The emergence of Reflective Writing Analytics

In 2015 I blogged about the emergence of what I dubbed “Writing Analytics” as a stream within the wider Learning Analytics field. Five years on, we see regular Writing Analytics workshops at LAK and ALASI (see the Events menu on that link), publications appearing in top tier conferences and journals (e.g. see below), and even the launch of a new conference and journal in the last two years.

This blog is to mark the emergence of Reflective Writing Analytics as a sub-stream. Reflective writing is quite different from the more widely used forms of academic writing (literature review, persuasive essay, research paper; etc.), but is growing in importance as we seek to place learners in authentic learning contexts (e.g. internships; work placements; or simulated teams), specifically so that they experience something of the complexity of real workplaces.

Honest reflection can make the writer vulnerable, as they reflect on their uncertainties, failings, and how they are changing as a learner/professional. They are often deeply personal, connecting to different threads across the many areas of their life. That’s almost the opposite of the other genres of writing that dominate students’ and professionals’ lives, which emphasise rational distance, mastery of the material, and confident rhetoric. Deep reflection can even share transformational moments in someone’s life and learning. Speaking personally, I find some student reflections profound, and even moving — an inspirational reminder of why we do what we do. Yet reflecting is not something that people are always (or even often) given the opportunity to learn how to do well.

As noted in a recent paper:

“Helping people make sense of their thoughts, feelings, reactions and approaches when stretched out of their comfort zones is core business for educators and coaches. Suitably supported, honest reflection makes it safe to question assumptions and consider change, but we also know that this is often difficult to teach, and challenging to learn.

[…] Reflective Writing is a strategy used in education and many professions to help learners, professionals and leaders make sense of challenging experiences, and prepare for the future. It integrates “head and heart”: valuing not only technical/academic knowledge, but how this interplays with experiential/professional ways of knowing, and recognising the fact that learning and working engage our emotions and feelings.

[…] However, while we know there is nothing as valuable as detailed coaching feedback to build this capacity, this is a scarce, costly skillset and labor-intensive. The practical consequence is that most students and leaders do not understand how to reflect deeply, and do not receive good feedback.” (Buckingham Shum & Lucas, 2020)

The desire to provide people with useful and timely feedback on reflection on a widespread basis has led to interest in developing Reflective Writing Analytics — broadly, the use of natural language processing and automated feedback methods to understand and support the process of reflection. This is a nascent area. There are not many people (that we know of) working on the challenge of providing automated analysis of, and feedback on, reflective writing, so it was a delight to convene a post-LAK20 call last week with teams from the USA, UK and AUS, when we spent 2 hours comparing notes on what we’re wrestling with, and how we might collaborate to move the field forward.

Examples of current work are below to help you get up to speed with this emerging field, the different emphases within it, and the scholarly communities who participate. There’s a significant existing body of work outside the field of analytics on the nature of reflection, and how to teach reflective writing (reviewed in the papers). The intriguing challenge is to translate that, with integrity, into the world of text analytics and automated feedback. As we noted during our call, it’s a really exciting nexus of the cognitive, social, affective, pedagogical, ethical, user experience and technical.

Do get in touch if you want to join forces — everyone is most welcome, and we’re sure there must be more people out there doing this we haven’t met!

Thanks to the kickoff videoconference participants for co-authoring this blog: Alyssa Wise (NYU), Andrew Gibson (QUT), Huda Alrashidi (Warwick), Ming Liu (UTS), Qiujie Li (NYU), Sameen Reza (NYU), Thomas Ullmann (OU), Yeonji Jung (NYU)

New York University (Lead: Alyssa Wise)

Cui, Y., Wise, A. F., & Allen, K. L. (2019). Developing Reflection Analytics for Health Professions Education: A Multi-dimensional Framework to Align Critical Concepts with Data Features. Computers in Human Behavior, 100, 305-324. https://doi.org/10.1016/j.chb.2019.02.019

Jung, Y. and Wise, A.F. (2020). How and How Well Do Students Reflect?: Multi-Dimensional Reflection Assessment in Health Professions Education. In Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK’20). ACM, New York, NY, USA, pp.595-604. https://doi.org/10.1145/3375462.3375528 [Preprint]

Wise, A. F., & Cui, Y. (2019,). Top Concept Networks of Professional Education Reflections. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge (LAK’19). ACM, New York, NY, USA pp. 260-264. https://dl.acm.org/doi/pdf/10.1145/3303772.3303840

