Leaping the chasm moving from buzzwords to implementation of learning analytics
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Leaping the chasm: moving from buzzwords to implementation of learning analytics. George Siemens Technology Enhanced Knowledge Research Institute (TEKRI) Athabasca University February 1, 2012. Slides (with citations and links) http://www.slideshare.net/gsiemens/educause2012.

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Leaping the chasm: moving from buzzwords to implementation of learning analytics

George Siemens

Technology Enhanced Knowledge Research Institute (TEKRI)

Athabasca University

February 1, 2012


Slides (with citations and links)

http://www.slideshare.net/gsiemens/educause2012


1. Roots of learning analytics and context of deployment

2. Becoming at data-intensive university


1. Roots of learning analytics and context of deployment

2. Becoming at data-intensive university


Won’t make the argument for why analytics are growing


“Imagination no longer comes as cheaply as it did in the past. The slightest move in the virtual landscape has to be paid for in lines of code.”

Latour (2007)


What’s different today?

volume (apparently, there’s lots of data)

velocity (processing capacity)

variety (internet of things, social media)

variability (meaning variance)


“Analytics, and the data and research that fuel it, offers the potential to identify broken models and promising practices, to explain them, and to propagate those practices.”

Grajek, 2011


http://www.dataqualitycampaign.org/

A different way of thinking and functioning


EMC: Data Science Revealed: A Data-Driven Glimpse into the Burgeoning New Field


Reading a book (or any interaction with data) is analytics


Check my activity

Predictive Analytics Reporting


Methods, techniques & evidence


Metrics, or analytics on analytics, are hard (and contextual)

What is the impact of effective use of data?

Argument: “more precise and accurate information should facilitate greater use of information in decision making and therefore lead to higher firm performance.”

Brynjolfsson, Hitt, Kim (2011)


LA resources, publications, archive:


Student success/completion

Astin (1996)

Tinto (1993)


Distributed, multi-level analytics

Suthers & Rosen (2011)


Attention metadata

Duval (2011)


Learning networks, crowds, communities

Haythornthwaite (2011)


Discourse analysis (automated and manual)

De Liddo & Buckingham Shum (2011)


Social learning analytics

Buckingham Shum & Ferguson (2011)


Participatory learning and reputation

Clow & Makriyannis (2011)


Early warning

Macfayden & Dawson (2010)

Campbell et al (2006)


Semantic Web to Social Machines

“People do the creative work and the machine does the administration”

Web=unlimited scaling of info

Web should=unlimited social interaction

Hendler & Berners-Lee (2010)


1. Roots of learning analytics and context of deployment

2. Becoming at data-intensive university


We collect enough data. We need to focus on connecting.

Multiple data sources:

Social media

University help resources

LMS

Student information system

Course progression, etc


Privacy as a transactional entity

Share my data to improve learning support from the university (school)


“All-embracing technique is in fact the consciousness of the mechanized world. Technique integrates everything. It avoids shock and sensational events”

Ellul, 1964


Analytics as a complex system:

multiple interacting entities,

more meaningful when connected


Challenges:

Broadening scope of data capture

- data outside of the current model of LMS

- sociometer: Choudhury & Pentland (2002)

- classroom/library/support services,

- quantified self

Timeliness of data (real-time analytics)


Three communities that don’t communicate

Systems/enterprise level

Researchers

Educators (cobbling)


What does a data-intensive university look like?


Kron, et al (2011)


Acquisition: how do we get the data – structured and unstructured?

Storage: how do we store large quantities?

Cleaning: how do we get the data in a working format

Integration: How do we “harmonize” varying data sets together

Analysis: which tools and methods should be used?

Representation/visualization: tools and methods to communicate important ideas


“A university where staff and students understand data and, regardless of its volume and diversity, can use it and reuse it, store and curate it, apply and develop the analytical tools to interpret it.”


Principles of a systems-wide analytics tool

1. Algorithms should be open, customizable for context

2. Students should see what the organization sees

3. Analytics engine as a platform: open for all researchers and organizations to build on

4. Connect analytics strategies and tools: APIs

5. Integrate with existing open tools

6. Modularized and extensible


Learning Analytics & Knowledge 2012:

Vancouver

http://lak12.sites.olt.ubc.ca/

Open online course:

http://lak12.mooc.ca/


Twitter/randomly popular social media: gsiemens

www.learninganalytics.net

http://www.solaresearch.org/


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