Emotional scaffolding with respect to time factors in Networking Collaborative Learning Environments

With regard to learning, emotional considerations have been included in the research agenda for a long time and literature offers a variety of studies evaluating the role of emotions in different settings (class, tests and exams, studying at home, etc.). This knowledge and experience has tentatively begun to endow intelligent network systems with emotion assessment and affective feedback capabilities, although the process is still in its infancy. This paper reviews emotional aspects in learning and affect recognition as well as feedback strategies. In the described strategies, the need for considering the time factor is also stressed.

A study of using SmartBox to embed emotion awareness through stimulation into e-learning environments

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Emotions strongly influence human's behavior in individual and social situations and must be seriously considered in any human activity, such as e-Learning. Indeed, the embedding of emotional awareness features into virtual learning environments could offer a more authentic and challenging e-Learning experience, either individual or collaborative. However, the lack of empirical results together with the complexity attributed to the management by computers of human emotions and affective data, seriously limits the advances in e-Learning as it impedes to virtualize many real-world learning situations in which emotions play a significant role. In this paper, we investigate the use of the SmartBox device for emotion measurement of distance learners during their study as well as the development of affective strategies based on the SmartBox's stimulation capabilities. The aim is to collect emotion data from different sources in order to provide the most appropriate affective responses that positively influence distance learners' study and results and ultimately enhance the e-Learning process.Peer ReviewedPostprint (author's final draft

Ένα υπολογιστικό μοντέλο για την ενσωμάτωση συναισθηματικής ευαισθητοποίησης σε περιβάλλοντα ηλεκτρονικής μάθησης

