Data Science for Health and Education

Human-Centred Data Science for Health and Education

About the Human-Centred Data Science for Health and Education Node

Health and Education are rich application areas for data mining and data science, due to their increasingly wide adoption of technology to support their processes. The unprecedented amount of rich data captured as people interact through or with technology (such as eLearning, eHealth applications, use of wearable devices) offers the potential to discover and follow human processes and patterns and better support data-driven decision-making. In order to achieve this potential, human-centred approaches are crucial. This node is concerned with creating computational techniques and user interfaces that enable people to extract relevant and useful information from these large, multimodal, dynamic datasets.

Recent Projects

Extracting food intake patterns of young Australian adults

This interdisciplinary project explores the role food prepared outside the home versus within the home plays in the diets of young adults. This original approach is to use our newly designed and tested Smartphone application, that transforms data collection and processing, and continuous digital photography. The IT research of this project is to create novel data mining techniques to advance experts’ understanding about the outlets where young adults buy food,… more

Monitoring Physical Activity Behaviours for the iEngage Project

The iEngage project leverages on new technologies to help to promote healthier behaviours in children with regards to physical activity and nutrition. Its digital platform provides children with information, education and skills set to achieve their physical activity and nutrition goals. The platform also connects with the activity trackers to provide continuous feedback and summarise the daily activity on a dashboard. The richness of data collected can help various stakeholders (1)… more

Peer Recommenders for MOOCs

Lack of social relationships has been shown to be an important contributing factor for attrition in Massive Open Online Courses (MOOCs). This project aims at helping students to connect with other students to alleviate this phenomenon, by enhancing MOOCs with a peer-recommender system to encourage interactions, thus improving learning and reducing attrition. It explores various recommendation strategies and different ways to integrate the peer recommender system into the MOOC learning process.… more

Leaders

A/Prof Kalina Yacef

Information Technologies

Human-centred data mining, education technology, novel interfaces
kalina.yacef at sydney.edu.au

Members

A/Prof Irena Koprinska
Prof Rafael A Calvo
Prof Judy Kay