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Since their emergence and the massive hype surrounding massive open online courses (MOOCs) in the 2010s, these openly available sources for education have gone through massive changes over the past decade (Admiraal et al., 2015; Pilli et al., 2018). Over the years, researchers and designers of MOOCs have developed strategies and tools to enhance aspects of their courses and rise the quality of these educational environments, overall. Different types of research on MOOCs were done in the past. This ranges from research on retention- and completion-rates (Jordan, 2014) and explanations for these behaviors (Clow, 2013) to studies that analyzes the participant-behavior over a complete MOOC. These last approaches often lead to categories, grids or clustering-methods, such as Ferguson et al.’s (2015) or Yoon et al.’s (2021) study. Typically, videos are the core of MOOCs and are enriched by forum discussion or further readings. In many cases, researchers use data from video logs to predict completion rates (Halawa et al., 2014; Lemay & Doleck, 2020), develop visual representation methods of these data (Mubarak et al., 2021) or compare behavior between video-driven MOOCs and for-credit courses (Almeda, 2018). Other researchers focus on data gathered outside of the MOOC and try to connect these findings to aspects such as grades or dropout rates (Gamage et al., 2020). To further enhance the design process of such MOOCs and to lower down learner dropout rates, increase completion rates, enhance learner engagement, and better learning outcomes, metastudies (e.g., Yu, 2021b) have also been conducted.
However, research on interactivity, and especially the influence of certain methods, is still rather rare. Wilkie et al.’s (2018) work is an example of a study on a specific tool, in this case H5P. However, the results of the study were based upon anecdotal evidence from staff and learners at a university. To gain more insights into the influence of H5P, this study built upon the described positive findings.