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PhD – Learning Analytics

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Welcome to the UTS:CIC Doctoral Program

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We are delighted to announce the launch of CIC’s doctoral program in Learning Analytics, offering three UTS Scholarships to begin your research at the start of 2016.

CIC’s mission is to invent, evaluate and theorise the design of human-centered data science and learning analytics to advance the UTS Teaching & Learning program. As you will see from our projects, publications and the three PhD topics advertised, a core theme is analytics techniques to nurture in learners the creative, critical, sensemaking qualities needed for lifelong learning, employment and citizenship in a complex, data-saturated society.

While our first priority is the future of learning and teaching, we also anticipate broader applications of the tools we develop, to address data science challenges in other research fields and in UTS business operations.

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 2014-2016. UTS is ranked 1st in Australia and 21st globally in the Times Higher Education top 100 universities under 50 years of age, and is in the top 250 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 Prof. 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 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.


Three successful candidates will be eligible for a 3-year Scholarship of $35,000/pa (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 our 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 fees covered by the Australian government, and international students will receive a UTS International Research Scholarship covering tuition fees. 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 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 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 2016 Application in the subject line, to:

Georgia Markakis <[email protected]>


We aim to appoint for August 2016, the start of the second semester. We invite applications by close of Sunday 1st May 2016, with shortlisting for interview shortly after. However, there is an advantage to contacting us earlier to open discussions: you are 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.

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 30 October deadline does not apply to these Scholarships.

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.

LA Across Digital and Physical SpacesLA for Writing Practices3. Human-Centred-Design for Learning Analytics

Learning Analytics Across Digital and Physical Spaces


Roberto Martinez-Maldonado and Simon Buckingham Shum

The Challenge

While in blended learning we deploy a variety of educational technologies and pedagogical resources for online and face-to-face settings, as we build the qualities needed for lifelong learning, and increasingly authentic assessment within formal education, learner activities must necessarily span spaces and moments beyond formal educational contexts and tools (Kloss et al., 2012, Sharples & Roschelle, 2010).

The learning analytics challenge for this PhD is to research, prototype and evaluate approaches to automatically capture traces of students’ activity, using 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 the insights of students’ activity across 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 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 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 (see PhD Topic 2), and science and technology studies of information infrastructure (PhD Topic 3). 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, 2013), 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.


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-centered software

Interested candidates should contact [email protected] and [email protected] with informal queries. Please follow the application procedure for the submission of your proposal.


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.

Delgado Kloos, Carlos, D. Hernández-Leo, and J. I. Asensio-Pérez. (2012). Technology for Learning across Physical and Virtual Spaces: Special Issue. Journal of Universal Computer Science, 18(15), pp. 2093-2096.

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. and Yacef, K. (2013) An automatic approach for mining patterns of collaboration around an interactive tabletop.  International Conference on Artificial Intelligence in Education, AIED 2013, pages 101-110.

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.

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.

Learning Analytics for Writing Practices


Simon Knight and Simon Buckingham Shum

The Challenge

Literacy, including the abilities to comprehend rich multimedia, and effectively communicate through written texts, are key to learning, and full participation in society (OECD, 2013; OECD & Statistics Canada, 2010). In everyday contexts, for example, parents need to understand their child’s vaccination needs; voters want to weigh up the claims of politicians on climate change; and students want to assess the relative weight of argument around the causes of some historic event.

In each case, information seekers require more than just the ability to read content; they must make decisions about where to look for information, which sources to select (and corroborate), how to synthesise (sometimes competing) claims from diverse sources. In an educational, business or other work context, they must typically produce a high quality written synthesis making an argument for a point of view, or decision.

The learning analytics challenge is to research, design and evaluate techniques to make sense of the data traces produced from search, discourse and writing. These should illuminate the relationships between the above activities, and provide feedback for learners and educators that can inform productive reflection and action.

Analytic Approaches

One class of research into the sorts of “literacy practices” introduced above has studied multiple document processing (MDP), the ability to read, comprehend and integrate information from across sources (see, for examples, Bråten, 2008; Bråten, Britt, Strømsø, & Rouet, 2011; Ferguson, 2014; Foltz, Britt, & Perfetti, 1996; Goldman et al., 2011; Hastings, Hughes, Magliano, Goldman, & Lawless, 2012; Kobayashi, 2014; Rouet & Britt, 2011). We are particularly interested in research viewing these behaviours through the lens of epistemic cognition – beliefs about the certainty, simplicity, source, and justification of knowledge (see, for examples, Bråten, 2008; Bråten et al., 2011; Ferguson, 2014).