Wise, A.F., Reza, S. & Han, R. J. (2020). Becoming a Dentist: Tracing Professional Identity Development through Mixed-Methods Data Mining of Student Reflections. Proceedings of ICLS’20: International Conference of the Learning Sciences. Nashville, TN: ISLS. [Preprint]

Queensland University of Technology (Lead: Andrew Gibson)

Work in progress with RWA and GoingOK: http://goingok.org

Willis, J., and Gibson, A (2020). The Emotional Work of Being an Assessor: A Reflective Writing Analytics Inquiry into Digital Self-assessment. In Fox, J., Alexander, C., Aspland,T.(Eds.) Teacher Education in Globalised Times. (Sringer). https://doi.org/10.1007/978-981-15-4124-7 [pre-order]

Gibson, Andrew P. (2017) Reflective writing analytics and transepistemic abduction. PhD Thesis, Queensland University of Technology. https://doi.org/10.5204/thesis.eprints.106952 [Preprint]

Gibson, A., Aitken, A., Sándor, Á., Buckingham Shum, S., Tsingos-Lucas, C. and Knight, S. (2017). Reflective Writing AnalyticsFor ActionableFeedback. Proceedings of LAK17: 7th International Conference on Learning Analytics & Knowledge, March 13-17, 2017, Vancouver, BC, Canada. (ACM Press), pp.153-162. http://dx.doi.org/10.1145/3027385.3027436 [Preprint] [Replay]

The Open University & Warwick University (Lead: Thomas Ullmann)

Ullmann, T. D. (2019). Automated Analysis of Reflection in Writing: Validating Machine Learning Approaches. International Journal of Artificial Intelligence in Education, 29(2), 217–257. https://doi.org/10.1007/s40593-019-00174-2 [Preprint]

Ullmann, T. D., Wild, F., & Scott, P. (2012). Comparing Automatically Detected Reflective Texts with Human Judgements. 2nd Workshop on Awareness and Reflection in Technology-Enhanced Learning. CEUR-WS.org. http://ceur-ws.org/Vol-931/paper8.pdf

Try ReflectR – an online tool to classify sentences regarding reflection: http://qone.eu/reflectr

Alrashidi, Huda; Ullmann, Thomas; Ghounaim, Samiah and Joy, Mike (2020). A Framework For Assessing Reflective Writing Produced Within the Context of Computer Science Education. In: Companion Proceedings 10th International Conference on Learning Analytics & Knowledge (LAK20, 24/03/2020, Frankfurt, Germany). [Preprint]

University of Technology Sydney (Lead: Simon Buckingham Shum)

AcaWriter automated feedback tool: AcaWriter orientation for staff and students • Video intro to the reflective module and embedding in Pharmacy Masters program

A blog post on the challenge of sharing reflective writing datasets

Buckingham Shum, S., Á. Sándor, R. Goldsmith, R. Bass and M. McWilliams (2017). Towards Reflective Writing Analytics: Rationale, Methodology and Preliminary Results. Journal of Learning Analytics, 4, (1), 58–84. https://doi.org/10.18608/jla.2017.41.5 (Open Access)

Liu, M., Buckingham Shum, S., Mantzourani, E.,Lucas, C. (2019). Evaluating Machine Learning Approaches to Classify Pharmacy Students’ Reflective Statements. In: Isotani S., Millán E., Ogan A., Hastings P., McLaren B., Luckin R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science, vol 11625. Springer, Cham. https://doi.org/10.1007/978-3-030-23204-7_19 [Preprint]

Buckingham Shum, S. and Lucas, C. (2020). Learning to Reflect on Challenging Experiences: An AI Mirroring Approach. Proceedings of ACM CHI 2020 Workshop on Detection and Design for Cognitive Biases in People and Computing Systems, April 25, 2020 (online). [Preprint]

Tackling cognitive bias with CHI tools

CHI is the premier conference on Computer-Human Interaction, though this year’s isn’t meeting due to COVID-19 (but see the Proceedings if you’re not familiar with its amazing breadth and depth). However, some of the workshops are going ahead online, one of which is this new one on Detection and Design for Cognitive Biases in People & Computing Systems:

“With social computing systems and algorithms having been shown to give rise to unintended consequences, one of the suspected success criteria is their ability to integrate and utilize people’s inherent cognitive biases. Biases can be present in users, systems and their contents. With HCI being at the forefront of designing and developing user-facing computing systems, we bear special responsibility for increasing awareness of potential issues and working on solutions to mitigate problems arising from both intentional and unintentional effects of cognitive biases.

