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Reality-based Interfaces for
Collaborative Discovery
Liberact 2013
How reality-based interaction can enhance
collaborative learning and discovery?
Frameworks
&Tools
Interactive
Systems
Evaluation
Liberact 2013
Renaissance Team as Methodology
Outline
G-nome Surfer
Collaborative Exploration of
Genomic Data
MoClo Planner
A Collaborative Tool for Designing and
specifying biological constructs
Liberact 2013
ATabletop Interface for Collaborative Exploration of
Genomics
Liberact 2013
Preliminary User Studies
Position #
PIs 9
Postdocs 6
Industry Researchers 2
Student Researchers 12
Research Assistant 2
Master Student 1
Total 32
Goal
Design requirements for
synthetic biologists
Interview
Procedure
1 hour interviews in
laboratories
Interview
Questions
 Procedure walk-through
 Research goals
 Work practices
 Computational tools
 Future of the field
Data
Collection
 Video
 Voice recordings
 Screenshots
 Observations
Qualitative
Methods
• Affinity diagrams
• Iterative coding
Institutions
• Boston University
• Harvard Medical School
• MIT
• WellesleyCollege
• Wyss Institute
Heterogeneous Data
8
Design Goals
Locate
Learn Retrieve
Annotate
Compare
O. Shaer, M. Strait, C. Valdes, T. Feng, M. Lintz and H. Wang, Enhancing Genomic
Learning through Tabletop Interaction, Proc. ACM CHI 2011.
1. Microscopic:
Usability of concrete interaction techniques
2. Macroscopic:
Usefulness for real-world tasks
3. Holistic:
Impact on collaborative learning
Evaluation
Liberact 2013
Experimental task:
Identify the molecular basis of an unclassified motor mutation in mice
Existing web-based bioinformatics tools G-nome Surfer with multi-mouseGUI G-nome Surfer
Macroscopic Evaluation:
Comparative study with 48 students
Liberact 2013
Liberact 2013
Holistic evaluation
Intermediate college-level Neuroscience laboratory
Synthetic biology lab at BU
Evaluating Collaborative Problem Solving
• Quantitative measures
– Number and value of
insights
– time per activity
– subjective workload
– attitude
– physical and verbal
participation
– equality of participation
• Qualitative indicators
– collaboration style
– problem-solving strategy
– nature of discussion
Liberact 2013
Findings
• G-nome Surfer improves students performance,
reduces workload, and increases enjoyment
(compare to current state-of-the-art bioinformatics
tools)
• The tabletop prototype (compare to multi-mouse
interface):
– Increases physical participation
– Encourages reflection
– Fosters effective collaboration
– Facilitates more intuitive interaction
Liberact 2013
MoClo Planner
A collaborative tool for Modular Cloning
Liberact 2013
From Design to Synthesis
Clones multiple DNA fragments in a single reaction
Fusion sites – the cut and paste sites of Golden Gate cloning
Promoters
Fusion Site
Compatible Fusion Sites RBS
RBSPromoters
Modular Cloning
Requirements
• Process
visualization
• Resource
integration
• Efficiency
• Collaboration
Level 0: Basic Modules
Level 1:Transcriptional Units
Level 2: Multigene Constructs
S.Liu, K. Lu, N. Seifeselassie, C. Grote, N. Francisco, V. Lin, L. Ding, C. Valdes, R.
Kincaid, O. Shaer, MoClo Planner: Supporting Innovation in Bio-Design through
Multitouch Interaction , Demo abstract, ACM ITS 2012.
Research
Design
Build
Test
Evaluation
Position #
PIs 1
Postdocs 1
Student Researchers 22
Total 24
Institutions
• Boston University iGEM
• MIT iGEM
• WellesleyCollege
Goal
Evaluate the usability of the
program
Study
Procedure
Use PixelSense in user’s lab
environment
Usability
Task
 Select specific parts from Parts
Registry
 Build a Level 1 module
 Build a Level 2 module
 Create primers
Data
Collection
 Video recording
 Audio recording
 Post-task interview
 NASATLX questionnaire
 Engagement questionnaire
User Study Task
General findings
• Successful task completion
• User recommended
• Lowered complexity
• Quick and Efficient
Liberact 2013
Going forward
1) Reality-based interaction offers unique opportunities for
enhancing discovery and learning in data-intensive areas.
2) Designing interaction for problems in genomics can help move
forwards the theory and practice ofTEI.
3) Future research includes:
How to visualize and engage data, activity, progress spanning
many systems, people, places, timescales?
What representational forms, device ecologies, most
appropriate for large, abstract data?
Facilitating engagement with big data in ways that highlight
connections between multiple forms of evidence
Lessons learned and Going Forward
RBI can facilitate immediate, visible, and easily reversible manipulations
• How to designTEI for open-ended creative inquiries?
