DASS Research Assistant Programme: From Data to Insights – For students, by students
DASS Research Assistants (2026 Cohort)
About the DASS Research Assistant Programme
Launched in May 2026, the Data Analytics for Student Success (DASS) Research Assistant Programme provides students with exposure to real-world applications in the field of data and learning analytics while improving DASS capacity through student involvement. The programme also creates opportunities for DASS to explore and test new tools used by students.
Student Voice: From Conversation to Action
In March 2026, Sakhele Phakathi, a final-year BSc student majoring in Statistics & Data Science and Chairperson of the UCT StatsGeeks Society, approached the Data Analytics for Student Success (DASS) Team to discuss the possibility of creating part-time opportunities for students. DASS took this idea forward by developing five projects for the first cohort of Research Assistants. A total of 141 applications were received.
Meet the 2026 Cohort
S'bonokuhle Myeni: The Aspiring AI Engineer
S'bonokuhle holds an Honours degree in Geographic Information Systems at UCT and a Bachelor’s degree in Geomatics from Durban University of Technology. He is currently completing his MSc in Data Science at UCT, where his research focuses on evaluating an ensemble machine learning approach for credit card fraud detection.
“I am an aspiring AI Engineer, and I find automation particularly interesting. Recently, I have been exploring multi-agent systems (agentic AI) and the Model Context Protocol (MCP), which has deepened my interest in how AI can be used to automate and improve complex workflows.”
As part of the DASS Research Assistant Programme, S'bonokuhle is working on the Curriculum Change Impact on Student Success and Financial Sustainability project, which investigates curriculum changes implemented between 2021 and 2025 and their potential implications for student success, student throughput and programme-level financial sustainability. His work involves extracting curriculum structures, course credits and fee information from university handbooks and other sources, using large language models (LLMs) to assist with organising the information into structured datasets. The extracted data is then manually validated to ensure accuracy and reliability.
“So far, the experience has been valuable because it has exposed me to a different side of data science. In academic projects, data is often provided in a relatively structured form, whereas this project has required me to think about how to obtain reliable data in the first place. It has been particularly interesting to see how technical approaches such as LLM-assisted data extraction can be combined with human validation to address practical data challenges.”
Maziko Mangochi: Connecting People and AI
Maziko holds a Bachelor of Commerce in Marketing and Business Management from Damelin, and a Bachelor of Commerce Honours degree specialising in Marketing from UCT. He is currently pursuing a Master of Commerce at UCT, where his research focuses on AI-driven financial advice and the tension between accessibility and trust when consumers interact with AI-based financial advisory services.
“My professional experience has largely been built around combining marketing with technology and data. I have worked in digital marketing and social commerce in Malawi, where I have used platforms such as WhatsApp and Instagram not only for marketing but also as direct sales channels. This has made me particularly interested in how technology changes the way people interact with businesses and services.”
As part of the DASS Research Assistant Programme, Maziko is currently conducting desktop research on existing chatbots used in higher education and examining the different approaches researchers have used to evaluate their effectiveness and impact. He is also exploring how AI tools can be applied to analyse chatbot conversation data.
“My experience so far has been very positive. It has given me the opportunity to develop my research skills in a practical setting while also learning more about how AI can be evaluated beyond simply looking at whether a chatbot works. The programme has allowed me to see how research can be used to understand real problems and inform decisions.”
Oyedele Gbenro: From Aspiring Medical Doctor to Data Scientist
Oyedele holds a Bachelor of Science in Mathematical Sciences and an Honours in Statistics from Sefako Makgatho Health Sciences University. He has completed an MSc in Data Science at UCT and will be graduating on 8 September 2026. His research, titled "Spatial Analysis of Invasive Tree Species in the Eastern Cape", focused on mapping the spatial distribution of invasive tree species and investigating the environmental factors driving their spread.
“I initially wanted to be a medical doctor, but when I could not get into medical school, I found my way into data science. I realised that I could still be a source of hope and solutions — just in a different way. I believe that data, when used thoughtfully, can help people and organisations make better decisions.”
As part of the DASS Research Assistant Programme, Oyedele is working on Phase 3 of the Living and Learning project, focusing on data management, analysis, and reporting. His role involves working with data collected from residence engagements, helping to establish baseline data for student belonging, social cohesion, engagement, and help-seeking, and supporting the development of the Living and Learning Survey.
“My experience so far has been rewarding — I have enjoyed collaborating with a passionate team and seeing how data can inform decisions that improve student life.”
Tanweer Nujjoo: The Sportaholic and Taekwon-do Practitioner
Tanweer holds a BSc in Geomatics, specialising in Surveying, from UCT. He is currently enrolled in an MSc in Data Science at UCT and has recently submitted his dissertation. His MSc research, which is an expansion of his undergraduate work, focused on applying advanced spatiotemporal machine learning models to predict wildfire susceptibility in Table Mountain National Park.
“I am deeply motivated by work that creates a tangible, positive impact within the educational, environmental, and healthcare sectors. The DASS research assistant programme offered the perfect intersection to leverage my background in data science to drive meaningful, data-informed improvements in South African higher education through the National Benchmark Tests (NBT) project.”
As part of the DASS Research Assistant Programme, Tanweer is working within the Centre for Educational Assessment (CEA), focusing on translating complex NBT dataset outputs into actionable curriculum and instructional insights through user-friendly reporting and visualisations. He is currently designing automated workflows for annual NBT reporting cycles to streamline post-analysis delivery and secure long-term institutional utility.
“My experience so far has been immensely positive. Collaborating closely with colleagues like Precious Mudavanhu has created a supportive environment for growth, effective problem solving and exchange of knowledge.”
Corey Webb: The avid birder and competitive swimmer
Corey holds an undergraduate degree, majoring in Computer Science and Applied Statistics, and a BSc Honours in Computer Science from UCT. His Honours project focused on fine-tuning a large language model to solve isiZulu mathematics word problems. He is currently pursuing an MSc in Data Science, working on the validation and annotation of video footage captured by cameras attached to penguins in Antarctica.
“I have a passion for diving into data and finding patterns. So far, I've had experience with other types of data problems (images and Chain-of-Thought math problems), but I haven't yet dealt with a large and high-dimensional tabular dataset. I was super keen on getting my hands dirty with this real-world data and I'm super excited that my work will have some real impact.”
As part of the DASS Research Assistant Programme, Corey is using Power BI to create a visual tool for analysing Extended Curriculum Program (ECP) data. The project also involves performing statistical analysis to uncover hidden patterns that may not be immediately apparent in a simple visualisation.
“I'm equally excited for both of these parts of the project as they harmonise the two most important qualities of a data scientist - the ability to find patterns in data and the ability to convey that information effectively (often through visualisations).”