Welcome to the UTS:CIC Doctoral Program
CIC’s doctoral program in Learning Analytics offers UTS Scholarships to start in Autumn and Spring sessions for both domestic students (i.e. who do not require a visa), and international students. Applicants may be awarded a UTS Scholarship from CIC’s quota, or will be supported by CIC to compete against applicants from across the university for a scholarship.
Application Deadlines for Domestic Students
|Spring 2020||Closed||For commencement July 2020|
|Autumn 2021||30 September 2020||For commencement January 2021|
Application Deadlines for International Students
|Autumn 2021||Closed||For commencement January 2021|
|Spring 2021||15 January 2021||For commencement July 2021|
CIC’s primary mission is to maximise the benefits of analytics for UTS teaching and learning. The Learning Analytics Doctoral Program, launched in 2016, is part of our strategy to cultivate transdisciplinary innovation to tackle challenges at UTS, through rigorous methodologies, arguments and evidence. A core focus is the personalisation of the learning experience, especially through improved feedback to learners and educators.
As you will see from our work, and the PhD topics advertised, we have a particular interest in analytics techniques to nurture in learners the creative, critical, sensemaking qualities needed for lifelong learning, employment and citizenship in a complex, data-saturated society.
We invite you to apply for a place if you are committed to working in a transdisciplinary team to invent user-centered analytics tools in close partnership with the UTS staff and students who are our ‘clients’. (See the fun diagram to check a PhD is really for you!)
Please explore this website so you understand the context in which we work, and the research topics we are supervising. We look forward to hearing why you wish to join CIC, and how your background, skills and aspirations could advance this program. Please also take a look at the advice from the Director on approaching potential supervisors.
- Here are the current CIC Doctoral Researchers — can you see yourself joining them?
- And here are completed PhD Dissertations — are you excited to produce something like these?…
Shibani Antonette (2016—2019). Augmenting Pedagogic Writing Practice with Contextualizable Learning Analytics | CIC News | Now Lecturer in Transdisciplinary Innovation, University of Technology Sydney
Vanessa Echeverria Barzola (2016—2020). Designing Feedback for Collocated Teams using Multimodal Learning Analytics | CIC News | Now Postdoctoral Fellow, Human-Computer Interaction Institute, Carnegie Mellon University
“At UTS we are proud to be rated the top young university in Australia and within the top 200 universities globally.” [learn more]
CIC reports directly to Professor Shirley Alexander, Deputy Vice-Chancellor and Vice-President, Education & Students — whose learning and teaching strategy, through a $1.2B investment in new learning spaces, is ranked as world leading. Data and analytics are a core enabler of the UTS vision for learning.futures. Personalised learning through analytics-powered feedback is a priority in the UTS 2027 Strategy that CIC leads, so your work here will be right at the forefront of this. It is rare to have a Learning Analytics research centre positioned so strategically in a university, reflecting the boldness of the UTS leadership.
Our primary audience is UTS, working closely with faculties, information technology and student support units to prototype new analytics applications. Since we are breaking new ground, developing approaches that have wide applicability, we disseminate this for research impact. As you can see from our projects, we conduct both internally and externally-funded work.
CIC works closely with key teams in UTS who support the improvement of teaching and learning, including the Institute for Interactive Media & Learning (IML), Higher Education Language & Presentation Support (HELPS), and the Information & Technology Division to ensure that our tools can operate and scale in UTS as required. The annual Learning & Teaching Awards showcase leading educator practice, while the Assessment Futures program is defining the contours of assessment regimes relevant to the real world.
While you are expected to take charge of your own tool development, CIC’s application developer may well be able to support you with some web, mobile or script development to enable your research.
While CIC is inventing new analytics tools, we are also interested in evaluating open source and commercial learner-facing applications that have interesting potential for analytics.
PhD projects often add to and learn from ongoing projects, so think about whether your work connects to mainstream e-learning products or our research prototypes. You may bring expertise in particular data analysis tools. Those already in use in CIC include R, Weka, RapidMiner, ProM, Tableau.
Topic-specific technical skills and academic grounding that you will need for your PhD are specified in the PhD project descriptions, but there are some common skills and dispositions that we are seeking, given the way that we work.
- CIC is committed to multidisciplinarity, which we hope will become transdisciplinary as we build enough common ground for the disciplines to inform or even transform perspectives. Thinking outside your ‘home turf’ is not easy or comfortable, but we are seeking people with an appetite to stretch themselves with new worldviews.
- CIC is committed to user-centered participatory design of learning analytics tools, so you will need a passion for, and commitment to, working with non-technical users as you prototype new tools. We are seeking excellent interpersonal and communication skills in order to translate between the technical and educational worlds, and creative design thinking to help users engage with new kinds of tools. Ideally, you will already have had some design experience, but this can also be an area you want to learn.