Visual representations can facilitate multi-stage workflows
• Important for execution and tracking of complex analyses
• Need parametrized, annotatable representations of complex large datasets
RBI could facilitate collaboration for co-located teams
• Large interdisciplinary teams and distributed work are common in this area
• Users can jointly manipulate assumptions and see consequences
RBIs can support understanding and discovery
• Provide access to different pieces of the problem (data, reactions)
• Help users forms accurate mental models through tangible/embodied manipulation
Acknowledgements
Liberact 2013
Orit Shaer
oshaer@wellesley.edu
Liberact 2013
Thank you

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Liberact conference 2013 Gnome Surfer & Moclo Planner

  • 2. How reality-based interaction can enhance collaborative learning and discovery? Frameworks &Tools Interactive Systems Evaluation Liberact 2013
  • 3. Renaissance Team as Methodology
  • 4. Outline G-nome Surfer Collaborative Exploration of Genomic Data MoClo Planner A Collaborative Tool for Designing and specifying biological constructs Liberact 2013
  • 5. ATabletop Interface for Collaborative Exploration of Genomics Liberact 2013
  • 6. Preliminary User Studies Position # PIs 9 Postdocs 6 Industry Researchers 2 Student Researchers 12 Research Assistant 2 Master Student 1 Total 32 Goal Design requirements for synthetic biologists Interview Procedure 1 hour interviews in laboratories Interview Questions  Procedure walk-through  Research goals  Work practices  Computational tools  Future of the field Data Collection  Video  Voice recordings  Screenshots  Observations Qualitative Methods • Affinity diagrams • Iterative coding Institutions • Boston University • Harvard Medical School • MIT • WellesleyCollege • Wyss Institute
  • 9. Locate Learn Retrieve Annotate Compare O. Shaer, M. Strait, C. Valdes, T. Feng, M. Lintz and H. Wang, Enhancing Genomic Learning through Tabletop Interaction, Proc. ACM CHI 2011.
  • 10.
  • 11. 1. Microscopic: Usability of concrete interaction techniques 2. Macroscopic: Usefulness for real-world tasks 3. Holistic: Impact on collaborative learning Evaluation Liberact 2013
  • 12. Experimental task: Identify the molecular basis of an unclassified motor mutation in mice Existing web-based bioinformatics tools G-nome Surfer with multi-mouseGUI G-nome Surfer Macroscopic Evaluation: Comparative study with 48 students Liberact 2013
  • 13. Liberact 2013 Holistic evaluation Intermediate college-level Neuroscience laboratory Synthetic biology lab at BU
  • 14. Evaluating Collaborative Problem Solving • Quantitative measures – Number and value of insights – time per activity – subjective workload – attitude – physical and verbal participation – equality of participation • Qualitative indicators – collaboration style – problem-solving strategy – nature of discussion Liberact 2013
  • 15. Findings • G-nome Surfer improves students performance, reduces workload, and increases enjoyment (compare to current state-of-the-art bioinformatics tools) • The tabletop prototype (compare to multi-mouse interface): – Increases physical participation – Encourages reflection – Fosters effective collaboration – Facilitates more intuitive interaction Liberact 2013
  • 16. MoClo Planner A collaborative tool for Modular Cloning Liberact 2013
  • 17. From Design to Synthesis
  • 18. Clones multiple DNA fragments in a single reaction Fusion sites – the cut and paste sites of Golden Gate cloning Promoters Fusion Site Compatible Fusion Sites RBS RBSPromoters Modular Cloning
  • 20. Level 0: Basic Modules Level 1:Transcriptional Units Level 2: Multigene Constructs
  • 21. S.Liu, K. Lu, N. Seifeselassie, C. Grote, N. Francisco, V. Lin, L. Ding, C. Valdes, R. Kincaid, O. Shaer, MoClo Planner: Supporting Innovation in Bio-Design through Multitouch Interaction , Demo abstract, ACM ITS 2012. Research Design Build Test
  • 22. Evaluation Position # PIs 1 Postdocs 1 Student Researchers 22 Total 24 Institutions • Boston University iGEM • MIT iGEM • WellesleyCollege Goal Evaluate the usability of the program Study Procedure Use PixelSense in user’s lab environment Usability Task  Select specific parts from Parts Registry  Build a Level 1 module  Build a Level 2 module  Create primers Data Collection  Video recording  Audio recording  Post-task interview  NASATLX questionnaire  Engagement questionnaire
  • 24. General findings • Successful task completion • User recommended • Lowered complexity • Quick and Efficient Liberact 2013
  • 25. Going forward 1) Reality-based interaction offers unique opportunities for enhancing discovery and learning in data-intensive areas. 2) Designing interaction for problems in genomics can help move forwards the theory and practice ofTEI. 3) Future research includes: How to visualize and engage data, activity, progress spanning many systems, people, places, timescales? What representational forms, device ecologies, most appropriate for large, abstract data? Facilitating engagement with big data in ways that highlight connections between multiple forms of evidence
  • 26. Lessons learned and Going Forward RBI can facilitate immediate, visible, and easily reversible manipulations • How to designTEI for open-ended creative inquiries? Visual representations can facilitate multi-stage workflows • Important for execution and tracking of complex analyses • Need parametrized, annotatable representations of complex large datasets RBI could facilitate collaboration for co-located teams • Large interdisciplinary teams and distributed work are common in this area • Users can jointly manipulate assumptions and see consequences RBIs can support understanding and discovery • Provide access to different pieces of the problem (data, reactions) • Help users forms accurate mental models through tangible/embodied manipulation