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Towards more Visual Analytics in Learning Analytics

Towards more Visual Analytics in Learning Analytics

Promoting research in Learning Analytics in the Visual Analytics community, in the 5th EuroVis Workshop on Visual Analytics (EuroVA) 2014

Panagiotis D. Ritsos

June 10, 2014
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  1. TOWARDS MORE VISUAL ANALYTICS
    IN LEARNING ANALYTICS
    Panagiotis “Panos’’ D. Ritsos & Jonathan C. Roberts
    School of Computer Science, Bangor University, UK
    5th EuroVis Workshop on Visual Analytics (EuroVA) 2014

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  2. Outline
    •  Introduction to Learning Analytics (LA)
    •  Motivation for working on LA
    •  Our use-case of interpreter-mediated communication
    training
    •  The synergy of Visual Analytics (VA) and LA
    •  Future directions and recommendations

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  3. Introduction
    •  Analytics and Data Mining has transformed the field of scientific inquiry the
    last decades.
    •  Physics, environmental sciences and biology use analytics for some time –
    education/learning sciences have done so only recently:
    e.g., Computers in Biology and Medicine – 1970 vs
    Journal of Educational Data Mining – 2009
    •  When applied to education these methodologies are referred to as learning
    analytics (LA), academic analytics and educational data mining (EDM) –
    (similar but essentially different!).
    •  The field of LA has been emerging, over last decade, but has gained
    significant attention and momentum in the last three years.
    Baker, R., and Siemens, G., "Educational data mining and learning analytics." Cambridge Handbook of the Learning Sciences: (2014).

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  4. What is Learning Analytics?
    •  A recent and widely accepted definition of LA appeared in the call for papers of
    the first International Conference on Learning Analytics and Knowledge (LAK
    2011) and was adopted by the Society of Learning Analytics Research
    (SOLAR):
    Learning analytics is the measurement, collection, analysis and reporting of data
    about learners and their contexts, for purposes of understanding and optimising
    learning and the environments in which it occurs
    Siemens G., Long P, Penetrating the fog: Analytics in learning and education. Educause Review 46, 5 (2011)
    •  Academic Analytics addresses a mix of administrative and learning analytics (BI in academia)
    •  EDM is concerned with developing methods for exploring the unique types of data that come from
    educational settings and using those methods to better understand students, and the settings which
    they learn in

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  5. Why do we need LA
    •  Analyzing and visualizing that data, has the potential to depict underlying factors that
    affect the learning and teaching processes and allow us to improve them.
    •  The dynamic analysis and visualization of the data can also help students as they learn.
    •  There has been recent substantial growth (in the education sector) in the application of
    business intelligence, web analytics and data mining concepts at educational
    institutions.
    Technology-Enhanced Learning (TEL) environments
    Learning Management Systems (LMSs)
    Virtual Learning Environments (VLEs)
    ever-increasing amount of
    data, (potentially) collected
    by educational institutions.

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  6. LMSs, MOOCs and LAs
    •  In addition to the aforementioned use of LMSs many educational institutions offer their
    educational material online.
    •  Massive Open Online Courses (MOOCs) like Harvard’s and MIT’s eDX, and Standford’s
    Coursera, offer an unprecedented access and opportunity for people to access top-class
    educational programs.
    •  Consequently, all that online activity can yield a large amount of data that can be
    analyzed and give the opportunity for:
    –  better performance prediction,
    –  increase the opportunities of intervention and
    –  offer new levels of personalization of the teaching process.

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  7. Our motivation
    •  Our interest with LA stems from several sources and has been developing over
    many years through our:
    –  experience of teaching computing science and engineering in academic
    and vocational environments, over many years
    –  experience with student performance metrics and data from the perspective
    of various roles (e.g., Dir. Of Teaching)
    –  collaboration since 2011 with interpreting researchers for the
    implementation of a dedicated VLEs for the simulation and training of
    interpreter-mediated communications

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  8. Our use case
    Training and simulation for interpreter-mediated communications:
    •  The rise of migration and multilingualism in Europe requires professional interpreters
    in business, legal, medical and many other settings.
    •  Future interpreters therefore need to master an ever broadening range of interpreting
    scenarios and skills.
    •  This is difficult to achieve with traditional teaching methods and in times of reduced
    teaching contact hours.
    •  Also, a client-side understanding of what working with an interpreter involves is
    crucial, but efforts to educate potential clients of interpreters are scarce and normally
    separate from interpreter education.

