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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

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

George Siemens

Technology Enhanced Knowledge Research Institute (TEKRI)

Athabasca University

February 1, 2012

slide2
Slides (with citations and links)

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

slide3
1. Roots of learning analytics and context of deployment

2. Becoming at data-intensive university

slide4
1. Roots of learning analytics and context of deployment

2. Becoming at data-intensive university

slide6
“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
What’s different today?

volume (apparently, there’s lots of data)

velocity (processing capacity)

variety (internet of things, social media)

variability (meaning variance)

slide8
“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

slide9
http://www.dataqualitycampaign.org/

A different way of thinking and functioning

slide18
Check my activity

Predictive Analytics Reporting

metrics or analytics on analytics are hard and contextual
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)

slide22
Student success/completion

Astin (1996)

Tinto (1993)

slide24
Attention metadata

Duval (2011)

slide26
Discourse analysis (automated and manual)

De Liddo & Buckingham Shum (2011)

slide27
Social learning analytics

Buckingham Shum & Ferguson (2011)

slide28
Participatory learning and reputation

Clow & Makriyannis (2011)

slide29
Early warning

Macfayden & Dawson (2010)

Campbell et al (2006)

semantic web to social machines
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)

slide31
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
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
Privacy as a transactional entity

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

slide34
“All-embracing technique is in fact the consciousness of the mechanized world. Technique integrates everything. It avoids shock and sensational events”

Ellul, 1964

slide35
Analytics as a complex system:

multiple interacting entities,

more meaningful when connected

slide36
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
Three communities that don’t communicate

Systems/enterprise level

Researchers

Educators (cobbling)

slide40
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

slide41
“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
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

slide45
Learning Analytics & Knowledge 2012:

Vancouver

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

Open online course:

http://lak12.mooc.ca/

slide46
Twitter/randomly popular social media: gsiemens

www.learninganalytics.net

http://www.solaresearch.org/

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