In the fast-paced environment of higher education institutions, gathering student feedback can be a challenging endeavour. Traditional feedback mechanisms, such as online surveys or lengthy questionnaires, often suffer from low participation rates due to the cognitive load they impose on students, especially when presented after or during exam periods. These forms can be perceived as tedious and time-consuming, discouraging students from providing valuable insights into their educational experiences.

Cognitive load theory, a well-established concept in the field of human-computer interaction (HCI), suggests that individuals have limited cognitive resources available for processing information. When the demands of a task exceed these resources, cognitive overload can occur, leading to decreased performance and potentially abandonment of the task altogether (Hollender et al., 2010).

In the context of feedback collection, traditional forms can impose a significant cognitive burden on students. They often require students to recall and articulate their thoughts and experiences from scratch, necessitating mental effort and increasing the intrinsic cognitive load associated with the task. Additionally, the presence of extraneous information or complex user interfaces can further exacerbate cognitive overload, deterring students from engaging with the feedback process.

To address these challenges, this project introduces Anonfeedback.io, an innovative feedback system leveraging the power of AI and HCI techniques. It aims to reduce cognitive demands through intuitive design, encouraging higher engagement rates. This enables institutions to act promptly on contextualized, real-time feedback, in order to enable continuous improvement in the learning experience while overcoming limitations of conventional mechanisms.

The system's other strength lies in its use of sentiment analysis powered by large language models (LLMs) like GPT-4. These advanced natural language processing models excel at capturing linguistic nuances, detecting sarcasm, and providing nuanced insights from unstructured text comments, thereby obtaining far more accurate classification out of the box. By leveraging the latest advancements in AI, the system offers educational institutions the ability to promptly respond to contextualized, real-time feedback.

Dissertation

https://rgu.anonfeedback.io/

https://rgu.anonfeedback.io/fran/discussion