PhD in Learning Analytics

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.

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 some completed PhD Dissertations — are you excited to produce something like this?…

  • 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, Monash University

  • Carlos Prieto (2016—2020). Engaging stakeholders in the learning analytics design process | CIC News | Now Senior Learning Technologist, The University of Sydney

“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 the 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 positioned strategically in the university.

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 human-centred participatory design of learning analytics tools (itself an active research topic), so you will need a passion for, and commitment to, working with non-technical users as you pilot 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.

Deadlines

Please read the UTS Application Essentials information to see the deadlines for different “Research Sessions”, which also vary depending on whether you are a domestic or international student.

However, we strongly encourage you to contact us much earlier than the deadline, to open discussions. If we see potential, we will give you feedback to help you sharpen your thinking and application. 

The UTS application form and further guidance on preparation and submission of your research proposal are on the UTS Research Degrees website.

Fees

Please go to the UTS Fees page to check the tuition fees that apply to the CIC PhD for the year you are interested in. This is in the Analytics & Data Science area, and the course code to enter is 02062. Note that full-time study is 3 years (= 6 sessions), but should be completed in 4 years (= 8 sessions).

Scholarships

The UTS Scholarships website gives you all the details.

Successful candidates will be eligible for a 3-year Scholarship of $38,854/pa for a full-time student (a substantial increase on the standard Australian PhD stipend of $28,854). To this, you may be able to add potential teaching income (e.g.. teaching 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.

Every UTS doctoral program has a ranking scheme for applicant strength, which takes into account your research track record to date. Highly ranked international students can compete for an International Research Training Program (IRTP) Scholarship and the UTS International Research Scholarships (UTS IRS).

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.

Eligibility

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.

Selection Criteria

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.

Applications

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:

Gabrielle.Gardiner@uts.edu.au

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:

1. Scroll down to “Lodge your application”
2. Click on the blue “Register and Apply” button
3. 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.

Work-Integrated Learning Analytics: Equipping Students for Employment Through Reflective, Data-Informed Narratives

Supervisors

The successful applicant will be supervised by Prof. Simon Buckingham Shum, who is a leading researcher in Learning Analytics, Associate Prof. Franziska Trede, a leading researcher in Work Integrated Learning, and Prof. Ruth Crick, who leads research into tracking lifelong learning competencies using Learning Power as part of Work Integrated Learning Design.

Contact the team to open a conversation

The Challenge

This PhD project offers the chance to work on a strategically critical challenge for higher education: How do we equip students for a workplace of unprecedented turbulence and complexity? It is now well established that completing lectures, labs and projects within the walls of a traditional university fails to prepare students adequately. Employers are calling for more ‘job ready’ graduates, and while graduates will always need a thorough grounding in disciplinary knowledge and skills for certain professions, in addition, they now need to demonstrate multiple intelligences beyond academic aptitude, plus a mindset that enables them to step into socially, politically, culturally complex workplaces, continuously learn on the job, and rapidly add value to teams.

It is here that Work Integrated Learning (WIL) has a vital contribution to make (see refs). WIL is an active, embodied, relational learning strategy that is situated in professional practice and includes some form of industry interactions. It prepares students for professional roles and responsibilities. As such WIL is a strategy to strengthen students’ employability.

However, powerful though WIL is when implemented effectively, a key missing ingredient is the ability for educators and students to track and assess WIL competencies rigorously. The exploding field of Learning Analytics (i.e. data science in education) offers exciting new ways to evidence competencies in authentic contexts outside the formal classroom (face-to-face or online). Data science and visualization can make goals, activities and reflections visible in new ways to students and educators, provoking productive student reflection on how they are developing as professionals. In tandem, institutional analytics can help to identify which programs at UTS are having the greatest impact on graduate employability.

Working within a strong ethical data and research framework, the project offers the chance to work with platforms including Learning Power (a form of dispositional learning analytics), AcaWriter (reflective writing analytics), and data from other relevant sources associated with WIL contexts.

Candidates

In addition to evidencing the skills and dispositions that we seek in all candidates (see CIC’s PhD homepage), specific to this project are the following criteria:

• A Masters degree including a dissertation, or a Bachelors degree with Honours distinction, or equivalent experience demonstrating capacity to conduct and write up your own investigation
• Knowledge and experience working in further or higher educational institutions, particularly in programs involving WIL
• Experience with quantitative research methodologies, data analysis or data science, and a willingness to learn new qualitative methodological skills
• Very strong interpersonal skills to enable you to work with diverse stakeholders, likely spanning academics, employers, and providers of analytics services
• Strong personal planning and project management skills

It will be advantageous if you can evidence any of the following skills/experience which equip you to bring a distinctive approach to the challenge:

