Home / Research / PhD in Learning Analytics

PhD in Learning Analytics

WelcomeUTS ContextToolsSkills & DispositionsScholarships & Applications

Welcome to the UTS:CIC Doctoral Program

Screen Shot 2015-07-21 at 2.53.05 pm

CIC’s doctoral program in Learning Analytics offers UTS Scholarships to start in Autumn and Spring sessions for domestic students (i.e. who do not require a visa). There is also the possibility for international applicants to apply, who if judged strong enough, will be supported by CIC to compete against applicants from across the university for an International Scholarship.

Application Deadlines for Domestic Students

SESSION CLOSING DATE NOTE
Autumn 2019 30 September 2018 For commencement January 2019
Spring 2019 30 April 2019 For commencement July 2019
Autumn 2020 30 September 2019 For commencement January 2020

Application Deadlines for International Students

SESSION CLOSING DATE NOTE
Autumn 2019 CLOSED For commencement January 2019
Spring 2019 15 January 2019 For commencement July 2019
Autumn 2020 30 June 2019 For commencement January 2020

CIC’s primary mission is to maximise the benefits of analytics for UTS teaching and learning. The Learning Analytics Doctoral Program 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’.

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.

UTS has been awarded a significant five stars result for excellence in eight out of eight categories of higher education by QS™ for 2014-2017. UTS is ranked 1st in Australia for universities under 50 years of age (8th globally), and is in the top 200 universities by the Times Higher Education World University Rankings which judges world class universities across all of their core missions – teaching, research, knowledge transfer and international outlook. [learn more]

CIC sits in UTS within the portfolio of 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 in a recent analysis. Data and analytics are a core enabler of the UTS vision for learning.futures, while other initiatives such as Assessment Futures pose new challenges for authentic assessment that analytics should help to tackle.

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 tools used in UTS such as Blackboard, SPARK, ReView and A.nnotate, as well as more experimental products such as Glyma and Declara, and prototypes like AWA, CLA and Compendium.  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.

Scholarships

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.

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 person requirements are as specified on the CIC PhD website.

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 in the CIC PhD website, 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 2018 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 Point 4 (“Lodge your application”)
  2. Click on the blue “Register and Apply” button
  3. When you reach the section asking for a course code, do not try to type anything in.  Instead, just search the course name and then select it.

Deadline

We want to appoint for the start of the Spring session 2018. Students may start the Spring session anytime from 1st July onwards to the census date, 25th August. We invite applications by close of 30 April 2018, with shortlisting for an interview shortly after. 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. The scholarship deadline for Spring domestic students is 30 April 2018.

PhD Topics

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.

1. Classroom Analytics2. Data interoperability and analytics for lifelong personalised learning3. Scaling Dispositional Learning Analytics4. Learning Analytics & Learning Design

Multimodal Learning Analytics in the Classroom

Supervisors

Roberto Martinez-Maldonado and Simon Buckingham Shum

The Challenge

The learning analytics challenge for this PhD is to research, prototype and evaluate approaches to automatically capture traces of students’ activity, using multimodal analytics techniques to make sense of data from heterogeneous contexts. Depending on the trajectory that you take, examples of the questions that such a project could investigate include:

  • How can multimodal analytics approaches be applied to gain a holistic understanding of students’ activity in authentic learning spaces?
  • How can the insights of students’ activity in physical spaces be connected with higher-level pedagogies?
  • How can these insights promote productive behavioural change?
  • How can the teacher be supported with this information to provide informed feedback?
  • How can learners and teachers be supported with data in the classroom?
  • What are the ethical implications of rolling out analytics in the classroom?
  • How can this information support more authentic and holistic assessment?
  • What are the technical challenges that need to be overcome?
  • How do learning theories and learning design patterns map to the orchestration of such analytics tools?

Analytic Approaches

We are seeking a PhD candidate interested in working on designing and connecting Multimodal Learning Analytics solutions according to the pedagogical needs and contextual constraints of learning occurring across physical and digital spaces. Providing continued support in the classroom, for mobile experiences and using web-based systems has been explored to different extents and each poses its own challenges. An overarching concern is how to integrate and coordinate learning analytics in a coherent way. Synergies with educational frameworks and theories already drawn on by the CIC team will be encouraged, such as Learning Power (Crick et al, 2015; CLARA tool) Epistemic Cognition, and science and technology studies of information infrastructure. The Connected Learning Analytics toolkit is another candidate infrastructure.

