JoWUA

Abstract

Individualized e-learning systems plays a major role in the dynamic learning systems in which learners vary widely with respect to the background knowledge, the rate of learning, and the degree of engagement, as well as contextual factors like the availability of devices and the quality of the network. Nevertheless, the majority of current e-learning systems are based on the usage of either static or partially adaptive personalization models, which do not react to the changing contextual factors and the shift in learner behaviour in real-time. This drawback lowers learning performance, interaction, and knowledge retention. This paper aims at creating and testing a Context-Aware Adaptive Learning Model (CAALM) that can dynamically customize learning material and instructional approaches depending on dynamically monitored learner and environmental conditions. The proposed model combines multi-dimensional context sensing (learner performance, interaction behaviour, time-on-task, and device context) with an adaptive decision engine that manipulates content difficulty, sequencing, and presentation modality in real time. The model was deployed and tested on a real-world e-learning interaction dataset consisting of 1,200 learners, 18,000 learning sessions, and 45 contextual features divided into training (70%), validation (15%), and testing (15%) sets. It was measured in terms of performance against a non-contextual baseline and a static personalization model. The experimental outcomes indicate that the suggested method allows reaching the 17.8 % improvement in the learning gain, a 14.3 % improvement in the course completion rate, and a 21.6 % decrease in the mean response latency indicators. Statistics verification with paired t-tests proves that the process of improvements was significant (p < 0.01), and one-way ANOVA demonstrates that the performance improvement was the same in various groups of learners (F = 6.42, p < 0.05). The findings affirm that the use of real-time contextual awareness is an effective way of boosting adaptive learning. The proposed model presents a scalable and reliable model of next-generation personalised e-learning systems that could be used in the dynamic educational environment.