[ Academic, HCI & Learning Analytics Researcher, and Full Stack Developer. ]
I am currently a Research Fellow at Monash University's Centre of Learning Analytics (CoLAM) working under the esteemed mentorship of Professor Dragan Gasevic. My work primarily focuses on researching computational methods, innovative information visualisation strategies to understand how student's learn and regulate their learning process, and employ HCI and software engineering techniques to develop edtech solutions that enhance digital learning experiences. As a Research Fellow at CoLAM, I am currently contributing to the following projects: FLoRA, KMASS, and Data analytics-based tools and methods to enhance SRL. My academic contributions are predominantly concentrated within the domains of learning analytics, educational psychology, predictive modeling, curriculum development, behavioural analytics, human-computer interactions, and computer science education.
I undertook my Ph.D. studies under the expert guidance of Prof. Bernd Meyer and Assoc. Prof. Michael Wybrow at the Faculty of Information Technology, Monash University. My doctoral project showcased how rich trace data about students’ learning journey (pathways) can be explored via visual interfaces to support educators address complex educational needs and understand the effectiveness of their instructional design.
My academic journey spans a decade, and my professional interests are deeply rooted in the domains of data analytics, computational modeling, and the design and deployment of novel tools to support teaching and learning. I obtained my Bachelor's and Master's degrees in Computer Science from The University of the South Pacific, located in the Fiji Islands. Prior to my academic career, I had the privilege of amassing valuable experience as a Business Analyst - Finance & IT with a prominent regional food company, renowned for its operations in manufacturing, distribution, marketing, and sales throughout the Asia Pacific region.
Contact: | Resume | Google Scholar | ORCiD |
2022 | Runner-up Best Paper Award Full Paper: Using Learner Trace Data to Understand Metacognitive Processes in Writing from Multiple Sources at the 2022 edition of The International Conference on Learning Analytics & Knowledge (LAK22). |
2022 | The "Smart Linings for Pipe and Infrastructure" project becomes Finalist in R&D Excellence Award 2021 presented by the Australian Water Association, VIC Branch. |
2021 | FLoRA project receives recognition in annual EDUCAUSE Horizon Report. |
2020 | Runner-up Best Paper Award Full Paper: UserFlow: A Tool for Visualising Fine-grained Contextual Analytics in Teaching Documents at the 2020 ACM Conference on Innovation and Technology in Computer Science Education. |
2019 | AIED Conference Scholarship to attend 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, USA. |
2017 | Honourable Mention - Project Award (2nd Place) - Australian Summer School on Data Visualization ( SummerViz 2017) |
2016 | Monash Faculty of IT – Merit-based Tuition Scholarship |
2016 | Monash Graduate Scholarship (PhD) |
CRC-P: Smart Linings for Pipe and Infrastructure Project (Decision Tool)
Monash Pipe Evaluation Platform is designed to assist asset managers, project managers and liner installers by: i) Providing a tool to rank pipes by their likelihood of failure; ii) Assessing the condition of the ranked pipes; iii) Recommending suitable types of liners; and iv) Calculating the expected life of the lined pipe. Role: UI design, development, testing, and deploying the complete web application on Water Services Association of Australia (WSAA) server. WSAA is the peak industry body representing the urban water industry, with its members providing water and sewerage services to over 24 million customers in Australia and New Zealand. The Australian Government, through the Cooperative Research Centre, provided funding for the Smart Linings for Pipe and Infrastructure Project. The Pipe Evaluation Platform was a key deliverable of this project contributing to 1.2 Sub-Project 1: Intelligent decision tools and standards and 1.5 Sub-Project 4: Implementation and adoption of research. ★The "Smart Linings for Pipe and Infrastructure" project became a Finalist in R&D Excellence Award 2021 presented by the Australian Water Association VIC Branch.
| Link: [ Project Overview ] [ MPEP Platform ] [ Project Report ][ Short Video ] [ Other ]
FLoRA Project: Advancing Student Support through Personalised Scaffolds
The FLoRA project explores self-regulated learning with personalised teaching guides and suggestions offered through artificial intelligence technology. The project was a collaboration between Radboud University's Adaptive Learning Lab, the University of Edinburgh in the UK, the Technical University of Munich in Germany, and Monash University in Australia, and used in 5 countries (Australia, Netherlands, Germany, India, and China) by 2000+ students. FLoRA project was funded by DFG, ESRC and NWO as part of the Open Research Area (Call 5) under grant number BA 2044/10-1 | GA 2739/1-1 | MO 2698/1-1. ★ FLoRA was selected in the prestigious EDUCAUSE 2021 Horizon Report as one of six projects out of 141 key technologies and practices, which were voted in to have a significant impact on the future of higher education teaching and learning.
[ Project Website ] [ FLoRA Platform ] [ News Coverage ]
LearnerFlow: An Instructor-Facing Learning Analytics Dashboard
LearnerFlow Role: Researcher and Lead Developer (2018 to 2020)
The LearnerFlow Tool was an outcome of my doctoral research. This tool was piloted in two courses in S1 2019 (ENG1003 & FIT5145) with approximately 1500 students in the Faculty of IT at Monash University and aimed to support instructors in evaluating their learning designs. Its implementation provided valuable insights into optimising learning experiences for students. ★ The research paper based on this project was Nominated for Best Full Paper Award at The ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) 2020 .
[ Project Website ] [ Publication ]
Early Warning System - FSTE Learning & Teaching Project - USP
EWS Role: Researcher, Conceptualisation and Developer of Version 1 (2013 to 2014)
This project stemmed from my masters research and focused on using trace data to identify early indicators of "at-risk" students and produced various reports that facilitated implementing interventions to support these students and improve their academic outcomes. The tool was implemented in 16 courses (3500+ students) at USP between 2013 to 2016. ★ The impact of this project has featured in a prestigious Q1-ranked journal: Studies in Higher Education.
[Project Website] [ Features ] [ VC-Forum Presentation ]
Jun 2020 - Now | Monash University, Melbourne, Australia | |
Research Fellow - Centre of Learning Analytics, Faculty of Information Technology | ||
Mar 2020 - Nov 2021 | Monash University, Melbourne, Australia | |
Research Assistant/Developer (Part-time) - Department of Civil Engineering | ||
Jul 2017 - Jun 2020 | Monash University, Melbourne, Australia | |
Sessional Lecturer [FIT5211, FIT5145, FIT1043], Faculty of Information Technology | ||
Feb 2016 - Nov 2019 | Monash University, Melbourne, Australia | |
Teaching Associate, Faculty of Information Technology | ||
Jul 2011 - Jan 2016 | The University of the South Pacific, Suva, Fiji Islands | |
Subject Coordinator - School of Computing, Information & Mathematical Sciences | ||
Mar 2006 - Jun 2011 | The University of the South Pacific, Suva, Fiji Islands | |
Tutor in CS/IS - School of Computing, Information & Mathematical Sciences | ||
Dec 2004 - Mar 2016 | Goodman Fielder International (Fiji) Ltd | |
Business Analyst - Finance & IT |
Role: Panel member tasked to review Year 11 & Year 12 Computer Studies prescription and writing of the textbook.
Computer Studies - A Textbook for Year 12
Ministry of Education, Suva, Fiji Islands, 2014. Print.
Link: [website] [credit] [Online Copy]
Computer Studies - A Textbook for Year 11
Ministry of Education, Suva, Fiji Islands, 2013. Print.
Link: [website] [credit] [Online Copy]
Introductory Python Course
Exemplar Course at University
Industry-Academia Partnership