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Integrating laboratory work into interactive engineering eLearning contents augments theory with practice while simultaneously ameliorating the apparent theory-practice gap in traditional eLearning. The purpose of this paper is to assess and recommend media that currently fulfil this desirable dual pedagogical goal.

The qualitative approach compares the eLearner-content interactivity deriving from Mathematica’s Computable Document File (CDF) application, Pearson’s myLab and Lucas-Nuelle’s UniTrain-I. Illustrative interactive examples written in JavaScript and Java are thereby drawn from an engineering eLearning course developed at the University of Botswana (UB).

Based on its scientific rigour, wide application scope, engineering analytical depth, minimal programming requirements and cross-subject-cum-faculty application and deployment potential, the authors found the CDF to be a versatile environment for generating dynamically interactive eLearning contents. The UniTrain-I, blending a multimedia information and communication technology (ICT)-based interactive eLearner-content philosophy with practical laboratory experimentation, is recommended for meeting the paper’s dual eLearning goal as the most adept framework to-date, blending dynamic interactive eLearning content with laboratory hands-on engineering experimentation.

The lack of other competing frameworks limited the considerations to only the three mentioned above. Consequently, the results are subject to review as the ongoing research advances new insights.

The conclusions help eLearning designers plan ICT-based resources for integration into practical electrical engineering eLearning pedagogy and both CDF and UniTrain-I help dispel the prevailing apparent disquiet regarding the effectiveness of the eLearning-mediated electrical engineering pedagogy. In addition, the cited examples document an original electrical engineering eLearning course developed at the UB.

The conceptualisation of technology adoption has largely been based on the Bass or some Bass-derived model – notably, the logistic model. Logistic-type models offer limited insights regarding the adoption process dynamics or the utility value of their results. The purpose of this paper is to outline an alternative technology adoption framework based on complex adaptive networks.

An agent-based methodological approach is proposed. In it the actors, factors, goals, and adaptive learning influences driving solar energy technology adoption (SETA) process are first substantiated by empirical evidence gathered using field questionnaires and then incorporated in the simulation of a dynamic complex adaptive network of SETA. The complex adaptive network model is based on simple heuristic rules applied using a modified preferential attachment scheme within a NetLogo simulation environment.

The interim results suggest an emergent network where prominent hub “driver” agents underlining the robustness of the model are statistically discernible.

The research is limited to solar photovoltaic and solar water heating technology adoption in Botswana households; however, its results are far-reaching.

These results can be related to sustainable energy policy design. There, targeted incentive mechanisms can be formulated against the backdrop of the identified environmental factors and actors; the aim being to accelerate and cascade SETA.

The results could also be cascaded to other sectors and other non-solar technologies, thus providing a general alternative framework for enabling the widespread adoption of technologies.

This research therefore represents a novel way of utilizing the new science of networks to accelerate SETA.