Krakow presentation speak_appsmngm_final

Considering Learning Analytics: SpeakApps
and the Application of a Learning Analytics
Reference Model
Mairéad Nic Giolla Mhichíl
Dublin City University
www.speakapps.org

Project Overview
SpeakApps 2 speakapps.eu
Lifelong Learning Programme Nov 2013 – Oct 2014
KA2 LANGUAGES, Accompanying Measures
Development of tools and pedagogical tasks for oral production and spoken interaction
Partners Associated Partners
• Institut Obert de Catalunya
• University of Southern Denmark
• University of Nice
• University of Jÿvaskÿla
• Ruhr-Universitat Bochum
• Polskie Towarzystwo Kulturalne "Mikolaj Kopernik“
• Fundació Pere Closa

SpeakApps...
• Life Long Learning Project, 2011-2012 & 13-14
• Partners and Associate Partners:
– Universitat Oberta de Catalunya, Catalan, English
– Rijksuniversiteit Groningen, Dutch
– University of Jyväskylä, Swedish, Finnish
– Jagiellonian University Krakow, Polish
– Dublin City University, Irish
• Development of tools and pedagogical tasks for oral
production and spoken interaction
• Open Educational Resource using CEFR
• SpeakApps Moodle based Platform

Introduction to Analytics
 Pervasiveness of technology has facilitated the collection of
data and the creation of a variety of data sets, big data
 Application has spread across domains and prompted
business and societal applications
 Google analytics – Adwords etc. (http://www.web2llp.eu/)
 Smart cities – Policing using predicative models to prevent crime
(Santa Cruz police department -
http://edition.cnn.com/2012/07/09/tech/innovation/police-tech/)
 Ultimate aim to inform decision making from resource allocation to
improved services etc.
How? By using a variety of data mining techniques for
discovery of patterns and/ or validation of hypothesis/claims

Educational Analytics
 Data available in education from a variety of sources
 LMS
 Institutional systems Google for education
 User generated content, social networks
 Ferguson (2012)* provides a useful overview of the educational analytics field
and suggests the following divergence in focus between:
 Educational data mining focuses on the technical challenge: How can we
extract value from these big sets of learning-related data?
 Learning analytics focuses on the educational challenge: How can we
optimise opportunities for online learning?
 Academic analytics focuses on the political/economic challenge: How
can we substantially improve learning opportunities and educational results
at national or international levels?
)

Educational Analytics…
 Long and Siemens (2011:32) – aptly describes the challenge:
But using analytics requires that we think carefully about what we need to
know and what data is most likely to tell us what we need to know.
(http://net.educause.edu/ir/library/pdf/ERM1151.pdf)
* See: Ferguson, Rebecca (2012). Learning analytics: drivers, developments and
challenges. International Journal of Technology Enhanced Learning, 4(5/6) pp. 304–317.

Student or Learner Data**
Demographics
• Age, home/term address,
commuting distance, socio-economic
status, family composition,
school attended, census information,
home property value, sibling
activities, census information
Online Behaviour
• Mood and emotional analysis of
Facebook, Twitter, Instagram
activities, friends and their actual
social network, access to VLE
• (Moodle)
Physical Behaviour
• Library access, sports centre, clubs
and societies, network access
yielding co-location with others and
peer groupings, lecture/lab
attendance…
Academic Performance
• Second Level performance,
University exams, course
preferences, performance relative
to peers in school
Slide reproduced and adapted with permission from presentation Glynn, M. (2014) Using the Data from eAssessment,
eAssessment 2014: Final Answer: The question of summative eAssessment, 05th September 2014, University of Dundee
Scotland available from: http://www.slideshare.net/enhancingteaching/optimising-knowledge-assessment-data

Levels / Objectives of Analytics**
Descriptive
• What has happened?
Diagnostic
• Why this happened?
Predictive
• What will happen?
Prescriptive
• What to do?
Slide reproduced and adapted with permission from presentation Glynn, M. (2014) Using the Data from eAssessment,
eAssessment 2014: Final Answer: The question of summative eAssessment, 05th September 2014, University of Dundee
Scotland available from: http://www.slideshare.net/enhancingteaching/optimising-knowledge-assessment-data

Open University Analytics Principles**
Learning analytics is a moral practise which should align with
Learning analytics is a moral practise which should align with
core organisational principles
core organisational principles
The purpose and boundaries regarding the use of learning
The purpose and boundaries regarding the use of learning
Students should be engaged as active agents in the
analytics should be well defined and visible
analytics should be well defined and visible
implementation of learning analytics
Students should be engaged as active agents in the
implementation of learning analytics
The organisation should aim to be transparent regarding data
collection and provide students with the opportunity to update
their own data and consent agreements at regular intervals
Modelling and interventions based on analysis of data should
be free from bias and aligned with appropriate theoretical
The organisation should aim to be transparent regarding data
collection and provide students with the opportunity to update
their own data and consent agreements at regular intervals
Modelling and interventions based on analysis of data should
be free from bias and aligned with appropriate theoretical
and pedagogical frameworks wherever possible
and pedagogical frameworks wherever possible
Students are not wholly defined by their visible data or our
Students are not wholly defined by their visible data or our
interpretation of that data
interpretation of that data
Adoption of learning analytics within the organisation
requires broad acceptance of the values and benefits
Adoption of learning analytics within the organisation requires broad
acceptance of the values and benefits (organisational culture) and the
(organisational culture) and the development of appropriate
development of appropriate skills
skills
The organisation has a responsibility to all stakeholders to
use and extract meaning from student data for the benefit of
The organisation has a responsibility to all stakeholders to
use and extract meaning from student data for the benefit of
students where feasible
students where feasible
Slide reproduced and adapted with permission from presentation Glynn, M. (2014) Using the Data
from eAssessment, eAssessment 2014: Final Answer: The question of summative eAssessment,
05th September 2014, University of Dundee Scotland available from:
http://www.slideshare.net/enhancingteaching/optimising-knowledge-assessment-data

