1. ORLA – Ireland’s Online Library for Learning Analytics
Mark Glynn (DCU)
Geraldine Gray (ITB)
Lee O’Farrell (National Forum)
Mary O’Toole (Griffith College)
The National Forum for the Enhancement of Teaching and Learning is a new sectoral network established to enhance teaching and learning in Irish Higher Education through:
Advocacy and leadership
Connecting excellent practice
Collaboration
Innovation and the mobilisation of expertise throughout the entire higher education system in Ireland
In addition, it aims to find efficient ways of maximising teaching and learning excellence by focusing the energy and expertise of the sector on specific, strategic enhancement themes – with a view to addressing challenges of collective importance to the sector.
National Forum for the Enhancement of Teaching and Learning in Higher Education

2. LA & EDM for Learning Impact Project
National Forum for the Enhancement of Teaching and Learning in Higher Education

3. ORLA (Online Resource for Learning Analytics)
I have all this data; what can I DO with it?
What is Learning Analytics?
How can we design an effective implementation at our institution?
National Forum for the Enhancement of Teaching and Learning in Higher Education

4. What does ORLA include? Policy Development Data Protection & GDPR
Student Guides
Areas for research
Sources of data & potential applications
Choosing the right product(s)
In-house Reporting Platforms
Staff Guide
Implementation Checklist
Existing features in your VLE
Vendor profiles
Using analytics in your module
Effective Interventions
Data Quality
Identifying stakeholders

5. IT & Infrastructure Policy Development Data Protection & GDPR
Student Guides
Areas for research
Sources of data & potential applications
Choosing the right product(s)
In-house Reporting Platforms
Staff Guide
Implementation Checklist
Existing features in your VLE
Vendor profiles
Using analytics in your module
Effective Interventions
Data Quality
Identifying stakeholders

6. Outputs- Interventions Group
Student guide to using Feedback
(Steps to take to act on data)
Lecturer case studies
(How they use data to inform teaching?)
Measures for quantifying success
(Retention?, at risk students?, student learning?)
A guide to effective interventions
(Escalation steps, different types)
Learning Resources Template
(Template to personalise, available learning resources)
Outputs- Interventions Group

7. Case Study –Griffith College
‘What do you want to know about your learners?’
Cross Faculty research
Lee O’Farrell, Dr Fiona O’Riordan, Mary O’Toole
‘Do you have any special needs or require additional assistance for this module? And how can I help you?’
Case Study –Griffith College

8. Data aggregation and modeling group: perspective
Interactions with content management systems / virtual learning environments
Demographic information
Academic performance
Previous academic history
Interactions with the library
Interactions with student services
Information Communication Technology footprint
Scale: Student  Module  Programme  School …
Time: Now  Weekly  Semester  Historical profile
Utopian model: available in a single place; data warehouse comprised with all necessary KPI – Mammoth task
. . In the mean time what can be done that is useful and achievable

9. Data aggregation and modeling group: outputs
Potential research questions
Data Stories
Sample conceptual Model
Guide to data quality issues
A step by step guide on how to extract data into usable information
Data aggregation and modeling group: outputs
2)      A list of potential research questions/areas relating to Learning Analytics which could be further explored
3)      A practical, step-by-step guide for individual users showing how to extract data from existing resources and combine them into a single analytic repository (ie Excel) as well as how to manipulate the data to begin to turn it into useful and useable information
4)      A simple story showing broadly how data can be used to answer questions.
5)      A sample conceptual model of the data that is available in a given HEI, broken down by business area and system, including who is custodian of the data.
6)      A guide data quality issues

10. Ethics

11. Responsibilities of Data Controllers
Obtain and process student data fairly
Keep it only for one or more specified and lawful purposes
Process it only in ways compatible with the purposes for which it was initially given
Keep it safe and secure
Keep it accurate and up-to-date
Ensure that it is adequate, relevant and not excessive
Retain it no longer than is necessary for the specified purpose or purposes
Give a copy of his/her personal data to any student or graduate, on request.
Following principles of article 5 gdpr
Print out 20 copies

12. Lawfulness of Processing data
Consent
Necessary for the performance of contract
Necessary for legal obligation
Necessary in legitimate interests pursued by controller or third party
 the data subject has given consent to the processing of his or her personal data for one or more specific purposes;
 processing is necessary for the performance of a contract to which the data subject is party or in order to take steps at the request of the data subject prior to entering into a contract;
 processing is necessary for compliance with a legal obligation to which the controller is subject;
 processing is necessary in order to protect the vital interests of the data subject or of another natural person;
 processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller;
 processing is necessary for the purposes of the legitimate interests pursued by the controller or by a third party, except where such interests are overridden by the interests or fundamental rights and freedoms of the data subject which require protection of personal data, in particular where the data subject is a child. (This point shall not apply to processing carried out by public authorities in the performance of their tasks).

13. Recommendations