Published with Simon Knight in The Conversation: July 24, 2017 6.09am AEST

Artificial intelligence (AI) enables Siri to recognise your question, Google to correct your spelling, and tools such as Kinect to track you as you move around the room.

Data big and small have come to education, from creating online platforms to increasing standardised assessments. But how can AI help us use and improve it?

AI has a long history with education

Researchers in AI in education have been investigating how the two intersect for several decades. While it’s tempting to think that the primary dream for AI in education is to reduce marking load —…

“…analogy is anything but a bitty blip — rather, it’s the very blue that fills the whole sky of cognition — analogy is everything, or very nearly so, in my view” — Douglas Hofstadter (“Analogy as the core of cognition.” The analogical mind: Perspectives from cognitive science (2001): 499–538)

In their seminal book, Metaphors We Live By, George Lakoff and Mark Johnson argued that metaphors have cognitive implications, they aren’t mere idioms. Among other things, they map across domains enabling us to think of the source domain in terms of the target. In this thought piece, we reflect on the metaphors that are dominating current popular discourse about “Big Data”, and the not-so-obvious dangers they bring.

A joint reflection with Kailash Awati, which he’s hosting…

As the Carnegie Foundation Summit for Improvement in Education unfolds, I’ve revisited some personal reflections on 2 workshops and a lecture with Tony Bryk (President, Carnegie Foundation for the Advancement of Teaching), hosted last May by Ruth Deakin Crick at University of Bristol. What follows after a brief introduction to the concept of NICs, are my thoughts on the intersection of NICs with Learning Analytics.

I made a number of connection points between the features of the DEED+NIC approach, and learning analytics, which I’ll highlight with a sidebar.

A slide from Tony Bryk’s presentation

Introduction: NICs

If you know me then you know that the ideas of the…

Learning Analytics sits at the intersection of Computer Science (drawing on sub-disciplines such as Data Mining, Information Retrieval, Information Visualization, Web Semantics) and Education (e.g. Educational Research, Measurement Science, Learning Sciences, Computer-Supported Collaborative Learning, e-Assessment). In my view it is an educational incarnation of Human-Centred Informatics (the effective design of human/digital information systems) and arguably Computational Social Science where social phenomena and computational modelling meet (elegantly introduced by Hannah Wallach in her recent Medium posting, and explored in relation to Complexity Science elsewhere). Extending several recent talks (e.g.

Simon Buckingham Shum

Learning/Analytics/Sensemaking • Connected Intelligence Centre, U. Technology Sydney twitter@sbuckshum • http://cic.uts.edu.auhttp://Simon.BuckinghamShum.net

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