023, 28(8), 9715-9742.

[27] Tzenios N. Examining the impact of EdTech integration on academic performance using random forest regression. ResearchBerg Review of Science and Technology, 2020, 3(1), 94-106.

[28] Sathe M T, Adamuthe A C. Comparative study of supervised algorithms for prediction of students' performance. International Journal of Modern Education and Computer Science, 2021, 13(1), 1-21.

[29] Yu R, Yao T, Ba F. Educational Big Data Analytics for Futuristic Smart Learning Using Deep Learning Techniques. Scalable Computing: Practice and Experience, 2024, 25(4), 2728-2735.

[30] Li X, Zhang Y, Cheng H, et al. Student achievement prediction using deep neural network from multi-source campus data. Complex & Intelligent Systems, 2022, 8, 5143-5156.

[31] Ahmadi G, Mohammadi A, Asadzandi S, et al. What are the indicators of student engagement in learning management systems? a systematized review of the literature. International Review of Research in Open and Distributed Learning, 2023, 24(1), 117-136.

[32] Baker R, Xu D, Park J, et al. The benefits and caveats of using clickstream data to understand student self-regulatory behaviors: opening the black box of learning processes. International Journal of Educational Technology in Higher Education, 2020, 17, 1-24.

[33] Fischer C, Pardos Z A, Baker R S, et al. Mining big data in education: Affordances and challenges. Review of research in education, 2020, 44(1), 130-160.

[34] Baneres D, Rodríguez M E, Guerrero-Roldán A E, et al. An early warning system to detect at-risk students in online higher education. Applied Sciences, 2020, 10(13), 4427.

[35] Waheed H, Hassan S U, Nawaz R, et al. Early prediction of learners at risk in self-paced education: A neural network approach. Expert Systems with Applications, 2022, 213, 118868.

[36] Beaulac C, Rosenthal J S. Predicting university students' academic success and major using random forests. Research in Higher Education, 2019, 60(7), 1048-1064.

[37] Nahar K, Shova B I, Ria T, et al. Mining educational data to predict students' performance. Education and Information Technologies, 2021, 26, 6051-6067.

[38] Strikas K, Papaioannou N, Stamatopoulos I, et al. Predicting student specializations: A machine learning approach based on academic performance. Journal of e-Learning and Knowledge Society, 2024, 19(2), 45-58.

[39] Khan I, Ahmad A R, Jabeur N, et al. An artificial intelligence approach to monitor student performance and devise preventive measures. Smart Learning Environments, 2021, 8(17), 1-18.

[40] Fazil M, O'Riordan A, Pardo A. A novel deep learning model for student performance prediction using engagement data. Journal of Learning Analytics, 2024, 9(3), 150-171.

[41] El-Sabagh H A. Adaptive e-learning environment based on learning styles and its impact on development students' engagement. International Journal of Educational Technology in Higher Education, 2021, 18(1), 1-24.

[42] Mirata V, Hirt F, Bergamin P, et al. Challenges and contexts in establishing adaptive learning in higher education: Findings from a Delphi study. International Journal of Educational Technology in Higher Education, 2020, 17(1), 32.

[43] Deeva G, De Smedt J, Saint-Pierre C, et al. Predicting student performance using sequence classification with time-based windows. Expert Systems with Applications, 2022, 209, 118187.

[44] Alyahyan E, Düstegor D. Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Education, 2020, 17(1), 3.

[45] Kochmar E, Vu D D, Belfer R, et al. Automated data-driven generation of personalized pedagogical interventions in intelligent tutoring systems. International Journal of Artificial Intelligence in Education, 2022, 32(2), 323-349.

[46] Zhang J, Zou L, Miao J, et al. An individualized intervention approach to improving university students' learning performance and interactive behaviors in a blended learning environment. Interactive Learning Environments, 2020, 28(2), 231-245.

[47] Aljohani N R, Fayoumi A, Hassan S U. Predicting at-risk students using clickstream data in the virtual learning environment. Sustainability, 2019, 11(24), 7238.

[48] Hooshyar D, Pedaste M, Saks K, et al. Open learner models in supporting self-regulated learning in higher education: A systematic literature review. Computers & Education, 2020, 154, 103878.

[49] Akcapinar G, Altun A, Askar P. Using learning analytics to develop early-warning system for at-risk students. International Journal of Educational Technology in Higher Education, 2019, 16(1), 40.

[50] Buenano-Fernández D, Gil D, Luján-Mora S. Application of machine learning in predicting performance for computer engineering students: A case study. Sustainability, 2019, 11(10), 2833.

[51] Strelan P, Osborn A, Palmer E. The flipped classroom: A meta-analysis of effects on student performance across disciplines and education levels. Educational Research Review, 2020, 30, 100314.

[52] Herodotou C, Rienties B, Hlosta M, et al. The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study. The Internet and Higher Education, 2020, 45, 100725.

[53] Cruz-Jesus F, Castelli M, Oliveira T, et al. Using artificial intelligence methods to assess academic achievement in public high schools of a European Union country. Heliyon, 2020, 6(6), e04081.

[54] Ifenthaler D, Yau J Y K. Utilising learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development, 2020, 68(4), 1961-1990.

[55] Herodotou C, Hlosta M, Boroowa A, et al. Empowering online teachers through predictive learning analytics. British Journal of Educational Technology, 2019, 50(6), 3064-3079.

[56] Bond M. Facilitating student engagement through the flipped learning approach in K-12: A systematic review. Computers & Education, 2020, 151, 103819.

[57] Bernacki M L, Chavez M M, Uesbeck P M. Predicting achievement and providing support before STEM majors begin to fail. Computers & Education, 2020, 158, 103999.

[58] Baoshuo Wang. From "the light of the country" to "the phenomenal explosion" —— the audience relationship and the way to "break the circle" in decoding "nezha 2". AI and Data Science Journal, 2025, 2(1): 76-105. DOI: https://doi.org/10.61784/adsj3016.

[59] Tsiakmaki M, Kostopoulos G, Kotsiantis S, et al. Implementing AutoML in educational data mining for prediction tasks. Applied Sciences, 2019, 10(1), 90.

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

[61] Hao Peng, Li Chen Lim, Poh Kiong Te. Interplay of proactive personality, self-efficacy, and work engagement: a mediating role of job satisfaction. Trends in Social Sciences and Humanities Research. 2024, 2(8): 35-44. DOI: 10.61784/tsshr3079.

[62] Holmes W, Porayska-Pomsta K, Holstein K, et al. Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 2021, 32(3), 504-526.

[63] Baker R S, Hawn A. Algorithmic bias in education. International journal of artificial intelligence in education, 2022, 1-41.

[64] XinYi Huang. Enhancing Full-English Teaching Quality in Sino-foreign Cooperative Programs in Higher Vocational Colleges. Journal of Trends in Financial and Economics. 2025, 2(1): 20-23. DOI: https://doi.org/10.61784/jtfe3031.

[65] Christodoulou A, Angeli C. Adaptive learning techniques for a personalized educational software in developing teachers' technological pedagogical content knowledge. Frontiers in Education, 2022, 7, 789397.