Patrick Tran | BE, BSC, MBA, PhD, PRINCE2
Learning & Teaching Group| UNSW Canberra
Building 14 L1, Northcott Drive, Canberra ACT 2600
I am a data scientist by training, instructional designer by choice and educator at heart! My main interests lie at the intersection of innovation, technology, and leadership. I received a PhD in computer science for a thesis on improving performance of network Intrusion Detection Systems using Machine Learning. I am also a certified PRINCE2 project manager with an MBA in Accounting and Finance. In the education space, I have a wide range of research interests that are centered around learners:
- Learning Analytics and Educational Data Mining
- Educational Technologies
- Learning Theories
- Inventive problem solving
- Foresight – futures studies
Through my applied research and teaching, I seek to leverage data analytics to drive pedagogical decisions, revamp learner experience and revolutionize existing approaches to learning and teaching.
I am currently working at the confluence of educational technology, learning analytics and educational research at University of New South Wales (UNSW) Canberra. I am a regular contributor to the EdTechPosium (ETP) conference in the last few years, presenting topics on learning technologies: (1) Gamification: Learn computer coding with fun (ETP 2017); (2) Enhance learners’ engagement with iLesson, a four-in-one learning tool (ETP 2018); and (3) Making a Case for Online Exams: Efficiency, Integrity and Insights (ETP 2019). My online coffee courses on gamification and learning analytics can also be found on the ANU web blog.
My expertise is in the domain of learning analytics, but I have vast experience with all corners of the academic world, including research, teaching, academic program management and instructional design. Prior to UNSW, I held various positions in both teaching and management capacities at University of Technology Sydney, Victoria University and Australian National University. Over the years, I undertook several technology-enabled learning initiatives that involve analysing learners’ interaction data with intelligent tutoring systems and developing early intervention strategy for their success.
I consider myself to be a self-motivated lifelong learner and believe that everyone should learn something new each day. When I am not working, I love spending time with family and friends, and if I'm lucky, with a cold drink in my hand and sand between my toes. Cycling and watching movies are my favourite hobbies.
Educational Design and Technology
- DOI: 10.1109/TLT.2020.2989333
- Abstract: The use of learning management systems (LMSs) for learning and knowledge sharing has accelerated quickly both in education and corporate worlds. Despite the benefits brought by LMSs, the current systems still face significant challenges including the lack of automation in generating quiz questions and managing courses. Over the past decade, more attention has been accorded to analyzing the rich learning data captured by the systems and developing tools that support contemporary learning modes. This paper considers a popular LMS, Moodle, and showcases four innovative projects that aim to extend the system's capabilities and address the above problems: (1) the Quiz Making Language (QML) markup system, (2) an approach to learning analytics using ad-hoc reports and artificial intelligence (AI) enabled visualization, (3) automating course administration tasks, and (4) a 4-in-1 learning application for flipped learning. These projects target all main users of the system, including instructors, learners, and administrators. It is illustrated from this study that the innovative use of web technologies and learning analytics have great potentials in improving LMS user productivity, supporting, and informing learning.
Engineering / Machine Learning
X. Kong, G. Fang, L. Liu and T. P. Tran, "Low Computational Data Fusion Approach Using INS and UWB for UAV Navigation Tasks in GPS-Denied Environments," 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), Gold Coast, Australia, 2019, pp. 410-414.
T. P. Tran, P. Tsai and T. Jan, "A Multi-expert Classification Framework with Transferable Voting for Intrusion Detection," 2008 Seventh International Conference on Machine Learning and Applications, San Diego, CA, 2008, pp. 877-882.
T. P. Tran, P. Tsai and T. Jan, "An adjustable combination of linear regression and modified probabilistic neural network for anti-spam filtering," 2008 19th International Conference on Pattern Recognition, Tampa, FL, 2008, pp. 1-4.
Tich Phuoc Tran and T. Jan, "Boosted Modified Probabilistic Neural Network (BMPNN) for Network Intrusion Detection," The 2006 IEEE International Joint Conference on Neural Network Proceedings, Vancouver, BC, 2006, pp. 2354-2361.