A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system

Harley, Jason M.; Bouchet, François; Hussain, M. Sazzad; Azevedo, Roger; Calvo, Rafael A.

doi:10.1016/j.chb.2015.02.013

Cited by 163 publications

(146 citation statements)

References 42 publications

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“…It is possible that this might have biased participant responses such that participants who performed favorably recalled experiencing more positive emotions, valuing the task more and being more in control. The authors recognize the need to use multi-modal measurement tools during cognitively demanding tasks (Duffy et al 2015;Harley et al 2015). To overcome this limitation, future research should integrate multiple assessments of emotions, particularly non-invasive measurements, such as physiological and behavioral measures (Calvo and D'Mello 2010;Harley et al 2015).…”

Section: Discussionmentioning

confidence: 99%

“…The authors recognize the need to use multi-modal measurement tools during cognitively demanding tasks (Duffy et al 2015;Harley et al 2015). To overcome this limitation, future research should integrate multiple assessments of emotions, particularly non-invasive measurements, such as physiological and behavioral measures (Calvo and D'Mello 2010;Harley et al 2015). Future research will also aim to replicate the findings of this study with larger sample sizes, while also investigating other activity emotions such as relaxation and in other CBLEs in order to examine the robustness and generalizability of the results, in particular, between improved learning outcomes and lower levels of emotionality.…”

Section: Discussionmentioning

confidence: 99%

“…In these environments, emotions are typically reported with low intensities, particularly negative emotions (D'Mello 2013; Harley and Azevedo 2014;Harley et al 2015). These low intensities can restrict statistical variance, especially through floor effects, thus making it difficult to understand the nuanced relationship between emotions and their impact on learning and performance.…”

Section: Emotions and Instructional Interventions In Cblesmentioning

confidence: 99%

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The link between achievement emotions, appraisals, and task performance: pedagogical considerations for emotions in CBLEs

2016

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Achievement emotions have a powerful influence on how students interact with current and future learning and performance tasks. As such, pedagogical practices that support adaptive student emotions are critical for teaching and learning in computer-based learning environments (CBLEs). This research investigates the relationship between during-task achievement emotions and participants' appraisals of task control, value, perceived performance, and actual performance outcomes on a diagnostic reasoning task with a CBLE, BioWorld. Based on the emotions participants reported experiencing during the task, we found that participants could be organized into three groups using a k-means cluster analysis: a positive, negative, and low emotion group. Participants assigned to the positive emotion group had the highest subjective appraisals of task value, task control, and the highest perceived performance; however, these participants had lower levels of actual performance when compared to learners assigned to the low emotion cluster and had actual performance levels comparable to learners in the negative emotion cluster. These results provide preliminary evidence for fostering low emotionality rather than positive emotionality with pedagogical interventions in order to support better performance outcomes, while learners engage in academic achievement tasks in CBLEs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Emotions and Instructional Interventions In Cblesmentioning

confidence: 99%

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The link between achievement emotions, appraisals, and task performance: pedagogical considerations for emotions in CBLEs

2016

Self Cite

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“…Second, learners might be better off left alone if prompts are poorly calibrated to their present state, either because of an interaction between a particular message and individual difference, the message being poorly designed, or (more likely) because of a failure to accurately detect the learners' emotional state (Robison et al 2009). The latter can happen as the result of competing, incongruent emotion information from multiple data channels (Harley et al 2015c). Prompts designed to mitigate anxiety, for example, may in fact elicit more anxiety if the learner is suddenly made to think that maybe they should be anxious (for a review of meta-emotions see Bartsch et al 2008).…”

Section: Deployment Of Direct System-delivered Promptsmentioning

confidence: 99%

Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and Features

Harley

Lajoie

Frasson

et al. 2016

Int J Artif Intell Educ

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A growing body of work on intelligent tutoring systems, affective computing, and artificial intelligence in education is exploring creative, technology-driven approaches to enhance learners' experience of adaptive, positively-valenced emotions while interacting with advanced learning technologies. Despite this, there has been no published work to date that captures this topic's breadth. We took up this grand challenge by integrating related empirical studies and existing conceptual work and proposing a theoretically-guided taxonomy for the development and improvement of emotion-aware systems. In particular, multiple strategies system developers may use to help learners experience positive emotions are mapped out, including those that require different amounts and types of information about the user, as well as when this information is required. Examples from the literature are provided to illustrate how different emotionaware system approaches can be combined to take advantage of different types of data, both prior to and during the learner-system interaction. High-level system features that emotion-aware systems can tailor to learners in order to elicit positive emotions are also described and exemplified. Theoretically, the taxonomy is primarily informed by the control-value theory of achievement emotions (Pekrun 2006(Pekrun , 2011 and its assumptions about the relationship between distal and proximal antecedents and the elicitation and and Graesser (2015) and is intended to guide the design of emotion-aware systems that can foster positive emotions during learner-system interactions through the use of varied approaches, data sources, and design features.

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“…Emotions are a critical component of effective learning and problem solving, especially when it comes to interacting with computer-based learning environments (CBLEs; multi-agent systems, intelligent tutoring systems, serious games [17].…”

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confidence: 99%

Evaluating the Emotional State of a User Using a Webcam

Magdin¹,

Turčáni²,

Hudec³

2016

IJIMAI

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-In online learning is more difficult for teachers identify to see how individual students behave. Student's emotions like self-esteem, motivation, commitment, and others that are believed to be determinant in student's performance can not be ignored, as they are known (affective states and also learning styles) to greatly influence student's learning. The ability of the computer to evaluate the emotional state of the user is getting bigger attention. By evaluating the emotional state, there is an attempt to overcome the barrier between man and non-emotional machine. Recognition of a real time emotion in e-learning by using webcams is research area in the last decade. Improving learning through webcams and microphones offers relevant feedback based upon learner's facial expressions and verbalizations. The majority of current software does not work in real time -scans face and progressively evaluates its features. The designed software works by the use neural networks in real time which enable to apply the software into various fields of our lives and thus actively influence its quality. Validation of face emotion recognition software was annotated by using various experts. These expert findings were contrasted with the software results. An overall accuracy of our software based on the requested emotions and the recognized emotions is 78%. Online evaluation of emotions is an appropriate technology for enhancing the quality and efficacy of e-learning by including the learner´s emotional states.

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A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system

Cited by 163 publications

References 42 publications

The link between achievement emotions, appraisals, and task performance: pedagogical considerations for emotions in CBLEs

The link between achievement emotions, appraisals, and task performance: pedagogical considerations for emotions in CBLEs

Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and Features

Evaluating the Emotional State of a User Using a Webcam

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