Addressing these questions should lead to educationally grounded machine learning techniques that give insight into heterogeneous activity traces (e.g. Martinez-Maldonado et al, 2018), and the design and evaluation of teacher and/or student-facing dashboards that provoke productive sensemaking, and inform action (e.g. Martinez-Maldonado et al, 2012). We invite your proposals as to which techniques might be best suited to this challenge.

You will work in close collaboration with ‘clients’ from other faculties/units in UTS, and potentially industry 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 CrossLAK website.

Examples that can help you understand the kind of research we are currently associated with this PhD topic include the following:

HealthSimLAK: Multimodal Learning Analytics meet Patient Manikins

High Performance Teamwork Analytics in Physical Spaces

Candidates

In addition to the skills and dispositions that we are seeking in all candidates, you should have:

  • A Masters degree, Honours distinction or equivalent with at least 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
  • Strong programming skills in at least one relevant language (e.g. C/C++, .NET, Java, Python, R, etc.)
  • Experience with data mining, data analytics or business intelligence tools (e.g. Weka, ProM, RapidMiner). Visualisation tools are a bonus.

It is advantageous if you can evidence:

  • Experience in designing and conducting quantitative, qualitative or mixed-method studies
  • Familiarity with educational theory, instructional design, learning sciences
  • Peer-reviewed publications
  • A digital scholarship profile
  • Design of user-centred software

Interested candidates should contact Roberto.Martinez-Maldonado@uts.edu.au and Simon.BuckinghamShum@uts.edu.au with informal queries. Please follow the application procedure for the submission of your proposal.

References

Aljohani, Naif R. and Davis, Hugh C. (2012) Learning analytics in mobile and ubiquitous learning environments. In Proceedings of the 11th World Conference on Mobile and Contextual Learning: mLearn 2012, Helsinki, Finland.

Deakin Crick, R., S. Huang, A. Ahmed-Shafi and C. Goldspink (2015). Developing Resilient Agency in Learning: The Internal Structure of Learning Power. British Journal of Educational Studies 63(2): 121- 160.

Kitto, Kirsty, Sebastian Cross, Zak Waters, and Mandy Lupton. (2015). Learning analytics beyond the LMS: the connected learning analytics toolkit. In Proceedings of the 5th International Conference on Learning Analytics And Knowledge, Poughkeepsie, New York: ACM, pp. 11-15

Martinez-Maldonado, R., Clayphan, A., Yacef, K. and Kay, J. (2015) MTFeedback: providing notifications to enhance teacher awareness of small group work in the classroom. IEEE Transactions on Learning Technologies, TLT, 8(2): 187-200

 Martinez-Maldonado, R., Kay, J., Buckingham Shum, S., and Yacef, K. (2017). Collocated Collaboration Analytics: Principles and Dilemmas for Mining Multimodal Interaction DataHuman-Computer Interaction, HCI, In Press.

Martinez-Maldonado, R., Yacef, K., Kay, J., and Schwendimann, B. (2012) An interactive teacher’s dashboard for monitoring multiple groups in a multi-tabletop learning environment.  International Conference on Intelligent Tutoring Systems, pages 482-492.

Data interoperability and analytics for lifelong personalised learning

Supervisors

Kirsty Kitto, Roberto Martinez-Maldonado and Simon Buckingham Shum

The Challenge

It is likely that people entering the workforce today will need to change jobs multiple times throughout their lifetime (CEDA, 2015). Many existing job roles are likely to be automated, but new roles in the workforce of the future are emerging all the time. Higher education is likely to be just the start of a person’s learning journey; many people will need to up-skill, re-skill and retrain throughout their careers. This means that already thorny problems like the recognition of prior learning are going to become key; how can we recognise existing skills, knowledge and competency when they come from a wide array of domains and environments?