• Web development
• Text analytics
• UI design
• Information visualization
• Human-centred design
• Knowledge of WIL theory and practice
• Familiarity with the Learning Analytics field (in particular any of the analytics approaches mentioned above)
• Confidence working with non-technical stakeholders to involve them in the design of data-driven software
• Peer-reviewed research publications / contributions to research-informed reports

References

Artess, J., Hooley, T. and Mellors-Bourne, R. (2017), Employability: A Review of the Literature 2012-2016. York: Higher Education Academy

Byrne, C. (2020, What determines perceived graduate employability? Exploring the effects of personal characteristics, academic achievements and graduate skills in a survey experiment. Studies in Higher Education, 1-18. https://doi.org/10.1080/03075079.2020.1735329

Burke C., Scurry T., Blenkinsopp J., Graley K. (2017), Critical Perspectives on Graduate Employability. In: Tomlinson M., Holmes L. (eds) Graduate Employability in Context. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-57168-7_4

Weiss F, Klein M, Grauenhorst T. (2014), The effects of work experience during higher education on labour market entry: learning by doing or an entry ticket? Work, Employment and Society. 28(5):788-807. https://doi.org/10.1177/0950017013506772

Analytics for Belonging: Harnessing the Potential of Learning Analytics to Help Students Feel Valued, Included, and Accepted at University

Supervisors

The successful applicant will be supervised by Prof. Simon Buckingham Shum, leading researcher in Learning Analytics, and Dr. Lisa-Angelique Lim, who specialises in the area of learning analytics approaches to feedback. Both supervisors are from the Connected Intelligence Centre (CIC).

We invite interested candidates to reach out to any of the supervisory team for a conversation.

The Challenge

Students’ sense of belonging has long been recognised as a critical ingredient of student engagement, which itself is strongly related to student success at all levels (Kahu & Nelson, 2018). Research has established that students with a stronger sense of belonging also demonstrate greater academic self-confidence, higher motivation, higher levels of academic engagement, and higher achievement (Thomas, 2012; Ahn & Davis, 2020).

What’s the challenge? With contemporary higher education being characterised by large enrolments, modularisation, and moving into online spaces, students can find it difficult to find a strong sense of belonging within the institution. At the level of the specific course being studied, students can also feel a sense of isolation as they learn together, but remotely. The sense of isolation presents a significant barrier for students to sustain their motivation and engagement for learning, which has been found to lead to withdrawal and a less than satisfying learning experience (Maunder, 2018; Pedler et al 2022; Thomas, 2012) . The challenge, therefore, is for academics to foster a sense of belonging in students, so that they can feel connected with their course, peers, and teaching staff, for a more optimal learning experience. Especially within – but not limited to – the context of online learning and large enrolment subjects, academics have limited time and interaction opportunities with students, which exacerbates this challenge.

It is here that learning analytics holds the potential to leverage learner data to facilitate academics’ understanding of students and their learning, in order to design feedback and support in a personalised manner to each and every student. This personalised communication, together with effective learning design, can foster teacher presence, which in turn has been found to foster student belonging. While developmental work in learning analytics has seen a plethora of technological solutions developed for personalised feedback and support, research and evidence on the use of analytics for fostering students’ sense of belonging is only now emerging (e.g., Benedict et al., 2022; Hunkins, Kelly, & D’Mello, 2022). To address this important research agenda, this project aims to explore how learning analytics can be harnessed to help academics leverage data to foster students’ sense of belonging in their studies.

Analytical approaches

OnTask is a personalised messaging system that leverages learning analytics. The tool works by facilitating academics to create personalised messages based on “if-this-then-that” rules. And because academics are the ones writing the message, students get a sense of that personal connection and belonging which might be hard to achieve when they feel that they are just one out of a thousand-strong cohort.

Examples of research and evidence around OnTask which this PhD will build upon, includes:

