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Source: http://coil.psu.edu/

The Center for Online Innovation in Learning (COIL) is a research and development initiative that supports local, national, and international collaborations. COIL sponsors a wide range of research projects that explore the design, development and implementation of online innovations to improve teaching and learning.

The current COIL Research Initiation Grants include:

  1. The Penn State Lifelong Learning Landscape (L3) Project is designed to build, implement, and study the effects of an innovative educational delivery system designed to meet the needs of non-degree learners; promote lifelong connections between Alumni and the University; and attract new learners to Penn State.
  2. Teaching Behaviors that Enable Student Success examines the ability to accommodate for differences in learning style, student preferences, pace of study, and variations of delivery modalities, by identifying the specific teaching behaviors that positively impact the student online learning experience.
  3. Exploring an Innovative Framework of Evolving Best Practices for Effective Online Learning is designed to explore mechanisms used to cultivate/nourish the needed self-discipline and determine best practices for active learning to compensate for the missing on-campus pedagogical engagements will be investigated.
  4. Enhancing Online Geospatial Education with Sketch-based Geospatial Learning Objects will evaluate existing solutions for creating online sketch-based learning objects using tablets (iPad, Android) and identify those that best meet the usability and utility requirements of online educators and collect and disseminate so called sketch-based geospatial learning objects creating a digital library whose organization matches the one used in the GIS & T Body of Knowledge.
  5. Developing an Open-Source Case Based Environment to Support Higher Order Learning has the primary goal of outlining the instructional specifications for and developing a prototype for a case-based learning environment that will facilitate higher order learning among Applied Statistics students and enable them to demonstrate the ability to successfully engage in open-ended analysis, evaluation, and synthesis of content-specific knowledge within real- world contexts.
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