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Internet-Based Peer-Assisted Learning: Current Models, Future Applications, and Potential

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Abstract

Peer-Assisted Learning (PAL) is recognized as an effective academic support program designed to assist students’ learning needs. At the tertiary level, universities in Western countries have developed and implemented various forms of PAL programs catering for students across disciplines, commonly targeting transitional subjects which are perceived to be difficult. Studies have demonstrated that PAL programs positively contribute to better student performance and higher student retention rates.

However, there has been relatively little discussion on how new technological trends can be applied to extend the PAL platform to suit the evolving student lifestyle and changes in learning behaviors. In this chapter, several models for Internet-based PAL are discussed and evaluated. The chapter also outlines some of the technological and other requirements for the establishment and maintenance of these Internet-based PAL programs. Finally, an evaluation of the potential outcomes is presented. The discussion highlights that Internet-based PAL programs can be used as an instructive complement to existing face-to-face PAL programs, further extending the benefits of student peer learning and social exchange with the convenience of mobile technology.

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Correspondence to Tairan Kevin Huang .

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Huang, T.K., Cui, J., Cortese, C., Pepper, M. (2019). Internet-Based Peer-Assisted Learning: Current Models, Future Applications, and Potential. In: Zhang, Y., Cristol, D. (eds) Handbook of Mobile Teaching and Learning. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41981-2_18-2

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  • DOI: https://doi.org/10.1007/978-3-642-41981-2_18-2

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  • Print ISBN: 978-3-642-41981-2

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Chapter history

  1. Latest

    Internet-Based Peer-Assisted Learning: Current Models, Future Applications, and Potential
    Published:
    17 October 2018

    DOI: https://doi.org/10.1007/978-3-642-41981-2_18-2

  2. Original

    Internet-Based Peer Assisted Learning: Current Models, Future Applications, and Potential
    Published:
    08 April 2015

    DOI: https://doi.org/10.1007/978-3-642-41981-2_18-1