Abstract:

In the age of digitalization, individuals can acquire knowledge through various media. One focus is on e-learning software that supports university students. Not only universities, but also employees and schools use these standardized e-learning platforms, indicating their central role in lifelong learning. However, these e-learning software remain static and do not adapt to user's emotions or context. Therefore, it is crucial to enhance these existing systems and personalize for optimal efficiency. Currently these systems do not consider the learners stress or focus levels. This paper investigates on the research question: How to adapt the e-learning software user interface to users' current emotions? The methodological approach is a deductive literature review with a user study for validation through Wizard of Oz prototyping. The study aims to examine user acceptance of e-learning software that simulates emotion capture technologies (e.g., EMOTIV) in the context of smart home applications.

To achieve this, a prototype was created to assess key features and evaluate users' willingness to adopt such technology. The prototype provides a tangible representation of what emotion detection and e-learning software could potentially resemble. These software functionalities are not yet implemented but have been simulated using the Wizard of Oz method. A prototype was showcased to a group of students and employees. To assess if adaptive learning produces a more conducive environment, a questionnaire was provided to the participants before and after the prototype presentation. This article highlights various methods for recording emotions and reactions as well as methods for reducing stress. It also demonstrates the possibility of using smart home applications while using an e-learning software to positively influence learning behavior and enable more efficient learning.

This Wizard of Oz prototype simulates a system to minimize subject stress enhance focus during learning. If the system identifies a learner experiencing stress, it recommends stress-reducing measures, such as automatic opening of windows or notifications and adjustable desk height for continued standing. These measures aim to prevent the user from becoming emotionally overwhelmed or excessively stressed when learning over an extended period. Furthermore, they enhance the user's concentration level, as measured by their focus. Based on the questionnaire user study, about 60% of the 15 respondents preferred incorporating mindfulness techniques in their learning. Additionally, approximately 93% of participants expressed willingness to utilize e-learning programs integrating emotion detection technology, while roughly half already use smart home applications. This research presents an approach to merge adaptive e-learning systems within the context of smart home technology to facilitate learning environments with minimized stress and enhanced concentration.

Keywords:

Context-adaptive e-learning, personalized learning environment, prototype.