Category: Learning Analytics

Posts related to Learning Analytics

LAK25 at Dublin – Publications and Reflections

It was great to reconnect with the Learning Analytics community at LAK25 in Dublin (before coming straight back to a busy teaching semester). LAK15 was the first conference I ever attended, and I have fond memories of this first scholarly experience. I went down memory lane after 10 years, reflecting on how it is probably the reason why I chose to do a PhD and stay in academia. My publications at LAK25 are discussed below, but also check out the full UTS line-up from the Connected Intelligence Centre (CIC) website! 

In the writing analytics workshop I co-organised, our discussions spanned new methods that can inform and use generative AI for writing (tools and analytics), but we also asked some fundamental questions on the nature and evolving role of writing itself.

Rianne Conijn, Antonette Shibani, Laura Allen, Simon Buckingham Shum & Cerstin Mahlow (2025) Writing Analytics in the age of Large Language Models: Expanding horizons for assessing and designing writing with AI. In companion proceedings of the 15th International Conference on Learning Analytics & Knowledge (LAK25) pg 436-439.

Abstracts of the presented papers are on the workshop website, and you can access the organiser slides and the shared notes as well. As someone who values writing quite a bit (I’ve always found academic writing really helpful for my thinking, which enhances my own ideas as they evolve in my head), the diminishing role of writing especially for student assessments worries me (and many other folks I know, we had a great question on it at the workshop). But, having heard Prof. Rupert Wegerif talk about dialogic theory and education servicing technologies (Yes, that’s right!), I’ve come to peace with other ways of thinking. While writing is still important, it may become less essential; and maybe that’s okay. Because people think differently, writing is not the only medium for it (for example, Indigenous Knowledges have been around for centuries in various other forms). For those who prefer writing, it can continue to be of significant value, and I agree, the college essay is not dead (manifesto), we just need better, more relevant ones. I believe that we should still teach students how to write well, especially in finding their voice and being authentic (with text starting to sound very similar everywhere, I’ve personally started craving for more originality in tone of writing; Sounding professional is great, but looks like I could only take so many AI-generated emails)!

I was invited to take part in the full day Grand Challenges workshop, where workshop participants had a lot of fun identifying and voting for the grand challenges that the field of Learning Analytics should tackle in the next decade. Thanks, Sasha for the shoutout on LinkedIn.

The workshop had interesting follow-up events: an interactive poster, and an interactive panel session (keynote 3), and Prof. Simon Buckingham Shum was able to convince most (voted #1 challenge in the workshop), or me atleast, that LA should prioritize the soceity’s most pressing challenges. I’ve asked Simon how exactly we can make this happen (I can draw basic connections between learning analytics empowering learners to tackle skill challenges but I cannot extend it to how it can help save the world in this polycrisis!). He promised that he would write about it in detail, so I’m looking forward to reading it. Sidenote: I was reading Tomitsch (Head of School, TD School, UTS) and Baty’s Designing Tomorrow book and all about the climate challenges during my flight to Dublin, so it made sense – highly recommended read; it completely changed how I view Human-centred design!

Another challenge around defining core principles for LA in the rapidly changing GenAI context was something I contributed to quite a bit (stemming from the many conversations at LAK around futures and past failures of LA). There’s a lot happening around GenAI pretty quickly; its affordances are rapidly improving – but this is all the more the reason to be mindful and cautious in what we research and implement in education. We’ve contributed to some starter discussions in an upcoming editorial for the JLA Special Section on Generative AI and Learning Analytics – look out for this in the coming weeks!

At another workshop From Data to Discovery: LLMs for Qualitative Analysis in Education, Lisa did a fabulous job of presenting our team’s work (unfortunately, it conflicted with my writing analytics workshop, so I couldn’t make it). The title is self-explanatory, and the full paper has all the details:

Aneesha Bakharia, Antonette Shibani, Lisa-Angelique Lim, Trish McCluskey and Simon Buckingham Shum (2025) From Transcripts to Themes: A Trustworthy Workflow for Qualitative Analysis Using Large Language Models. In CEUR proceedings of the workshop ‘From Data to Discovery: LLMs for Qualitative Analysis in Education’. Companion proceedings of the 15th International Learning Analytics & Knowledge Conference (LAK ’25).

