DBIS

The bachelor thesis aims to contribute to the ongoing research project MILKI-PSY, which is centered around advancing Self-Regulated Learning (SRL) in psychomotor training through multimodal immersive mentoring. In the context of the project, we explore how various calculation mechanisms and hardware decision impact the latency of feedback in psychomotor learning.

Open Source Software (OSS) revolutionised the computing world about three decades ago. One of the principles of OSS guarantees software developers, companies, researchers, and students the freedom to change and improve the software. Characterised by active community involvement (bazaar-style software development), OSS development has produced category-killer Operating Systems (e.g., Debian, Ubuntu) and applications (e.g., the Apache HTTP Server, Firefox). 

The computer science community is now riding another revolution called the Large Language Models (LLMs) revolution. Various variants of LLMs (Commercial and Open Source) come with billions of parameters that developers can fine-tune to control how the system generates text (tokens). Commercial LLMs (e.g., ChatGPT) come with a copyright and are expensive to deploy and use. They have also been criticised for their hallucination, lack of transparency, and the potential for monopolisation by big corporations.

This thesis aims to develop a social bot designed to enhance self-regulated learning experiences by leveraging xAPI statements to track learning progress. The system will employ the “GRETA Kompetenzbilanz” model to visualize the learner’s progress comprehensively, recommend suitable learning materials, and facilitate reflective practices. The project also explores the potential integration of FAQ handling and considers the implementation of MLOps for continuous improvement through retraining of recommendation models.

This master’s thesis aims to advance the field of psychomotor learning within the context of Project MILKI-PSY, a research initiative focusing on multimodal immersive mentoring to facilitate Self-Regulated Learning (SRL) for psychomotor training. Psychomotor learning involves the acquisition of skills through the integration of physical and cognitive processes. In this project, we are developing an innovative framework that leverages immersive technologies to enhance SRL, offering a unique opportunity to revolutionize the way individuals acquire psychomotor skills.

This bachelor thesis aims to explore the development of a peer-assisted exergame using Unity as the framework and YOLOv8 for movement tracking. Exergames, which combine exercise and gaming elements, have gained popularity in recent years as a means to promote physical activity. However, they often lack a social component, which could enhance motivation and engagement. This research project seeks to address this gap by creating a peer-assisted exergame that encourages players to exercise together while having fun.