Increasingly we see claims emerging that technology will help to personalise learning, building upon the existing strengths of a learner and helping them to bolster their weaknesses. Many Adaptive Learning systems are already in existence and build upon a long line of work in Intelligent Tutoring (Nye, Graesser, & Hu, 2014; Ma, Adesope, et al., 2014) and Recommendation systems built for EdTech (Manouselis, Drachsler, et al., 2011). These systems claim to identify existing weaknesses in learners and to then personalise the learning experience, providing an individual journey that is adapted specifically for them. But as Caulfield (2016) correctly claims: “we have personalisation backwards” if we are attempting to provide the same remedy for students who come from very different backgrounds. Many others have called attention to the long history of attempts to “optimize” learning (e.g. Watters, 2015; Kohn, 2016), pointing out that it does nothing to innovate on an “old-school model that consists of pouring a bunch o’ facts into empty receptacles” (Kohn, 2016). Also worrying, the loss of autonomy associated with a tool that tells students precisely what to do next leads to a loss of serendipity and will discourage the development of growth mindsets and an ability to thrive in situations of ambiguity and uncertainty (Deakin Crick et al, 2015). This PhD project will seek to tackle these problems head-on, by investigating ways in which technology solutions can be provided that help in the construction of personal learning journeys that help learners to build upon their existing knowledge, skills and backgrounds, and then demonstrate the achievement of capabilities and competencies that map into both formal educational systems and work-based selection criteria.

Underlying such a project, we require a way of providing the learner of the future with a Personal Learning Record Store (PLRS) that they can access and make use of for life. This project will seek to develop early prototypes of a PLRS that satisfies core use cases identified by you. It will be important to keep in mind the long-term legal, ethical, and social implications of a technology of this form, and so your project will be about more than just developing tech, you will need to keep the learner firmly in mind while solving core technical problems concerning  interoperability and learner facing learning analytics. Depending on the trajectory that you take, examples of the questions that such a project could investigate include:

  • What data needs to be stored in a PLRS in order to facilitate lifelong personalised learning pathways?
  • What form should a PLRS take to facilitate lifelong learning?
  • How could high level educationally relevant constructs be discovered from low-level clickstream data and then mapped to the attainment of key skills and competencies?
  • What new learning designs and patterns can be created to take advantage of the large amount of learning data stored in a PLRS?
  • How can xAPI profiles and recipes be used to ensure that learning data collected from multiple educational systems and workplaces are both syntactically and semantically interoperable in a PLRS?
  • What analytics would enable a learner to understand key weaknesses (and strengths) that are evidenced by the low-level data contained in their PLRS?
  • How can we map between identified curriculum documentation and the data stored in a PLRS?
  • What analytics will help lifelong learners to understand the data in their PLRS, and to order it appropriately?
  • How can selected data from a PLRS be pulled into e-Portfolios and curriculum vitae?

Analytic Approaches

The challenge of developing learning analytics for lifelong learning competencies is at a relatively early stage of development (Buckingham Shum & Deakin Crick, 2016). Early work with the Connected Learning Analytics (CLA) toolkit (Kitto, Cross et al., 2015, Bakharia, Kitto, et al., 2016) has demonstrated that it is possible to create interoperable data structures from apparently disparate data sources with careful consideration. This project will seek to extend those results by developing frameworks and use cases for personalised lifelong learning that take full advantage of the fact that learning can happen anywhere, at anytime, and in many different places.

Depending upon the emphasis that your research project develops, you will need to make use of emerging educational data standards such as xAPI (ADL, 2013) and IMS Caliper (IMS, 2015) and couple them with existing frameworks to ensure that PLRS data is interoperable despite being collected across many different places and contexts. A good place to start might involve investigating the way in which the xAPI concept of a Learning Record Store can be extended to enable individual learners to link them with existing organisational Information Systems (e.g. Student Information Systems, and Learning Management Systems). The World Wide Web Consortium’s (W3C) Resource Description Framework (RDF) Linked Data (LD) technology stack could also be used to ensure that concepts such as “course”, “award”, and “badge” map between example educational domains (e.g. Europe and Australia), which will enable data to travel between them as a learner moves between institutions from e.g. UTS to Oxford and then into an increasingly globalised workforce.