There’s another detailed blog post on this work, so I’ll add its link here:

Theme Explorer: LLM-augmented Inductive Coding

We also presented a poster that expands analytics on writing for potential feedback on AI-human collaboration (student work, extended from CoauthorViz):

Antonette Shibani, Vishal Raj and Simon Buckingham Shum (2025). Towards Analytics for Self-regulated Human-AI Collaboration in Writing. In Companion proceedings of the 15th International Learning Analytics & Knowledge Conference (LAK25) pg 120-122.

There are ongoing discussions at LAK and beyond on the use of the term “collaboration” to denote human-AI partnerships (and “partnerships” as a term, too but we mean partners in cognition like this 1991 paper). Various disciplines have always interpreted terminologies differently (especially computer science, which makes loose analogies with human psychology – e.g., hallucinations), and it is an open question if we can even agree on how we must perceive terms. Maybe we need better definitions for what we mean by regulation, hybrid, augmenting, or just human-AI interaction? More on this is likely to come later!

I also chaired two sessions at LAK (one where the best papers from sister conferences were invited to present, and another on learning design). The Q&A parts were super interesting there and throughout LAK. We could really see the diversity of the audience’s backgrounds here – the constant switch between statistics, practitioners, LA, and LLMs is mind-boggling!

And more photo dumps because… why not 🙂

Team UTS

The Long Room

Book of Kells Experience

New publication: Untangling Critical Interaction with AI

I’ve had a longstanding interest in exploring how students engage critically with automated feedback and develop their AI literacy. In our LAK22 paper, we argued why it is so important that we develop these skills in learners. There is a heightened necessity in today’s educational landscape for learners in the age of generative AI (Gen AI) to engage with AI critically.

Our upcoming CHI publication investigates the fundamental question: Why do students engage with Gen AI for their writing tasks, and how can they navigate this interaction critically? In our paper, we define in concrete terms and stages how criticality can manifest when students write with ChatGPT support. We draw from theory and examples in empirical data (which are still unbelievably scarce in the literature) to understand and expand the notion of critical interaction with AI.

A pre-print version is available for download on Arxiv [PDF]. Full citation below:

Antonette Shibani, Simon Knight, Kirsty Kitto, Ajanie Karunanayake, Simon Buckingham Shum (2024). Untangling Critical Interaction with AI in Students’ Written Assessment. Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI ’24), May 11-16, 2024, Honolulu, HI, USA. Pre-print: https://arxiv.org/abs/2404.06955 

A short video presentation gives the gist of the paper [Follow along with the transcript]

Tamil Co-Writer: Inclusive AI for writing support

Next week, I’m presenting my work in the First workshop on
Generative AI for Learning Analytics (GenAI-LA) at the 14th International Conference on Learning Analytics and Knowledge LAK 2024:

Antonette Shibani, Faerie Mattins, Srivarshan Selvaraj, Ratnavel Rajalakshmi & Gnana Bharathy (2024) Tamil Co-Writer: Towards inclusive use of generative AI for writing support. In Joint Proceedings of LAK 2024 Workshops, co-located with 14th International Conference on Learning Analytics and Knowledge (LAK 2024), Kyoto, Japan, March 18-22, 2024.

With colleagues in India, we developed Tamil Co-Writer, a GenAI-supported writing tool that offers AI suggestions for writing in the regional Indian language Tamil (which is my first language). The majority of AI-based writing assistants are created for English language users and do not address the needs of linguistically diverse groups of learners. Catering to languages typically under-represented in NLP is important in the generative AI era for the inclusive use of AI for learner support. Combined with analytics on AI usage, the tool can offer writers improved productivity and a chance to reflect on their optimal/sub-optimal collaborations with AI.

The tool combined the following elements:

  1. An interactive AI writing environment that offers several input modes to write in Tamil
  2. Analytics of writer’s AI interaction in the session for reflection (See post on CoAuthorViz for details, and related paper here)

A short video summarising the key insights from the paper is below:

Understanding human-AI collaboration in writing (CoAuthorViz)

Generative AI (GenAI) has captured global attention since ChatGPT was publicly released in November 2022. The remarkable capabilities of AI have sparked a myriad of discussions around its vast potential, ethical considerations, and transformative impact across diverse sectors, including education. In particular, how humans can learn to work with AI to augment their intelligence rather than undermine it greatly interests many communities.

My own interest in writing research led me to explore human-AI partnerships for writing. We are not very far from using generative AI technologies in everyday writing when co-pilots become the norm rather than an exception. It is possible that a ubiquitous tool like Microsoft Word that many use as their preferred platform for digital writing comes with AI support as an essential feature (and early research shows how people are imagining these) for improved productivity. But at what cost?

