A Study of Using SmartBox to Embed Emotion Awareness through Stimulation into E-learning Environments

Caballé, Santi; Barolli, Leonard; Feidakis, Michalis; Matsuo, Keita; Xhafa, Fatos; Daradoumis, Τhanasis; Oda, Tetsuya

doi:10.1109/incos.2014.9

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…For example, in a learning task on a computer, a video signal from a web camera was used to measure head poses and hand movements, in order to detect emotions such as frustration, engagement, distraction, and boredom (Asteriadis, Tzouveli, Karpouzis, & Kollias, 2009). In another study, a wireless sensor tracking overall body movements was used to distinguish several emotional states including confusion and boredom, in order to provide automatic feedback to students in an IDLE (Caballe et al., 2014). Another possible methodology for measuring body postures is to use pressure sensor sheets placed on the seat and the backrest of the chair of participants (Tan, Lu, & Pentland, 1997).…”

Section: The Detection Of Confusionmentioning

confidence: 99%

Inside Out

Arguel

Lockyer

Lipp

et al. 2016

Journal of Educational Computing Research

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Confusion is an emotion that is likely to occur while learning complex information.This emotion can be beneficial to learners in that it can foster engagement, leading to deeper understanding. However, if learners fail to resolve confusion, its effect can be detrimental to learning. Such detrimental learning experiences are particularly concerning within digital learning environments, where a teacher is not physically present to monitor learner engagement and adapt the learning experience accordingly. However, with better information about a learner's emotion and behaviour, it is possible to improve the design of interactive digital learning environments (IDLE) in promoting productive confusion but also in preventing overwhelming confusion. This article reviews different methodological approaches for detecting confusion, such as self-report, behavioural and physiological measures, and discusses their implications within the theoretical framework of a zone of optimal confusion. The specificities of several methodologies and their potential application in IDLEs are discussed.

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Section: The Detection Of Confusionmentioning

confidence: 99%

Inside Out

Arguel

Lockyer

Lipp

et al. 2016

Journal of Educational Computing Research

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Providing Timely Support to Students in Educational Virtual Worlds

Makhija

Richards

Caballé

et al. 2018

Advances in Intelligent Networking and Collaborative Systems

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Educational virtual worlds (EVWs) are immersive educational tools that have the potential to engage students using interactive virtual avatars in the simulated multi-media environments. As with any learning approach, EVWs should have the capacity to adapt according to individual student needs and differences such as their personality characteristics, engagement levels, navigational behaviour, and emotional level. Based on such factors, the EVWs should be capable of providing tailored support and feedback when required. This experimental study conducted with 144 undergraduate students investigates the relationship between individual learner factors, including personality, cognitive and affective engagement, and the value of providing either tailored, generic or no hints on academic performance in an EVW. Results suggest that extravert and agreeable personality traits are positively linked to academic performance and affective and cognitive engagement levels. Contrary to our hypothesis, participants who were provided with hints did not perform better than the control group in performance. Further studies are needed to gain deeper insights to design appropriate support strategies within EVW.

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Identification of Affective States in MOOCs

Souza

Perry

2018

Int J Innov Educ Res

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Massive Open Online Courses (MOOCs) are a type of online coursewere students have little interaction, no instructor, and in some cases, no deadlines to finisch assignments. For this reason, a better understanding of student affection in MOOCs is importantant could have potential to open new perspectives for this type of course. The recent popularization of tools, code libraries and algorithms for intensive data analysis made possible collect data from text and interaction with the platforms, which can be used to infer correlations between affection and learning. In this context, a bibliographical review was carried out, considering the period between 2012 and 2018, with the goal of identifying which methods are being to identify affective states. Three databases were used: ACM Digital Library, IEEE Xplore and Scopus, and 46 papers were found. The articles revealed that the most common methods are related to data intensive techinques (i.e. machine learning, sentiment analysis and, more broadly, learning analytics). Methods such as physiological signal recognition andself-report were less frequent.

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A Study of Using SmartBox to Embed Emotion Awareness through Stimulation into E-learning Environments

Cited by 3 publications

References 19 publications

Inside Out

Inside Out

Providing Timely Support to Students in Educational Virtual Worlds

Identification of Affective States in MOOCs

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