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Honours and Minor Thesis projects

Displaying 51 - 60 of 216 honours projects.


Primary supervisor: Delvin Varghese

In the evolving landscape of data reporting, traditional text-based and quantitative methods are increasingly being supplemented by rich, community-generated qualitative data, including audio and video content. This shift presents unique challenges and opportunities in how non-profits, government bodies, and community organizations present and utilize this data.

Primary supervisor: Bernhard Jenny

Become part of the Monash Immersive Analytics Lab, and explore exciting new ways to visualise, interact, and analyse all types of data with VR and AR! We are looking for enthusiastic students to work on immersive visualisation using latest technology, such as head-mounted displays with integrated eye-trackers (Microsoft HoloLens and others), gesture recognition devices, and large wall displays.

Primary supervisor: Roberto Martinez-Maldonado

This project focuses on modeling heart rate data captured via FitBit Sense devices worn by team members in collaborative situations such as supervision meetings, group teaching, or nursing simulation scenarios. The primary goal is to identify stressful situations or similar events by analysing heart rate variations.

Primary supervisor: Roberto Martinez-Maldonado

This project focuses on the automated classification of teachers' activities and co-teaching behaviors using positioning data captured via sensors and microphone data. The main task involves developing and applying machine learning techniques to analyse multimodal datasets, combining positioning and speech data to identify and categorize various teaching activities. By leveraging large language models (LLMs), Generative AI (GenAI), and Natural Language Processing (NLP), the project aims to extract features that enhance the accuracy and effectiveness of these classification tasks.

Primary supervisor: Roberto Martinez-Maldonado

I am seeking students doing Honours or a minor thesis in a Masters interested in working on designing Learning Analytics innovations to study classroom proxemics by analysing and visualising indoor positioning data (along with other sources of data such as audio, physiological activity and characteristics of the students).

Primary supervisor: Roberto Martinez-Maldonado
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Are you ready to dive into the future of education and revolutionise how software projects are assessed? Join this innovative project aimed at creating cutting-edge learning analytics capabilities within the Faculty of IT at Monash University.

Primary supervisor: Roberto Martinez-Maldonado
Data Comics Banner

This project aims to enhance student engagement and comprehension by combining Data Comics with Generative AI. Data Comics present complex information in an engaging, accessible format, and by leveraging AI, we seek to automate their creation, making the process efficient and scalable.

Primary supervisor: Ehsan Shareghi

Web is filled with content, and language agents (as an emerging family of AI systems) are still far from capable in tapping into the information available on the web during their course of action. This project will move on this exciting direction by building a language agent that for any given web page can (1) write a python crawler on-the-fly, and (2) identify its core content.

Primary supervisor: Guanliang Chen

In education, writing is a prevalent pedagogical practice employed by teachers and instructors to enhance student learning. Yet, the timely evaluation of students' essays or responses represents a formidable challenge, consuming considerable time and cognitive effort for educators. Recognizing the need to alleviate this burden, Automatic Essay Scoring (AES) has emerged, which refers to the process of using machine learning techniques to evaluate and assign scores to student-authored essays or responses.

Primary supervisor: Guanliang Chen

In education, writing is a prevalent pedagogical practice employed by teachers and instructors to enhance student learning. Yet, the timely evaluation of students' essays or responses represents a formidable challenge, consuming considerable time and cognitive effort for educators. Recognizing the need to alleviate this burden, Automatic Essay Scoring (AES) has emerged, which refers to the process of using machine learning techniques to evaluate and assign scores to student-authored essays or responses.