Successful candidates will be eligible for a 3-year Scholarship of $35,000/pa for a full-time student (a substantial increase on the standard Australian PhD stipend of $25,849). To this, you may be able to add potential teaching income from the many opportunities to work with Master of Data Science & Innovation students. In addition, as far as possible, CIC will fund you to present peer-reviewed papers at approved, high-quality conferences.
Domestic students have their tuition fees covered by the Australian Government’s Research Training Program (RTP) Fees Offset Scholarship. Please note, all scholarships at UTS are dependent upon satisfactory progress throughout the three years.
We are also open to applications from self-funded full-time and part-time candidates, in which case you may propose other topics that fit CIC’s priorities.
To be eligible for a scholarship, a student must minimally:
- have completed a Bachelor Degree with First Class Honours or Second Class Honours (Division 1), or be regarded by the University as having an equivalent level of attainment;
- have been accepted for a higher degree by research at UTS in the year of the scholarship;
- have completed enrolment as a full-time student
Additional requirements are detailed under each of the topic areas.
Appointments will be made based on the quality of the candidates, their proposals, the overall coherence of the team, the potential contribution to UTS student and educator experience, and the research advances that will result.
The criteria are specified under each of the topic areas, both generic and specific to advertised projects. Evidence will be taken from an applicant’s written application, face-to-face/video interview, multimedia research presentation at the interview, and references.
Applicants for a Studentship should submit:
- Covering letter
- Curriculum Vitae
- Research Proposal, maximum 4 pages, applying for one of the advertised PhD topics
Please email your scholarship application as a PDF, with PhD Application in the subject line, to:
Following discussion with the relevant potential supervisors, you will be required to go through the UTS application process as a formal part of the application.
To begin this formal application process, click here and complete the following steps:
- Scroll down to “Lodge your application”
- Click on the blue “Register and Apply” button
- When you reach the section asking you to select your course, enter ‘data science’ into the free text search and the CIC Doctor of Philosophy – C02062 should come up.
The deadlines for applications are noted in the table above. However, there is an advantage to contacting us earlier to open discussions: you are strongly encouraged to get in touch with project leads informally in advance of that because if we like you, we will offer you a place as soon as we can, and you need to know where you stand.
So please get in touch with the Director if you have queries about CIC in general, and with the relevant supervisors about the topic of interest to you.
The UTS application form and further guidance on preparation and submission of your research proposal are on the UTS Research Degrees website.
We invite scholarship applications to investigate the following topics, which are broad enough for you to bring your own perspective. If you have your own funding, then you may propose another topic which fits with CIC’s priorities. Note that previously advertised projects for which we have found candidates have been removed.
You are strongly recommended to take this one hour UTS Open taster, an interactive tutorial (developed by Sophie Abel, one CIC’s doctoral researchers) on how to write a research abstract. This explains the key building blocks in an archetypal abstract — if you make these moves in your proposal, it will have a sound structure. This also enables you to try out AcaWriter, one of the instant feedback tools CIC’s developed, on your own writing.
Writing Analytics for Deep Reflection
Ming Liu and Simon Buckingham Shum (UTS:CIC), Cherie Lucas (UTS:Pharmacy)
The supervision team for this PhD is a partnership between CIC and the School of Pharmacy, who together have pioneered reflective writing analytics.
The societal challenge:
“We do not learn from experience… we learn from reflecting on experience.”
(paraphrasing John Dewey)
The problems now confronting society place an unprecedented urgency on learning from experience. Such is the pace of change that before we can plan for them, citizens and professionals in all sectors find themselves immersed in novel, complex problems. Moreover in education, the learning sciences tell us that crafting authentic experiences is a powerful trigger for learning.
“White water is the new normal”, as they say. But as Dewey noted, critical to this is our capacity to reflect on the experience of shooting those rapids. If we can’t learn how we could do better next time — individually and collectively — we are in deep trouble. From school age students, through higher education, and into professional leadership, we have to make sense of challenging experiences, recognise how we were challenged, how we are changing, and how we can improve.
At the heart of deep learning is our sense of identity. People rarely shift from entrenched positions by force of argument alone. However, when we undergo challenging experiences that force us to question assumptions and worldviews at the heart of our identity, this can indeed be transformational if we are assisted in making sense of this, and can emerge with our identify intact but now under reconstruction. Without such shifts, it’s hard to see how we will move beyond current polarisations around how we relate to each other, and the planet. Given our current political and cultural climate, applied research to help people reflect on how they adjust to threatening transitions is both timely, and of first order importance.
