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An Analysis of a Scalable Modularized Framework for Designing and Implementing Adaptive Learning Environments

Graduate #52
Discipline: Computer Sciences and Information Management
Subcategory: Computer Science & Information Systems

Candice Adams - Auburn University
Co-Author(s): Cheryl Seals, Auburn University, Auburn, AL



For over 28 years, HCI researchers and developers have been challenged with improving usability of products. More recently, the Computer-Supported Cooperative Learning (CSCL) community has focused on developing collaborative systems with an emphasis on increasing learning through social interaction. These significant innovations in learning technologies are encouraging individuals, especially teenagers between the ages of 13-17, to seek enrichment learning opportunities that they normally would not take advantage of due to limitation of traditional classroom offerings within their educational facility. The widespread use of the Internet by this particular age group for socializing is a new phenomenon that raises new issues for researchers and developers. Just designing for usability is not enough; we need to understand how technology can support social interaction and design for sociability in order to make learning effective within these environments. Moreover, increasing accessibility to adaptive and collaborative based learning environments through cloud-based solutions is essential and can transform the way many students learn inside and outside of the traditional classroom. This presentation will review the development and evaluation of various learning technologies and environments, explore adaptive-based algorithms to support real-time presentation of user content based upon selection with a prototype of adaptive-based algorithm and propose a system design for a collaborative and adaptive-based application that will support informal e-learning.

Not Submitted

Funder Acknowledgement(s): This research is supported in part by the National Science Foundation.

Faculty Advisor: Cheryl Seals, sealscd@auburn.edu

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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