This project will help to progress our understanding of how we might be able to create an open source Learning Analytics API for any data stored in a PLRS that meets the requirements of specific identified xAPI recipes and profiles (ADL, 2016). This will help us to understand how learners might provide evidence for competency in 21st-century skills like “creativity” and “communication”, and other core graduate employability skills (Bridgstock,  2017) by pulling data from their PLRS. This project also offers an opportunity to work towards rethinking the way in which people might use extracurricular activities to add further weight to their claims of competency and achievement.

Candidates

In addition to the skills and dispositions that we are seeking in all candidates, you should have:

  • A Masters degree, Honours distinction or equivalent with at least above-average grades in computer science or equivalent
  • An analytical, creative and innovative approach to solving problems
  • A strong interest in data interoperability, linked data, SKOS, RDF, etc.
  • Strong programming skills in at least one relevant language (e.g. Python, C/C++, Java) and associated programming frameworks

It is advantageous if you can evidence:

  • Experience in designing APIs
  • Familiarity with at least one of Experience API (xAPI) and/or IMS Caliper
  • Experience with systems architecture and design
  • Peer-reviewed publications
  • A digital scholarship profile
  • Design of user-centred software

Interested candidates should contact Kirsty.Kitto@uts.edu.au and Roberto.Martinez-Maldonado@uts.edu.au with informal queries. Please follow the application procedure for the submission of your proposal.

References

ADL. (2013). xAPI-Spec. https://github.com/adlnet/xAPI-Spec, version 1.0.3 accessed 11/4/2017

ADL. (2016). Companion Specification for xAPI Vocabularies, https://adl.gitbooks.io/companion-specification-for-xapi-vocabularies/content/ , version 1.0 accessed 11/4/2017

Bakharia, A., Kitto, K., Pardo, A., Gašević, D., & Dawson, S. (2016, April). Recipe for success: lessons learnt from using xAPI within the connected learning analytics toolkit. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 378-382). ACM.

Bridgstock, Ruth (2017). Graduate employability 2.0: Social Networks for learning, career development and innovation in the digital age. Available at:  http://www.graduateemployability2-0.com/

Buckingham Shum, S., & Deakin Crick, R. (2016). Learning analytics for 21st century competencies. Journal of Learning Analytics, 3(2), 6–21.  http://dx.doi.org/10.18608/jla.2016.32.2

Caulfield, Mike (2016) We have personalization backwards,  http://mfeldstein.com/we-have-personalization-backwards/

CEDA. (2015). Australia’s future workforce? Technical report, Committee for Economic Development of Australia (CEDA). http://www.ceda.com.au/research­and­policy/policy­priorities/workforce.

IMS. (2015). Caliper Analytics, http://www.imsglobal.org/activity/caliperram

Kitto, Kirsty, Sebastian Cross, Zak Waters, and Mandy Lupton. (2015). Learning analytics beyond the LMS: the connected learning analytics toolkit. In Proceedings of the 5th International Conference on Learning Analytics And Knowledge, Poughkeepsie, New York: ACM, pp. 11-15

Kitto, K., Lupton, M., Davis, K & Waters, Z.(2016). Incorporating student-facing learning analytics into pedagogical practice. In S. Barker, S. Dawson, A. Pardo, & C. Colvin (Eds.), Show Me The Learning. Proceedings ASCILITE 2016 Adelaide (pp. 338-347)

Kohn, Alfie (2016). The overselling of Education Technology, Edsurge: https://www.edsurge.com/news/2016-03-16-the-overselling-of-education-technology

Ma, W., Adesope, O., Nesbit, J.,  Liu, and Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901-918.

Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., & Koper, R. (2011). Recommender systems in technology enhanced learning. In Recommender systems handbook (pp. 387-415). Springer US.

Nye, B. D., Graesser, A. C., & Hu, X. (2014). AutoTutor and family: A review of 17 years of natural language tutoring. International Journal of Artificial Intelligence in Education, 24(4), 427-469.

Sharples, Mike, and Jeremy Roschelle. (2010). Guest editorial: Special section on mobile and ubiquitous technologies for learning. IEEE Transactions on Learning Technologies, (1), pp. 4-6.