So, we need to get better at deep reflection, and clearly, there’s nothing as valuable as detailed feedback to provoke further reflection. But this is a scarce skillset and very labour-intensive. The practical consequence is that most students and leaders do not understand what good reflective writing is, and do not receive good feedback. For these reasons, there’s interest in educational and professional sectors in the potential of automated techniques to deliver real-time, personalised coaching.
In sum, this PhD is fundamentally about harnessing computational intelligence to deepen human learning in contexts spanning formal education, professional practice, and community transformation.
The writing challenge:
Effective written communication is an essential skill which promotes educational success for university students. However, far too many students have never had the features of good rhetorical moves explained well to them, and most educators are subject matter experts, not skilled writing coaches (Lucas, Gibson & Buckingham Shum, 2018). CIC initiated its Academic Writing Analytics (AWA) project in 2015, as it became clear through consultations across faculties that student writing was a strategically important area for UTS teaching and learning (and indeed, for most other educational institutions). The goal is to more effectively teach the building blocks of good academic writing by providing instant, personalised, actionable feedback to students about their drafts (Knight, Buckingham Shum, Ryan, Sándor, & Wang, 2018).
To deliver on this vision requires integrated expertise including natural language processing, linguistics, academic language pedagogy, learning design, feedback design, user experience, and cloud computing. This is truly a transdisciplinary effort, which has been enormously stimulating. To date, we have worked on critical, argumentative, analytical writing of the sort typically found in literature reviews, persuasive essays and research articles, as well as reflective writing, in which learners make sense of their workplace experiences, try to integrate this with their academic understanding, and share their own uncertainties, emotions and sense of personal challenge/growth (Gibson, Aitken, Sándor, Buckingham Shum, Tsingos-Lucas, & Knight, 2017).
Learning Analytics tools are most effective when co-designed with effective Learning Designs: the features constructed by the analytics align with the assessment criteria, and the tool is coherently embedded in authentic student learning tasks. Our program has demonstrated how this can be accomplished (Knight, Shibani & Buckingham Shum, 2018; Shibani, Knight, Buckingham Shum & Ryan, 2017).
Depending on your interests and skillset, critical advances that this PhD might range across technical and pedagogical contributions to educational technology:
- Integration between rule-based modelling and machine learning approaches
- Accelerated customisation of the parsers to different disciplinary domains and genres of writing
- Definition of new computational proxies that can serve as indicators of deep reflection
- User experience and machine learning to enable user feedback that teaches the tool when it make errors
- Curation of text corpora to advance the field
- Design and validation of analytics-augmented learning design patterns
- Radical improvements in the user experience of automated feedback, e.g. through novel educator/student co-design processes, or user interfaces
We currently implement the underlying concept matching model using a rule-based grammar and human-curated lexicons, which for those not familiar with this kind of work, brings both pros and cons (Buckingham Shum, Sándor, Goldsmith, & McWilliams 2017; Ullmann, 2017). The rules are grounded in scholarly literature on the features of academic research writing, and have been tested on diverse texts by the team through close manual analysis. The lexicons can be edited to tune them to the language used in different disciplines and subjects. This relatively traditional AI approach provides familiar intellectual credentials when introducing the system to educators, and when we’re testing it, the underlying behaviour is easier to explain, and errors can be diagnosed very precisely. However, it brings the limitations associated with any rule-based approach: given the richness of open-ended reflective writing, there are exception cases to debug, and improvements to the system’s performance require manual edits to the rules and lexicon.
Quick intro from an educator’s perspective:
We are now beginning work to investigate if a machine learning approach can augment the current infrastructure (Liu, et al. 2019; Ullmann, 2019). Recent years, with the availability of “big data”, such as large question answer banks (Rajpurkar, Zhang, Lopyrev, & Liang, 2016), and effective machine learning algorithms, e.g. deep neural networks (Lecun, Bengio, & Hinton, 2015), data driven approaches based on new data processing architectures have attracted a great attention in natural language processing tasks, such as neural text summarization (Liu & Manning, 2017) and neural machine translation (Bahdanau, Cho, & Bengio, 2014), mainly because these approaches do not require human defined rules and have good generalization power. However, such data driven approaches require a large amount of data, and some statistical learning models such as deep neural networks are not easy to comprehend. Preliminary results are reported
Therefore, we invite your proposals as to which techniques might be best suited to this challenge. What’s more, the creation of a corpus for writing raises ethical challenges, and we invite your thoughts on what these are, and how we might address them.
You will work in close collaboration with one or more academics from other faculties/units in UTS, using co-design methods with academics, potentially external partners, with opportunities for synergy with existing projects and tools as described on the CIC website. For more information about ongoing research in this area, please visit the Academic Writing Analytics homepage and the Writing Analytics blog.
