Abstract

Secure and reliable operations with information are needed for the sustainability of a context-aware mobile open learning environment. Students' attendance prediction can be maximized to optimize resource usage, enhance operational effectiveness, and increase confidence in the institution. The challenge is that conventional statistical techniques don't take into account temporal, contextual, and resource security issues in a decentralized mobile learning setting. This paper presents a model that provides the background for LSTM networks (Long Short-Term Memory) in the context of real-time student attendance prediction. The model can capture an intricate time series while preserving confidential student data, and possessing a touch of privacy-protected lightweight mobile streams keeps the model's ability to interface intact. In the evaluation of the model within the framework, it is evident that the model exhibits satisfactory performance (training: accuracy = 99%, precision = 98%, recall = 99%, F1 = 98%). Furthermore, the model's performance has been sustained even in the presence of adversarial attacks, data silos, and data exfiltration. The MAX LOAD behavior is considered good support for the viability of the model and as supportive evidence that AI-based prediction in the mobile environment can be undertaken with reasonable confidence. This effort addresses the task of reasonably integrating predictive modeling into the problem of obtaining appropriate measures of security for use in modeling, towards intelligent mobile learning systems that are adaptive, sustainable, private yet trusted, and hence maximize institutional trust in digital learning spaces.