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  9. Learning systems on Interpreter-mediated communications
    2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
    EVIVA
    StC, Surrey Surrey, StC, UVEG
    Bangor, Surrey, StC, UAM, UCY, Bar-llan
    Bangor, Surrey, StC, UAM, UCY
    ELISA
    BACKBONE
    IVY
    EVIVA - Evaluation & analysis
    of user behaviour in relation
    to UX, pedagogy in VLEs
    used for interpreting, including
    virtual worlds
    BACKBONE & ELISA -
    Repositories of
    audiovisual corpus
    material of interpreting-
    specific scenarios
    ü  time spent in each exercise
    ü  interpreting performance audio
    with annotations
    ü  performance improvement over
    time
    ü  Performance audio/video
    analysis
    ü  UX metrics (clicks, timings etc.)
    ü  Mentor commentary
    ü  interpreter-specific metrics etc.
    Examples of related data:

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  10. The potential of VA in LA
    •  While the current learning tools do utilize several visualization types, these mostly are
    ``traditional’’ styles, i.e., bar charts, line graphs, scatterplots or sparklines.
    •  They are also predominantly static by nature, and afford little interaction.
    •  These visualizations typically represent simple statistical information such as grades from tests,
    dials to illustrate user-load, line-graphs of user logins etc
    •  Visualization and VA can transform LA; it has potential to go beyond mere analysis.
    •  The VA community has extensive experience in non-traditional visualization types and can offer
    novel ways of data exploration
    •  There are therefore challenges and opportunities for VA researchers to provide
    more informative visualizations,
    new interaction methodologies,
    better ways to manipulate data,
    and, ultimately, provide value for teachers and learners.

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  11. Privacy, control, ownership
    •  LA primarily intends to be a moral and educational practice, resulting in
    more successful learning.
    •  However, a series of important implications arise in terms of usage ethics,
    privacy and access.
    •  These are implications also often encountered in VA systems, where real
    data are concerned.
    •  For instance, questions such as:
    –  what type of data a learning system collects
    –  how the data leads to genuine insights about a learner’s performance
    –  who controls and owns the data
    –  who can access it
    –  and where it is stored
    …require careful consideration.
    Slade S., Prinsloo P.: Learning analytics ethical issues and dilemmas. American Behavioral Scientist 57, 10 (2013), 1510–1529.

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  12. Our intention
    •  Engender the VA community into researching LA and to consider the issues and
    opportunities such and endeavour entails.
    •  Our long-term hypotheses are as follows:
    –  there is a need to develop tools that assist the learner throughout the whole
    process of learning - i.e., tools that aid and supports the learners from the very
    beginning when the users are novices, and help them advance into expert level
    –  a wider range of data should be captured - from assessment and exam results,
    to usage statistics, attitudes and emotions.
    –  more analytics will aid the users themselves, as they learn - they will become
    more active and reflective learners. Ultimately, as they become more aware of
    the processes of their own learning they will be more effective learners.

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  13. Recommendations
    •  Make VA a first-class citizen and move beyond simple dashboards
    –  integrating monitoring, analysis and interactive visualizations throughout various
    stages of the learning process.
    •  Apply knowledge and practices from the VA domain in order to design better LA
    interfaces
    –  e.g., learn from ideas in uncertainty visualization to create more informative
    interfaces
    •  Utilize data from multiple users for more accurate outcomes
    •  Integrate visualization techniques of data provenance to create more accountable
    learning environments
    •  Consider how learners can be more active in their learning
    •  Integrate new interaction modalities, like affective computing techniques,
    eye-tracking, tangible and haptic interfaces

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  14.  
     
             
           
     
    We are grateful to the members of the IVY consortium, for their assistance and contributions. This work
    was supported by the European Commission through projects (IVY) 511862-LLP-1-2010-1-UK-KA3-
    KA3MP and (EVIVA) 531140-LLP-1-2012-1-UK-KA3-KA3MP in the Lifelong Learning Programme.
    This presentation reflects the views only of the authors, and the Commission cannot be held responsible
    for any use which may be made of the information contained therein.

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  15. LA visualizations (concepts)

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  16. IVY-VE – architecture
    time spent in each exercise, interpreting
    performance audio with annotations •
    performance improvement over time •
    audio analysis • interpreter-specific
    metrics etc.
    Web Application

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  17. Our projects…
    Interpreting in Virtual Reality (2011 – 2012)
    •  …address the needs of future interpreters and users of interpreters in higher
    education, vocational training and adult learning contexts
    •  …use 3D virtual environment technology to create an adaptive, learning
    environment – IVY-VE – that supports the acquisition and application of skills
    required in interpreter-mediated communication
    Evaluating the Education of Interpreters and their Clients through Virtual
    Learning Activities (2013 – 2014)
    •  …evaluate the educational opportunities that three types of virtual learning
    environments — 3D virtual worlds, videoconference tools and video
    repositories of training material— offer for future interpreters and their clients

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  18. Research Field
    •  Several venues promote and publish research in this area:
    –  Journal of Educational Data Mining
    –  Journal of Learning Analytics
    –  International Conference on Educational Data Mining
    –  Conference on Learning Analytics and Knowledge (LAK)
    –  International Conference on Artificial Intelligence in Education,
    –  ACM Knowledge Discovery in Databases
    –  International Conference of the Learning Sciences
    –  Annual meeting of the American Educational Research Association
    Baker, R., and Siemens, G., "Educational data mining and learning
    analytics." Cambridge Handbook of the Learning Sciences: (2014).

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