This dissertation provides initial empirical evidence for Expectancy Value Theory for Help Sources and generates design recommendations for online courses based on the newfound understanding between theory and student behavior. My high-level research goals are pursued in the context of help seeking in the presence of reputation systems in MOOC discussion forums. Educational technology can be intentionally designed and introduced in such a way as to maintain the benefits of existing technology while reducing negative impact on learning-relevant behaviors. I do this through the lens of student expectancy and values for the help source, and costs of pursuing that help. Within this thesis I present three online survey experiments, one is intended to provide empirical evidence for the connection between Expectancy Value Theory for Help Sources and student help seeking outcomes. The remaining two survey experiments are designed to further investigate the results of a system for help exchange through the lens of Expectancy Value Theory for Help Sources. The first survey supports the existence of beliefs for help sources, although careful design of value manipulations is necessary to isolate value beliefs from expectancy beliefs for the help source. In a field experiment investigating the design of a help exchange system, I explore the connection between common reputation system features and Expectancy Value Theory for Help Sources. This provides support for the theory outside of a controlled laboratory setting. This Quick Helper MOOC Experiment and the supporting Quick Helper Theory Survey Experiment show that voting within a reputation system context decreases the number of peers invited to be helpers possibly through an increase in evaluation anxiety. Help giver badges ca reduce this evaluation anxiety and mitigate the negative impact of voting. I performed a final field experiment in a small private online course to examine these issues in a more naturalistic setting outside of the Quick Helper help exchange system. I explored learning expectancy-emphasizing email prompts and voting in the course discussion forum, and how these manipulations impacted larger, more nuanced dependent variables such as help seeking and learning. Results from this experiment are not as strong as the more tightly controlled survey experiments and Quick Helper MOOC field experiment, but we still see support in the general direction of our original hypotheses. From these experiments I generate a series of design recommendations for instructors of online courses implementing discussion forums: (1) reputation systems can have a positive effect on student engagement in discussion forums, but there may be a negative effect on help seeking and other vulnerable learning-relevant behaviors, (2) The negative impact of evaluation anxiety from voting can be mitigated through the use of either help giver badges or using only upvoting instead of up/downvoting which may reduce evaluation anxiety, and (4) Email prompts with dilute implementation have questionable impact on student contributions in discussion forums.

The phenomenon of technostress can also be seen as a worldwide pandemic (Boyer-Davis, 2020). The transition to e-learning due to the sudden COVID-19 pandemic outbreak has created new demands on teachers and students and their interaction with technology (Galvin et al. 2021). Previous research has discovered higher levels of technostress for both students and teachers when using technology at home. The aim of this study was to investigate the potential effects of the implemented distance-based form of teaching that was enforced on students and teachers at Uppsala University during the pandemic outbreak. The study collected data from students and teachers on a survey comprising eight questions related to technostress and the use of technology. The data were then linked to different stressors introduced by Tarafdar et al. (2007) and Nimrod (2017) to explain the effects of e-learning. The P-E Fit theory was then used to further explain why various effects were prominent. The study found that teachers experienced high levels of primarily techno-overload, with an example being more preparation before lectures. Students experienced a more mixed result, where they saw techno-invasion as the highest effect, while techno-complexity and techno-overload were also prominent. An example of an effect related to techno-invasion that students experienced was the lack of balance between time reserved for studies and free time.