Unveiling Key Concepts of Learner Analytics
Introduction to
Learner Analytics
Key Concepts
24/11/21
The aim of this
session :
•
Learner Analytics is a developing area for universities – but
it is not always clear what is meant by the term. This session
will cover key aspects such as skills needed, technology
required, the data that may be used, as well as practical
implementation and ethical issues.
•
The purpose of this event is to give those working in a university
environment an understanding of the main aspects, challenges
and benefits of a Learner Analytics project. It will also aim to
facilitate networking between those interested in this area.
•
A big question: Individual projects in each institution or a trans-
national effort?
Session
outcomes:
Gain an understanding of core components of a
Learner Analytics initiative:
o
Data issues.
o
Descriptive dashboards
o
Predictive models
o
Using the outcomes
o
Skills and Tech
o
GDPR, Ethics and Digital Citizenship
Co-create an example of a Learner Analytics project
will other delegates.
Network with other professionals interested in this
area.
Programme
for today:
1.
Introduction to Key Concepts
2.
UEA as an example
3.
Skills and Technology
4.
GDPR and Ethics (group
discussion)
5.
Group work
6.
Bringing it back together
Key aspects of
Learner
Analytics:
A learning
analytics
architecture
example:
Descriptive
statistics:
Predictive
s
tatistics:
KPIs 1:
Identifying
variables
KPIs 2:
Shifting
variables
Case
Management
Feedback
L
oop
Fitbit for
students…
•
Should a student see
data and if, so why?
What are we looking to
achieve?
•
What are the risks?
•
What are the benefits?
Practical
Concerns:
Skills, Tech
and Process
•
Project Management
•
Process Review
•
Testing
•
Coding language
•
What model?
•
How is code stored?
•
How is code run?
•
How are the results disseminated?
•
How do you ensure operational robustness?
•
Change Control
Ethics and
GDPR
Group discussion.
Five minute
break
We made it half way!
Group work.
Some
questions to
think about:
1.
What do YOU want to achieve with
analytics?
2.
What data do you have available?
3.
How will you disseminate results?
4.
What processes will it impact on?
5.
What problems might there be?
Plenary
session:
Welcome Back!
Finishing off:
Final questions/discussion points.
Please contact me at
g.fincham@uea.ac.uk
if you have any more questions.
Dive into the world of Learner Analytics with a focus on essential aspects such as data issues, predictive models, skills and technology, GDPR, ethics, and more. Explore the core components of Learner Analytics projects and engage in activities aimed at enhancing understanding and collaboration in this evolving field.
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Presentation Transcript
Introduction to Learner Analytics Key Concepts 24/11/21
Learner Analytics is a developing area for universities but it is not always clear what is meant by the term. This session will cover key aspects such as skills needed, technology required, the data that may be used, as well as practical implementation and ethical issues. The aim of this session : The purpose of this event is to give those working in a university environment an understanding of the main aspects, challenges and benefits of a Learner Analytics project. It will also aim to facilitate networking between those interested in this area. A big question: Individual projects in each institution or a trans- national effort?
Gain an understanding of core components of a Learner Analytics initiative: o Data issues. o Descriptive dashboards o Predictive models o Using the outcomes o Skills and Tech o GDPR, Ethics and Digital Citizenship Co-create an example of a Learner Analytics project will other delegates. Network with other professionals interested in this area. Session outcomes:
1. Introduction to Key Concepts 2. UEA as an example 3. Skills and Technology Programme for today: 4. GDPR and Ethics (group discussion) 5. Group work 6. Bringing it back together
Architecture what are you trying to achieve? GDPR opt in, or object? Comparative or internal? Key aspects of Learner Analytics: Data: calculated KPIs or grey data ? Predictive or descriptive? Links to process Visible to students? Measures of success
A learning analytics architecture example:
Descriptive statistics:
Predictive statistics:
KPIs 1: Identifying variables
KPIs 2: Shifting variables
Case Management Feedback Loop
Should a student see data and if, so why? What are we looking to achieve? What are the risks? What are the benefits? Fitbit for students
Project Management Process Review Practical Concerns: Skills, Tech and Process Testing Coding language What model? How is code stored? How is code run? How are the results disseminated? How do you ensure operational robustness? Change Control
Ethics and GDPR Group discussion.
Five minute break We made it half way!
Group work. 1. What do YOU want to achieve with analytics? 2. What data do you have available? 3. How will you disseminate results? 4. What processes will it impact on? 5. What problems might there be? Some questions to think about:
Plenary session: Welcome Back!
Final questions/discussion points. Finishing off: Please contact me at g.fincham@uea.ac.uk if you have any more questions.
