Next-Gen Digital Learning for Health Education with Adaptive Pathways for Enhanced Engagement
Keywords:
Adaptive Health Learning Algorithms (AHLA), Digital Health Education, Knowledge Retention, Health Education Technology, Educational EffectivenessAbstract
The field of health education has historically faced challenges related to learner engagement and the efficacy of content delivery. Traditional methods, including lectures and textbooks, often fail to address the individual learning needs and varying paces of students. In the context of health education, where the retention and understanding of complex topics such as disease prevention, medical ethics, nutrition, and Digital health are critical, these issues become even more pronounced. Digital education has revolutionized the way health education is delivered, offering personalized learning experiences that are more engaging and effective. This paper presents an innovative approach called Adaptive Health Learning Algorithms (AHLA), which integrates machine learning models to create tailored learning paths based on individual learning progress and engagement. AHLA continuously adjusts content delivery, ensuring that learners grasp foundational knowledge before advancing to more complex concepts. By combining multimedia, gamification, and real-time performance analytics, this technique fosters deeper understanding and retention of health-related knowledge. Simulation results evaluate the effectiveness of the Adaptive Health Learning Algorithms (AHLA) system, 200 students were divided into two groups: one using AHLA and the other using a traditional static learning path. The results showed significant improvements in several key metrics for the AHLA group. The average completion rate for the AHLA group was 95%, compared to 75% in the static learning path group, indicating a 20% higher completion rate. When tested on their knowledge retention through a final exam, students using AHLA scored an average of 85%, while the static group scored only 70%, marking a 15% improvement in knowledge retention for the AHLA group. Engagement was also significantly higher for the AHLA group, with students spending an average of 25 minutes per session, compared to 18 minutes in the static group, a difference of approximately 38% more time spent interacting with the learning platform. Additionally, multimedia content engagement was higher in the AHLA group, with 72% of learners interacting with videos, quizzes, and interactive simulations, versus 50% in the static group, representing a 22% increase in multimedia engagement.
References
[1] Q. Shi, N. Cai and W. Jiao, “Monitoring and Evaluating College Students' Digital Health Based on Big Data Analysis,” American Journal of Health Behavior, vol.46, no.2, pp.164-176, 2022.
[2] W. Zheng, “Cluster Analysis Algorithm in the Analysis of College Students' Digital Health Education,” Applied bionics and biomechanics, vol.2022, 2022.
[3] S. Yang, L. Lin and X. Zhang, “Adjustment method of college students' Digital health based on data analysis under the background of positive psychology,” Frontiers in Psychology, vol.13, pp.921621, 2022.
[4] B. Liang, “Exploration and application of college students’ management model during the epidemic based on big data technology,” Mathematical Problems in Engineering, vol.2022, 2022.
[5] X. Liu, “Optimization of college students’ Digital health education based on improved intelligent recognition model,” Mathematical Problems in Engineering, vol.2022, 2022.
[6] C. Deng, Q. Yu, G. Luo, Z. Zhao and Y. Li, “Big data-driven intelligent governance of college students' physical health: System and strategy,” Frontiers in Public Health, vol.10, pp.924025, 2022.
[7] T. Guo, W. Zhao, M. Alrashoud, A. Tolba, S. Firmin et al., “Multimodal educational data fusion for students’ Digital health detection,” IEEE Access, vol.10, pp.70370-70382, 2022.
[8] H. Han, “Fuzzy clustering algorithm for university students' psychological fitness and performance detection,” Heliyon, vol.9, no.8, 2023.
[9] E. O. Ogunseye, C. A. Adenusi, A. C. Nwanakwaugwu, S. A. Ajagbe and S. O. Akinola, “Predictive analysis of Digital health conditions using AdaBoost algorithm,” ParadigmPlus, vol.3, no.2, pp.11-26, 2022.
[10]P. Muzumdar, G. P. Basyal and P. Vyas, “An empirical comparison of machine learning models for student's Digital health illness assessment,” arXiv preprint arXiv:2202.13495, 2022.
[11]S. Bhatnagar, J. Agarwal and O.R.Sharma, “Detection and classification of anxiety in university students through the application of machine learning,” Procedia Computer Science, vol.218, pp.1542-1550, 2023.
[12]J. Zhao and Q. Li, “Big data–Artificial Intelligence fusion technology in education in the context of the new crown epidemic,” Big Data, vol.10, no.3, pp.262-276, 2022.
[13]T. Guo, X. Bai, X. Tian, S. Firmin and F.Xia, “Educational anomaly analytics: features, methods, and challenges,” Frontiers in big Data, vol.4, pp.811840, 2022.
[14]Y. Cui, Z. Ma, L. Wang, A. Yang, Q. Liu et al., “A survey on big data-enabled innovative online education systems during the COVID-19 pandemic,” Journal of Innovation and Knowledge, vol.8, no.1, pp.100295, 2023.
[15]L. Cai and X.Wang, “Prediction and influencing factors of college students’ career planning based on big data mining,” Mathematical Problems in Engineering, vol.2022, 2022.
[16]F. Ouatik, M. Erritali, F. Ouatik and M. Jourhmane, “Predicting student success using big data and machine learning algorithms,” International Journal of Emerging Technologies in Learning (iJET), vol.17, no.12, pp.236-251, 2022.
[17]J. Angskun, S. Tipprasert and T.Angskun, “NEXT-GEN on social networks for real-time depression detection,” Journal of Big Data, vol.9, no.1, pp.69, 2022.
[18]M. Ayala-Chauvin, F. Avilés-Castillo and J. Buele, “Exploring the landscape of data analysis: a review of its application and impact in Ecuador,” Computers, vol.12, no.7, pp.146, 2023.
[19]Z. Xu and D. Zou, “Big data analysis research on the deep integration of intangible cultural heritage inheritance and art design education in colleges and universities,” Mobile Information Systems, vol.2022, pp.1-12, 2022.
[20]A. C. Ikegwu, H. F. Nweke, C. V. Anikwe, U. R. Alo and O. R. Okonkwo, “NEXT-GEN for data-driven industry: a review of data sources, tools, challenges, solutions, and research directions,” Cluster Computing, vol.25, no.5, pp.3343-3387, 2022.
[21]M. Rezapour and S.K. Elmshaeuser, “Artificial intelligence-based analytics for impacts of COVID-19 and online learning on college students’ Digital health,” PLoS One, vol.17, no.11, pp.e0276767, 2022.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal of Computing in Education, Sports and Health (JCESH)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Fringe Global Scientific Press publishes all the papers under a Creative Commons Attribution-Non-Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/) license. Authors have the liberty to replicate and distribute their work. Authors have the ability to use either the whole or a portion of their piece in compilations or other publications that include their own work. Please see the licensing terms for more information on reusing the work.
