The document discusses learning analytics, defined as the measurement and analysis of learner data to optimize educational environments. It highlights the importance of data sources, the implementation process, and ethical considerations. Key takeaways include the necessity for actionable insights and stakeholder involvement to effectively enhance student success and institutional competitiveness.

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Learning Analytics
Your next movement towards the future
of education
Jose A Omedes
Jose.omedes@itoptraining.com

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What raises interest in LA area?
Analysis of around 300 posts on the topic
20.11%
11.41%
9.24%
8.70%8.15%
4.89%

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Index
• Defining Learning Analytics
• The three elements of analytics: data, analysis and action
• Learning Analytics maturity and the predictive bridge.
• Learning Analytics benefits and experiences
• A new learning era
• Implementing learning analytics
• Learning analytics ethics
• Key take aways

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Learning Analytics Defined
“Learning analytics is the measurement, collection, analysis and
reporting of data about learners and their contexts, for purposes
of understanding and optimizing learning and the environments
in which it occurs”
International conference on learning analytics

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Learning Analytics Defined
Learning analytics is the measurement, collection, analysis and reporting of data about
learners and their contexts, for purposes of understanding and optimizing learning and
the environments in which it occurs
DATA
Basic asset.
Raw material
to be transformed into
analytical insights.
ANALYSIS
Process to add
intelligence
to data using
algorithms.
ACTION
Critical step towards
achieving the purpose:
Understanding
& optimizing learning
International conference on learning analytics

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Learning Analytics and EDM
Educational Data Mining (EDM)
EDM focuses on the development of methods for exploring the
unique types of data that come from an educational context. […]
the objective of data mining in education is largely to improve
learning […]
Handbook of educational data mining
Educational Data Mining (EDM) ≈ Learning Analytics

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LA and EDM on Google

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Data Types
Internal External
PROVIDED / OBSERVED
Learning Analytics
INFERRED
DERIVED
Learning
Analytics
Algorithms
PROVIDED: Consciously given
OBSERVED: Recorded automatically
DERIVED: Produced from other data
INFERRED: Produced using analytics
Usually based on correlation
between data sets

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Data Sources
Demographic Data
Not Sensitive
Sensitive
Name, birthdate, Sex
Ethnicity, Disability, Scholarship
Academic Data
Prior Performance
Current Performance
Learner Content
Maths: A, Physics: B, Electromagnetism: B
Course 1: Assignment 1: 89% Assignment 2: 56%
Course 2: Assignment 1: 35%, Assignment 2: 64%
Essay 1, Group Report B, Chat 1 …
Learning Activity Data Activity Records
2017/10/02- 10:50 Logged into LMS
2017/10/02- 11:50 Accessed Library Catalog
2017/10/02- 12:00 Check out library book “Human body anatomy”
Educational Context Data Context Info
Course 1: start: 2017/09/02, duration: 10 weeks, instructor: Allan Green
Course 2: start: 2017/08/05, duration: 15 weeks, instructor: Mike Brown
External Data
Social Account Profiles
Other apps info
Facebook, Twitter, Google +
eBook apps, xAPI enabled apps …

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Data Sources
SIS
(Student Information
Systems)
LMS / LRS
(Learning Management
System)
Other Internal Systems
External Systems
Learning Analytics
go beyond
the LMS !!!

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Analysis
Process of obtaining insights from data based on
a set of statistical and machine learning based algorithms
ANALYSIS
Data Learning Analytics

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Analytics maturity model
analysis
complexity
(imposes
demands on
data: volume,
type, timeframe,
etc.)
Diagram Source: https://www.ibm.com/developerworks/community/blogs/jfp/entry/the_analytics_maturity_model?lang=en

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Predictive Analytics Bridge
Descriptive
Diagnostic
Predictive
Algorithms
Data
Reactive
Understand the past
Proactive
Influence the present

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Action
Is the overall target of the learning analytics process
No action = Failure
Having in place the internal processes that lead to
action is critical
CultureLeadership

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Is it worth? Benefits
Reduce drop out rates
Increase learners’ performance
Targeted proactive tutoring
Increase retention
Targeted proactive tutoring
Offer personalized learning experiences
Adaptive Learning / Adaptive content
Understand content consumption
patterns & quality issues
Improve content &
course quality
Instructional design

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Is it worth? Benefits
Cost efficient
allocation
Understand which resources work and
which don`t
Data-driven investment decisions
Identify and promote student
success factors
Create student structured pathways
towards graduation
Proactively drive
success
Curriculum design

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Source: https://www.jisc.ac.uk/reports/learning-analytics-in-higher-education
Does it work?

