Predictive Analytics to Enhance Learning Outcomes: Cases from UK Schools
Article Sidebar
-
Educational Data, Learning Outcomes, Personalized Learning, Predictive Analytics, UK Schools
Abstract
Background. The increasing integration of data-driven technologies in education has positioned predictive analytics as a promising tool for enhancing student learning outcomes. In the UK, schools are beginning to leverage predictive models to identify at-risk learners, personalize instruction, and inform pedagogical decisions.
Purpose. This study investigates the practical application and impact of predictive analytics in secondary education settings across selected schools in England and Scotland. The primary objective is to assess how predictive tools are used to improve academic performance, engagement, and targeted interventions.
Method. A qualitative case study approach was employed, involving interviews with school leaders, data analysts, and teachers in six institutions, alongside document analysis and system usage observations.
Results. The findings reveal that predictive analytics, when implemented with pedagogical alignment and ethical oversight, significantly supports early identification of student needs and enables timely academic interventions. However, challenges persist in terms of data literacy among staff, algorithmic transparency, and balancing predictive insights with professional judgment.
Conclusion. The study concludes that predictive analytics can enhance learning outcomes when embedded within a holistic educational framework that prioritizes equity, accountability, and human-centered decision-making.
Full text article
References
Alhwaiti, Y., Khan, M., Asim, M., Siddiqi, M. H., Ishaq, M., & Alruwaili, M. (2025). Leveraging YOLO deep learning models to enhance plant disease identification. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-92143-0
Bonney-King, J., Fischer, J., & Miller-Cushon, E. (2025). Effects of reward type and previous social experience on cognitive testing outcomes of weaned dairy calves. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-91843-x
Borycka, K., M?y?czak, M., Roso?, M., Korzeniewski, K., Iwanowski, P., He?man, H., Janku, P., Uchman-Musielak, M., Dosedla, E., Diaz, E. G., Sudo?-Szopi?ska, I., Mik, M., Ratto, C., & Spinelli, A. (2025). Detection of obstetric anal sphincter injuries using machine learning-assisted impedance spectroscopy: a prospective, comparative, multicentre clinical study. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-92392-z
Chandel, P., & Lim, F. V. (2025). Generative AI and Literacy Development in the Language Classroom: A Systematic Review of Literature. Ubiquitous Learning, 18(2), 31–49. https://doi.org/10.18848/1835-9795/CGP/v18i02/31-49
Chandra, S. S., Kumar, R., Arjunasamy, A., Galagali, S., Tantri, A., & Naganna, S. R. (2025). Predicting the compressive strength of polymer-infused bricks: A machine learning approach with SHAP interpretability. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-89606-9
DeFeo, D. J., Gerken, S., Tran, T. C., Khodyakov, D., & Fink, A. (2025). General education biology labs: a Delphi study of student learning outcomes. Discover Education, 4(1). https://doi.org/10.1007/s44217-025-00428-3
Dell, T., Voigt, M. B., Isaak, A., Boehner, A., Pieper, C., Mesropyan, N., Kupczyk, P., Luetkens, J., & Kuetting, D. (2025). Impact of robotic assistance on the learning curve in endovascular interventions: exploring the role of operator experience with the CorPath GRX system. CVIR Endovascular, 8(1). https://doi.org/10.1186/s42155-025-00529-y
Dorathi Jayaseeli, J. D., Briskilal, J., Fancy, C., Vaitheeshwaran, V., Patibandla, R. S. M. L., Syed, K., & Swain, A. K. (2025). An intelligent framework for skin cancer detection and classification using fusion of Squeeze-Excitation-DenseNet with Metaheuristic-driven ensemble deep learning models. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-92293-1
Ganiyu, I. O., Plotka, G., Seuwou, P., & Ige-Olaobaju, A. (2025). Examining the use of LEGO Serious Play to enhance postgraduate research capacity. Humanities and Social Sciences Communications, 12(1). https://doi.org/10.1057/s41599-024-03930-5
Ghavami Hosein Pour, B., Karimian, Z., & Hatami Niya, N. (2025). A narrative review of advancing medical education through technology: the role of smart glasses in situated learning. BMC Medical Education, 25(1). https://doi.org/10.1186/s12909-025-06949-7
Giesler, L. P., O’Brien, W. T., Bain, J., Spitz, G., Jaehne, E. J., van den Buuse, M., Shultz, S. R., Mychasiuk, R., & McDonald, S. J. (2025). Investigating the role of the brain-derived neurotrophic factor Val66Met polymorphism in repetitive mild traumatic brain injury outcomes in rats. Behavioral and Brain Functions , 21(1). https://doi.org/10.1186/s12993-025-00270-5
Grosso, F. (2025). Integrating psychological and mental health perspectives in disease management: improving patient well-being. Humanities and Social Sciences Communications, 12(1). https://doi.org/10.1057/s41599-025-04359-0
Houston, N., Manrique, F., Mo, S., Ruoqian, W., Wenjing, J., & Yuting, L. (2025). Designing an effective educational toy: incorporating design-thinking in the design classroom. Discover Education, 4(1). https://doi.org/10.1007/s44217-025-00402-z
