Affective Profiles in a Massive Open Online Course and Their Relationship with Engagement

Abstract

The importance of affects in learning has been firmly established in face-to-face learning environments, and now researchers are examining the roles of affects in online learning environments, including massive open online courses (MOOCs). The purpose of this research was to identify profiles of common achievement affects (i.e., relief, anxiety, boredom, and guilt) over the duration of one MOOC and examine the differences in academic engagement. Results from the latent profile analysis revealed unique affective profiles, and these profiles differed significantly in cognitive, behavioral, and social engagement in the MOOC. Our findings therefore highlight the importance of understanding affective profiles in MOOCs and address potential difficulties to engage these learners with a vast diversity in backgrounds.Keywords: massive open online courses, achievement affects, engagement, longitudinal, profilesThe importance of affects in learning has been firmly established in face-to-face learning environments (Pekrun, 2006), and now researchers are examining the roles of affects in online learning environments. A special issue of Internet in Higher Education showed strong similarities between the experience and effects of emotions/affects in online learning environments and traditional classrooms (Daniels and Stupnisky, 2012). However, little is known about the role of affective experience in perhaps the newest and arguably least traditional online learning environment: massive open online courses (MOOCs). Not only do MOOCs represent a novel learning environment but they also consist of a highly diverse set of learners united from all parts of the world by a shared interest in content and an Internet connection (Saadatdoost et al., 2015). The purpose of this research was threefold. First, we identified profiles of common achievement affects, namely, anxiety, relief, boredom, and guilt over the duration of one MOOC. Second, we investigated the extent to which learners' interpretation of the MOOC as providing support for their competence and autonomy predicted affective profiles. And third, we examined the differences in cognitive, behavioral, and social engagement associated with each profile. MassiVe OPen Online cOUrsesIn their original form, MOOCs were designed to generate knowledge through connectivity. These "connectivist MOOCs" were based on a pedagogy of sharing information through digital networks (Kop and Hill, 2008;Siemens, 2012), and providing an open platform for students to discuss content and create learning through connecting. Indeed, the first MOOC called "Connectivism and Connective Knowledge" was run through the University of Manitoba, Canada and involved 25 learners with official student status and 2,300 learners unaffiliated with the university (Daniel, 2012).

A motivation perspective on achievement appraisals, emotions, and performance in an online learning environment

“…In sum, these findings contribute to the achievement motivation literature in that they unveil psychosocial profiles that arise in an online learning environment (e.g., Tze et al, 2017). The academic experiences of students in online learning settings that involve co-activated cognitive and affective processes have received little attention to date and the addition of online technology can bring further complexity to learning tasks (Daniels & Stupnisky, 2012).…”

Momentary emotion profiles in high school science and their relations to control, value, achievement, and science career intentions.

“…1Tze, Daniels, Buhr, and Le (2017) examined emotion profiles, but profiles were based on repeated measures of the same emotion (not multiple emotions). For this reason, we excluded the study from our literature review.…”

Recognizing Multidimensional Engagement of E-Learners Based on Multi-Channel Data in E-Learning Environment

“…Leony et al [12] infer four kinds of more complex emotions (frustration, confusion, boredom and happiness) in the MOOC platform, based on the four corresponding detection model. The similar taxonomy of learning emotions can be found in [29]- [31]. Some researchers have shown that learner's attention or motivation can be utilized to identify learning engagement in e-learning environment.…”