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Does it work?
Source: https://www.jisc.ac.uk/reports/learning-analytics-in-higher-education
Case studies show:
• Validity of the predictive models applied to learning analytics
systems
• Interventions with at-risk students are effective
• There are other benefits to taking a data-driven approach

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A new learning era … or not?
Learning Analytics: The coming third wave
Malcolm Brown, Director, EDUCAUSE Learning Initiative
The Predictive Learning Analytics Revolution
EDUCASE ECAR Working Group Paper
Adaptive Learning Holds Promise for the Future
of Higher Education BARNES6NOBLE at EDUCATION DIVE

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Reality …
The world is more and more data-driven … Education is no exception.
“Learning analytics” is an important tool to improve education and to
make high education institutions more competitive.
“Learning analytics” are successful only if there is action as a result of
its implementation.
Institutions must cross the predictive analytics bridge to benefit from
a new way of driving students success.

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The decision automation leap
Prescriptive Prescriptive
Human supported Fully automated
Learning Analytics

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The decision automation leap
Impressive and completely
automated
Really impressive !!!
80% Unknown …
Never forget the human factor in education !!!

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Implementing Learning Analytics
How do I implement my
learning analytics project?

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Implementing Learning Analytics
1 Start small
Duration
Scope
2 Don’t focus on technology Focus on specific problems
you want to address
3 Go after quick wins
Show there are positive outcomes
and possible impact
4
Involve from day one
all the critical stakeholders
Don’t forget the people that
would use the technology in
the end

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Learning Analytics Readiness
Understand whether institutions are ready for learning analytics from multiple dimensions
LEARNING ANALYTICS
READINESS JISC
Culture & Vision
Ethics & Legal Issues
Strategy & Investment
Structure & Governance
Technology & Data
LEARNING ANALYTICS
READINESS INSTRUMENT
Culture & Processes
Data Management Expertise
Data Analysis Expertise
Governance / Infrastructure
Readiness perception
Kimberly E. Arnold
Steven Lonn
Matthew D. Pistilli
ANALYTICS MATURITY INDEX

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What is Readiness about?
Strategy & Vision
Culture
Governance and Processes
Ethics and Legal Issues
Investment
Technology
Data
ACTION
ANALYTICS MATURITY INDEX

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Don’t get trapped by the readiness loop
Strategy & Vision
Culture
Governance and Processes
Ethics and Legal Issues
Investment
Technology
Data
ACTION
Readiness
Assessment
1 Start small
2 Don’t focus on technology
3 Go after quick wins
4
Involve from day one
all the critical stakeholders
Start your seed project!

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Technology Challenges
Data Capture Not all educational interactions happen in a digital environment
Predictions Accuracy All statistical processes draw conclusions subject to an estimated error
Partial View Learning processes go beyond what data tell us
Data Literacy Analytics consumers need the skills to interpret analytics properly
Data Variety Combine data coming from multiple sources and systems
Comparable Analytics
Not all systems use same algorithms or measure the same way
Open standards?

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Learning Analytics Ethics
Source: Accenture Tecnology: https://www.accenture.com/us-en/insight-data-ethics
Data Supply Chain
DATA ANALISYS ACTION

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Learning Analytics Ethics
DATA ANALISYS ACTION
• Consent
• Privacy
• Access
• Validity
• Right to opt out
• Safety
• Transparency
• Accuracy
• Validation
• Systematic &
random errors
• Obligation to act
• Failure to act
• Adverse impact
• Abuse / Gaming
• Discrimination /social
status
• Pedagogical impact
Complete ethical issues taxonomy by Sclater: https://analytics.jiscinvolve.org/wp/2015/03/03/a-taxonomy-of-ethical-legal-and-logistical-issues-of-learning-analytics-v1-0/

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Key Take Aways
• Learning analytics are an important asset for institutions providing
important benefits but also competitiveness
• Institutions must cross the predictive analytics bridge and start
influencing the future by changing the present.
• Learning analytics are about action. No action = Failure. Get ready to
act and change !
• Launch your analytics seed project: start small, don’t focus on
technology, go after quick wins, involve stakeholders.
• Develop your code of conduct and run analytics protecting all the
stakeholders and specially the students.

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What raises interest in LA area?
Analysis of around 300 posts on the topic
20.11%
11.41%
9.24%
8.70%8.15%
4.89%

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For more info …
Thanks a lot!
Jose A Omedes
Research and Development Director
Jose.omedes@itoptraining.com