Distributed Development and Teaching of Algorithmic Concepts

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Distributed Development and Teaching of
Algorithmic Concepts

• Saman Amarasinghe, John Chapin, Seth Teller
• MIT Lab for Computer Science
• www.lcs.mit.edu
• saman,jchapin,seth_at_lcs.mit.edu

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Motivations

• Teaching
• Students demonstrate conceptual mastery
• Collaborative distance learning
• Web distribution, evaluation, feedback
• Over-the-shoulder interaction
• Live, integrated staff/student connections
• Explore broader applications
• Capture, analysis, sharing mechanisms
• Distributed engineering

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Fusion Project

• Teach algorithmic concepts
• Students demonstrate mastery byexhibiting
  correct algorithm for concept
• Increase efficiency of teaching staff
• Less administration more involvement
• Web distribution, evaluation, feedback
• Templated development kit
• Distributed creation of new modules

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Foundational Work

• Algorithm animation
• Zeus (Brown 91) Interactive Illustrations
  (Trychin 97)
• Data-flow software architectures
• Conman (Haeberli 88), AVS (Upson et al. 89)
• Web-based teaching
• WebCT (Goldberg et al. 96), JScheme (Hickley
  97)
• Web chat, whiteboard, video, etc.
• Vat, Wb, Vic (McCanne 94)
• Stages of learning
• Perceiving structure integration
  actingfeedback reflection (Laurillard 93)

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Fuse-N system Advances

• Web-based curriculum distribution, coursework,
  evaluation, feedback
• Four distinct pedagogical modes
• Reference Manual Implement Difference
• Shared lab bench metaphor for student, staff
  work interaction
• Authoring kit to facilitate module development by
  other educators

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Student Perspective

• Concept Module
• Represents algorithmic concept
• Pedagogical modes
• Reference (correct, but source hidden)
• Manual (non-algorithmic)
• Implement (algorithmic)
• Difference (validation)

-Demo-
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Data-Flow Desktop Framework

• Data-Flow metaphor supports
• Per-module, typed, input/output ports
• Concatenation of modules (chaining)
• Aggregation of modules (abstraction)
• Per-student state (persistence)
• Network transparency
• Test suites, event logging, etc.

-Demo-
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Interaction Feedback

• Avatars for each student, staff person
• Visual status, location of person
• Mechanism for message exchange, etc.
• Desktop sharing (e.g., with a TA)
• Allows selective/wholesale state sharing
• Mechanisms for distributed data-flow
• Logging, checkpointing of student work
• Forms-based feedback, grading

-Demo-
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Teaching programming concepts

• Synergy among student, teaching staff, and
  automated analysis tools
• Example common semantic errors
• Deploy to introductory programmingclass
• Incorporate second language (Scheme)

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Collaborative dynamic analysis

• Today Debugging across the web
• Java debugger as applet
• Tomorrow Sharing of dynamic state
• Test cases
• Coverage results
• Failure analyses
• Group access to analysis information generated by
  individuals

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Future Directions

• Enhanced collaboration capability
• Analysis, meta-data repository
• Authorization security improvements
• Audio, video streaming live avatars
• Consistent shared access to info spaces
• Enhanced analysis capability
• Bi-directional debugging (w/ Boothe)
• Testing, grading, feedback, aggregates

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Technical underpinnings

• Client-server architecture
• Server-side compilation
• No development environment necessary on client
• All code in Java for cross-platform,
  cross-browser use server retargeting
• Integrated editor, chat, whiteboard (now),
  debugger, video (planned)