Blended Training on Scientific Software: A Study on How Scientific Data are Generated ARTICLE
Efrosyni-Maria Skordaki, Susan Bainbridge, Athabasca University
IRRODL Volume 19, Number 2, ISSN 1492-3831 Publisher: Athabasca University Press
This paper presents the results of a research study on scientific software training in blended learning environments. The investigation focused on training approaches followed by scientific software users whose goal is the reliable application of such software. A key issue in current literature is the requirement for a theory-substantiated training framework that will support knowledge sharing among scientific software users. This study followed a grounded theory research design in a qualitative methodology. Snowball sampling as well as purposive sampling methods were employed. Input from respondents with diverse education and experience was collected and analyzed with constant comparative analysis. The scientific software training cycle that results from this research encapsulates specific aptitudes and strategies that affect the users\u2019 in-depth understanding and professional growth regarding scientific software applications. The findings of this study indicate the importance of three key themes in designing training methods for successful application of scientific software: (a) responsibility in comprehension; (b) discipline; and (c) ability to adapt.
Skordaki, E.M. & Bainbridge, S. (2018). Blended Training on Scientific Software: A Study on How Scientific Data are Generated. The International Review of Research in Open and Distributed Learning, 19(2),. Athabasca University Press. Retrieved November 11, 2018 from https://www.learntechlib.org/p/183600/.
© 2018 Athabasca University Press
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