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

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

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

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

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

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

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

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

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

Ethics

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

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).

Recommendations