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Learning Analytics

Learning

Analytics

Learning analytics (LA) is….

The collection and analysis of usage data associated with student

learning.

The purpose of LA is….

To observe and understand learning behaviors in order to enable

appropriate interventions

http://blog.edmentum.com/personalized-learning-design-your-framework

We have all this data. You have to tell us how to make use of it to improve our teaching !

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Learning Analytics CycleLe

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An

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Cyc

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Educators use analytics to:

• Monitor the learning process

• Explore student data

• Identify problems

• Discover patterns

• Find early indicators for success

• Find early indicators for poor marks or drop-out

• Assess usefulness of learning materials

• Increase awareness, reflect and self reflect

• Increase understanding of learning environments

• Intervene, supervise, advise and assist

• Improve teaching, resources and the environment

Learners use analytics to:

• Monitor their own activities and interactions

• Monitor the learning process

• Compare their activity with that of others

• Increase awareness, reflect and self reflect

• Improve discussion participation

• Improve learning behavior

• Improve performance

• Become better learners

• Learn!

Learning Analytics

Staf

f

Admin Data

Activity DataPredict likelihood of

withdrawal

Predict module grades

View profile of student interactions

Module Outcome Model

Retention Model

Activity Profile

Stu

de

nt

Comparison to similar students

Cluster students

Which things can we change that could

make a difference?

Administrative Data Activity Data

Academic performance at

entrance

UCAS Application

Attendance

Engagement

Contact with support services

VLE Usage

Library Usage

Proximity Door access

Social background Module Grades

Course Enrolment

Fees

Engagement and Academic Integration

Predictive Model

Demographics Contact with tutors

Campus PC Usage

Social interaction

Possible future data source

Student factors

Administrative Data

• Student Administration System

• Known at time of enrolment

Activity Data

• User interaction with a system

• Patterns of usage

• Real time

• Collected at scale

• Change over time

Initial assessment of

risk

On going assessment of

risk

What is Learning Analytics?

Learning Analytics

Educational Data

Mining

Academic Analytics

Predictive modellingExtract value from big data sets

Business Intelligence applied to education at an institutional, regional and national level

Understand how students are learning and optimise the learning process

2012 Jasig Sakai Conference 28

Learning Analytics:

The use of analytic techniques to help targetinstructional, curricular, and support

resources to support the achievement of specific learning goals through applications that directly influence educational practice

van Barneveld, Arnold, & Campbell, 2012adapted from Bach

2012 Jasig Sakai Conference 29

Educational Data Mining:

A process for analyzing datacollected during teaching and

learning to test learning theoriesand inform educational practice

Bienkowski, Feng, & Means, 2012

2012 Jasig Sakai Conference 30

Analytics at Your Institution RIGHT NOW

Business / Academic Analytics:

A process for providing higher education institutions with the data

necessary to support operational and financial decision making

van Barneveld, Arnold, & Campbell, 2012adapted from Goldstein and Katz

Many, Many concerns

Privacy

Security

Ethics

Ownership

Technical infrastructure and protocols

Skills needed?

References

• Ferguson, R. (2013, July 10). Cassandra Colvin. Retrieved July 9, 2016, from http://www.slideshare.net/R3beccaF/planning-for-learning-analytics?qid=220f51f6-e546-4e6c-804c-9cc7ca02f9b6&v=&b=&from_search=24

• Harfield, Timothy. (2014) Learning Analytics Whatis it ? Why do it ? And How ?. http://www.slideshare.net/tdharfield/learning-analytics-what-is-it-why-do-it-and-how

• Khalil, M. & Ebner, M. (2015). Learning Analytics: Principles and Constraints. In Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2015. pp. 1326-1336. Chesapeake, VA: AACE

• Lonn, S. (2012, June 11). J D freeman. Retrieved July 9, 2016, from http://www.slideshare.net/stevelonn/learning-analytics-101

Type of analytics Who Benefits?

Course-level: social networks, conceptual development, language analysis

Learners, faculty

Aggregate: predictive modeling,patterns of success/failure

Learners, faculty

Institutional: learner profiles,performance of academics, knowledge flow

Administrators, funders, marketing

Regional (state/provincial): comparisons between schools

Funders, administrators

National & International National governments

Identify questions

• Which elements are learners struggling with?

• Which sections engage them the most?

• What prompts them to ask questions?

• Are they finding assessment challenging?

• What misconceptions have learners shown?

• Are there any accessibility issues?

• How can analytics be used to obtain desired

learning outcomes?