The starting point for this paper is the eLearning Political Strategies (ELPoS) model. The model is based on two dimensions: 1) the direction of the political strategy (upward or downward), and 2) the scope of the political strategy (individual or group based). The interaction between the above dimensions defines four different types of eLearning political strategies, which result in different political outcomes. The model is accompanied with one case study that is divided into four parts (“acts”). Each of the acts provides an example of one of the four strategies in the model. The discussion and conclusions sections integrate the findings from the case study, outline the rules that govern the application of political strategies in the context of eLearning, and list some of the theoretical and practical implications from a better understating of the politics of eLearning.
TopPolitics And Elearning
Cross (2004) is considered the person who coined the term eLearning. Since then, a range of definitions have been offered for this term. The first type of definitions focus on the fact that eLearning is based on the Internet. For example, Rosenberg (2001, p. 5) defines eLearning as: “the use of internet technologies to deliver a broad array of solutions that enhance knowledge and performance, Rossett and Sheldon (2001) defines eLearning as: “Web-based training (WBT)”, and Adrich (2004, p. 240) defines eLearning as: “a broad combination of processes, content, and infrastructure to use computers and networks to scale and/or improve one or more significant parts of a learning value chain, including management and delivery”.
Other definitions of eLearning are so general that they include just about all interactions and experiences in organizations. For example, Manville (2003, p. 50) defines eLearning as: “Not only Internet-published courseware, but also the tools for managing, modularizing and handling: different kinds of content and learning objects (including both electronic and non-electronic forms, and even traditional classroom instruction), just-in-time and asynchronous learning, such as virtual labs, virtual classrooms and collaborative work spaces, simulations, document repositories and publishing programs, tools for prescribing learning, managing development pathways and goals and handling e-commerce and financial transactions related to learning, and the utilities and capabilities for supporting informal learning, mentoring, communities of practice and other non-training interventions”. In other words, according to Manville, eLearning includes just about everything that happens in the corporate world except training.
For the purpose of this paper, we define eLearning as both electronically based and related to teaching and learning. However, this does not mean that we restrict eLearning to activities that occur in a classroom (even an on-line classroom) or that we consider eLearning a process that takes place during the delivery of content. Thus, activities that occur between students, instructors, and administrators, within an organization that engages in eLearning (such as a university) that involve electronic means and are related to the teaching and learning process would meet our definition of eLearning.
There are a number of central themes in the literature on eLearning that are directly related to our study. First, is the discussion of factors that predict attitudes to eLearning. Previous research lists many factors as affecting attitudes to eLearning, including: age: gender, personality, and computer experience (e.g., Miller & Varma, 1994; Taylor & Todd, 1995; Sanchez et al., 2006). Thus, the literature shows that users who are young, male, with a higher risk tolerance and more experience with computers, are more inclined to have positive views of eLearning than users who do not meet these criteria.
The effectiveness of eLearning programs has attracted much research as well. Studies focusing on this issue identified three sets of factors that impact the effectiveness of eLearning programs:
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Technology factors (Masie, 2001; Brown, 2002);
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Psychological factors (Henning, 2003); and
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Organizational factors (Guest, 1998; Herriot et al., 1998; Martin et al.,1998; Capelli, 1998; Martin and Beaumont, 2003).
Applying the above framework, Mital (2011) found that learner perceptions of the characteristics of learning technology can explain acceptance behavior, particularly in relation to the inclination to adopt eLearning technologies. Learner perceptions also explain the effectiveness of utilizing eLearning technologies in the long term. Most importantly, according to Mital (2011), acceptance behavior correlates with learner’s expectations of the technology in terms of the suitability of the learning to the task, the applicability of the technology to the learner’s needs, and the availability of the right incentives to motivate learners to use the technology.