Adaptive Learning Systems: Personalizing Mathematics Instruction for At-Risk Students
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Background. Mathematics learning among at-risk students is frequently constrained by heterogeneous learning gaps, low engagement, and limited access to individualized instruction. Conventional teaching approaches often fail to accommodate these differences, leading to persistent disparities in academic achievement.
Purpose. This study aims to examine the effectiveness of adaptive learning systems in personalizing mathematics instruction and improving learning outcomes for at-risk students, as well as to explore the role of student engagement in this process.
Method. A quasi-experimental mixed-methods design was employed involving 112 students assigned to experimental (adaptive learning system) and control (traditional instruction) groups. Data were collected through pretest–posttest assessments to measure achievement, engagement questionnaires to assess behavioral and emotional involvement, and system-generated learning analytics to track progress and interaction patterns. Quantitative data were analyzed using descriptive and inferential statistics, while qualitative insights were derived through thematic analysis.
Results. The findings indicate that adaptive learning systems significantly improve mathematics achievement and student engagement compared to conventional instructional methods. Personalized learning pathways, immediate feedback, and continuous progress monitoring contribute to enhanced performance and reduced variability among learners. Engagement is identified as a mediating factor that strengthens the relationship between adaptive instruction and academic achievement.
Conclusion. Adaptive learning systems offer an effective and scalable solution for supporting at-risk students in mathematics education. The results underscore the importance of integrating personalization and engagement-focused strategies to promote equitable learning outcomes. Educators and institutions are encouraged to adopt adaptive technologies to create more responsive and inclusive instructional environments.
Abramson, J. (2024). Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature, 630(8016), 493–500. https://doi.org/10.1038/s41586-024-07487-w
Al-Tohamy, R. (2022). A critical review on the treatment of dye-containing wastewater: Ecotoxicological and health concerns of textile dyes and possible remediation approaches for environmental safety. Ecotoxicology and Environmental Safety, 231(Query date: 2026-03-28 14:02:47). https://doi.org/10.1016/j.ecoenv.2021.113160
Arbelo, E. (2023). 2023 ESC Guidelines for the management of cardiomyopathies: Developed by the task force on the management of cardiomyopathies of the European Society of Cardiology (ESC). European Heart Journal, 44(37), 3503–3626. https://doi.org/10.1093/eurheartj/ehad194
Burke, K. (2023). A Grammar of motives. Dalam A Grammar of Motives (hlm. 530). https://doi.org/10.2307/jj.8501167
Byrne, D. (2022). A worked example of Braun and Clarke’s approach to reflexive thematic analysis. Quality and Quantity, 56(3), 1391–1412. https://doi.org/10.1007/s11135-021-01182-y
Byrne, R. A. (2023). 2023 ESC Guidelines for the management of acute coronary syndromes. European Heart Journal, 44(38), 3720–3826. https://doi.org/10.1093/eurheartj/ehad191
Chang, Y. (2024). A Survey on Evaluation of Large Language Models. ACM Transactions on Intelligent Systems and Technology, 15(3). https://doi.org/10.1145/3641289
Elsayed, N. A. (2023). 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes—2023. Diabetes Care, 46(Query date: 2026-03-28 14:02:47). https://doi.org/10.2337/dc23-S002
Franchis, R. de. (2022). Baveno VII – Renewing consensus in portal hypertension. Journal of Hepatology, 76(4), 959–974. https://doi.org/10.1016/j.jhep.2021.12.022
Giaquinto, A. N. (2022). Breast Cancer Statistics, 2022. CA Cancer Journal for Clinicians, 72(6), 524–541. https://doi.org/10.3322/caac.21754
Guo, M. H. (2022). Attention mechanisms in computer vision: A survey. Computational Visual Media, 8(3), 331–368. https://doi.org/10.1007/s41095-022-0271-y
Han, B. (2024). Cancer incidence and mortality in China, 2022. Journal of the National Cancer Center, 4(1), 47–53. https://doi.org/10.1016/j.jncc.2024.01.006
Han, K. (2023). A Survey on Vision Transformer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1), 87–110. https://doi.org/10.1109/TPAMI.2022.3152247
Heidenreich, P. A. (2022). 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation, 145(18). https://doi.org/10.1161/CIR.0000000000001063
Humbert, M. (2022). 2022 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension. European Heart Journal, 43(38), 3618–3731. https://doi.org/10.1093/eurheartj/ehac237
Ji, S. (2022). A Survey on Knowledge Graphs: Representation, Acquisition, and Applications. IEEE Transactions on Neural Networks and Learning Systems, 33(2), 494–514. https://doi.org/10.1109/TNNLS.2021.3070843
Joglar, J. A. (2024). 2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation, 149(1). https://doi.org/10.1161/CIR.0000000000001193
Kasneci, E. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103(Query date: 2026-03-28 14:02:47). https://doi.org/10.1016/j.lindif.2023.102274
Kerbl, B. (2023). 3D Gaussian Splatting for Real-Time Radiance Field Rendering. ACM Transactions on Graphics, 42(4). https://doi.org/10.1145/3592433
Larsson, D. G. J. (2022). Antibiotic resistance in the environment. Nature Reviews Microbiology, 20(5), 257–269. https://doi.org/10.1038/s41579-021-00649-x
Li, J. (2022). BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation. Proceedings of Machine Learning Research, 162(Query date: 2026-03-28 14:02:47), 12888–12900.
