The emotional state of a learner plays a pivotal role in the effectiveness of the learning process. Emotions such as excitement, curiosity, boredom, or frustration, often directly correlate with retention, understanding, and overall engagement. While in a physical classroom, educators can somewhat gauge these emotional responses through body language or facial expressions, the digital landscape of e-learning often feels like navigating in the dark.
Imagine a system where, as students engage with digital content, their emotional states are subtly and ethically monitored. This could be done through facial expression analysis, keyboard dynamics, or even feedback loops built into the learning platform. The immediate benefit? A dynamic e-learning environment that adjusts content pacing, throws in engaging activities when boredom is detected, or perhaps offers additional resources when a learner is struggling.
However, the implications of emotional analysis extend beyond real-time adjustments. Such data, when aggregated and analyzed, can provide invaluable insights into curriculum design, identifying challenging topics, and refining overall learning pathways.
Creating such a system is no small feat. It requires meticulous design, ethical considerations regarding privacy, and maybe sophisticated AI-driven analysis.
A Master's thesis in learning technology can be done individually or in collaboration with other master's students. Here, there are vast opportunities to delve deep into topics that you are passionate about.
Please get in touch with Omid Mirmotahari, omidmi@uio.no, and we'll figure it out together.
A possible proposal for a thesis could be (not limited to):
"Develop a system using cameras, skin conductance sensors, or other tech to monitor students' emotional states. Adjust the learning experience based on this feedback, perhaps by changing the pace or suggesting breaks. Address privacy concerns and ensure opt-in participation."