This workshop brings together designers, developers, and thinkers across disciplines to re-define computing systems by focusing on inherent biases in people and systems and work towards a research agenda to mitigate their effects. By focusing on cognitive biases from a content or system as well as from a human perspective, this workshop will sketch out blueprints for systems that contribute to advancing technology and media literacy, building critical thinking skills, and depolarization by design.”

The papers are all open access, so jump in and see what an interesting range of contributions this event attracted. The workshop was an eclectic mix of expertises and interests, and also used a Miro board in a fun way to support activities with sticky note exercises.

I wrote a position paper with Cherie Lucas, contextualising our work on the automated detection of written reflection. Our vision is that such tools could make citizens more self-aware of their biases, making them less reactive, and more open to new perspectives when their assumptions are challenged.

Buckingham Shum, S. and Lucas, C. (2020). Learning to Reflect on Challenging Experiences: An AI Mirroring Approach. Proceedings of the CHI 2020 Workshop on Detection and Design for Cognitive Biases in People and Computing Systems, April 25, 2020. [slides]

Abstract. As citizens are confronted by major societal changes, they find their assumptions being challenged and their identities threatened, bringing the risk that they retreat to like-minded ‘bubbles’ rather than ask whether they might have something to learn. Algorithmically driven media platforms exacerbate this process by amplifying cognitive biases and polarizing debate. This paper argues for a distinctive role that Artificial Intelligence (AI) can play, by holding up a metaphorical ‘mirror’ to online writers, with carefully designed feedback making them more aware of, and reflective about, their reactions and approaches to challenging situations. As an example, we describe a web application that uses Natural Language Processing to annotate written accounts of personal responses to challenging experiences, highlighting where the author appears to be reflecting shallowly or deeply. This open source tool is already in use by students to help them make sense of work placement challenges they encounter, but could find wider application. Our vision is that such tools could make citizens more self-aware of their biases, making them less reactive, and more open to new perspectives when their assumptions are challenged.

Running an online Doctoral Consortium

Following COVID-19, last week I co-chaired the LAK20 Doctoral Consortium as an online 1-day workshop. This followed the tried and tested format we’ve developed since 2015/2016 and have used fairly consistently for the face-to-face events. The question was, would this work 100% online?

Following acceptance, the doctoral researchers populate the Google Doc program with their websites/papers/posters/slides, with specific instructions also to prepare feedback for their colleagues in the breakout sessions.

The shift online led to the following adjustments, which may help anyone else running a DC, or indeed, any online workshop where you want to combine conference + unconference mode.

Program sequencing

We were now covering 8 timezones. Frankfurt (the intended location for the conference) just happened to be the central, most convenient timezone, but it might not have been. World Clock Meeting Planner gives you a nice calendar to help everyone coordinate:

Vancouver to Sydney timezones chart

So instead of the usual thematic clustering of talks, presentation sequencing now had to ‘follow the sun’ through the 8 timezones. So the biggest hit of moving online is that you don’t have everyone there all the time. Oceania had to go first, then Europe, and then North America. This meant that Australians, for instance, dropped out at lunchtime (their midnight), and the US joined around then. The only way around this would be to chunk the day into 2-3 smaller sessions that are tolerable for all timezones.

Logistical implications:

You need co-chairs spanning the timezones so they can pass the baton. We had 6 academics covering, which worked well (1 AUS, 3 EU, 2 US).
Prior to the decision to move fully online, this was being planned as a hybrid event, which just imposed lighter scheduling constraints since the co-present students could be slotted in anywhere.
The plenary room with student presentations was recorded, so that all talks could be replayed (pause and resume recording during breakouts). With Zoom handling unprecedented load, it took 7 days to receive the link to the 2Gb recording. Then you need to split the video file up into each presentation, which is pretty simple in iMovie etc, and a channel to upload to.
- Option: You could set up each plenary session as a separate room (or even for each talk) to keep recordings shorter, but just a bit more hassle to create and navigate.
We might possibly have used a Zoom Webinar, designed for large audiences (like the LAK main program), but this drastically restricts participants’ ability to engage fully (use video; share screens) which would impact the small workshop feel.
Dropping thematic clustering in fact meant that students who would otherwise have been in parallel breakouts on the same theme, were now able to use the breakouts to give feedback to colleagues in their area of expertise, an unexpected benefit. Normally, they would have chatted informally outside the formal sessions.