Watters, Audrey (2015). The algorithmic future of education. http://hackeducation.com/2015/10/22/robot-tutors

Scaling Dispositional Learning Analytics

Supervisors

Simon Buckingham Shum (UTS:CIC) &  and Ruth Crick

The Challenge

Complex problem solving, critical thinking and creativity are the three most important capabilities for thriving in the ‘Fourth Industrial Revolution’.  Improving these capabilities requires real-world, purposeful contexts, the ability to work across silos, and new measurement models with data science making possible new kinds of analytics.

A strategic educational response to a world of constant change is to focus explicitly on nurturing the personal qualities, assessed under authentic conditions, that equip learners to cope with novel, complex situations. Thus, even if we do not know what the future holds, we can be better equipped for the only thing we can be sure of — change. The qualities that learners need have thus been dubbed “21st century” in nature — not because they were of no use before (although they may take novel forms today) — but because of their central importance in times of turbulence, in a jobs marketplace where routine cognitive work will be increasingly automated. A specific quality that has attracted significant interest are Learning Dispositions, also referred to as Mindsets, sometimes referred to as “non-cognitive skills” (e.g. Gutman and Schoon, 2013).

There is an established body of evidence on how learners’ dispositions and mindsets impact engagement (Deakin Crick et al. 2015; Dweck, 2006), driving efforts to develop practical formative assessment tools. Deakin Crick, et al. (2015) report progress in a 15-year research program defining a multi-dimensional construct termed “learning power,” focusing on the evidence for and relationships between a set of malleable learning dispositions (rather than skills), namely: Mindful Agency, Sensemaking, Creativity, Curiosity, Belonging, Collaboration, Hope and Optimism, and Orientation to Learning. (CLARA, 2016).

Analytics Approaches

We refer to this class of approach as Dispositional Learning Analytics (DLA) (Buckingham Shum & Deakin Crick, 2012). Dispositions such as these may be assessable through conventional means (teacher observation, student self-report), or more recently, analysing student behavioural trace data from digital platforms.

At UTS we have been piloting the Crick Learning for Resilient Agency (CLARA) self-diagnostic tool, developed from a 15 year educational research program by Ruth Crick. To date this has been on a small scale through ‘boutique’ deployments as part of experimental innovation pilots in courses, in partnership with early adopter academics, creating a corpus of about 3000 student profiles. In this methodology, CLARA does not exist in a vacuum, but is intended to be the springboard for learners to define their own enquiry-based learning journey — a process which itself requires scaffolding.

As DLA platforms evolve into mobile, personalised analytics tools (analogous to fitness, dieting and other lifelogging apps), new possibilities, and challenges, for scaling DLA emerge:

  • Scaling the number of learners by making the software available on commodity mobile devices
  • Scaling personalised support to students for making sense of the diagnostic feedback, and navigating their enquiry project, through the use of AI techniques such as user modelling and chatbots (previously one required access to a scarce, expensive human coach)
  • Scaling the computational power that can be applied to the dataset, and related datasets, in order to generate feedback for learners, educators and leaders.
  • Scaling personalised scaffolding for educators wishing to integrate DLA into their teaching.

Research Question

An overarching concern is how to integrate and coordinate learning analytics in a coherent way focusing on the learner’s journey – key events, dispositions and needs. A rich array of research questions emerges around these challenges, e.g.

  • What other data is it productive to connect to CLARA or enquiry learning journey data, and which statistical or AI approaches yield insights?
  • Can we generate intelligible DLA feedback from learners’ behavioural traces (what they do with digital tools), rather than rely solely on self-report?
  • What are the requirements to support rapid educational improvement cycles, and what coverage of the design space do current platforms provide?

As with all CIC PhDs, this research program will be in partnership with one or more UTS academics to evaluate learning analytic holistically in the UTS context. Additionally, this PhD is a collaboration between CIC and Learning Emergence, a learning analytics start-up spun out of the educational research underpinning CLARA. The goal is to investigate research questions of mutual interest, such as the above, but with academic freedom assured. Learning Emergence will be providing access to the analytics powering their digital learning infrastructure, and to suitably de-identified data for cohort analysis. The international network of leading edge researchers and practitioners that Learning Emergence brings is an added attraction for a PhD candidate on this project.

