Analytics: Improving teaching and learning

Using analytics to improve the teaching and learning environment

George SiemensNovember 21, 2011

Sydney, Australia Data Intensive University Forum

“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.”

We’re living in data. We’re all doing analytics.

Next-Generation Analytics. Analytics is growing along three key dimensions:

(1) From traditional offline analytics to in-line embedded analytics. This has been the focus for many efforts in the past and will continue to be an important focus for analytics.

(2)From analyzing historical data to explain what happened to analyzing historical and real-time data from multiple systems to simulate and predict the future.Over the next three years, analytics will mature along a third dimension, (3) from structured and simple data analyzed by individuals to analysis of complex information of many types (text, video, etc…) from many systems supporting a collaborative decision process that brings multiple people together to analyze, brainstorm and make decisions.

Data reveals our sentiments, our attitudes,our social connections,our intentions,and what we might do next.

Learning Analytics

Business Intelligence

Big Data

EDM

Statistical methods

Intelligent Tutors

Personalization

Adaptive learning

Roots of learning analytics

Analytics processesData Sources Repositories Tools and

MonitoringAnalytics Methods

Permissions

LMS, library, social media, support services, mobiles, profile, attendance

Data warehouse (institutional, national)

Dashboards, visualization, query & drill down, automated monitoring, “quantified self” monitoring

Predictive, course-path, social network, data mining, learner profile

Admin, faculty, learners, reporting agencies

Siemens, Long, 2011. EDUCUASE Review

1. Data Trails

2. Machine-human readable content

3. Learner Profile Development

4. Analytics tools and Methods

5. Prediction & Intervention

6. Adapting and personalizing

Siemens, Long, 2011. EDUCAUSE Review

Open Learning Analytics

Challenge: Organizational

capacity building for analytics deployment and use

Why invest in analytics?

1. Unbox the “black box of learning”

2. Identify students at the margins

3. Adapt teaching process to context/learners

4. Target support resources to those who need it

5. Personalize and adapt content

6. More effective planning and allocation of institutional resources

7. (in the future) Restructure education processes to account for the architecture of information today: social, network, fragmented participatory

“Knewton analyzes learning materials based on thousands of data points—concepts, structure, difficulty level, media format—and uses sophisticated algorithms to piece together the perfect bundle of content for each student, every day.

The more students who use the platform, the more accurate it becomes.”

Predictive Analytics Reporting

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Open online course: Learning AnalyticsJanuary 23 - March 17, 2012

http://www.solaresearch.org/

Simon Buckingham Shum

Shane Dawson Erik Duval Dragan Gasevic George Siemens

change.mooc.ca

Twitter: gsiemens

www.elearnspace.org/blog

http://www.solaresearch.org/

Learning Analytics & Knowledge 2012: Vancouver

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