A Wearable-Supported Contextual Learning Model for On-the-Move Knowledge Acquisition

Employees in today's world work remotely, fueling the need for flexible and contextual learning solutions. This paper introduces a new model of learning – wearable supported contextual learning – which aims to help learners acquire the required knowledge in real-time, dynamic environments. The model proposes the use of wearables for collecting data in real-time, adapting context and delivering tailored content which improves learning by providing educational materials that suit the environment and activities the learner is engaged in at that precise time. Through multimodal sensor fusion, natural language processing, and adaptive learning techniques, the system offers timely motivational feedback with relevant information. Such an approach not only closes the gap between knowing something theoretically and applying it in practice, but also enables the learner to develop the required skills on a sustained basis adding value to the learning experience. Initial assessments and prior enactment of a prototype showcase the model’s ability to boost retention, learning engagement, and overall satisfaction in varied educational settings. In the future, the study will focus on looking into scalability, application across domains, and converging with emerging technologies like Augmented Reality and 5G technology to enhance learning on-the-go.