Patrick Tran

Educational Designer/Developer
ADFA Administrative

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
  • Gamification
  • 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.  

Selected publications

Educational Design and Technology


    T. P. Tran, L. Sidhu, D. Tran, "A Framework for Navigating and Enhancing the Use of Digital Assessment", 5th International Conference on E-Society, E-Education and E-Technology ICSET2021, August 2021, Taipei, Taiwan (Accepted) 

    • Abstract: Digital assessment (DA) has been recognized as one of the most critical elements of modern education due to the ever-growing uptake of information and communication technologies in learning and teaching. This article provides a systemic approach to navigating and enhancing the use of DA in the higher education context. A multi-dimensional framework for DA development is first introduced which not only captures important aspects of DA but also highlights the trade-off between these competing dimensions. We then take a closer look at the online testing landscape and use this framework to analyze the pedagogical and technical dimensions of DA in practice. 

    T. P. Tran and D. Meacheam, "Enhancing Learners’ Experience Through Extending Learning Systems," in IEEE Transactions on Learning Technologies, vol. 13, no. 3, pp. 540-551, 1 July-Sept. 2020, 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.

    P. Tran and M. Hilier "Making a Case for Online Exams: Efficiency, Integrity and Insights”, 2019, EdTechPosium 

    • Abstract: Technology is undoubtedly changing the way learning takes place and how students are being evaluated. Many institutions have implemented Digital Assessments (DA) strategies not only to optimize their assessment processes but also enhance students’ learning experience through the use of online learning platforms. This presentation discusses a comprehensive approach to an emerging DA area, online exams, with strong focus on exam efficiency, integrity and insights. We will look at the use of online quizzes in Moodle and technical tools that help create and maintain questions, safeguard online exams and enhance our understanding of assessment data. Experience learned from our DA pilots will also be presented. 

    P. Tran "Learning Analytics Explained", 2019, ANU Coffee Courses

    • Abstract: The increasing use of technology for teaching and learning has led to a significant amount of data being collected on how learning occurs in today’s world. To capitalize on this rich data, Learning Analytics (LA) has emerged in recent years as an important field that enables the measurement, collection, analysis and reporting of educational data for the purpose of understanding and optimizing learning. The applications of LA in education can include leaner-facing dashboards showing individual’s course progress, staff-facing reports tracking student use of educational resources, and intelligent systems that predict students’ academic performances and risks of dropping out, and enable teachers to take corrective actions. In this short espresso course, we will explore what LA is and why it is widely adopted by institutions. We will then explain and showcase how LA is used to support learning and teaching. The course will conclude by examining key issues in the use of LA, including concerns over ethics and privacy relating to LA applications, and how LA insights should be interpreted. This course will be of interest to anyone interested in leveraging educational data for better learning outcomes and the challenges involved with it. 

    P. Tran, "Enhance learners’ engagement with iLesson, a four-in-one learning tool", 2018, EdTechPosium 

    • Abstract: In this demonstration, Patrick will show how iLesson allows instructors to build an interactive lesson around a video on the fly. As a simple alternative to the native Moodle lesson activity, iLesson aims to increase student engagement and retention by bringing four major learning activities together: Watch, Check, Explore, and Discuss. 

    P. Tran, "Play to Learn: Gamification for Educators", 2018, ANU Coffee Courses

    • Abstract:  There has to be a good reason why many people spend a lot of their time, voluntarily, playing games. Most of these games are not only fun but also can help players satisfy their psychological needs such as competency, autonomy and relatedness. What if we borrow the elements and mechanics that make gaming so appealing and apply them to our teaching and learning? What are the effective strategies and approaches we can take to apply game thinking in education? This course offers a quick overview of gamification and its potential use to enhance learners’ motivation and engagement. We will explore different design techniques and discuss powerful tools to build a successful gamified learning experience. Participants in this course will develop a good understanding of gamification principles and start thinking like a game designer when developing their own courses. 

    P. Tran, "Gamification: Learn computer coding with fun", 2017, EdTechPosium 

    • Abstract: Gamification is a creative way of making things more interesting in a non-gaming context. Over the years, educators have used extrinsic reward systems such as badges, levels, achievements and leaderboards as a tool to boost their students’ motivation and productivity. A superficial approach to gamification would apply game psychology underhandedly and blindly slab simple game elements onto a course in high hopes that learners will be engaged. Though this might attract student attention initially, the best way to actually make something fun and memorable is not through the hall of fame and high-pitched bleeps and bloops, but to give it intrinsic value. This presentation aims to showcase a classroom in which gamification is applied to the fullest extent. Various gamification techniques are discussed in the context of a course teaching students computer coding. Specifically, gamification appears in two levels. First, the coding exercises themselves are real games with different levels of difficulty, but the learning activities in the course are also gamified with badges and leaderboards. Beyond extrinsic motivations, students are told that the more they play, the better they learn important coding skills that enable them to solve a larger class of problems. The presentation is concluded by discussing the challenges and the ingredients to a successful gamified learning experience. 

    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.

    Organisational units
    lensLearning and Teaching Group