We invite 4 page proposals demonstrating how you would scope and tackle this project in order to advance DLA in UTS, but with the potential to impact how dispositional data is gathered and analysed far beyond.

Candidates

In addition to the skills and dispositions that we are seeking in all candidates (see CIC guidelines), specifically for this project you should bring:

  • A Masters degree, or Bachelors Honours with distinction, in education, the learning sciences, statistics, or computer/data sciences (ideally a combination of disciplines spanning “learning” and “analytics”)
  • Analytical strength in statistics or other data science methods
  • Fluency in one or more statistical/data science software tools, ideally open source (e.g. R, Python)
  • An understanding of issues surrounding pedagogy, assessment, and data analysis
  • Strong interest in designing and conducting quantitative, qualitative or mixed-method studies involving learners and educators

It is advantageous if you can evidence:

  • Software coding skills
  • Experience in text analytics
  • Experience in the design of user-centred software and/or information visualisations
  • A strong interest, and ideally experience, in at least one learning analytics technique that you want to consider developing in this PhD
  • Peer-reviewed publications
  • A digital scholarship profile
  • Experience in designing and conducting quantitative, qualitative or mixed-method studies

Interested candidates are strongly encouraged to contact Ruth.Crick@uts.edu.au and Simon.BuckinghamShum@uts.edu.au to discuss ideas informally. We aim to give you feedback on your suitability and on draft proposals.

Please follow the application procedure for the formal submission of your proposal.

References

Buckingham Shum, S. and Deakin Crick, R. (2012). Learning dispositions and transferable competencies: Pedagogy, modelling and learning analytics. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ʼ12), 29 April–2 May 2012, Vancouver, BC, Canada (pp. 92–101). New York: ACM. doi: http://dx.doi.org/10.1145/2330601.2330629 (replay and open access eprint)

Buckingham Shum, S. and Deakin Crick, R. (2016). Learning analytics for 21st century competencies. Journal of Learning Analytics, 3(2), 6–21. http://dx.doi.org/10.18608/jla.2016.32.2

CLARA. (2016). The Crick Learning for Resilient Agency (CLARA) Survey Resource Website. Learning Emergence LLC, UK. http://clara.learningemergence.com

Deakin Crick, R. (2017). Learning Analytics: Layers, Loops and Processes in a Virtual Learning Infrastructure, in G. Siemens & C. Lang (eds), Handbook of Learning Analytics, 1st Edn, Society for Learning Analytics Research, pp. 291-307. https://solaresearch.org/hla-17

Deakin Crick, R., Huang, S., Ahmed-Shafi, A., & Goldspink, C. (2015). Developing resilient agency in learning: The internal structure of learning power. British Journal of Educational Studies, 63(2), 121–160. http://dx.doi.org/10.1080/00071005.2015.1006574

Dispositional Learning Analytics workshop, Learning Analytics Summer Institute, Stanford University, 2013: http://learningemergence.net/events/lasi-dla-wkshp

Dweck, C. (2006). Mindset: The new psychology of success. New York: Random House.

Gutman, L.M. and Schoon, I. (2013). The impact of non-cognitive skills on outcomes for young people: literature review. University of London, Institute of Education/Education Endowment Fund. https://educationendowmentfoundation.org.uk/public/files/Publications/EEF_Lit_Review_Non-CognitiveSkills.pdf

 

 

Aligning Student-Facing Learning Analytics with Learning Designs

Kirsty Kitto, Roberto Martinez Maldonado and Simon Buckingham Shum (UTS:CIC)

The Challenge

A growing number of educational technology products and research prototypes are developing student-facing dashboards, intended to provide students with actionable insights into their progress (Schwendimann, Rodriguez-Triana et al., 2017). The state of the art is, therefore, at an immature stage of development. Few dashboards are grounded in educational principles (Jivet et al., 2018), and they are rarely integrated with learning design and assessment, to ensure that use of the dashboard fits coherently into the student activity (Kitto et al., 2017).

