This study aims to examine studies on affective computing in e-learning. To do this, first e-learning, theoretical framework, emotions, affective computing, significance, and purpose of the study were explained. As for the method of the study, data collection tools, search details, search results, and data analysis were adverted, respectively. Following this, findings, conclusion, and suggestions were presented.
MOOCs:
A recent trend in e-learning is MOOCs. In MOOCs, free courses are delivered to a great many learners from the top-tier universities all around the world. However, MOOCs are criticized on the ground of high dropout rates, and low interaction (Morris, 2013). In helping to solve the low interaction problem, Moore’s (1989) theory of interaction seems to be appropriate.
Theoretical Framework:
Moore (1989) developed a theory about interaction in distance education. This theory classified interaction in distance education into three, which are student-content, student-teacher, and student-student interaction. This framework could help solve low interaction problem in e-learning, more specifically in MOOCs. On the other hand, another construct that should be taken into consideration to help solve the problem of high drop-out rates and low interaction in e-learning could be use of emotions.
Academic emotions:
In education, mostly the term of academic emotion is used. In e-learning, academic emotions are at the prime importance because the physical distance reduces level of transmittance of emotions between teacher and students and gives rise to negative emotions, which might lead to disengagement (Qin et al., 2010). The most popular academic emotion, on the other hand, is anxiety (Pekrun, Goetz, Titz & Perry, 2002). However, boredom and enthusiasm are also considered important emotions in e-learning.
Affective Computing:
The application of academic emotions to e-learning is said to be affective computing. In affective computing, the main purpose is to detect students’ or even teacher’s emotions during an e-learning course through eye-movements, head movements, voice, facial gestures using eye-tracking devices, cameras, microphones and advanced software. Affective computing is highly related to adaptive learning systems because e-learning courses can be customized for students utilizing data collected through affective computing.
Justification and Purpose:
Emotions play a key role in e-learning process. Recent studies have shown the effect of emotions on academic achievement (D'Errico, Paciello & Cerniglia, 2016; Krithika & Priya, 2016; Qin, Zheng, Li, & Zhang, 2010). In these studies, it was pointed out that positive emotions increase learning, enthusiasm, and engagement.
Because of the reasons mentioned above, emotion detecting systems are intensely studied in the literature. Through using these systems, detecting and evaluating emotions could help improve e-learning experience of learners by disseminating content, and giving real time and delayed feedback tailored for learners (Krithika & Priya, 2016; Ray & Chakrabarti, 2012). In short, detecting emotions in e-learning could help improve effectiveness of e-learning through adaptive learning systems (Caballé et al., 2014).
As there are a very large number of studies on affective computing in e-learning, there is a need for a quantitative content analysis on affective computing studies in e-learning. Therefore, a quantitative content analysis regarding aforementioned topic was carried out to lead the way for both implementers and researchers. To that end, following six research questions were answered.
1) What are the years in which emotion-related studies in e-learning were published?
2) Which publication types are preferred in publishing emotion-related studies in e-learning?
3) Which academic units conduct studies on emotion-related studies in e-learning?
4) Which countries are leading the emotion-related studies in e-learning?
5) What are the main purposes of studies conducted on emotion-related issues in e-learning?
6) What are the correct detection ratios of emotions in emotion-related studies in e-learning?