Analytics Process*
 Establish the objective or claim of the LA exercise
Three Stage iterative process
1. Data collection and Pre-processing
 Data preparation and cleaning, removal of redundant data etc. time stamps
 Application of established and evolving data mining techniques to complete
this
2. Analytics and action
 Explore/analyse the data to discover patterns
 Data visualisation and representation
3. Post-processing
 Adding new data from additional data sources
 Refining the data set
 Identify new indicators/metrics
 Modify variables of analysis
 Choose a new analytics method
* See: Chatti, M.A., Dyckhoff, A.L., Schroeder, U. and Thüs, H. (2012) ‘A reference model
for learning analytics’, Int. J. Technology Enhanced Learning, Vol. 4, Nos. 5/6, pp.318–33

 What? What kind of data does the system gather, manage,
and use for the analysis?
 Who? Who is targeted by the analysis?
 Why? Why does the system analyse the collected data?
 How? How does the system perform the analysis of the
collected data?
* See: Chatti, M.A., Dyckhoff, A.L., Schroeder, U. and Thüs, H. (2012) ‘A reference model for learning
analytics’, Int. J. Technology Enhanced Learning, Vol. 4, Nos. 5/6, pp.318–33
11
Learning Analytics Reference Model*

 What? Data and Environment:
 Which systems
 Structured and/or unstructured data
 Who? Stakeholder
Teachers
 Students
 Instructional designers
 Institutional stakeholders
12

 Why? Objective
 Monitoring and analysis
 Prediction and intervention
 Tutoring and Mentoring
 Assessment and feedback
Adaptation
Personalization and recommendation
Reflection
 Challenge to identify the appropriate indicators/metrics
13

 How? Method
 Statistics: most LMS produce statistics based on
behavioural data
Data mining techniques and others (long list)
Classification (categories known in advance) many different
techniques from Data mining
Clustering (categories created from the data similar data clustered
together based on similar attributes not known in advance)
Association rules mining leads to the discovery of interesting
associations and correlations within data
Social Network Analysis
…
14

15
Dublin City University’s Moodle Analytics*
What? What kind of data
does the system gather,
manage, and use for the
analysis
Using the generic Moodle Log Data i.e. behavioural data
Who? Who is targeted by
the analysis?
Students and Lecturers
Why? Why does the
system analyse the
collected data?
Students – adapt their behaviour
Teachers – review course
Institutional – monitor engagement particularly of first
year students
How? How does the
system perform the
analysis of the collected
data?
- Model created based on c. six years of user data
based on user participation within courses – identify
which modules are suitable i.e. must posses a strong
confidence level i.e. .6/.7
- Students who are not engaging with the Module are
provided with feedback of engagement – student
centred

DCU Moodle Analytics
Slide reproduced and adapted with permission from presentation Glynn, M. (2014) Using the Data
from eAssessment, eAssessment 2014: Final Answer: The question of summative eAssessment,
05th September 2014, University of Dundee Scotland available from:
http://www.slideshare.net/enhancingteaching/optimising-knowledge-assessment-data

 Claim is that student and teacher oral & video recordings
should be time limited to maintain the attention of the listener
 Currently we recommend a maximum of one minute for
learner recordings and two minutes for teacher recordings
 At present this claim is based on experience
 Evidence to support decision-making which will impact:
 Resource allocation to refine the tool – time limitation
 Learner agency
 Instructional and task design
17
SpeakApps Pilot

18
SpeakApps Pilot & LA Reference Model
What? What kind of data
does the system gather,
manage, and use for the
analysis
LMS data i.e.
technical information i.e. device, browser, versions etc.
behavioural data i.e. time stamps, click tracking, user
generated content such as surveys, peer-feedback
Who? Who is targeted by
the analysis?
Students, Teachers, Instructional Designers and
Developers
Why? Why does the
system analyse the
collected data?
Students – adapt
Teachers – tutoring
Instructional designers – adapt task
Developers – interface adaptation
How? How does the
system perform the
analysis of the collected
data?
Statistics based on behavioural data and the analysis of
user generated data – possible qualitative follow-up

Data Types and Sources
 Aggregate and integrate data produced by students from multiple
sources
 Challenge to source, combine and manipulate data from a wide variety of
sources and in many formats
 Over reliance on behavioural data from LMS, varied data sources
 Structured data i.e. data from LMS etc., other institutional systems, connected
devices
 Unstructured data i.e. other sources user generated content/data i.e.
Facebook - social network modelling, online dictionaries, translation tools
thesaurus etc.
19
Concluding Remarks

Student Agency in LA
 Students as active agents – voluntarily collaborate in
providing and accessing data
 Designing interventions (if appropriate in the context) and the
agency of the student:
 Student at the centre of interpretation
 Data representation to facilitate interpretation
 Requires specific skills of interpretation
20
Concluding Remarks…

Ethical and Educational Concerns
 Use of data based on transparent opt-in permission of
students following established research principles
 Students understand that data is collected about them and actively
buy-in
 Privacy and stewardship of data
 Emphasis on learning as a moral practice resulting in
understanding rather than measuring (Reeves, 2011)
21

 Challenging to realise the specific objectives of stakeholders
 Teachers v Instructional designers in online education
 Institutions v funders
 Designing and focusing indicators
 Necessary skills for interpretation and communicating outcomes
 Representation in clear and usable formats for stakeholders
 DCU to research impact on students
22

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