Adaptive Learning Strategies Using Technology in German Schools
Abstract
Background. The increasing integration of technology in education has transformed teaching and learning practices across the globe. In Germany, adaptive learning strategies have gained traction as personalized approaches to meet diverse student needs. However, the effectiveness of these strategies, particularly when combined with technological tools, remains an area of active research.
Purpose. This study aims to explore the implementation of adaptive learning strategies using technology in German schools. The research focuses on understanding how these strategies impact student engagement, performance, and individualized learning outcomes.
Method. A mixed-methods approach was employed, combining quantitative data from student performance assessments and qualitative insights from teacher interviews. A total of 200 students across five German schools participated in the study, using adaptive learning platforms designed to personalize educational content. Teachers were interviewed to assess their experiences with these tools and strategies.
Result. The findings suggest that adaptive learning strategies enhanced student engagement and performance, with significant improvements in individualized learning outcomes. Students who interacted with technology-driven adaptive platforms showed increased motivation and better retention of subject matter compared to those in traditional learning environments. Teachers reported positive experiences, noting that the strategies allowed for more tailored support for students.
Conclude. The integration of adaptive learning strategies using technology has shown positive effects on student learning outcomes in German schools. These findings suggest that further adoption and refinement of such strategies can play a key role in fostering personalized education.
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References
Abouelenein, Y. A. M., Selim, S. A. S., & Aldosemani, T. I. (2025). Impact of an adaptive environment based on learning analytics on pre-service science teacher behavior and self-regulation. Smart Learning Environments, 12(1). https://doi.org/10.1186/s40561-024-00340-7
Achuthan, K., Sankaran, S., Roy, S., & Raman, R. (2025). Integrating sustainability into cybersecurity: insights from machine learning based topic modeling. Discover Sustainability, 6(1). https://doi.org/10.1007/s43621-024-00754-w
Ackermann, H., Lange, A. L., Hafner, V. V, & Lazarides, R. (2025). How adaptive social robots influence cognitive, emotional, and self-regulated learning. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-91236-0
Agrawal, S. P., Jangir, P., Pandya, S. B., Parmar, A., Hourani, A. O., Trivedi, B. I., & Khishe, M. (2025). Exploring the effectiveness of adaptive randomized sine cosine algorithm in wind integrated scenario based power system optimization with FACTS devices. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-91778-3
Alruwaili, M., Ali, A., Almutairi, M., Alsahyan, A., & Mohamed, M. (2025). LSTM and ResNet18 for optimized ambulance routing and traffic signal control in emergency situations. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-89651-4
Anbazhagan, T., & Rangaswamy, B. (2025). Early prediction of CKD from time series data using adaptive PSO optimized echo state networks. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-91028-6
Badjatia, N., Podell, J., Felix, R. B., Chen, L. K., Dalton, K., Wang, T. I., Yang, S., & Hu, P. (2025). Machine Learning Approaches to Prognostication in Traumatic Brain Injury. Current Neurology and Neuroscience Reports, 25(1). https://doi.org/10.1007/s11910-025-01405-x
Bahrami, Z., Bashipour, F., & Baghban, A. (2025). Application of machine learning approach to estimate the solubility of some solid drugs in supercritical CO2. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-89858-5
Birk, F., Mahler, L., Steiglechner, J., Wang, Q., Scheffler, K., & Heule, R. (2025). Flexible and cost-effective deep learning for accelerated multi-parametric relaxometry using phase-cycled bSSFP. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-88579-z
B?çakc? Ye?ilkaya, H., & Guest, R. (2025). Activity-aware electrocardiogram biometric verification utilising deep learning on wearable devices. Eurasip Journal on Information Security, 2025(1). https://doi.org/10.1186/s13635-025-00193-8
Bonneville, C., Bieberdorf, N., Hegde, A., Asta, M., Najm, H. N., Capolungo, L., & Safta, C. (2025). Accelerating phase field simulations through a hybrid adaptive Fourier neural operator with U-net backbone. Npj Computational Materials, 11(1). https://doi.org/10.1038/s41524-024-01488-z
Chhillar, I., & Singh, A. (2025). An improved soft voting-based machine learning technique to detect breast cancer utilizing effective feature selection and SMOTE-ENN class balancing. Discover Artificial Intelligence, 5(1). https://doi.org/10.1007/s44163-025-00224-w
