Adaptive Teaching Path Modeling and Dynamic Intervention Mechanisms for Higher Education Driven by Interactive Mobile Technologies.
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| Title: | Adaptive Teaching Path Modeling and Dynamic Intervention Mechanisms for Higher Education Driven by Interactive Mobile Technologies. |
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| Authors: | Wang, Jianjiang1 wangjianjiang0091@hbun.edu.cn, Cui, Yangyang1 Cuiyangyang_1015@163.com, Sun, Lingling1 sunlingling0378@hbun.edu.cn |
| Source: | International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 16, p118-133. 16p. |
| Subjects: | Mobile learning, Individualized instruction, Data fusion (Statistics), Privacy, Edge computing, Higher education, Federated learning |
| Abstract: | The proliferation of mobile intelligent terminals and ubiquitous networks has established mobile learning as a core modality within the digital--intelligent higher education ecosystem. Conventional adaptive teaching systems, which rely on centralized cloud architectures, suffer from limited contextual awareness dimensionality, delayed intervention responses, and pronounced privacy risks--deficiencies that render them ill-suited for the fragmented and dynamically fluctuating contexts characteristic of mobile learning. To address these challenges, a three-tier adaptive teaching intervention framework was constructed, integrating on-device perception, edge-side inference, and cloud-based optimization. A gated adaptive multi-modal context fusion mechanism was designed to integrate multiple contextual dimensions and cognitive states into a unified joint representation. A lightweight efficiency prediction network was trained via knowledge distillation, enabling real-time local path fine-tuning on mobile terminals through constrained optimization, while raw data remained entirely on the device. An edge-empowered end--edge--cloud collaborative intervention strategy was proposed, wherein structured pruning facilitated low-latency inference at the edge layer, and a heterogeneity-aware personalized federated learning algorithm was introduced to perform global distributed optimization. A dual-timescale closed-loop feedback mechanism was established to synchronously enable instantaneous policy iteration and long-term knowledge system refinement. This study achieves a deep integration of mobile terminal sensing capabilities with adaptive teaching logic, reconciling real-time adaptability, global optimization, and privacy preservation, thereby providing technical support for the scalable deployment of digital--intelligent learning ecosystems in higher education. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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