A fuzzy-based approach for classifying students' emotional states in online collaborative work

(c) 2016 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.Emotion awareness is becoming a key aspect in collaborative work at academia, enterprises and organizations that use collaborative group work in their activity. Due to pervasiveness of ICT's, most of collaboration can be performed through communication media channels such as discussion forums, social networks, etc. The emotive state of the users while they carry out their activity such as collaborative learning at Universities or project work at enterprises and organizations influences very much their performance and can actually determine the final learning or project outcome. Therefore, monitoring the users' emotive states and using that information for providing feedback and scaffolding is crucial. To this end, automated analysis over data collected from communication channels is a useful source. In this paper, we propose an approach to process such collected data in order to classify and assess emotional states of involved users and provide them feedback accordingly to their emotive states. In order to achieve this, a fuzzy approach is used to build the emotive classification system, which is fed with data from ANEW dictionary, whose words are bound to emotional weights and these, in turn, are used to map Fuzzy sets in our proposal. The proposed fuzzy-based system has been evaluated using real data from collaborative learning courses in an academic context.Peer ReviewedPostprint (author's final draft

A model for providing emotion awareness and feedback using fuzzy logic in online learning

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.Peer ReviewedPostprint (author's final draft

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

Analyzing the effects of emotion management on time and self-management in computer-based learning

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

Emotional intelligence and teacher leaders

The purpose of this study was to understand principals’ perceptions and perceived attributes of strong teacher leaders, determine how these attributes link to emotional intelligence and learn how these attributes are developed. In this study, emotional intelligence will be defined as “the abilities to recognize and regulate emotions in ourselves and in others” (Goleman, 2001, p.14). This study summarizes data collected through semi-structured interviews with 11 school principals and assistant principals from a variety of schools, school districts and across multiple states. Overall, this study lead to three main discoveries: 1) top perceived attributes of teacher leaders are closely connected to EI skills 2) teacher leader development strategies that best support EI are comprehensive, job-embedded, sustained over time, peer-focused, goal-focused, differentiated for teacher’s needs and leverage a variety of practices and tools, and 3) teacher leaders with high EI have a positive impact on their schools

Sharing emotions impacts computer-supported collaborative processes: effect of an emotion awareness tool

There is a large consensus among researchers on the significant role of emotions in group effectiveness and performance. Emotion awareness tools (EATs) have been developed in recent years allowing members of a group to identify with their own- and their partner's emotions through a computer-mediated collaboration. In this study, we report the impact of an EAT on socio-cognitive and relational processes, and its differential effect depending on gender. This study shows that the EAT was beneficial for processes contributing to the quality of working relationships and the mutual modeling process, by which group members gain a better awareness of their partner's knowledge