Data analytics in education can benefit students by providing insights and knowledge on various aspects of their academic journey, such as their academic performance, effectiveness of study strategies, course selection, career planning, academic challenges, preferences and learning styles. These insights can help students make informed decisions, set achievable goals, and ultimately achieve academic success at NTU.
Through the use of data and learning analytics, the Centre for the Applications of Teaching & Learning Analytics for Students, or ATLAS, aims to enhance student learning experiences and outcomes by empowering decisions and actions on teaching and learning, as well as on student well-being. This Centre was formed in January 2023 and headed by Dr Lim Fun Siong.
ATLAS intends to provide a seamless suite of data services from data governance to data collection, cleaning, management, modelling, analysis, visualisation and deployment. ATLAS will integrate and leverage on leading technologies used in the field of data science and machine learning, which include Denodo, Dataiku, Qliksense, Tableau, and Microsoft Power BI.
Note: In accordance with the University Data Governance Framework, personally identifiable information of students and staff are masked and safeguarded. These confidential data are highly restricted and only available to those with the appropriate access rights.
Holistic Student Learning Profile – Working with the Centre for IT Services (CITS) and the Student and Academic Services Department (SASD), we intend to build an educational data hub with Denodo that would provide a holistic learning profile of students. This learning profile will include data catalogues of student learning needs, outcomes, experiences, and other insights used to advance the teaching and learning goals of the University. The data can be used by faculty for research and educational evaluation needs. The data catalogue can be found here (require intranet access).
Early AleRT for Learning Intervention (EARLI) – Using artificial intelligence and prediction modelling, we will work with schools to identify at-risk students in need of additional attention and assistance.
Course Analytics Dashboard of Students (CADS) We intend to provide faculty of large core classes aggregated data of their students before, during and after each run of their course, to support them in understanding their students and plan their teaching.
Skill and Course Advising for Learning Excellence (SCALE) Working with the Experiential & Collaborative Learning (ECL) Office and the Career & Attachment Office (CAO), we intend to use data analytics to recommend courses and co-curricular activities for students based on their learning history and career aspirations.
NTU AI Learning Assistant (NALA) Leveraging existing infrastructure in NTU, we intend to develop customised generative AI tutoring chatbot to personalise learning for large classes.
Translational Research – We will work with faculty, students, school leaders, and external organisations to apply analytics solutions for identified areas of improvement in teaching and learning so as to enhance student learning experiences, engagement, and outcomes.