2 private programs for students and chairs

A clone of the public program was made for all participants, where we provided additional instructions, Zoom links (it’s an invitational event only) and recordings for those who missed sessions, and feedback boxes for everyone to share ideas:

In turn, the chairs had a clone of this which they could privately annotate with other org stuff, e.g. who is going to cover which breakouts, backup Zoom rooms (see next).

Breakout groups that students and chairs can roam at will

Zoom provides a breakout groups function which is great if you (as chair) want to do all the assigning of students to groups (Zoom supports automated or manual assignment of students to groups). Either way, however, students are then stuck with that breakout group — which may of course be entirely appropriate, depending on the students and learning design.

For our consortium, however, while each breakout had a designated focal student (the one getting feedback on their presentation and paper), everyone else operates in ‘unconference‘ mode, choosing which group(s) they join, switching to another at will. This is obviously designed to give students more agency, and Doctoral Consortium students who have prepared in advance can of course be trusted to make those calls.

Flexible breakout group instructions

This meant creating different Zoom rooms, which we discovered needed to be created by different hosts – the same host can’t be running and inhabiting multiple Zoom rooms.

If this had run as a hybrid physical+virtual event, co-present students would be asked to join their breakout Zoom room with their laptop, so that remote participants could tune in.

Plan B in case your planned videoconference tool fails

We used Zoom, which worked great overall. But whatever your platform, it’s good to know that if it goes down, you can hop into another one.

Find out which chairs can launch their own Zoom rooms, Google Hangouts, etc. at short notice. We had backup rooms linked in our chair’s version that we ended up calling on.

Finally, if someone can’t make attend live, the CHI guidelines on preparing a recorded research presentation are handy.

SoLAR webinar: Learning Analytics as Educational Knowledge Infrastructure

With thanks to SoLAR for inviting me to kick off this new series, this webinar was a deeper dive into the concept of Learning Analytics as Educational Knowledge Infrastructure, building on the thoughts I first shared at ICLS 2018. I very much welcome follow-up discussion in the webinar’s Google Group thread.

Here’s the replay and the slides [PDF].

Learning Analytics as Educational Knowledge Infrastructure from Simon Buckingham Shum

Learning Analytics & AI go to School

Here’s the replay of my keynote to the UTS STEM EdFutures Data in Schools Symposium [#UTSDataInSchools].

A non-technical introduction for school leaders and teachers to the promise and pitfalls around the explosion in data, including different roles for analytics and AI, the role of in-house data scientists, and how we build the bridge between educational constructs and log data [slides].

ALASI2018: Co-designing learning analytics with LA-DECK

AUSTRALIAN LEARNING ANALYTICS SUMMER INSTITUTE

Learning Analytics Design Cards (LA-DECK): Unpacking interstakeholder codesign through strategic cards.

Carlos Gerardo Prieto Alvarez, Roberto Martinez-Maldonado and Simon Buckingham Shum (Connected Intelligence Centre, University of Technology Sydney)

This workshop introduces participants to LA-DECK — Learning Analytics Design Cards [preview the cards]. This workshop will work particularly well for a team who want to explore the provision of learning analytics for a system you all understand. In this way, you can assess the potential of LA-DECK for your context.

However, you can definitely come along solo, and then work in an ad hoc team. You’ll just have to agree either to work on a fictional learning analytics challenge we’ll provide (below), or one that a team member can easily explain from their context (but we want to minimise the time you spend deciding this).

Learning Analytics Challenge: You want to give personalized feedback to 1,000 students to help them improve their communication skills.

After a brief intro, the core of the workshop will be hands-on with the LA-DECK simulating a co-design process structured by the LA-DECK, concluding with a plenary debrief to share experiences, reflections and show examples.

ALASI2018: Innovating for Impact Workshop

We’re pleased to announce this ALASI 2018 Workshop

Innovating Learning Analytics for Sustainable Impact

Simon Buckingham Shum, Cassandra Colvin, Shane Dawson,
Danny Liu, Pablo Munguia, Yi-Shan Tsai

THE CHALLENGE: INNOVATION + IMPACT

This workshop will explore the strategies that institutions are adopting to balance two key drivers of learning analytics initiatives:

Research & Innovation: the desire and need to conduct rigorous research (e.g. in order to develop learning analytics that are not available in current products to advance future-oriented teaching and learning strategies; to answer complex questions specific to the institution’s context; to build the university’s research profile)
Sustainable Solutions: the need for robust, usable analytics infrastructure that is trusted by, and useful for, educators, students, the IT division, the data warehouse team, the academic development team, etc… (e.g. to ensure that tools work smoothly, and are easily learnt; to tackle immediate, pressing needs around data; to ensure that data is secure, and reliably captured ).