Lockyer, Heathcote, and Dawson (2013) contrast checkpoint analytics with process analytics. The former are rarely coupled with pedagogical approaches, and often consist of a one-step process: students engage in a class activity, analytics are made available to inform them about their participation, but students are not required to engage with or respond to this feedback in any way. In contrast, process analytics are designed to provide specific insights about how a student has engaged in a task. To date, few studies have investigated how students actually make use of student facing analytics (either checkpoint or process). One study conducted by Corrin and de Barbara (2014), demonstrated that students often fail to understand simple reports, and another by Khan and Pardo (2016) showed that while they often approach checkpoint analytics with initial interest, this quickly tails off. In other words, the failure to link the analytics to learning design means that students often fail to see what they mean to them.

We have been seeking to address these challenges in our recent work. Kitto, et. al, (2016, 2017) have demonstrated how to link student-facing analytics to learning design by requiring students to act on the dashboard feedback. Echeverria, et. al (2018) explore how dashboards can be contextualised to specific activities, by enhancing process visualisations with Data Storytelling features, that focus attention on the most important aspects of their activity, as defined by the teacher’s intended learning design. Martinez-Maldonado et al (2016; 2018) consider the state of the art and challenges in orchestrating collocated collaboration analytics with learning designs.

In this PhD project you will be challenged to develop new ways in which student facing analytics can be developed to help students navigate complex learning tasks, by focusing attention on learning-to-learn more effectively (Buckingham Shum & Crick, 2017):

  • uncovering information about how they behave in different learning tasks
  • reflecting on their weaknesses and strengths as they approach different problems
  • developing stronger metacognitive skills
  • reflecting on their practice and improve
  • improving their data literacy

In the future, could we imagine dashboards adapting to student activity, or changing each week to reflect each new assignment? What technical and usability challenges might this raise? We want to know in what directions you would take this project.

Analytics Approaches

A key requirement when selecting analytics approaches is that we seek to build higher order skills for learning-to-learn. How will the feedback you design equip students in this way? All PhD research in CIC is in partnership with one or more academics in UTS who will deploy the analytics tool with their students. Depending on the collaborations that we forge, the kind of learning activity must obviously match the kind of analytics feedback provided. Your expertise in specific techniques will obviously be a factor, e.g. any of those already in use in CIC (ensure you have browsed these), or new ones. Depending on how the project develops, there may be scope to integrate your analytics into existing student facing platforms, else to deploy a research prototype.

Methodologically, this project could cover a wide variety, according to your interests and prior skills. Candidates include protocols for lab-based experimental studies, field trials in classrooms, qualitative analysis of transcripts. There is no doubt that you will engage in quantitative analysis of digital traces left by learners, candidate methods for this include statistical and machine learning methodologies. In your proposal, you should consider which you would consider relevant, and whether you bring, or require training in, these methods.

Candidates

In addition to the skills and dispositions that we are seeking in all candidates (see guidelines), specifically for this project you should bring:

  • A Masters degree, Honours distinction or equivalent, with at least above-average grades, in education, the learning sciences, computer/data sciences
  • An analytical, creative and innovative approach to solving problems
  • An understanding of issues surrounding pedagogy, assessment, and data analysis
  • Strong interest in designing and conducting quantitative, qualitative or mixed-method studies

It is advantageous if you can evidence:

  • Software coding skills for dashboard implementation
  • Experience in the design of user-centred software and/or information visualisations
  • A strong interest, and ideally experience, in at least one learning analytics technique that you want to consider developing in this PhD
  • Peer-reviewed publications
  • A digital scholarship profile
  • Experience in designing and conducting quantitative, qualitative or mixed-method studies

All members of the supervision team are actively engaged in this topic, and well connected to the international research community, making this the ideal project for a PhD on this topic. Interested candidates are strongly encouraged to contact Kirsty.Kitto@uts.edu.au and Roberto.Martinez-Maldonado@uts.edu.au to discuss ideas informally. We aim to give you feedback on your suitability and on draft proposals.

Please follow the application procedure for the formal submission of your proposal.

References

Bakharia, A., Kitto, K., Pardo, A., Gašević, D., Dawson, S. (2016), Recipe for success: lessons learnt from using xAPI within the connected learning analytics toolkit. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (LAK16). ACM, New York, NY, USA, 378-382. [Open Access ePrint]

Bakharia, A., Corrin, L., de Barba, P., Kennedy, G., Gašević, D., Mulder, R., … Lockyer, L. (2016). A conceptual framework linking learning design with learning analytics. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, Edinburgh, 329-338.

