Archive Page 2

Doug’s blogs from LAK14

I have not made it to LAK14 but am following the conference through Doug Clow’s blog posts.  I commend them to you if you are interested in Learning Analytics.

See: http://dougclow.org/2014/03/25/lak14-tuesday-am-3/ 

 

 

Why Educators Need to Know Learning Theory

I highly recommend the following blog post on Learning Theory: Why Educators Need to Know Learning Theory.

Personalisation for Accessibility (EU4ALL)

An animation illustrating the principles of personalisation for accessibility. Produced by the EU4ALL project in 2011.

JISC Digital Festival 2014 – Notes Day 1 (Cont…)

The other presentation I attended on day 1 of the festival was that given by Prof. David de Roure of the  University of Oxford.  He spoke on “Big Data for the Social Sciences“, which I hoped would be relevant to my own work on Learning Analytics.  This blog post is my notes from his talk.

How does technology get used in research?

-> What is this new “big data” and what does it tell us?

  • Big data does not respect disciplinary boundaries
  • Data has been around a long time
  • There is a lot of “hype” around big data that has led to inflated expectations of it
  • Can consider 2013 as the year we sort to define big data and 2014 the year we begun to use it effectively
  • It is big data because of both the velocity and volume of the data being generated
  • “Data deluge” is now a phenomenon across the disciplines
  • In the past analysis moved from the universities to business, now it is from the business world to the universities.
  • There is  huge unsatisfied demand for “data scientists”
  • Mores Law vs The Big Social

Moore's Law vs Big Social diagram

  • We use digital tools because it is the ecosystem – Research 2.0
  • What is the relevance of Social Science to Big Data?
    • We need to think through the implications
  • RCUK’s definition of “big data” is: big enough that we can’t deal with it as we did before
  • Why do we want it?
    • To do things in new ways
    • To do new things
    • e.g. Twitter data – we can look at the evolution of social processes in real-time
  • We need the expertise of those from classical Social Science
    • e.g. food vs consumption
    • can obtain new data from new sources (e.g. supermarket loyalty cards)
  • We can use different data sets to correlate
  • Real-time uses of big data, e.g. Twitter
    • spread of infectious diseases
    • riots
  • Visualisation can lead to better analysis
  • Underpinned by available infrastructure
  • Wikipedia is an example of  a social medium
    • behaviorally it is socially constructed
    • different in different countries/languages

— end —

A bit of light relief!

A little humour as light relief.  Open the image and enlarge to view the jokes or visit the source: http://www.tickld.com/x/20-jokes-that-only-intellectuals-will-understand

20 Interlectual Jokes

Internal project proposal: Learning Analytics for Disabled Students in STEM subjects

I am currently working on an internal project proposal: Learning Analytics for Disabled Students in STEM subjects (LA4DS-STEM). Hopefully it will run from April – December 2014.

The LA4DS-STEM project will review the potential of Learning Analytics in higher education, specifically in STEM, and with an emphasis on supporting disabled students and facilitating accessibility enhancements.

Learning Analytics is defined as the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.  The LA4DS-STEM project will specifically explore the following STEM application areas for Learning Analytics. A key output of the project will be an external funding bid for a larger-scale collaborative project. The work of LA4DS-STEM will inform pilots in this project. Provide envisaged benefits are confirmed, this should lead to enterprise level implementation within the OU and across HE.

The findings of the LA4DS-STEM project will be disseminated, firstly throughout the Science and MCT faculties, then to the wider university. External dissemination will highlight the OU’s lead in this field.

JISC Digital Festival – Notes (Day 2)

I have spent most of the morning interacting with reps of the various exhibitors here.  Now to rest my legs I have settled down in Hall 1 for the keynote by  Sugata Mitra, Prof. Of Educational Technology at Newcastle University.

Notes from Keynote

Sugata was the originator of the ‘Hole in the Wall Experiment‘. He plans to review the last 15 years of work and review trends.

The hole in the wall experiment

ATM like computer  in a hole in the wall. They (the slum kids in New Dehli) did not know English and the interfaces were in English. Street children were browsing within 6 to 8 hours and teaching each other.  Conclusion groups of children left with a computer would reach the level of the average office secretary in the West in about 9 months. [Video shown of this work].

The children’s achievement of their proficiency happened because not despite of the absence of an adult teacher/supervisor.  After 4 to 5 months the teachers reported that their English was much improved. Discovered they were using a search engine to find quality content and copying it down on to paper. Question – why we’re they copying down the right things?  They seemed to know what they were writing.  Then gave them educational objects.  Working in groups they seemed to be able to locate the right information and select it.  Groups of children could reach educational objectives of their own if they wished to. People supposed that when got to in depth learning or skills acquisition they would need human intervention. However, could not find the limits of this learning.

In England turned the hole in the wall upside down. Created the chaotic environment of the hole in the wall inside the clasroom with just a few computers. Made up some rules: free discussion and free movement allowed. In period 2008-2010 this led to the descriptor of self- organising learning events. E.g. For 7 year-olds “why is a polar bears coat white”.  Given the the choice between a hard and easy question the children opted for the harder questions. They were able to do GCSE questions about 6 to 7 years ahead of time. Called these Self Organising Learning Environments (SOLE).

In other countries around the world similar results.  C.F emergent phenomena or self ordering or spontaneous order in the Natural Sciences.  Tested limit of this method in Southern India. Research Question: can 11 year-olds learn the process of DNA replication?  Experiment was a failure but the students self studied why DNA replication sometime went wrong causing disease.  Pre and post testing showed those working 10 years ahead of their time. Used a non scientist and the method of the grandmother.  Using an older adult to stand behind and encourage.

[Slides: Schools in the cloud]

Constructing 7 pilots trying to level the playing field in primary education comparing India with UK.

Q&A

Experience with older students?  – Used to think method applied to ages 6 to 14 but beginning to show that it is not restricted to this. Experiences reported with 16-18 year olds, in FE and he is using SOLE approaches in his university courses.


Martyn Cooper

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