Typically, these two drivers pull in different directions. Innovative educational technologies typically fail to move beyond the “exploratory, exciting prototype” stage (Scanlon, et al. 2013). In their analysis of the state of the field in Australia, Colvin, et al. (2016) identified two clusters of universities: those who saw analytics as a more research-intensive vehicle for pedagogical innovation, and those seeking vendor solutions for pressing problems (such as student attrition). These conceptions implicate different stakeholders, with different success criteria.

However, are these tensions inevitable, or irreparable?Can the academic invention and rigour of good learning analytics research be harnessed to innovate solutions to strategic problems? Can R&D be accelerated and augmented so that it benefits more end users, more quickly, in sustainable and ethical ways ? Although far from being solved, progress is being made on these questions (e.g. Buckingham Shum and McKay, 2018). We might also ask how do we advance research in institutions that are not encouraging it, seeing analytics ‘simply’ as a technical solution to be licensed from a vendor?

Please see the full workshop description (pdf), register for ALASI, and post your thoughts here to help seed the event…

ALASI2018: Educational Data Scientists: A (Less) Scarce Breed?

We’re pleased to announce this ALASI 2018 Workshop

Educational Data Scientists: A (Less) Scarce Breed?

Simon Buckingham Shum, Kathryn Bartimote, Vitomir Kovanovic, Mike Pracy

At LAK13, a panel was convened entitled Educational Data Scientists: A Scarce Breed (position statements and video on website). The panel was framed as follows:

The Educational Data Scientist is currently a poorly understood, rarely sighted breed. Reports vary: some are known to be largely nocturnal, solitary creatures, while others have been reported to display highly social behaviour in broad daylight. What are their primary habits? How do they see the world? What ecological niches do they occupy now, and will predicted seismic shifts transform the landscape in their favour? What survival skills do they need when running into other breeds? Will their numbers grow, and how might they evolve? In this panel, the conference will hear and debate not only broad perspectives on the terrain, but will have been exposed to some real-life specimens, and caught glimpses of the future ecosystem.

Five years on, it is well worth asking if we have a better understanding of this (even more) highly valued profession. Can we now discern different ‘species’ of educational data scientists, as they establish niches for themselves? What patterns of connection and interaction maximise their success? Do they identify as technicians, analysts, or researchers? Is their mission to display, to analyse, to predict, to understand, or to advise? How can they be supported, and how do we raise the next generation?

Please see the full workshop description (pdf), register for ALASI, and post your thoughts here to help seed the event…

ICLS 2018 Keynote: Transitioning Education’s Knowledge Infrastructure

Keynote, International Conference of the Learning Sciences 2018, London Festival of Learning

Transitioning Education’s Knowledge Infrastructure: Shaping Design or Shouting from the Touchline?

Download HD / other • Slides PDF / Slideshare

Abstract: Bit by bit, a data-intensive substrate for education is being designed, plumbed in and switched on, powered by digital data from an expanding sensor array, data science and artificial intelligence. The configurations of educational institutions, technologies, scientific practices, ethics policies and companies can be usefully framed as the emergence of a new “knowledge infrastructure” (Paul Edwards).

The idea that we may be transitioning into significantly new ways of knowing – about learning and learners – is both exciting and daunting, because new knowledge infrastructures redefine roles and redistribute power, raising many important questions. For instance, assuming that we want to shape this infrastructure, how do we engage with the teams designing the platforms our schools and universities may be using next year? Who owns the data and algorithms, and in what senses can an analytics/AI-powered learning system be ‘accountable’? How do we empower all stakeholders to engage in the design process? Since digital infrastructure fades quickly into the background, how can researchers, educators and learners engage with it mindfully? If we want to work in “Pasteur’s Quadrant” (Donald Stokes), we must go beyond learning analytics that answer research questions, to deliver valued services to frontline educational users: but how are universities accelerating the analytics innovation to infrastructure transition?

Wrestling with these questions, the learning analytics community has evolved since its first international conference in 2011, at the intersection of learning and data science, and an explicit concern with those human factors, at many scales, that make or break the design and adoption of new educational tools. We are forging open source platforms, links with commercial providers, and collaborations with the diverse disciplines that feed into educational data science. In the context of ICLS, our dialogue with the learning sciences must continue to deepen to ensure that together we influence this knowledge infrastructure to advance the interests of all stakeholders, including learners, educators, researchers and leaders.

