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Aggregating the CIC Team’s Blogs…
Master of Data Science & InnovationMaster of Data Science & Innovation sbs-avatar rdc-avatar sjgk-avatar shawn-avatar shibani Kailash Awati 0-guvm1o6wqbdmncqt

mdsi-avatar from the Master of Data Science & Innovation. . .

  • MDSI Digest Spring Semester 2015 – issue 13
    Posted by Theresa Anderson on November 25, 2015 at 3:15 am

    With the end of the semester, the MDSI Digests are taking on a more abbreviated form. The focus this week is on enrolments for next semester and updates on MDSI activities – including updates on the fabulous results of our MDSI participants in the NSWDAC challenge! Student Survival Tips Enroling in subjects for next semester I’ve heard from […]

  • MDSI Digest Spring Semester 2015 – issue 12
    Posted by Theresa Anderson on November 13, 2015 at 4:26 am

    Hi all, It’s been an ueber-busy fortnight (no doubt for us all), so running a little behind on my Digests. With the Spring semester just about behind us, it is time for final assessments, exams (in some subjects) and planning for next semester. Here are some quick pointers to keep you on track with what’s […]

  • MDSI Digest Spring Semester 2015 – issue 11
    Posted by Theresa Anderson on October 30, 2015 at 5:48 am

    Hi everyone, As I mentioned in my tweets earlier this week, an attack of flu at the end of last week forced the cancellation of last week’s Digest. With the semester quickly drawing to a close, I suspect you are all deeply immersed in final assignments and preparation for the exams that some of you taking […]



from the Learning Emergence blog. . .

  • Learning Analytics for 21st Century Competencies?
    Posted by Simon Buckingham Shum on September 20, 2016 at 12:49 pm

    We’re delighted to announce a Special Section of the Journal of Learning Analytics, published this week, focusing on the challenge of Learning Analytics for 21st Century Competencies. In our editorial we introduce the nature of the challenge, and after summarising the different researcher and practitioner papers, propose a complex systems approach which takes seriously the ‘layers, loops and processes’ of learning infrastructures and the iterative relationship between the human and the digital, where people learn at the nodes of networked flows of information. Learning analytics is an emerging field powered by the paradigm shifts of the information age. Pedagogy and learning that produce students capable of thriving in conditions of complexity, risk, and challenge by taking responsibility for their own learning journeys, and using technology and analytics[... […]

  • Scaffolding deep reflection with automated feedback?
    Posted by Simon Buckingham Shum on August 27, 2016 at 9:47 pm

    We’ve all got used to the idea that computers can understand writing and speech to some degree — Google adverts that match your search queries… asking Siri simple questions… IBM Watson winning Jeopardy! But how does natural language processing fit into learning that goes beyond getting the right answer to a focused question, or matching some key concepts? Language is clearly front and centre in the way that we learn from others, share our understanding, and narrate to ourselves. However, the idea that computers have any substantive contribution to make to the teaching and assessment of writing elicits strong reactions from educators, and understandably so (learn more from this workshop). In recent work that we’ve been doing at University of Technology Sydney, we’re exploring to what extent text[... […]

  • Customer Journeys: Learning Journeys – beyond ‘nudge’ to digital decisioning at scale
    Posted by Ruth Deakin Crick on February 12, 2016 at 11:06 am

    The Learning Emergence team hosted an International Centre for Infrastructure Futures workshop at the Systems Centre  yesterday to explore the synergy between ‘customer journeys’ as developed in the digital architecture of retail banking – and ‘learning journeys’ as developed by the Bristol team to capture the personal and social processes which contribute to the development of ‘resilient agency’. the achievable goal of true ‘customer at a time’ value management with many millions of customers Dec Blue Partner  Tim Crick, also a Learning Emergence Partner, showed how advanced customer decisioning technology can help organisations deliver agile and adaptive ‘customer at a time’ value management strategies across digital, assisted and face-to-face  channels. This transformation in approach to customer management is radical – from a product/campaign centred approach to a[... […]



sbs-avatar from Simon Buckingham Shum. . .

  • Writing Analytics update
    Posted by sbs on February 8, 2017 at 10:30 pm

    Since I arrived at UTS we’ve been researching and prototyping our way forward on writing analytics, in close collaboration with academics here, as part of our broader program to develop learning analytics for 21st century competencies/graduate attributes. We want to understand the potential of the (sometimes controversial) topic of providing students with automated feedback on their writing. […]

  • Learning Analytics for C21 Competencies
    Posted by sbs on September 17, 2016 at 4:14 am

    A mega-triple special issue of the Journal of Learning Analytics has just hit the streets, with three thematic sections: LEARNING ANALYTICS FOR 21ST CENTURY COMPETENCIES MULTIMODAL LEARNING ANALYTICS DATASET DESCRIPTIONS FOR LEARNING ANALYTICS Very satisfying to have worked with co-editor Ruth Deakin Crick, and all the authors on the first section. Enjoy! This is accompanied by […]

  • Knowledge Art learning resources
    Posted by sbs on May 6, 2016 at 12:33 am

    Following the tragically premature death of Al Selvin last October, I have continued to think about how his inspirational research can live on in more than research writings. Throughout 2015, we were discussing ways in which we could communicate the ideas behind Knowledge Art to reflective practitioners, in contrast to the more academic audiences we’ve been engaging with. One outcome of […]



rdc-avatar from Ruth Crick. . .



sjgk-avatar from Simon Knight. . .

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    shawn-avatar from Shawn Wang. . .

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      shibani from Antonette Aileen Shibani. . .

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        Kailash Awati from Kailash Awati. . .

        • A prelude to machine learning
          Posted by K on February 23, 2017 at 5:12 am

          What is machine learning? The term machine learning gets a lot of airtime in the popular and trade press these days. As I started writing this article, I did a quick search for recent news headlines that contained this term. Here are the top three results with datelines within three days of the search: http://venturebeat.com/2017/02/01/beyond-the-gimmick-implementing-effective-machine-learning-vb-live/ […]

        •  A gentle introduction to support vector machines using R
          Posted by K on February 7, 2017 at 10:27 am

          Introduction Most machine learning algorithms involve minimising an error measure of some kind (this measure is often called an objective function or loss function).  For example, the error measure in linear regression problems is the famous mean squared error – i.e. the averaged sum of the squared differences between the predicted and actual values. Like the […]

        • The dark side of data science
          Posted by K on January 17, 2017 at 10:38 am

          Data scientists are sometimes blind to the possibility that the predictions of their algorithms can have unforeseen negative effects on people. Ethical or social implications are easy to overlook when one finds interesting new patterns in data, especially if they promise significant financial gains. The Centrelink debt recovery debacle, recently reported in the Australian media, […]


        Carlosfrom Carlos Gerardo Prieto Alvarez. . .

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