Informal Learning, Cyberlearning and Innovative

Education

Informal Learning, Cyberlearning and Innovative

Education

Diana G. Oblinger, Ph.D.

―

EmergentEmergent

UnpredictableUnpredictable

Self-organizingSelf-organizing

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

Finding information

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

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