Speaking from the perspective of leading an institutional analytics innovation centre, I hope that our experiences designing code, competencies and culture for learning analytics sheds helpful light on these questions.

Biography: Simon Buckingham Shum is Professor of Learning Informatics at the University of Technology Sydney, which he joined in August 2014 as inaugural director of the Connected Intelligence Centre: https://utscic.edu.au. Prior to this, he was Professor of Learning Informatics and Associate Director (Technology) at the UK Open University’s Knowledge Media Institute. He brings a background in Psychology, Ergonomics and Human-Computer Interaction, and a career-long fascination with making thinking visible using software. He co-founded the Compendium Institute to connect the international community using his team’s Compendium visual hypermedia tool, used widely for Dialogue, Issue and Argument Mapping in both education and business. He co-edited Visualizing Argumentation (2003, with Kirschner & Carr) followed by Knowledge Cartography (2008, 3^rdEdition now in prep., with Okada & Sherborne), and wrote Constructing Knowledge Art (2015, with Selvin). He has been active in shaping the field of Learning Analytics since the inaugural LAK 2011 conference, serving as a Program Chair (2012/2018), convening many workshops, and a regular keynote speaker. He co-founded the Society for Learning Analytics Research, serving as a V-P and on the Executive. Homepage: http://Simon.BuckinghamShum.net

Learning Analytics Summer Institute 2017

This year’s field-building Learning Analytics Summer Institute (twitter #lasi17), organised by SoLAR, was once again hosted at U. Michigan with satellite LASI-Locals running internationally (e.g. for reference, here’s the 2016 program). I joined, with Andrew Gibson, where we made contributions on several fronts…

Opening keynote address Learning Analytics vs. Cognitive Automation (replay the talk below • PDF slides • replay the other keynotes).
Andrew led training in Writing Analytics, explaining our approach to the design of natural language processing to enable instant feedback to students on their writing (workshop 4)
I ran a briefing on how the learning analytics community can think about the ways in which data and algorithms take on ethical dimensions (tutorial 8 slides below • PDF)

Video opens with a welcome to LASI from U. Michigan professor and SoLAR President Stephanie Teasley, followed by some incisive introductory comments on the state of Learning Analytics from UM Vice-Provost Academic Innovation James Hilton.

Learning Analytics vs. Cognitive Automation: Rationale, Examples and Organisational Strategy (starts 19min 50sec)

Writing Analytics: LAK & EDM workshops

It’s a sign of the times that we now have two independently organised workshops on Writing Analytics in 2016, connecting two sister communities to the topic with their slightly different flavours: Learning Analytics, and Educational Data Mining.

In a few weeks, we’re in Edinburgh for the LAK16 workshop Critical Perspectives on Writing Analytics, and then in June, there’s an EDM16 workshop on Writing Analytics, Data Mining, & Writing Studies.

Please consider attending, and watch this space for the outcomes…

LAK16 Panel: Institutional Learning Analytics Centres

Here’s a heads-up on a panel discussion coming up at LAK16. Join us 11am-12 on Friday morning (Prestonfield Rm, Macintyre Centre)) to hear how educational institutions are configuring different kinds of centres to build their learning analytics capacity.

Since we want to minimise time in lecture mode, and maximise conversation, we invite you to come armed having read the panellists’ position statements [pdf] (and this post-hoc addition of their slides). Note the line-up has changed slightly.

Institutional Learning Analytics Centres: Contexts, Strategies and Insights

Simon Buckingham Shum, Kevin Mayles, Leah Macfadyen, Shane Dawson & Lisa Berry

An indicator of the maturing field of learning analytics is the creation of new organizational entities dedicated to using learning analytics services to improve the student experience through institutional research. Going beyond traditional Business Intelligence (BI), these groups operate firmly at the intersection of learning and analytics — they can speak the language of pedagogy and assessment with educators, invent/deploy novel analytics tools, while engaging IT and BI colleagues around mainstreaming services. The end-users targeted by these learning analytics centres are educators and learners. In this panel, the leaders of seven differently configured centres, from diverse universities, share insights on issues such providing rapid value from pilots, research-based innovation, ways to engage stakeholders, vendor partnerships, data quality, and alignment with university strategy. Our hope is that attendees will leave with fresh ideas on the options they have to advance learning analytics in their own contexts.