Across this work, analysis of processes and products of writing have emerged. Emerging language technologies raise the potential for work on the detection of features in output texts that are related to features of literacy and high-level epistemic cognition. For example, analysis of the written outputs for: rhetorical parsing to detect typical scholarly moves (see, for example, de Waard, Buitelaar, & Eigner, 2009; Groza, Handschuh, & Bordea, 2010; Simsek, Buckingham Shum, Sandor, De Liddo, & Ferguson, 2013); text cohesion (McNamara, Louwerse, McCarthy, & Graesser, 2010); and topic coverage (see, for example, Hastings et al., 2012).

Proposals are welcomed that address the use of writing-practice based learning analytics to support student learning. Writing practices, here, are broadly construed to include activities such as: Information seeking; reading; annotation; writing itself (both the process and the output); and self and peer assessment. We invite proposals that address the kinds of: experimental paradigms, such as the MDP tasks, to investigate student writing; analytic techniques to explore semantic and meta-discourse properties of written outputs, and their relation to source documents (see discussion above); analysis of writing processes, including temporal analyses (further resources); and assessment tools to explore the best methods for feedback and constructive peer and self-assessment, or calibrated peer review (Balfour, 2013). We also welcome proposals with a focus on collaborative knowledge practices, including: co-writing; the giving of constructive formative feedback; and joint enterprise on the writing practices described above.

This PhD will contribute intellectually and technically to the ongoing research program being developed around the Academic Writing Analytics platform. 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.


Proposals are welcomed from candidates with a range of backgrounds and skills. We welcome proposals from language technologists, computational linguists and other computer or information science backgrounds, and from those with backgrounds in education, psychology, or related social science disciplines. All proposals should be trans-disciplinary in nature; orient your proposal to your particular strengths and interests, within the CIC context as a technology and innovation directed centre aiming at impact on student learning.

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

  • A higher degree in a computer, information, or learning science discipline, other relevant social science subject, or computational linguistics
  • Demonstrated knowledge of educational contexts (through applying work in education, etc.)
  • Previous experience using computational language technologies

It is advantageous if you can evidence:

  • Previous research experience e.g. at Masters level
  • Knowledge of, or willingness to learn, a relevant programming language (e.g. R, Python)
  • Experience in designing and conducting quantitative, qualitative or mixed-method studies
  • Peer reviewed publications
  • A digital scholarship profile
  • Design of user-centered software

Interested candidates should contact [email protected]. and [email protected] with informal queries. Please follow the application procedure for the submission of your proposal.


Balfour, S. P. (2013). Assessing writing in MOOCs: Automated essay scoring and calibrated peer review. Research & Practice in Assessment, 8(1), 40–48.

Bråten, I. (2008). Personal Epistemology, Understanding of Multiple Texts, and Learning Within Internet Technologies. In M. S. Khine (Ed.), Knowing, Knowledge and Beliefs (pp. 351–376). Dordrecht: Springer Netherlands. Retrieved from http://www.springerlink.com/content/j664674514614405/

Bråten, I., Britt, M. A., Strømsø, H. I., & Rouet, J.-F. (2011). The role of epistemic beliefs in the comprehension of multiple expository texts: Toward an integrated model. Educational Psychologist, 46(1), 48–70. http://doi.org/10.1080/00461520.2011.538647

de Waard, A., Buitelaar, P., & Eigner, T. (2009). Identifying the epistemic value of discourse segments in biology texts. In Proceedings of the Eighth International Conference on Computational Semantics (pp. 351–354). Stroudsburg, PA, USA: Association for Computational Linguistics. Retrieved from http://dl.acm.org/citation.cfm?id=1693756.1693802

Ferguson, L. E. (2014). Epistemic Beliefs and Their Relation to Multiple-Text Comprehension: A Norwegian Program of Research. Scandinavian Journal of Educational Research, 0(0), 1–22. http://doi.org/10.1080/00313831.2014.971863

Foltz, P. W., Britt, M. A., & Perfetti, C. A. (1996). Reasoning from multiple texts: An automatic analysis of readers’ situation models. In G. W. Cottrell (Ed.), Proceedings of the 18th Annual Cognitive Science Conference (pp. 110–115). Lawrence Erlbaum, NJ. Retrieved from http://www-psych.nmsu.edu/~pfoltz/reprints/cogsci96.html

Goldman, S. R., Ozuru, Y., Braasch, J. L. G., Manning, F. H., Lawless, K. A., Gomez, K. W., & Slanovits, M. (2011). Literacies for learning: A multiple source comprehension illustration. In N. Stein L. & S. Raudenbush (Eds.), Developmental science goes to school: Implications for policy and practice (pp. 30–44). Abingdon, Oxon: Routledge.