Li, Z. (2022). A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Transactions on Neural Networks and Learning Systems, 33(12), 6999–7019. https://doi.org/10.1109/TNNLS.2021.3084827
Liu, Z. (2022). A ConvNet for the 2020s. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2022(Query date: 2026-03-28 14:02:47), 11966–11976. https://doi.org/10.1109/CVPR52688.2022.01167
Lu, T. (2024). A comprehensive electron wavefunction analysis toolbox for chemists, Multiwfn. Journal of Chemical Physics, 161(8). https://doi.org/10.1063/5.0216272
Lyon, A. R. (2022). 2022 ESC Guidelines on cardio-oncology developed in collaboration with the European Hematology Association (EHA), the European Society for Therapeutic Radiology and Oncology (ESTRO) and the International Cardio-Oncology Society (IC-OS). European Heart Journal, 43(41), 4229–4361. https://doi.org/10.1093/eurheartj/ehac244
Mancia, G. (2023). 2023 ESH Guidelines for the management of arterial hypertension the Task Force for the management of arterial hypertension of the European Society of Hypertension: Endorsed by the International Society of Hypertension (ISH) and the European Renal Association (ERA). Journal of Hypertension, 41(12), 1874–2071. https://doi.org/10.1097/HJH.0000000000003480
Martin, S. S. (2024). 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data from the American Heart Association. Circulation, 149(8). https://doi.org/10.1161/CIR.0000000000001209
McDonagh, T. A. (2022). 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). With the special contribution of the Heart Failure Association (HFA) of the ESC. European Journal of Heart Failure, 24(1), 4–131. https://doi.org/10.1002/ejhf.2333
Mehrabi, N. (2022). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys, 54(6). https://doi.org/10.1145/3457607
Miller, K. D. (2022). Cancer treatment and survivorship statistics, 2022. Ca A Cancer Journal for Clinicians, 72(5), 409–436. https://doi.org/10.3322/caac.21731
Rajpurkar, P. (2022). AI in health and medicine. Nature Medicine, 28(1), 31–38. https://doi.org/10.1038/s41591-021-01614-0
Reig, M. (2022). BCLC strategy for prognosis prediction and treatment recommendation: The 2022 update. Journal of Hepatology, 76(3), 681–693. https://doi.org/10.1016/j.jhep.2021.11.018
Rinella, M. E. (2023). A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Journal of Hepatology, 79(6), 1542–1556. https://doi.org/10.1016/j.jhep.2023.06.003
Siegel, R. L. (2025). Cancer statistics, 2025. Ca A Cancer Journal for Clinicians, 75(1), 10–45. https://doi.org/10.3322/caac.21871
Solmi, M. (2022). Age at onset of mental disorders worldwide: Large-scale meta-analysis of 192 epidemiological studies. Molecular Psychiatry, 27(1), 281–295. https://doi.org/10.1038/s41380-021-01161-7
Soriano, J. B. (2022). A clinical case definition of post-COVID-19 condition by a Delphi consensus. Lancet Infectious Diseases, 22(4). https://doi.org/10.1016/S1473-3099(21)00703-9
Terven, J. (2023). A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS. Machine Learning and Knowledge Extraction, 5(4), 1680–1716. https://doi.org/10.3390/make5040083
Vahanian, A. (2022). 2021 ESC/EACTS Guidelines for the management of valvular heart disease. European Heart Journal, 43(7), 561–632. https://doi.org/10.1093/eurheartj/ehab395
Varadi, M. (2022). AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Research, 50(Query date: 2026-03-28 14:02:47). https://doi.org/10.1093/nar/gkab1061
Wei, J. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Advances in Neural Information Processing Systems, 35(Query date: 2026-03-28 14:02:47). https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85163157409&origin=inward
Xia, C. (2022). Cancer statistics in China and United States, 2022: Profiles, trends, and determinants. Chinese Medical Journal, 135(5), 584–590. https://doi.org/10.1097/CM9.0000000000002108
Zeng, A. (2023). Are Transformers Effective for Time Series Forecasting? Proceedings of the 37th Aaai Conference on Artificial Intelligence Aaai 2023, 37(Query date: 2026-03-28 14:02:47), 11121–11128. https://doi.org/10.1609/aaai.v37i9.26317
Zeppenfeld, K. (2022). 2022 ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death. European Heart Journal, 43(40), 3997–4126. https://doi.org/10.1093/eurheartj/ehac262
Zhang, L. (2023). Adding Conditional Control to Text-to-Image Diffusion Models. Proceedings of the IEEE International Conference on Computer Vision, (Query date: 2026-03-28 14:02:47), 3813–3824. https://doi.org/10.1109/ICCV51070.2023.00355
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