1.
Authentic oral language production and
interaction in CALL: An evolving conceptual
framework for the use of learning analytics
within the SpeakApps project
www.speakapps.eu

2.
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

3.
SpeakApps...
 Partners and Associate Partners with various
competencies, skills, backgrounds and contexts:
 Computer Assisted Language Learning
 Traditional universities and open/distance universities
 Learning technologists, linguists, pedagogists, information
technologists and programmers – Educators!
 Variety of linguist backgrounds and contexts including lesser used
languages, to national/international languages
 Challlenging at times but extremely engaged
 Binding was the conceptual and theoretical framework of
the project

4.
An Evolving Conceptual Framework*
 Maxwell (2013) *
 Fundamental concepts, assumptions, theories, experience and
expectations
 Theories, Concepts, Assumptions and Experience
 Materials that promote meaningful language interaction
opportunities (Brandl, 2002)
 Authentic materials provide learners with the opportunity to fulfil a
social purpose in the language community for which it was intended
(Grellet, 1981; Lee, 1995; Little, Devitt, & Singleton, 1989)
* Maxwell, J.A. (2013) Qualitative Research Design an Interactive Approach, 3rd
Ed., Thousand Oaks: Sage

5.
Conceptual Framework…
Theories, Concepts, Assumptions and Experience
 Task-based approaches closely aligned with action-oriented approaches to language derived
from sociocultural and cultural historical activity theoretical to language teaching and learning
(see for example Blin, 2010; Blin & Appel, 2011; Blin & Thorne, 2011)
 Teacher and learner agency being at the centre of curriculum and task design (van Lier,
2004; Engeström, 2006; Lipponen & Kumpulainen, 2011)
 Emphasis in many online language classes is usually placed on three of the core language
skills writing, reading and listening (Appel, Santanach, Jager, 2012)
 Developing oral language skills is accepted as being problematic in part because of time
constraints independent of the learning environment
 Ephemeral nature of speaking makes it difficult for both students and teachers to provide
feedback and to remember what was said
 Provide a way of offering students more and enhanced practice outside the classroom
 Data-driven decision making

6.
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

7.
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?
)

8.
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.

9.
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

10.
 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
10
Learning Analytics Reference Model*

11.
 What? Data and Environment:
 Which systems
 Structured and/or unstructured data
 Who? Stakeholder
Teachers
 Students
 Instructional designers
 Institutional stakeholders
11
Learning Analytics Reference Model*

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
12
Learning Analytics Reference Model*

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
…
13
Learning Analytics Reference Model*

14.
 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
14
SpeakApps Pilot

15.
15
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

16.
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.
16
Concluding Remarks

17.
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
17
Concluding Remarks…

18.
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)
18
Concluding Remarks…

19.
 Challenging to realise the specific objectives of stakeholders
 Teachers v Instructional designers in online education
 Designing and focusing indicators
 Quantitative and qualitative subjectivity and tension
 Answering the why?
 Sentiment analysis and forums
19
Concluding Remarks…

20.
 We welcome your input and thank you
20
SpeakApps – www.speakapps.eu