Emotions are present in any form of learning and social interaction. The realistic use of the emerging educational technologies has drawn attention to the consideration of emotions in learning environments. Towards this direction, Affective Computing is offering remarkable system implementations that detect and recognise students’ emotional states with high accuracy using machine learning algorithms, and provide feedback aiming at both students’ cognitive performance and their emotional regulation. However, these technologies often employ expensive sensors and complex computer intelligence that require special expertise or extra resources, while they introduce obtrusiveness and invasiveness in the learning process. Moreover, research on emotions in e-learning is still rather limited while the enrichment of learning environments with emotion awareness capabilities it is still in its infancy. There is still a need for more realistic, in-context studies to investigate successful affective learning sequences that propel students’ self-motivation and engagement. The main objective of this thesis is to investigate the importance of emotion awareness in e-learning environments. To this end, a conceptual model has been developed, including affective states and moods of interest that usually appear in e-learning settings with emphasis in Computer Supported Collaborative Learning (CSCL) scenarios. In the basis of this model, a computational model has been implemented consisting of a usable, expressive and effective multimedia interface for students to report their affective state and an affective virtual agent, employed with expressive faces to provide affective and task-based feedback in response to the students’ reported emotions. The integration was also enhanced with effective visualisations of students’ individual and group affective states. Both models have been tested in real education settings by conducting experiments with university students in Virtual Learning Environments. A qualitative and quantitative analysis of the results suggests 12 final affective states and 5 moods of interest in e-learning environments, raising, however, a speculation considering the adequacy and the potential of verbal or pictorial labels, to express the respondent’s exact feelings. Patterns of affective sequences have been also identified, which that can be exploited by computer intelligence to provide effective emotional scaffolds. With respect to self-reporting of emotions, it was found that students are willing to participate and express their affective state, once a tool provides an easy and usable way for them to do it, improving in that way their engagement. In response to their emotions’ sharing, students need to see an adaptation from the system immediately or after a very short period of time. The provision of affective feedback, enriched with task-oriented scaffolds, can improve student cognitive performance and emotion regulation, at least when it is human guided. Group emotion awareness also appears to improve social interaction.Τα συναισθήματα υπάρχουν σε κάθε μορφή μάθησης και εκπαίδευσης. Ως προς αυτή την κατεύθυνση, η Συναισθηματική Υπολογιστική έχει να επιδείξει αξιόλογα συστήματα που μπορούν και ανιχνεύουν και αναγνωρίζουν τη συναισθηματική κατάσταση του χρήστη με υψηλή ακρίβεια, και επιπλέον παρέχουν ανατροφοδότηση τόσο γνωστική όσο και συναισθηματική. Ωστόσο, η διερεύνηση των συναισθημάτων και ιδιαίτερα σε περιβάλλοντα ηλεκτρονικής μάθησης είναι περιορισμένη, ενώ ο ευρύτερος εμπλουτισμός των εκπαιδευτικών τεχνολογιών με μηχανισμούς συναισθηματικής ευαισθητοποίησης είναι μάλλον ανύπαρκτος. Ένας κύριος λόγος είναι ότι αυτές οι τεχνολογίες συχνά συνοδεύονται από υψηλές απαιτήσεις τόσο σε εξοπλισμό, όσο και σε τεχνογνωσία, δυσκολεύοντας την προσαρμογή τους στην εκπαιδευτική πραγματικότητα. Η παρούσα διατριβή εξετάζει τη σημασία και την αναγκαιότητα ενσωμάτωσης μηχανισμών συναισθηματικής ευαισθητοποίησης σε περιβάλλοντα η-μάθησης, η οποία λαμβάνει υπόψη τις πραγματικές εκπαιδευτικές συνθήκες. Η συναισθηματική ευαισθητοποίηση ορίζεται από την εκτίμηση και αξιολόγηση της συναισθηματικής κατάστασης του χρήστη και την αντίστοιχη συναισθηματική ανάδραση από το σύστημα. Ως προς αυτό το σκοπό, αναπτύχθηκε και αξιολογήθηκε ένα εννοιολογικό μοντέλο το οποίο εμπεριέχει συναισθηματικές καταστάσεις που συνήθως εμφανίζονται σε περιβάλλοντα η-μάθησης, με ιδιαίτερη έμφαση τις ομαδο-συνεργατικές δραστηριότητες και εργασίες. Βάσει του εννοιολογικού μοντέλου, υλοποιήθηκε ένα υπολογιστικό μοντέλο το οποίο παρέχει στους μαθητές μία εύχρηστη και εκφραστική πολυμεσική διεπαφή αυτό-αναφοράς της συναισθηματική τους κατάστασης, η οποία να μπορεί να χρησιμοποιείται παράλληλα με την εργασία τους. Για τη συναισθηματική και γνωστική ανατροφοδότηση των μαθητών, υλοποιήθηκε ένας συναισθηματικός εικονικός βοηθός, ο οποίος παρέχει γνωστικά βοηθήματα, επιστρατεύοντας συναισθηματικές εκφράσεις προσώπου. Επιπρόσθετα, το σύστημα παρέχει στους συμμετέχοντες χρήσιμες οπτικοποιήσεις των συναισθημάτων, τόσο των ίδιων, όσο και των ομάδων τους. Τα προτεινόμενα μοντέλα εξετάστηκαν και αξιολογήθηκαν σε πραγματικές εκπαιδευτικές συνθήκες, από φοιτητές του Πανεπιστημίου Αιγαίου και του Ανοικτού Πανεπιστημίου της Βαρκελώνης. Σύμφωνα με την ποσοτική και ποιοτική ανάλυση των αποτελεσμάτων, επιβεβαιώνεται η σημασία και η ανάγκη ενσωμάτωσης μηχανισμών συναισθηματικής ευαισθητοποίησης στα μαθησιακά περιβάλλοντα. Ωστόσο, αν και η συλλογή των συναισθημάτων μέσω των εργαλείων αυτο-αναφοράς είχε σημαντικό αντίκτυπο, η δυσκολία του συστήματος να παρέχει αυτόματα αποτελεσματική συναισθηματική ανατροφοδότηση, αποτελεί μάλλον το «αγκάθι» της καταγραφής των συναισθημάτων των μαθητών. Τα κύρια συμπεράσματά μας συνοψίζονται στην ακόλουθη παράγραφο: Οι μαθητές είναι πρόθυμοι να συμμετάσχουν και να εκφράσουν τη συναισθηματική τους κατάσταση, εφόσον κάποιο εργαλείο τους παρέχει έναν εύκολο και εύχρηστο τρόπο για να το κάνουν, ενδυναμώνοντας με αυτόν τον τρόπο τη συμμετοχικότητά τους. Ωστόσο, σε απόκριση αυτής της συναισθηματικής τους καταγραφής, έχουν ανάγκη να δουν μία ανάδραση από το σύστημα, μία προσαρμογή του στο πως νιώθουν, άμεσα ή σχεδόν άμεσα. Όταν αυτή η συναισθηματική ανατροφοδότηση είναι και εμπλουτισμένη με γνωστικές υποδείξεις, μπορεί να βελτιώσει τόσο τη γνωστική τους απόδοση, όσο και τη διάθεσή τους, τουλάχιστον όταν καθοδηγείται από τον ανθρώπινο παράγοντα. Τέλος, η ανταλλαγή συναισθηματικών πληροφοριών μεταξύ των μελών της ομάδας φαίνεται να βελτιώνει την κοινωνική τους αλληλεπίδραση

