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Interfaces for Learning Data Visualizations. Judy Kay CHAI: Computer Human Adapted Interaction Research Group School of Information Technologies, University of Sydney President of the AIED Society (2009-11), Programme co-Chair ITS2010, General Chair AIED2011

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Interfaces for Learning Data Visualizations

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    1. Interfaces for Learning Data Visualizations Judy Kay CHAI: Computer Human Adapted Interaction Research Group School of Information Technologies, University of Sydney President of the AIED Society (2009-11), Programme co-Chair ITS2010, General Chair AIED2011 Advisory Board User Modeling Programme co-Chair Pervasive 2012, Chair of the Joint Ubicomp and Pervasive Steering Committee

    2. About me • Inventing future technology to tackle important problems, notably in learning • Personalisation • Personal data and its management • Putting people in control • Open Learner Models (OLMs) • Metacognition and OLMs • Interactive surfaces… walls, tables…

    3. Learning analytics as a form of Learner/User Modelling with interfaces

    4. How to create interfaces for LA? • User-centred approaches • Stake-holders • Mental models • The problem? • Core tools and principles • Case studies • Institution • Class • Individual learner

    5. Interfaces… visualisations

    6. Why visualisations?

    7. Fekete, J. D., Van Wijk, J. J., Stasko, J. T., & North, C. (2008). The value of information visualization. In Information Visualization (pp. 1-18). Springer Berlin Heidelberg.

    8. “…easy and fast to see that there is no red circle, or to evaluate the relative quantity of red and blue circles. Color is one type of feature that can be processed preattentively, but only for some tasks and within some limits. [eg]if there were more than seven colors …, answering the question could not be done with preattentiveprocessing and would require sequential scanning, a much longer process.

    9. But how to create the right visualisations? Are there simple rules? Simple principles? Simple and constant solutions?

    10. Principle: individual data takes on more meaning…. When comparisons are supported: Others Temporal Contextual

    11. This user’s footprints Overall population footprints Patina: Dynamic Heatmaps for Visualizing Application Usage (CHI2013) Justin Matejka, Tovi Grossman, and George Fitzmaurice

    12. Extended Case Study Concrete example of my work to underpin the activities

    13. Defining features • The problem: • Group work is hard but it is important • Group work in learning context has many problems that cause great anguish, inefficiency • Target stakeholders: • Learner as individual • Team leaders (manager, tracker) • Facilitators (tutor, lecturer)

    14. Trac: Tool supporting long term group work Used by team members, facilitators, teachers, some clients

    15. SVNsource repository Wiki page editor Ticket Manager TRAC • open source tool for supporting software development projects • Not a learning system but used in a learning context.

    16.  Huge amounts of data about the group members and their interactions

    17. Narcissus Upton, K., and J. Kay. (2009) Narcissus: interactive activity mirror for small groups. In UMAP09, User Modeling, Adaptation and Personalisation, Springer-Verlag, 54-65

    18. Integrated of mirror tool Narcissus tab

    19. Lifelong modelling – mirrors and mining

    20. Header – Group view Display for one user Time – activity on that day is shown for each user, on each medium

    21. ticket contribitions svn contribitions Wiki contribitions

    22. …to see details Click on cell …

    23. Explains scoring

    24. Individual summary Group average

    25. Click on ticket activity for a day Associated details Click on ticket label

    26. Details of that ticket

    27. Sequence mining Group 1 – 1 person had sequences characteristic of managers. * That person had the manager role Group 1 – 3 members had developer activity sequences Group 3 – dysfunctional and here we might see why Group 5 – another way to be dysfunctional

    28. Activity 1 • Your Stakeholders?

    29. Activity 1 • Stakeholders? • Learners • Parents, Mentors, Facilitators • Teachers • Supervisors • Institutions • Quality assessors • Researchers

    30. Activity 2 • Problems you would like to tackle?

    31. Activity 2 • Current problems we aim to tackle? • Teacher: Early identification of at-risk individuals • Learner: Decision support • Am I doing well enough? • Am I doing what is expected of me? • Institution: Effectiveness of teaching and learning?

    32. Building from SMILI Bull, S., & Kay, J. (2007). Student Models that Invite the Learner In: The SMILI:() Open Learner Modelling Framework. International Journal of Artificial Intelligence in Education, 17(2), 89-120.

    33. What is an Open Learner Model? • Any interface to data that a system keeps about the learner • Came from AI + personalisation where learner model drives personalisation • OLM has become a first-class citizen! • Link to Learning Analytics….

    34. SMILI questions • How does the open learner model fit into the overall interaction? • What problem does it aim to address? • WHAT is open? • HOW is it presented? • WHO controls access?

    35. The purposes for opening the learner model are: • Improving accuracy • Promoting learner • Helping learners to plan and/or monitor their • Facilitating collaboration and/or competition • Facilitating navigation of the learning system • Assessment • Complex of issues of managing personal data: • right of access to data about themselves • Right of control over their learner model • increasing trust

    36. Scrutable user models and personalised systems Research systems only, so far But hints of their being ready to emerge in mainstream software

    37. Interfaces to substantial learner models Core concepts in a whole semester long subject

    38. HCI subject with online lectures • Exploit data from: • logs of interaction with lecture “slides” • class assessments • Lightweight ontology for tagging • automatic analysis of online dictionary • augmented with class-specific concepts (as class glossary) • enabling combination of multiple data sources about each concept • and inference up/down ontology

    39. SIV Lots of green means learner doing well Weak aspects visible as red Overview visualisation

    40. SIV Kay, J and ALum. "Exploiting readily available web data for scrutable student models.” Proceedings of the conference on Artificial Intelligence in Education 2005.

    41. Little detail

    42. Mental models

    43. Mental models A set of beliefs that the user holds

    44. Mental models A set of beliefs that the user holds eg. A whale is a fish The subject requires rote learning I expect to perform at about the median in this class

    45. Mental models come from: • Formal education • And so much else • Experience • Cultural expectations • Context • Emotional state • …. • Determining what the user • Believes to be true • Trusts • Feels permitted to consider and do • Feeling of competence

    46. Why do mental models matter for interface designers?

    47. Why do mental models matter for interface designers? They define what a user can “see” and “hear” How they interpret information Clashes between user, programmer, expert MMs

    48. Activity • Mental models • What are key elements for your LA needs?

    49. Pervasive technologies Case study Lots of embedded interaction devices, ready for interaction Where things may be headed….

    50. User models in real classrooms For orchestration For in-class monitoring to inform teacher actions For post-hoc reflection by the teacher