Coates, W. C. (2025). Precision education – a call to action to transform medical education. International Journal of Emergency Medicine, 18(1). https://doi.org/10.1186/s12245-025-00819-1
Colledani, D., Barbaranelli, C., & Anselmi, P. (2025). Fast, smart, and adaptive: using machine learning to optimize mental health assessment and monitor change over time. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-91086-w
ElHaj, K., & Alshamsi, D. (2025). GeoTemporal clustering for aquifer delineation: a big data approach to synchronizing and analyzing variable-length groundwater time series. Journal of Big Data, 12(1). https://doi.org/10.1186/s40537-025-01060-6
Gholami, S., Jannat, F.-E., Thompson, A. C., Ong, S. S. Y., Lim, J. I., Leng, T., Tabkhivayghan, H., & Alam, M. N. (2025). Distributed training of foundation models for ophthalmic diagnosis. Communications Engineering, 4(1). https://doi.org/10.1038/s44172-025-00341-5
Govindharaj, I., Rampriya, R., Michael, G., Yazhinian, S., Dinesh Kumar, K., & Anandh, R. (2025). Capsule network-based deep learning for early and accurate diabetic retinopathy detection. International Ophthalmology, 45(1). https://doi.org/10.1007/s10792-024-03391-4
Guiraud, M.-G., MaBouDi, H., Woodgate, J., Bates, O. K., Rodriguez, O. R., Gallo, V., & Barron, A. B. (2025). How bumblebees manage conflicting information seen on arrival and departure from flowers. Animal Cognition, 28(1). https://doi.org/10.1007/s10071-024-01926-x
Manivannan, R., & Senthilkumar, S. (2025). Intrusion Detection System for Network Security Using Novel Adaptive Recurrent Neural Network-Based Fox Optimizer Concept. International Journal of Computational Intelligence Systems, 18(1). https://doi.org/10.1007/s44196-025-00767-x
Mojumder, B., Uddin, M. J., & Dey, K. (2025). Perspectives, preparedness and challenges of the abrupt transition of emergency online learning to traditional methods in higher education of Bangladesh in the post-pandemic era. Discover Education, 4(1). https://doi.org/10.1007/s44217-025-00417-6
Natarajan, K., Baskaran, D., & Kamalanathan, S. (2025). An adaptive ensemble feature selection technique for model-agnostic diabetes prediction. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-91282-8
Nejatiyanpour, E., Ghorbanzadeh, O., Strobl, J., Yousefpour, R., Kakhki, M. D., Amirnejad, H., Gholamnia, K., & Sabouni, M. S. (2025). Assessing Hyrcanian forest fire vulnerability: socioeconomic and environmental perspectives. Journal of Forestry Research, 36(1). https://doi.org/10.1007/s11676-025-01832-z
Prabanand, S. C., & Thanabal, M. S. (2025). Advanced financial security system using smart contract in private ethereum consortium blockchain with hybrid optimization strategy. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-89404-3
Qinglong, L., Xiaodong, W., Song, X., Xiang, X., & Bo, P. (2025). Analysis of distribution network reliability based on distribution automation technology. Energy Informatics, 8(1). https://doi.org/10.1186/s42162-025-00478-9
Sanjalawe, Y., Al-E’mari, S., Fraihat, S., Abualhaj, M., & Alzubi, E. (2025). A deep learning-driven multi-layered steganographic approach for enhanced data security. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-89189-5
Sankova, M. V, Nikolenko, V. N., Litvinova, T. M., Volel, B. A., Oganesyan, M. V, Rizaeva, N. A., Vovkogon, A. D., Sankov, S. V, Bulygin, K. V, Zharikova, T. S., Sankov, A. V, Panas, A., Pontes-Silva, A., & Zharikov, Y. O. (2025). Effects of the COVID-19 pandemic on the health of medical students transitioning from traditional education to distance learning: a prospective cohort. BMC Medical Education, 25(1). https://doi.org/10.1186/s12909-024-06407-w
Saraireh, J., Agoyi, M., & Kassaymeh, S. (2025). Adaptive Ensemble Learning Model-Based Binary White Shark Optimizer for Software Defect Classification. International Journal of Computational Intelligence Systems, 18(1). https://doi.org/10.1007/s44196-024-00716-0
Suryawanshi, R., & Patil, B. P. (2025). Optimized antenna selection for mm-wave MIMO communication systems. Eurasip Journal on Wireless Communications and Networking, 2025(1). https://doi.org/10.1186/s13638-025-02436-1
Tudor, C., & Sova, R. (2025). An automated adaptive trading system for enhanced performance of emerging market portfolios. Financial Innovation, 11(1). https://doi.org/10.1186/s40854-025-00754-3
Yuan, Y., Li, R., Wang, G., & Lv, X. (2025). Improved sand cat swarm optimization algorithm assisted GraphSAGE-GRU for remaining useful life of engine. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-91418-w
Authors
Copyright (c) 2025 Claudia Fischer, Sebastian Koch, Laura Richter

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