Embedding emotion awareness into e-learning environments

In this work we investigate the importance of emotion awareness in e-learning environments, with emphasis to Computer Supported Collaborative Learning (CSCL) activities. Our presented solution involves a new conceptual model of emotions of interest in e-learning context. In the basis of this model, a computational model has been implemented employing self-report of emotions, affective feedback and effective emotion visualizations. Both models have been tested in real education settings, contributing to the research agenda

Emotion Recognition in Human–Robot Interaction Using the NAO Robot

Affective computing can be implemented across many fields in order to provide a unique experience by tailoring services and products according to each person’s needs and interests. More specifically, digital learning and robotics in education can benefit from affective computing with a redesign of the curriculum’s contents based on students’ emotions during teaching. This key feature is observed during traditional learning methods, and robot tutors are adapting to it gradually. Following this trend, this work focused on creating a game that aims to raise environmental awareness by using the social robot NAO as a conversation agent. This quiz-like game supports emotion recognition with DeepFace, allowing users to review their answers if a negative emotion is detected. A version of this game was tested during real-life circumstances and produced favorable results, both for emotion analysis and overall user enjoyment

A Dual-Modal System that Evaluates User's Emotions in Virtual Learning Environments and Responds Affectively

Endowing learning systems with emotion awareness features (capture user's affective state and provide affective feedback), seems quite promising. This paper describes a system implementation that provides emotion awareness, both explicitly, by self-reporting of emotions through a usable web tool, and implicitly, via sentiment analysis. Prominent theories, models and techniques of emotion, emotion learning, emotion detection and affective feedback are reviewed. We also present findings from our experiment with university students, validating the explicit mechanism in real education settings. Finally, we set open issues for future experimentation, contributing to the research agenda

Embedding emotion awareness into e-learning environments

In this work we investigate the importance of emotion awareness in e-learning environments, with emphasis to Computer Supported Collaborative Learning (CSCL) activities. Our presented solution involves a new conceptual model of emotions of interest in e-learning context. In the basis of this model, a computational model has been implemented employing self-report of emotions, affective feedback and effective emotion visualisations. Both models have been tested in real education settings, contributing to the research agenda

Embedding emotion awareness into e-learning environments

In this work we investigate the importance of emotion awareness in e-learning environments, with emphasis to Computer Supported Collaborative Learning (CSCL) activities. Our presented solution involves a new conceptual model of emotions of interest in e-learning context. In the basis of this model, a computational model has been implemented employing self-report of emotions, affective feedback and effective emotion visualisations. Both models have been tested in real education settings, contributing to the research agenda

A study of using SmartBox to embed emotion awareness through stimulation into e-learning environments

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Emotions strongly influence human's behavior in individual and social situations and must be seriously considered in any human activity, such as e-Learning. Indeed, the embedding of emotional awareness features into virtual learning environments could offer a more authentic and challenging e-Learning experience, either individual or collaborative. However, the lack of empirical results together with the complexity attributed to the management by computers of human emotions and affective data, seriously limits the advances in e-Learning as it impedes to virtualize many real-world learning situations in which emotions play a significant role. In this paper, we investigate the use of the SmartBox device for emotion measurement of distance learners during their study as well as the development of affective strategies based on the SmartBox's stimulation capabilities. The aim is to collect emotion data from different sources in order to provide the most appropriate affective responses that positively influence distance learners' study and results and ultimately enhance the e-Learning process.Peer Reviewe