Groza, T., Handschuh, S., & Bordea, G. (2010). Towards automatic extraction of epistemic items from scientific publications. In Proceedings of the 2010 ACM Symposium on Applied Computing (pp. 1341–1348). New York, NY, USA: ACM. http://doi.org/10.1145/1774088.1774377

Hastings, P., Hughes, S., Magliano, J. P., Goldman, S. R., & Lawless, K. (2012). Assessing the use of multiple sources in student essays. Behavior Research Methods, 44(3), 622–633. http://doi.org/10.3758/s13428-012-0214-0

Kobayashi, K. (2014). Students’ consideration of source information during the reading of multiple texts and its effect on intertextual conflict resolution. Instructional Science, 42(2), 183–205.

McNamara, D. S., Louwerse, M. M., McCarthy, P. M., & Graesser, A. C. (2010). Coh-Metrix: Capturing Linguistic Features of Cohesion. Discourse Processes, 47(4), 292–330. http://doi.org/10.1080/01638530902959943

OECD. (2013). PISA 2015: Draft reading literacy framework. OECD Publishing. Retrieved from http://www.oecd.org/pisa/pisaproducts/Draft PISA 2015 Reading Framework .pdf

OECD, & Statistics Canada. (2010). Literacy in the Information Age – Final Report of the International Adult Literacy Survey. OECD. Retrieved from http://www.oecd.org/edu/skills-beyond-school/41529765.pdf

Rouet, J.-F., & Britt, M. A. (2011). Relevance processes in multiple document comprehension. In M. T. McCrudden, J. P. Magliano, & G. Schraw (Eds.), Text relevance and learning from text (pp. 19–52). Information Age Publishing (IAP). Retrieved from http://www.niu.edu/britt/recent_papers/pdfs/RouetBritt_chapter_for_McCrudden.pdf

Simsek, D., S. Buckingham Shum, Á. Sándor, A. D. Liddo and R. Ferguson (2013). XIP Dashboard: Visual Analytics from Automated Rhetorical Parsing of Scientific Metadiscourse. 1st International Workshop on Discourse-Centric Learning Analytics, 3rd International Conference on Learning Analytics & Knowledge, Leuven, BE (Apr. 8-12, 2013). . Open Access Eprint: http://oro.open.ac.uk/37391


Human-Centred-Design for Learning Analytics


Theresa Anderson, Simon Buckingham Shum and Ruth Crick

The Challenge

We are in the midst of a profound shift to an algorithmically pervaded society, with learning analytics (as a research field and marketplace) offering a stage for how this will play out in education. In this PhD, we seek a candidate who wants to engage with the debate on how socio-technical infrastructures deliver computational intelligence in society in accountable, ethical ways, in order to enhance rather than erase human agency.

This debate needs to be contextualised to education, and specifically learning analytics design and research — not only because algorithms are central to analytics, but because agency is central to learning. For example, learning analytics technologies provoke concerns on the part of some educators, who fear that an algorithmic mindset is incompatible with one that values the qualities and processes associated with agency: creativity, critical thinking, community, deep learning. For others, analytics provide the exact opposite: the chance to make such learning processes (not just products) a quality which can be evidenced rigorously for the first time.

This PhD will engage with this debate, exploring to what extent the concerns are justified, whether the critiques can be addressed through better design processes and software, and the state of the art in using analytics to enhance rather than reduce learner agency and mindfulness.

Analytic Approaches

Knight and Buckingham Shum (In Press) note some of the discourse now emerging around algorithms and agency in society at large. We can begin to contextualise these to learning analytics.

Data — especially “Big Data” — has a misleading aura of completeness around it. This must be subjected to critical scepticism and questioning if it is to serve as a societal good (boyd & Crawford, 2011). The curation of large datasets invariably requires human effort and when examined, is replete with compromises and limitations at odds with the dominant rhetoric of objectivity (Leonelli, 2014).

Learning analytics as scientific infrastructures. The design of an analytics lifecycle, from data capture to analysis, rendering, interpretation and action, is pervaded with human judgements and intentions. In the historical study of scientific infrastructures, we recognise that, “Classification systems provide both a warrant and a tool for forgetting… what to forget and how to forget it… The argument comes down to asking not only what gets coded in but what gets coded out of a given scheme” (Bowker & Star, 1999, pp. 277–279). Thus: “raw data is both an oxymoron and a bad idea; to the contrary, data should be cooked with care” (Bowker, 2006, p. 185). Data cannot therefore ‘speak for itself’ — it must be given voice and action through human sensemaking, or by a computational agent which nonetheless embodies assumptions.