Resources to help you understand the current state of the technology and its educational applications include the references cited, plus:
- Orientation website for students and staff
- Reflective Writing Analytics introduction
- R&D project website
- Open source release
We’re looking for the broad skills and dispositions that we are seeking in all candidates (see CIC’s PhD homepage). In addition, we envisage that applicants will either come from strong technical backgrounds and are passionate to see these make a difference in education, or from strong educational backgrounds seeking to shape the design of analytics/AI.
Core strengths that we expect from applicants with technical backgrounds:
- A Masters degree, Honours distinction or equivalent with above-average grades in computer science, mathematics, statistics, or equivalent
- Analytical, creative and innovative approach to solving problems
- Strong interest in designing and conducting quantitative, qualitative or mixed-method studies
- Knowledge and experience of natural language processing/text analytic
- Strong programming skills in at least one relevant language (e.g. C++, .NET, Java, Python)
- Experience with statistical and data mining, deep learning, or data science tools (e.g. R, Weka, Tensorflow, ProM, RapidMiner).
Core strengths that we expect from applicants with educational backgrounds:
- A Masters degree, Honours distinction or equivalent with above-average grades in teaching, educational theory, instructional design, learning sciences (ideally experience in the pedagogy and scholarship of writing)
- Knowledge and experience in Design-Based Research, or a related methodology for authentic design and evaluation of educational technologies
- Qualitative and quantitative data collection and analysis skills
It is advantageous if you can evidence:
- Skill in working with non-technical clients to involve them in the design and testing of software tools
- Peer-reviewed publications
- Design and Implementation of user-centred software
Interested candidates should contact the team to open a conversation: Ming.Liu@uts.edu.au; Simon.BuckinghamShum@uts.edu.au; Cherie.Lucas@uts.edu.au We will discuss your ideas to help you sharpen up your proposal, which will be competing with others for a scholarship. Please follow the application procedure for the submission of your proposal.
Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural Machine Translation by Jointly Learning to Align and Translate. In Proceedings of the 3rd International Conference on Learning Representations.
Buckingham Shum, S., Sándor, Á.,Goldsmith, R.,Bass R.,and McWilliams M.(2017). Towards Reflective Writing Analytics: Rationale, Methodology and Preliminary Results. Journal of Learning Analytics, 4, (1), 58–84.
Gibson, A., Aitken, A., Sándor, Á., Buckingham Shum, S., Tsingos-Lucas, C.,and Knight, S. (2017). Reflective Writing Analytics for Actionable Feedback. In Proceedings of LAK17: 7th International Conference on Learning Analytics & Knowledge.
Knight, S., Shibani, A. and Buckingham Shum, S. (2018). Augmenting Formative Writing Assessment with Learning Analytics: A Design Abstraction Approach. London Festival of Learning (ICLS/AIED/L@S Tri-Conference Crossover Track), London (June 2018).
Knight, S., Buckingham Shum, S., Ryan, P., Sándor, Á.,and Wang, X. (2018). Designing Academic Writing Analytics for Civil Law Student Self-Assessment. International Journal of Artificial Intelligence in Education, 28, (1), 1-28.
Liu, M., Buckingham Shum, S., Mantzourani, E. and Lucas, C. (2019). Evaluating Machine Learning Approaches to Classify Pharmacy Students’ Reflective Statements. Proceedings AIED2019: 20th International Conference on Artificial Intelligence in Education, June 25th – 29th 2019, Chicago, USA. Lecture Notes in Computer Science & Artificial Intelligence: Springer
Lucas, C., Gibson, A. and Buckingham Shum, S. (2018). Utilization of a novel online reflective learning tool for immediate formative feedback to assist pharmacy students’ reflective writing skills. American Journal of Pharmaceutical Education.
Lecun, Y., Bengio, Y.,and Hinton, G. (2015). Deep Learning. Nature, 521(7553): 436–444.
Rajpurkar, P., Zhang,J., Lopyrev, K., and Liang P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.
See, A., Liu, P. J., and ManningC. D. (2017). Get To The Point: Summarization with Pointer-Generator Networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.
Shibani, A., Knight, S., Buckingham Shum S. and Ryan, P. (2017). Design and Implementation of a Pedagogic Intervention Using Writing Analytics. In Proceedings of the 25th International Conference on Computers in Education. New Zealand: Asia-Pacific Society for Computers in Education.
Ullmann, T.D. (2019). Automated Analysis of Reflection in Writing: Validating Machine Learning Approaches. Int. J. Artif. Intell. Educ. 1–41.