Seeking optimal confusion: a review on epistemic emotion management in interactive digital learning environments

Arguel, Amaël; Lockyer, Lori; Kennedy, Gregor; Lodge, Jason M.; Pachman, Mariya

doi:10.1080/10494820.2018.1457544

Cited by 59 publications

(47 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Taken together, these studies demonstrated the importance of learning-centered emotions during learning with digital learning environments (for a recent review see Arguel et al, 2019). However, they also demonstrated a profoundly controversial relation between (negative) emotions and learning.…”

Section: Introductionmentioning

confidence: 74%

“…Digital learning environments include specific affordances that are directly linked to students’ emotions. For example, research has demonstrated that the design of digital learning environments (e.g., shapes and colors; Plass et al, 2014), their structure (e.g., complex, non-linear structure; Arguel et al, 2019), and scaffolds incorporated in such systems (e.g., prompts and feedback by pedagogical agents; Harley et al, 2017) can impact students’ emotions. More specifically, digital learning environments can elicit and alter emotional processes or assist the learner in regulating them and provide unique opportunities for research to investigate emotions in ways hardly achievable in other contexts.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiple Negative Emotions During Learning With Digital Learning Environments – Evidence on Their Detrimental Effect on Learning From Two Methodological Approaches

et al. 2019

View full text Add to dashboard Cite

Emotions are a core factor of learning. Studies have shown that multiple emotions are co-experienced during learning and have a significant impact on learning outcomes. The present study investigated the importance of multiple, co-occurring emotions during learning about human biology with MetaTutor, a hypermedia-based tutoring system. Person-centered as well as variable-centered approaches of cluster analyses were used to identify emotion clusters. The person-centered clustering analyses indicated three emotion profiles: a positive, negative and neutral profile. Students with a negative profile learned less than those with other profiles and also reported less usage of emotion regulation strategies. Emotion patterns identified through spectral co-clustering confirmed these results. Throughout the learning activity, emotions built a stable correlational structure of a positive, a negative, a neutral and a boredom emotion pattern. Positive emotion pattern scores before the learning activity and negative emotion pattern scores during the learning activity predicted learning, but not consistently. These results reveal the importance of negative emotions during learning with MetaTutor. Potential moderating factors and implications for the design and development of educational interventions that target emotions and emotion regulation with digital learning environments are discussed.

show abstract

Section: Introductionmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Multiple Negative Emotions During Learning With Digital Learning Environments – Evidence on Their Detrimental Effect on Learning From Two Methodological Approaches

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Building on previous work, Arguel presented strategies and features for interactive digital learning environments based on a review of the literature [30].…”

Section: A Confusion On Learning Environmentsmentioning

confidence: 99%

Towards Higher Sense of Presence: A 3D Virtual Environment Adaptable to Confusion and Engagement

Pedro

Silva

2021

IEEE Access

View full text Add to dashboard Cite

Virtual Reality scenarios where emitters convey information to receptors can be used as a tool for distance learning and to enable virtual visits to company physical headquarters. However, immersive Virtual Reality setups usually require visualization interfaces such as Head-mounted Displays, Powerwalls or CAVE systems, supported by interaction devices (Microsoft Kinect, Wii Motion, among others), that foster natural interaction but are often inaccessible to users. We propose a virtual presentation scenario, supported by a framework, that provides emotion-driven interaction through ubiquitous devices. An experiment with 3 conditions was designed involving: a control condition; a less confusing text script based on its lexical, syntactical, and bigram features; and a third condition where an adaptive lighting system dynamically acted based on the user's engagement. Results show that users exposed to the less confusing script reported higher sense of presence, albeit without statistical significance. Users from the last condition reported lower sense of presence, which rejects our hypothesis without statistical significance. We theorize that, as the presentation was given orally and the adaptive lighting system impacts the visual channel, this conflict may have overloaded the users' cognitive capacity and thus reduced available resources to address the presentation content.

show abstract

“…Sentiments occurring in the online learning context are specifically identified in recent research. Based on the control-value theory (Arguel, Lockyer, Kennedy, Lodge, & Pachman, 2019), Daniels and Stupnisky (2012) categorized learning sentiments into three dimensions: positive (e.g., enjoyment, hope), negative (e.g., anxiety, frustration, boredom) and neutral. As suggested by Tian et al (2014), the three types of sentiments are further recognized by using neural networks in e-learning environments.…”

Section: E-learningmentioning

confidence: 99%

A review on sentiment discovery and analysis of educational big‐data

Han

Wu²,

Huang

et al. 2019

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Sentiment discovery and analysis (SDA) aims to automatically identify the underlying attitudes, sentiments, and subjectivity towards a certain entity such as learners and learning resources. Due to its enormous potential for smart education, SDA has been deemed as a powerful technique for identifying and classifying sentiments from multimodal and multisource data over the whole process of education. For big educational data streams, SDA faces challenges in unimodal feature selection, sentiment classification, and multimodal fusion. As such, a large body of studies in the literature explores diverse approaches to SDA for educational applications. This paper provides a self‐contained, uniform overview of the SDA techniques for education. In particular, we focus on prominent studies in unimodal sentiment features and classifications (e.g., text, audio, and visual). In addition, we present a novel SDA framework of multimodal fusions, together with description of their crucial components. Based on this framework, we review different approaches to SDA on education from the perspectives of approaches and applications. After comprehensively reviewing the SDA techniques on education, we present the trends and prospectives of the future SDA research under ubiquitous education contexts. This article is categorized under: Application Areas > Education and Learning

show abstract

Seeking optimal confusion: a review on epistemic emotion management in interactive digital learning environments

Cited by 59 publications

References 44 publications

Multiple Negative Emotions During Learning With Digital Learning Environments – Evidence on Their Detrimental Effect on Learning From Two Methodological Approaches

Multiple Negative Emotions During Learning With Digital Learning Environments – Evidence on Their Detrimental Effect on Learning From Two Methodological Approaches

Towards Higher Sense of Presence: A 3D Virtual Environment Adaptable to Confusion and Engagement

A review on sentiment discovery and analysis of educational big‐data

Contact Info

Product

Resources

About