1. Lim, L.-A., Gentili, S., Pardo, A., Kovanović, V., Whitelock-Wainwright, A., Gašević, D., & Dawson, S. (2021). What changes, and for whom? A study of the impact of learning analytics-based process feedback in a large course. Learning and Instruction, 72, 101202. https://doi.org/10.1016/j.learninstruc.2019.04.003
2. Lim, L.-A., Dawson, S., Gašević, D., Joksimović, S., Pardo, A., Fudge, A., & Gentili, S. (2020). Students’ perceptions of, and emotional responses to, personalised LA-based feedback: An exploratory study of four courses. Assessment & Evaluation in Higher Education. https://doi.org/10.1080/02602938.2020.1782831
3. Lim, L.-A., Dawson, D., Gašević, D., Joksimović, S., Fudge, A., Pardo, A., & Gentili, S. (2020). Students’ sense-making of personalised feedback based on learning analytics. Australasian Journal of Educational Technology, 36(3), 15-33. https://doi.org/10.14742/ajet.6370
4. Lim, L.-A., Fudge, A. & Dawson, S. (2019). Feeling supported: Enabling students in diverse cohorts through personalised, data-informed feedback. In Y. W. Chew, K. M. Chan, and A. Alphonso (Eds.), Personalised Learning. Diverse Goals. One Heart. ASCILITE 2019 Singapore (pp. 206-215). https://2019conference.ascilite.org/assets/papers/Paper-111.pdf
5. Matcha, W., Gašević, D., Uzir, N. A. A., Jovanović, J., & Pardo, A. (2019). Analytics of Learning Strategies: Associations with Academic Performance and Feedback. In The 9th International Learning Analytics and Knowledge Conference (LAK19), March, 2019, Tempe, AZ, USA. (pp. 461-470). New York, NY: ACM. https://doi.org/10.1145/3303772.3303787
6. Pardo, A., Jovanovic, J., Dawson, S., Gašević, D., & Mirriahi, N. (2019). Using learning analytics to scale the provision of personalised feedback. British Journal of Educational Technology, 50(1), 128-138. https://doi.org/10.1111/bjet.12592

The above studies evaluating the impact of OnTask implementation in teaching contexts have found the following benefits:

• Enhanced the course experience
• Elicited a ‘safe anxiety’
• Spurred motivation
• Fostered greater feelings of support
• Fostered more optimal study habits
• Enhanced academic performance
• Supported all phases of self-regulated learning

Click here to read more about OnTask, and the research behind this learning analytics platform.

This PhD project presents an opportunity to work at the cutting edge of learning analytics research. Potential candidates will have the opportunity to work with other novel analytics-powered platforms such as AcaWriter and Learning Power. Importantly, candidates will learn how to embed ethical considerations into research and practice around the use of data-informed feedback and support to students. 

Candidates

In addition to evidencing the skills and dispositions that we seek in all candidates (see CIC’s PhD homepage), specific to this project are the following criteria:

• A Masters degree including a dissertation, or a Bachelors degree with Honours distinction, or equivalent experience demonstrating capacity to conduct and write up your own research investigation
• At least intermediate skills in quantitative research methodologies, data analysis or data science, and a willingness to learn new qualitative methodological skills
• Strong interpersonal skills to enable you to work with stakeholders such as academics and students
• Strong personal planning and project management skills

It will be advantageous if you can evidence any of the following skills/experience which equip you to bring a distinctive approach to the challenge:

• Experience in the higher education or teaching sector
• Programming ability
• Human-centred design
• Peer-reviewed research publications / contributions to research-informed reports

References

Ahn, M. Y., & Davis, H. H. (2020). Four domains of students’ sense of belonging to university. Studies in Higher Education, 45(3), 622-634. https://doi.org/10.1080/03075079.2018.1564902  

Benedict, A., Al-Hossami, E., Dorodchi, M., Benedict, A., & Wiktor, S. (2022). Pilot Recommender System Enabling Students to Indirectly Help Each Other and Foster Belonging Through Reflections. LAK22: 12th International Learning Analytics and Knowledge Conference, Online, USA. https://doi.org/10.1145/3506860.3506903 

Hunkins, N., Kelly, S., & D’Mello, S. (2022). “Beautiful work, you’re rock stars!”: Teacher Analytics to Uncover Discourse that Supports or Undermines Student Motivation, Identity, and Belonging in Classrooms. LAK22: 12th International Learning Analytics and Knowledge Conference, Online, USA. https://doi.org/10.1145/3506860.3506896 

Kahu, E. R., & Nelson, K. (2018). Student engagement in the educational interface: understanding the mechanisms of student success. Higher Education Research & Development, 37(1), 58-71. https://doi.org/10.1080/07294360.2017.1344197  

Maunder, R. E. (2018). Students’ peer relationships and their contribution to university adjustment: the need to belong in the university community. Journal of Further and Higher Education, 42(6), 756-768. https://doi.org/10.1080/0309877X.2017.1311996  

Pedler, M. L., Willis, R., & Nieuwoudt, J. E. (2022). A sense of belonging at university: Student retention, motivation and enjoyment. Journal of Further and Higher Education, 46(3), 397-408. https://doi.org/10.1080/0309877X.2021.1955844  

Thomas, L. (2012). Building student engagement and belonging in higher education at a time of change: A summary of findings and recommendations from the What works? Student retention & success programme. Paul Hamlyn Foundation. Retrieved from https://www.phf.org.uk/wp-content/uploads/2014/10/What-Works-Summary-report.pdf .