Informal Learning, Cyberlearning and Innovative Education

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Informal Learning, Cyberlearning and Innovative
Education

• Diana G. Oblinger, Ph.D.

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Emergent
Unpredictable
Self-organizing
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Emerging educational ecology

• Learners have almost unlimited access to content,
  tools, resources, faculty, experts
• Research and scholarship have become more
  conversational
• Digital environments are places for scholarship

• Interdisciplinarity is growing
• Original research is conducted by non-scholars,
  e.g., undergraduates, citizen scientists
• Distributed access to resources

?Henry, 2009
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Learning beyond the classroom

• Undergraduate students spend only 7.7 of their
  time in formal learning environments
• Grad students spend 5.1 in formal learning
  environments
• Who are the educators?
• Faculty
• Academic advisors
• Student affairs staff
• Students
• Community members

Dey, 2008
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Finding information
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Games and scientific thought

• 86 of comments aimed at analyzing rules of the
  game
• gt50 used systems-based reasoning analyzing the
  game as a complex, dynamic system

• 10 constructed specific models to explain
  behavior, often using the model to make
  predictions
• 25 of commentators built on someone elses
  previous argument
• 25 issued rebuttals

Steinkuehler, 2008 image courtesy of Smith,
2008
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Experiencing learning

• Problem-solving
• Virtual worlds
• Simulations
• Haptics
• Remote instruments

?Hackathorn, 2007 del Alamos, 2007 Bertolini,
2007
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Community hubs

• nanoHUB
• Science gateway for nanotechnology
• Learning modules lectures, podcasts
• Industry-level tools
• Community

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Cyberlearning

• Access to educational resources, mentors,
  experts, online activities, virtual environments
• Engage with
• Scientific models
• Simulations
• Data sets
• Sensors
• Instruments

Borgman, et al., 2009
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Engagement of distributed communities

• Virtual organizations
• Distributed across space participants span
  locales and institutions (can include citizen
  scientists)
• Distributed across time synchronous and
  asynchronous
• Computationally enabled collaboration support
  systems
• Computationally enhanced simulations, databases,
  analytic services
• Establishing trust, reputation

NSF, 2008
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Data as an infrastructure

• Large collaborations are emerging to collect and
  aggregate data
• Vast amounts of data allow use to ask new
  questions in new ways
• Learner data can be valuable
  to educators
• Policy issues emerge for
  using and managing data

?Campolargo, 2008 Borgman et al., 2009
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Infrastructure for innovation

• Digital libraries
• Books, journals
• Artifacts
• Data sets
• Place for social interaction

• Community exchange
• Rapid prototyping
• Embedded sensors
• Computational approaches

?Henry, 2009
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Policies needed

• Managing and using massive data stores
• Interoperability and common standards
• Open access to data and educational resources

• Identity management
• Security, privacy
• Confidentiality, FERPA
• Data breach policies
• Indemnification
• Sustainability plans