Show me the data! Actionable insight from open courses

transcript

Analytics

“actionable insights through problem definition and the application of

statistical models and analysis against existing and/or simulated future data”

Cooper, A. 2012 – Cetis Analytics Series What-is-Analytics-Vol1-No-5

Overview

Data Analytic

Overview

Data Analytic Insight

Overview

Who?InstitutionTutorSelf…EducationalCommercial

Overview

How?Social networkDiscourse ContentDispositionContext…Administration

Overview

What?PlatformService…AvailabilityAccess

Overview

Misconception detection

Daphne Koller

Optimal Video Length for Student Engagement

Philip Guo

Deconstructing Disengagement

Kizilcec, R. F., Piech, C., & Schneider, E. (2013)Deconstructing disengagement: analyzing learner subpopulations in massive open online courses

©Abode of Chaos 2011-11-10 16:44:58http://www.flickr.com/40936370@N00/6332465890/

Data Access

Data Shapes

Data Flows

©Patrick Goossens 2009-03-01 12:11:05http://www.flickr.com/8862589@N07/3333965118/

YouTube Analytics

Google Analytics

http://mashe.hawksey.info/?p=15238

Google Analytics

Define events to track in GA

Google Analytics

Define events to track in GADevelop custom

segmentations with eye on

actionable insight

Google Analytics

Define events to track in GADevelop custom

segmentations with eye on

actionable insight

Provide data using developed templated

queries

Canvas API

Network effects

The social network diagrams can be used to identify:• isolated

students• group

malfunction• users that

are information brokers

Hansen, D. L., Shneiderman, B., & Smith, M. (2010). Visualizing threaded conversation networks: mining message boards and email lists for actionable insights.

ocTEL (WordPress)

Analytically cloaked

“Learning and knowledge creation is often distributed across multiple media and sites in

networked environments. Traces of such activity may be fragmented across multiple

logs and may not match analytic needs.”

Suthers, D. D., & Rosen, D. (2011). A unified framework for multi-level analysis of distributed learning

Count% of Total

Total Inputs 250

# Matched 178 71.20%

# No Match 72 28.80%

# Bad Input 0 0.00%

Count% of Total

Total Inputs 250

# Matched 178 71.20%

# No Match 72 28.80%

# Bad Input 0 0.00%

In sample 41% (n.103) emails returned bio

Count% of Total

Total Inputs 250

# Matched 178 71.20%

# No Match 72 28.80%

# Bad Input 0 0.00%

In sample 41% (n.103) emails returned bio

API returns other social profiles

• Detecting and Analyzing Subpopulations within Connectivist MOOCs

• Retrospective investigation into learner subpopulation detection within the connectivist courses.

• Using free and open source tools we will attempt to resolve activity data from multiple sources to permit the analysis of any engagement patterns.

Summary

Thank you!Questions?

Martin Hawkseymashe.hawksey.info

@mhawksey