In our recent full paper, we explored an analytic approach to study writers’ support seeking behaviour and dependence on AI in a co-writing environment:

Antonette Shibani, Ratnavel Rajalakshmi, Srivarshan Selvaraj, Faerie Mattins, Simon Knight (2023). Visual representation of co-authorship with GPT-3: Studying human-machine interaction for effective writing. In M. Feng, T. K¨aser, and P. Talukdar, editors, Proceedings of the 16th International Conference on Educational Data Mining, pages 183–193, Bengaluru, India, July 2023. International Educational Data Mining Society [PDF].

Using keystroke data from the interactive writing environment CoAuthor powered by GPT-3, we developed CoAuthorViz (See example figure below) to characterize writer interaction with AI feedback. ‘CoAuthorViz’ captured key constructs such as the writer incorporating a GPT-3 suggested text as is (GPT-3 suggestion selection), the writer not incorporating a GPT-3 suggestion
(Empty GPT-3 call), the writer modifying the suggested text (GPT-3 suggestion modification), and the writer’s own writing (user text addition). We demonstrated how such visualizations (and associated metrics) help characterise varied levels of AI interaction in writing from low to high dependency on AI.

Figure: CoAuthorViz legend and three samples of AI-assisted writing (squares denote writer written text, and triangles denote AI suggested text)

Full details of the work can be found in the resources below:

Several complex questions are yet to be answered:

  • Is autonomy (self-writing, without AI support) preferable to better quality writing (with AI support)?
  • As AI becomes embedded into our everyday writing, do we lose our own writing skills? And if so, is that of concern, or will writing become one of those outdated skills in the future that AI can do much better than humans?
  • Do we lose our ‘uniquely human’ attributes if we continue to write with AI?
  • What is an acceptable use of AI in writing that still lets you think? (We know by writing we think more clearly; would an AI tool providing the first draft restrict our thinking?)
  • What knowledge and skills do writers need to use AI tools appropriately?

Edit: If you want to delve into the topic further, here’s an intriguing article that imagines how writing might look in the future: https://simon.buckinghamshum.net/2023/03/the-writing-synth-hypothesis/

Questioning Learning Analytics – Cultivating critical engagement (LAK’22)

Gist of LAK 22 paper

Our full research paper has been nominated for Best Paper at the prestigious Learning Analytics and Knowledge (LAK) Conference:

Antonette Shibani, Simon Knight and Simon Buckingham Shum (2022, Forthcoming). Questioning learning analytics? Cultivating critical engagement as student automated feedback literacy. [BEST RESEARCH PAPER NOMINEE] The 12th International Learning Analytics & Knowledge Conference (LAK ’22).

Here’s the gist of what the paper talks about:

  • Learning Analytics (LA) still requires substantive evidence for outcomes of impact in educational practice. A human-centered approach can bring about better uptake of LA.
  • We need critical engagement and interaction with LA to help tackle issues ranging from black-boxing, imperfect analytics, and the lack of explainability of algorithms and artificial intelligence systems, to the required relevant skills and capabilities of LA users when dealing with such advanced technologies.
  • Students must be able to, and should be encouraged to, question analytics in student-facing LA systems as Critical engagement is a metacognitive capacity that both demonstrates and builds student understanding.
  • This puts the power back to users and empowers them with agency when using LA.
  • Critical engagement with LA should be facilitated with careful design for learning; we provide an example case with automated writing feedback – see the paper for details on what the design involved.
  • We show empirical data and findings from student annotations of automated feedback from AcaWriter, where we want them to develop their automated feedback literacy.

The full paper is available for download at this link: [Author accepted manuscript pdf].

This paper was the hardest for me to write personally since I was running on 2-3 hours of sleep right after joining work part-time following my maternity leave. Super stoked to hear about the best paper nomination, as my work as a new mum paid off. Good to be back at work while also taking care of the little bubba 🙂 Thanks to my co-authors for accommodating my writing request really close to the deadline!

Also, workshops coming up in LAK22:

  • Antonette Shibani, Andrew Gibson, Simon Knight, Philip H Winne, Diane Litman (2022, Forthcoming). Writing Analytics for higher-order thinking skills. Accepted workshop at The 12th International Learning Analytics & Knowledge Conference (LAK ’22).
  • Yi-Shan Tsai, Melanie Peffer, Antonette Shibani, Isabel Hilliger, Bodong Chen, Yizhou Fan, Rogers Kaliisa, Nia Dowell and Simon Knight (2022, Forthcoming). Writing for Publication: Engaging Your Audience. Accepted workshop at The 12th International Learning Analytics & Knowledge Conference (LAK ’22).

