Informal Learning, Cyberlearning and Innovative Education Diana G. Oblinger, Ph.D.
Jan 01, 2016
Informal Learning, Cyberlearning and Innovative
Education
Informal Learning, Cyberlearning and Innovative
Education
Diana G. Oblinger, Ph.D.
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
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
Games and scientific thought
• 86% of comments aimed at analyzing rules of the game
• >50% used “systems-based reasoning” analyzing the game as a complex, dynamic system
—Steinkuehler, 2008; image courtesy of Smith, 2008
• 10% constructed specific models to explain behavior, often using the model to make predictions
• 25% of commentators built on someone else’s previous argument
• 25% issued rebuttals
Experiencing learning
• Problem-solving
• Virtual worlds
• Simulations
• Haptics
• Remote instruments
―Hackathorn, 2007; del Alamos, 2007; Bertolini, 2007―Hackathorn, 2007; del Alamos, 2007; Bertolini, 2007
Community hubs
• nanoHUB
• Science gateway for nanotechnology
• Learning modules: lectures, podcasts
• Industry-level tools
• Community
Cyberlearning
• Access to educational resources, mentors, experts, online activities, virtual environments
• Engage with―Scientific models―Simulations―Data sets―Sensors―Instruments
—Borgman, et al., 2009
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
Data as an infrastructure
―Campolargo, 2008; Borgman et al., 2009
• 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
Infrastructure for innovation
• Digital libraries―Books, journals―Artifacts―Data sets
• Place for social interaction
• Community exchange
• Rapid prototyping
• Embedded sensors
• Computational approaches
―Henry, 2009