Buckingham Shum, S. and Crick, R. (Eds.) (2016). Learning Analytics for 21st Century Competencies. Special Issue: Journal of Learning Analytics, 3 (2), 6-212. http://learning-analytics.info/journals/index.php/JLA/issue/view/381

Corrin, L., & de Barba, P. (2014). Exploring students’ interpretation of feedback delivered through learning analytics dashboards. Proceedings of the ASCILITE 2014 Conference, Otago.

Echeverria, V., Martinez-Maldonado, R.,  Granda, R., Chiluiza, K., Conati, C., and Buckingham Shum, S. (2018) Driving Data Storytelling from Learning Design, In Proceedings of the Eighth International Learning Analytics & Knowledge Conference (LAK ’18). ACM, New York, NY, USA. https://doi.org/10.1145/3170358.3170380 [Open Access ePrint]

Gibson, A., Kitto, K., Bruza, P. (2016). Towards the Discovery of Learner Metacognition From Reflective Writing. Journal of Learning Analytics, 3(2), 22-36. http://dx.doi.org/10.18608/jla.2016.32.3

Jivet, I., Scheffel, M., Specht, M. and Drachsler, H. (2018). License to evaluate: Preparing learning analytics dashboards for educational practice. In Proceedings of International Conference on Learning Analytics and Knowledge, Sydney, NSW, Australia, March 7–9, 2018 (LAK ’18), 10 pages. https://doi.org/10.1145/3170358.3170421

Khan, I., & Pardo, A. (2016). Data2U: Scalable real time student feedback in active learning environments. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, Edinburgh, 249-253.

Kitto, K., Buckingham Shum, S., Gibson, A., (2018). Embracing Imperfection in Learning Analytics, In Proceedings of the Eighth International Learning Analytics & Knowledge Conference (LAK ’18). ACM, New York, NY, USA, In press (Accepted 21/11/2017). [Open Access ePrint]

Kitto, K., Lupton, M., Davis, K., Waters, Z. (2017). Designing for Student Facing Learning Analytics, Australasian Journal of Educational Technology, 33(5), 152-168. [Open Access ePrint]

Kitto, K., Lupton, M., Davis, K., Waters, Z. (2016). Incorporating student-facing learning analytics into pedagogical practice. In S. Barker, S. Dawson, A. Pardo, & C. Colvin (Eds.), Show Me The Learning. Proceedings ASCILITE 2016 Adelaide, pp. 338-347. [Open Access ePrint]

Kitto, K., Cross, S., Waters, Z., Lupton, M. (2015). Learning Analytics beyond the LMS: the Connected Learning Analytics Toolkit. In Proceedings of the Fifth International Conference on Learning Analytics and Knowledge (LAK15). ACM, New York, NY, USA, 11-15. [Open Access ePrint]

Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing pedagogical action: Aligning learning analytics with learning design. American Behavioral Scientist, 57(10) 1439–1459. [Open Access ePrint]

Martinez-Maldonado, R., Buckingham Shum, S., Schneider, B., Charleer, S., Klerkx, J., and Duval, E. (2017). Learning Analytics for Natural User InterfacesJournal of Learning Analytics, 4(1), (March 2017): 24-57.

Martinez-Maldonado, R., Kay, J., Buckingham Shum, S., and Yacef, K. (2018, In Press). Collocated Collaboration Analytics: Principles and Dilemmas for Mining Multimodal Interaction DataHuman-Computer Interaction.

Schwendimann, B. A., Rodriguez-Triana, M. J., Vozniuk, A., Prieto, L. P., Boroujeni, M. S., Holzer, A., Gillet D,  & Dillenbourg, P. (2017). Perceiving learning at a glance: A systematic literature review of learning dashboard research. IEEE Transactions on Learning Technologies, 10(1), 30-41.

Wise, A. F. (2014). Designing pedagogical interventions to support student use of learning analytics. Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, Indianapolis, IN, 203-211. https://doi.org/10.1145/2567574.2567588

 

Top