Automated Writing Feedback in AcaWriter

You might be familiar with my research in the field of Writing Analytics, particularly Automated Writing Feedback during my PhD and beyond. The work is based off an automated feedback tool called AcaWriter (previously called Automated Writing Analytics/ AWA) which we developed at the Connected Intelligence Centre, University of Technology Sydney.

Recently we have come up with resources to spread the word and introduce the tool to anyone who wants to learn more. First is an introductory blog post I wrote for the Society for Learning Analytics Research (SoLAR) Nexus publication. You can access the full blog post here: https://www.solaresearch.org/2020/11/acawriter-designing-automated-feedback-on-writing-that-teachers-and-students-trust/

We also ran a 2 hour long workshop online as part of a LALN event to add more detail and resources for others to participate. Details are here: http://wa.utscic.edu.au/events/laln-2020-workshop/

Video recording from the event is available for replay:

Learn more: https://cic.uts.edu.au/tools/awa/

Automated Revision Graphs – AIED 2020

I’ve recently had my writing analytics work published at the 21st international conference on artificial intelligence in education (AIED 2020) where the theme was “Augmented Intelligence to Empower Education”. It is a short paper describing a text analysis and visualisation method to study revisions. It introduced ‘Automated Revision Graphs’ to study revisions in short texts at a sentence level by visualising text as graph, with open source code.

Shibani A. (2020) Constructing Automated Revision Graphs: A Novel Visualization Technique to Study Student Writing. In: Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12164. Springer, Cham. [pdf] https://doi.org/10.1007/978-3-030-52240-7_52

I did a short introductory video for the conference, which can be viewed below:

I also had another paper I co-authored on multi-modal learning analytics lead by Roberto Martinez, which received the best paper award in the conference. The main contribution of the paper is a set of conceptual mappings from x-y positional data (captured from sensors) to meaningful measurable constructs in physical classroom movements, grounded in the theory of Spatial Pedagogy. Great effort by the team!

Details of the second paper can be found here:

Martinez-Maldonado R., Echeverria V., Schulte J., Shibani A., Mangaroska K., Buckingham Shum S. (2020) Moodoo: Indoor Positioning Analytics for Characterising Classroom Teaching. In: Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12163. Springer, Cham. [pdf] https://doi.org/10.1007/978-3-030-52237-7_29

New Research Publications in Learning Analytics

Three of my journal articles got published recently, two on learning analytics/ writing analytics implementations [Learning Analytics Special Issue in The Internet and Higher Education journal], and one on a text analysis method [Educational Technology Research and Development journal]. that I worked on earlier (many years ago in fact, which just got published!).

Article 1: Educator Perspectives on Learning Analytics in Classroom Practice

The first one is predominantly qualitative in nature, based on instructor interviews of their experiences in using Learning Analytics tools such as the automated Writing feedback tool AcaWriter. It provides a practical account of implementing learning analytics in authentic classroom practice from the voices of educators. Details below:

Abstract: Failing to understand the perspectives of educators, and the constraints under which they work, is a hallmark of many educational technology innovations’ failure to achieve usage in authentic contexts, and sustained adoption. Learning Analytics (LA) is no exception, and there are increasingly recognised policy and implementation challenges in higher education for educators to integrate LA into their teaching. This paper contributes a detailed analysis of interviews with educators who introduced an automated writing feedback tool in their classrooms (triangulated with student and tutor survey data), over the course of a three-year collaboration with researchers, spanning six semesters’ teaching. It explains educators’ motivations, implementation strategies, outcomes, and challenges when using LA in authentic practice. The paper foregrounds the views of educators to support cross-fertilization between LA research and practice, and discusses the importance of cultivating educators’ and students’ agency when introducing novel, student-facing LA tools.

Keywords: learning analytics; writing analytics; participatory research; design research; implementation; educator

Citation and article link: Antonette Shibani, Simon Knight and Simon Buckingham Shum (2020). Educator Perspectives on Learning Analytics in Classroom Practice [Author manuscript]. The Internet and Higher Education. https://doi.org/10.1016/j.iheduc.2020.100730. [Publisher’s free download link valid until 8 May 2020].

