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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' 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.
Keywords: blended learning, grounded theory, scientific software, training, distance learning, snowball sampling, purposive sampling
Introduction
Scientific Software Training
Scientific software is becoming increasingly important to the realms of science and engineering. It is a tool that is used to process data and solve models expressed mathematically in an augmented, timelier manner. Scientific software is employed in research areas that can directly affect public safety, such as nuclear power generation computer systems, groundwater quality monitoring and engineering designs. Academic researchers and industry professionals depend on such software in order to answer their scientific inquiries. Scientific software also provides infinite opportunities to share and collaborate. Howison and Herbsleb (2011) argue that the creation of new scientific knowledge requires the combination of evolving scientific methods, validated instruments, and theory. However, the value of training scientists and engineers on this type of software is underestimated. Literature has already acknowledged a general lack of formal scientific software training among users, especially for large research projects with societal importance. An overwhelming majority of researchers in natural sciences and engineering wish for increased computational skills, as they need to have sufficient knowledge of what the software is doing and whether it is, in fact, doing what is expected (Hannay et al., 2009; Joppa et al., 2013; Skordaki, 2016). As society's important scientific decisions...