Algorithmic accountability and stakeholder trust. Algorithms sit at the heart of analytics, but their design and debugging is the preserve of the few. In an era when software is embedded in appliances and online everywhere, one might ask if learners or educators should be troubling themselves with why the system is behaving as it does. For others, however, there is an ethical and pedagogical need to articulate the possible definitions and meanings of “algorithmic accountability” (Diakopoulos, 2014), such that learning analytics have appropriate transparency to different stakeholders.

Learning analytics design processes that give the end-user voice. Academic and design fields such as Human-Computer Interaction, Social Informatics and Values-Sensitive Design provide ways to design learning analytics that could reduce the risk of such tools fostering student and faculty suspicion, and build a greater sense of agency, and hence trust, of such tools. Research undertaken in these fields recognises that design is not a neutral activity, which is why sensitivity to all stakeholder perspectives and their values are foregrounded along the lines discussed in Jaifari, Nathan and Hargraves (2015) for instance.  For this reason, the approach taken in this project is most appropriately framed using a participatory, value-sensitive methodology.

Agency: from clicks to constructs. Aspects of “learner agency” that are of particular interest to us are qualities such as resilience in the face of challenge and uncertainty, creativity and playfulness in problem solving, one’s ability to engage in social learning, and one’s ability to make connections across contexts (Anderson, 2012; 2013; 2014; Crick et al. 2015). These are among the highest order competencies that humans display, and which we seek to nurture in learners. While these can be assessed through direct observation and self-report, can these be meaningfully identified in data traces from student activity, or is attempting to quantify such qualities one step too far in algorithmic intelligence hubris?

A key outcome from this PhD is an account of how the wider critical discourse around algorithms in society inform, and is informed by, the design of learning analytics. A second outcome is an analysis of whether it is reasonable to design analytics as proxy indicators for ‘agency’ — to quantify what some consider to be unquantifiable.

Depending on the candidate’s interests and expertise, the argument might be backed in terms of theory, empirical evidence and design prototypes. Thus, the PhD might:

  • translate theoretical implications into practical design guidance for learning analytics designers, researchers and educators, to help them understand and engage with the issues of values and ethics that lie at the heart of any computational model
  • prototype analytics tools embodying those principles, or make modifications to existing tools and how they are deployed, in the light of the principles
  • devise and validate participatory design processes which empower learners and educators in ways that address concerns about privacy violations, algorithmic opacity, or inappropriate educational interventions from limited data
  • engage with the legal and ethical ramifications of a growing dependency on algorithms in educational systems.


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

  • A Masters degree, Honours distinction or equivalent in a relevant discipline, e.g. Science & Technology Studies, Education, Design, Information Sciences, Human-Computer Interaction, Ethics of IT
  • Analytical, creative and innovative approach to solving problems
  • Experience in designing and conducting quantitative, qualitative or mixed-method studies

It is advantageous if you can evidence:

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


Anderson, T.K. 2012, ‘Information Science and 21st Century Information Practices: creatively engaging with information‘ in Bawden, D. & Robinson, L. (eds), Introduction to Information Science, Facet Publishing, UK, pp. 15-17.

Anderson, T.K. 2013, ‘The 4Ps of innovation culture: conceptions of creatively engaging with information‘, Information Research – Proceedings of the Eighth International Conference on Conceptions of Library and Information Science, Copenhagen, Denmark, pp. 1-15.

Anderson, T.K. 2014, ‘Making the 4Ps as important as the 4Rs’, Knowledge Quest, vol. 42, no. 5, pp. 42-47.

Bowker, G. C. (2006). Memory Practices in the Sciences. Cambridge, MA: MIT Press.

Bowker, G. C., & Star, L. S. (1999). Sorting Things Out: Classification and Its Consequences. Cambridge, MA: MIT Press.

boyd, d. and K. Crawford (2012). Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon. Information, Communication & Society 15(5): 662-679.

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.

Diakopoulos, N. (2014). Algorithmic Accountability. Digital Journalism, 3(3), 398–415.

JafariNaimi, N., Nathan, L., and Hargraves, I. (2015) Values as hypotheses: design, inquiry, and the service of values. Design Issues 31(4): 91-104.

Knight, S. and Buckingham Shum, S. (In Press). Theory and Learning Analytics. Handbook of Learning Analytics and Educational Data Mining.

Leonelli, S. (2014). What difference does quantity make? On the epistemology of Big Data in biology. Big Data & Society, Apr-June 2014, pp.1-11. [Supplementary Media]