Article 2: Implementing Learning Analytics for Learning Impact: Taking Tools to Task

The second one led by Simon Knight provides a broader framing for how we define impact in learning analytics. It defines a model addressing the key challenges in LA implementations based on our writing analytics example. Details below:

Abstract: Learning analytics has the potential to impact student learning, at scale. Embedded in that claim are a set of assumptions and tensions around the nature of scale, impact on student learning, and the scope of infrastructure encompassed by ‘learning analytics’ as a socio-technical field. Drawing on our design experience of developing learning analytics and inducting others into its use, we present a model that we have used to address five key challenges we have encountered. In developing this model, we recommend: A focus on impact on learning through augmentation of existing practice; the centrality of tasks in implementing learning analytics for impact on learning; the commensurate centrality of learning in evaluating learning analytics; inclusion of co-design approaches in implementing learning analytics across sites; and an attention to both social and technical infrastructure.

Keywords: learning analytics, implementation, educational technology, learning design

Citation and article link:  Simon Knight, Andrew Gibson and Antonette Shibani (2020). Implementing Learning Analytics for Learning Impact: Taking Tools to Task. The Internet and Higher Education. https://doi.org/10.1016/j.iheduc.2020.100729.

Article 3: Identifying patterns in students’ scientific argumentation: content analysis through text mining using LDA

The third one led by Wanli Xing discusses the use of Latent Dirichlet Allocation, a text mining method to study argumentation patterns in student writing (in an unsupervised way). Details below:

Abstract: Constructing scientific arguments is an important practice for students because it helps them to make sense of data using scientific knowledge and within the conceptual and experimental boundaries of an investigation. In this study, we used a text mining method called Latent Dirichlet Allocation (LDA) to identify underlying patterns in students written scientific arguments about a complex scientific phenomenon called Albedo Effect. We further examined how identified patterns compare to existing frameworks related to explaining evidence to support claims and attributing sources of uncertainty. LDA was applied to electronically stored arguments written by 2472 students and concerning how decreases in sea ice affect global temperatures. The results indicated that each content topic identified in the explanations by the LDA— “data only,” “reasoning only,” “data and reasoning combined,” “wrong reasoning types,” and “restatement of the claim”—could be interpreted using the claim–evidence–reasoning framework. Similarly, each topic identified in the students’ uncertainty attributions— “self-evaluations,” “personal sources related to knowledge and experience,” and “scientific sources related to reasoning and data”—could be interpreted using the taxonomy of uncertainty attribution. These results indicate that LDA can serve as a tool for content analysis that can discover semantic patterns in students’ scientific argumentation in particular science domains and facilitate teachers’ providing help to students.

Keywords: text mining, latent dirichlet allocation, educational data mining, scientific argumentation

Citation and article link:  Wanli Xing, Hee-Sun Lee and Antonette Shibani (2020). Identifying patterns in students’ scientific argumentation: content analysis through text mining using Latent Dirichlet Allocation. Educational Technology Research and Development. https://doi.org/10.1007/s11423-020-09761-w.

LAK 2019 in Tempe, Arizona

I attended the Learning Analytics and Knowledge Conference LAK this year in the midst of my tight thesis writing schedule, and did not regret it 🙂 This 9th International LAK (4-8 Mar, 2019) was held in Tempe, Arizona which meant a flight travel of 15 hours + transit from Sydney one way; I survived, thankfully.

First of all, I was excited to have been awarded a scholarship from ACM-W that supports Women in Computing for conference travel. And super excited to have received it for LAK which competes with journals in publishing some of the most influential work in educational technology.

I kicked off LAK2019 with the full day Writing Analytics workshop I chaired on Advances in Writing Analytics: Mapping the state of the field. While the other workshop organizers could not make it that day which was unfortunate, I’m thankful for the support from UTS CIC colleagues and the participants for helping to run a successful workshop. This fourth workshop in the series of Writing Analytics workshops in LAK had great participation and discussions. We saw interesting presentations on writing analytics from various speakers, and tried a demo version of AcaWriter to see the tool in action – check out tweets with #WaLAK19 and #LAK19. We brainstormed utopian and dystopian visions of how writing analytics in 2030 would look like, and discussed ways to get to a desirable future from where we are now. The potential formation of a Special Interest Group on Writing Analytics (SIGWA) was discussed to facilitate a community of researchers in the area. Notes from the workshop are shared here.

