Emotional learning involves the acquisition of skills to recognize and manage emotions, develop care and concern for others, make responsible decisions, establish positive relationships, and handle challenging situations effectively. Time is an important variable in learning context and especially in the analysis of teaching-learning processes that take place in collaborative learning, whereas time management is crucial for effective learning. The aim of this work has been to analyze the effects of emotion management on time and self-management in e-learning and identify the competencies in time and self-management that are mostly influenced when students strive to achieve effective learning. To this end, we run an experiment with a class of high school students, which showed that increasing their ability to manage emotions better and more effectively enhances their competency to manage the time allocated to the learning practice more productively, and consequently their learning performance in terms of behavioral engagement and achievement and partly, in terms of cognitive engagement and self-regulation. Teacher affective feedback was proved to be a crucial factor to enhance cognitive engagement.Peer ReviewedPostprint (author's final draft
Designing an automated assessment tool in online distributed programming can provide students with a meaningful distributed learning environment that improves their academic performance. However, it is a complex and challenging endeavor that, as far as we know, has not been investigated yet. To address this research gap, this work presents a new automated assessment tool in online distributed programming, called DSLab. The tool was evaluated in a real long-term online educational experience by analyzing students' perceptions with the aim of improving its design. A quantitative analysis method was employed to collect and analyze data concerning students' perceptions as to whether using the DSLab tool was really a worthwhile experience. Our study shows that the DSLab tool includes acceptable utility and efficiency features. It also identifies factors that influence current design efficiency with the aim of improving DSLab design by suggesting new functionalities and ideas.
Aquesta és una còpia de la versió author's final draft d'un article publicat a la revista Soft computing.La publicació final està disponible a Springer a través de http://dx.doi.org/10.1007/s00500-016-2399-0 This is a copy of the author 's final draft version of an article published in the journal Soft computing.The final publication is available at Springer via http://dx.doi.org/10.1007/s00500-016-2399-0 Article publicat / Published article:Arguedas, M. [et al.] (2016) A model for providing emotion awareness and feedback using fuzzy logic in online learning. " Soft computing". Doi: 10.1007/s00500-016-2399-0 Abstract: Monitoring users' emotive states and using that information for providing feedback and scaffolding is crucial. In the learning context, emotions can be used to increase students' attention as well as to improve memory and reasoning. In this context, tutors should be prepared to create affective learning situations and encourage collaborative knowledge construction as well as identify those students' feelings which hinder learning process. In this paper, we propose a novel approach to label affective behavior in educational discourse based on fuzzy logic, which enables a human or virtual tutor to capture students' emotions, make students aware of their own emotions, assess these emotions and provide appropriate affective feedback. To that end, we propose a fuzzy classifier that provides a priori qualitative assessment and fuzzy qualifiers bound to the amounts such as few, regular, and many assigned by an affective dictionary to every word. The advantage of the statistical approach is to reduce the classical pollution problem of training and analyzing the scenario using the same dataset. Our approach has been tested in a real online learning environment and proved to have a very positive influence on students' learning performance. Section/Category: Methodologies & Application Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation A MODEL FOR PROVIDING EMOTION AWARENESS AND FEEDBACK USING FUZZY LOGIC IN ONLINE LEARNINGAbstract Monitoring users' emotive states and using that information for providing feedback and scaffolding is crucial. In the learning context, emotions can be used to increase students' attention as well as to improve memory and reasoning. In this context, tutors should be prepared to create affective learning situations and encourage collaborative knowledge construction as well as identify those students' feelings which hinder learning process. In this paper, we propose a novel approach to label affective behavior in educational discourse based on fuzzy logic, which enables a human or virtual tutor to capture students' emotions, make students aware of their own emotions, assess these emotions and provide appropriate affective feedback. To that end, we propose a fuzzy classifier that provides a priori qualitative assessment and fuzzy qualifiers bound to the amounts such as few, regular, and many assigned by an affective dictionary to every word. The advan...
There is a lack of studies that examine the role of a pedagogical agent on student development in a specific learning situation that involves psychological and cognitive preparatory activities in high school settings. We examined the effectiveness of pedagogical agent (APT) cognitive and affective feedback on learner motivation and well‐being. We applied an experimental research design, involving 45 fourth‐year high school students, divided in experimental and control groups (APT vs. human tutor). We performed a quantitative analysis to collect and analyse data of students using our APT. APT cognitive feedback had a positive effect on students' motivation for learning by encouraging students' proposals and initiatives and arousing students' interest in the topic. In addition, APT affective feedback fostered an appropriate emotional climate and creative environment for learning by enhancing students' curiosity, creativity and confidence for carrying out the activity, while reducing students' negative emotions such as boredom and anger. This study provided us useful insights about the affective (and cognitive) competencies that a virtual affective pedagogical agent needs to have in order to support students' mental and emotional health throughout a learning situation. Yet, further research is needed to consolidate these findings and make APT more adaptive to different learning situations.
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