Jiang, D., Wang, S., Xiao, Y., Zhi, P., Zheng, E., Lyu, Z., & Guo, Q. (2025). Risk factors and prediction model of metabolic disorders in adult patients with pituitary stalk interruption syndrome. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-91461-7
Karume, A. K., Sugut, J., Sankei, P., Kimathi, P. M., Guleid, A., Kimonge, D., Ebert, E., Wanjiku, G., Myers, J. G., & Beck, A. (2025). Improvement in clinician confidence in and knowledge of Diabetic Ketoacidosis management following a case-based curriculum in Kenya. BMC Medical Education, 25(1). https://doi.org/10.1186/s12909-025-06898-1
Kidayi, P. L., Dausen, E. J., Ndile, M., Sixsmith, J., Mawona, Z. M., Berntsen, K., Manangwa, S. E., Smit, J. M., Rogathi, J., & de Zeeuw, J. (2025). Development of a training programme to improve health literacy and respectful compassionate care competencies among undergraduate student nurses: a quantitative study. BMC Medical Education, 25(1). https://doi.org/10.1186/s12909-025-06894-5
Linardon, J. (2025). Navigating the Future of Psychiatry: A Review of Research on Opportunities, Applications, and Challenges of Artificial Intelligence. Current Treatment Options in Psychiatry, 12(1). https://doi.org/10.1007/s40501-025-00344-1
Ljungblad, L. W., Murphy, D., & Fonkalsrud, H. E. (2025). A mixed reality for midwifery students: a qualitative study of the technology’s perceived appropriateness in the classroom. BMC Medical Education, 25(1). https://doi.org/10.1186/s12909-025-06919-z
Mancilla, S., Wences, G., Hernández-López, E., & Cohen, I. (2025). Sub-spatial prediction of votes integrating socioeconomic, educational, and age strata with machine learning and topological data analysis. Journal of Big Data, 12(1). https://doi.org/10.1186/s40537-025-01112-x
Muthmainnah, M., Cardoso, L., Marzuki, A. G., & Al Yakin, A. (2025). A new innovative metaverse ecosystem: VR-based human interaction enhances EFL learners’ transferable skills. Discover Sustainability, 6(1). https://doi.org/10.1007/s43621-025-00913-7
Ominyi, J., Nwedu, A., Agom, D., & Eze, U. (2025). Leading evidence-based practice: nurse managers’ strategies for knowledge utilisation in acute care settings. BMC Nursing, 24(1). https://doi.org/10.1186/s12912-025-02912-5
Palladino, P., Trotta, E., Bonvino, A., Carlucci, L., & Cottini, M. (2025). How do you feel during English class? Emotions and metacognition in primary school children learning English as a second language. Metacognition and Learning, 20(1). https://doi.org/10.1007/s11409-025-09414-4
Patwary, M. N., & Sajib, M. N. F. (2025). Exploring Tertiary Students’ Perceptions of Using Smartphones to Enhance EFL Writing: Pedagogical Implications. International Journal of Technology, Knowledge and Society, 21(2), 51–72. https://doi.org/10.18848/1832-3669/CGP/v21i02/51-72
Qader, M. A., Hosseini, L., Abolhasanpour, N., Oghbaei, F., Maghsoumi-Norouzabad, L., Salehi-Pourmehr, H., Fattahi, F., & Sadeh, R. N. (2025). A systematic review of the therapeutic potential of nicotinamide adenine dinucleotide precursors for cognitive diseases in preclinical rodent models. BMC Neuroscience, 26(1). https://doi.org/10.1186/s12868-025-00937-9
Rubio-López, A., García-Carmona, R., Zarandieta-Román, L., Rubio-Navas, A., González-Pinto, Á., & Cardinal-Fernández, P. (2025). Analysis of stress responses in medical students during simulated pericardiocentesis training using virtual reality and 3D-printed mannequin. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-92221-3
Seiler, J., Wetscher, M., Harttgen, K., Utzinger, J., & Umlauf, N. (2025). High-resolution spatial prediction of anemia risk among children aged 6 to 59 months in low- and middle-income countries. Communications Medicine, 5(1). https://doi.org/10.1038/s43856-025-00765-2
Sirocchi, C., Urschler, M., & Pfeifer, B. (2025). Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping. BioData Mining, 18(1). https://doi.org/10.1186/s13040-025-00430-3
Sueda, T., Yasui, M., Nishimura, J., Kagawa, Y., Kitakaze, M., Mori, R., Matsuda, C., Ushimaru, Y., Sugase, T., Mukai, Y., Komatsu, H., Yanagimoto, Y., Kanemura, T., Yamamoto, K., Wada, H., Goto, K., Miyata, H., & Ohue, M. (2025). Learning curve analysis for prophylactic bilateral robot-assisted lateral lymph node dissection for lower rectal cancer: a retrospective study. Techniques in Coloproctology, 29(1). https://doi.org/10.1007/s10151-025-03119-1
Yenew, C., Bayeh, G. M., Gebeyehu, A. A., Enawgaw, A. S., Asmare, Z. A., Ejigu, A. G., Tsega, T. D., Temesgen, A., Anteneh, R. M., Yigzaw, Z. A., Yirdaw, G., Tsega, S. S., Ahmed, A. F., & Yeshiwas, A. G. (2025). Scoping review on assessing climate-sensitive health risks. BMC Public Health, 25(1). https://doi.org/10.1186/s12889-025-22148-x
Zhang, Z., Zhao, Y., Ma, Y.-J., Chen, C.-Q., Li, Z.-Y., Wang, Y.-K., Zhang, S.-J., Li, H.-M., Li, Y., Tian, Y., & Tian, H. (2025). Prediction of STAS in lung adenocarcinoma with nodules ? 2 cm using machine learning: a multicenter retrospective study. BMC Cancer, 25(1). https://doi.org/10.1186/s12885-025-13783-z
Authors
Copyright (c) 2025 David Green, Emily Thompson, Isaac Ochieng
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