In the main conference, I presented our full research paper, co-authored by Dr. Simon Knight and Prof. Simon Buckingham Shum on Contextualizable Learning Analytics Design: A Generic Model and Writing Analytics Evaluations. We emphasized the need for flexible Learning Analytics Applications that can provide contextualized support, and demonstrated the CLAD model with our example.

I recommend watching the key note recordings from LAK’19, which are added in the SOLAR youtube channel. I would have loved to go into more detail to highlight some of the interesting work across LAK, but my notes for this conference are shorter than my usual notes since I’m now back to thesis writing and frantically managing time 😂. I did come across exciting work and meet lots of interesting people, most of whom I followed-up (I think!), so hope there would be new collaborations! I also officially joined the Society of Learning Analytics (SOLAR) executive committee as the elected student member. Thrilled and looking forward to serving on the committee!


Contextualizable learning analytics for writing support

Recently I gave a talk on Augmenting pedagogical writing support with contextualizable learning analytics at the CRLI seminar series in the University of Sydney.  It was a great opportunity to share and discuss ideas from my PhD research, and indeed a privilege to be invited to present at this seminar. Long time slot means less time constraints, so I enjoyed doing the 1 hour+ session. The talk is recorded and available for viewing on Youtube, and the slides are here. This post is a summary of the key ideas from this talk and an upcoming paper on ‘Contextualizable Learning Analytics Design (CLAD)’.

Big data, learning analytics and education:

Big data and artificial intelligence are changing many ways we do things to improve our lives (for better or for worse). Companies around the world including Facebook, Google, Apple and Amazon use data everyday to get big insights to support us. What can the more traditional organizations like educational institutions use data for? Can we harness this technology and data to improve learning? To answer these questions, Learning Analytics (LA) emerged as a field to attempt tackling huge amounts of data in education. Although data was previously available in education research for decades, different granularities of data from multiple sources in authentic scenarios and technical affordances of new tools can now support many causes which were not previously plausible. This root cause for the inception of the field has probably been a reason for its emphasis on ‘big impact’ and generalizable solutions that can cater to and scale up to huge numbers. Massive Open Online Courses (MOOCS) are a classic example of how we can scale teaching to a large number of learners using technology. However, the problem with scalable, generalizable solutions in learning analytics is that education is inherently contextual, and a one-size-fits all approach would not work in all contexts the same way. This has led to the argument on moving from big data to meaningful data for learning analytics.

Bringing in the context:

To bring the educational context to Learning Analytics (LA), it must be coupled with pedagogical approaches. This involves the integration of LA in pedagogical contexts to augment the learning design and provide analytics that are aligned with the intended learning outcomes. Learning Design (LD) describes an educational process, and involves the design of units of learning, learning activities or learning environment which are pedagogically informed. LA can provide the necessary data, methodologies and tools to test the assumptions of the learning design, and LD can add value to the analytics by making it meaningful for the learner. By bringing LA and LD together, they can contribute to each other and close the gap between the potential and actual use of technology.

Contextualizable Learning Analytics Design:

We introduce the Contextualizable Learning Analytics Design (CLAD) model in a forthcoming article by bringing together the elements of LA and LD for context. The educators are involved with LA developers to co-design this contextualization. This involves LD elements of assessment and task design, and LA elements of features and feedback working dynamically and in sync for different contexts, rather than being rigidly fixed. The CLAD model is demonstrated by implementing the Writing Analytics tool ‘AcaWriter’ in different learning contexts (Law essay writing, Accounting business report writing). AcaWriter, developed by the Connected Intelligence Centre, UTS provides automated feedback on student writing based on rhetorical moves. To contextualize the use of this LA tool for students, the elements of the CLAD model were employed as follows:

  • Assessment formed the basis of contextualization to align AcaWriter with the intended learning outcomes.
  • The features of data that are important for the context were picked so that AcaWriter can bring them to the attention of the learners.
  • The feedback from AcaWriter was tuned to make it relevant for the context of writing by mapping it back to assessment criteria.
  • Task design ensured that AcaWriter activities are relevant to the learner and grounded by pedagogic theory.

With such contextualized LA, the educator has agency to design learning analytics that is relevant to the learning context, and the learner finds it meaningful due to its embedding in the curriculum. This ensures that LA contributes to learning in authentic practice by augmenting existing good pedagogic practice. The approach scales over multiple learning contexts by transferring good design patterns from one learning context to another (for example from law essay writing to accounting business report writing).

More details on the above can be found in the following article, and related resources are available on the HETA project website.

References: