25,111 research outputs found
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
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
Expressing social attitudes in virtual agents for social training games
The use of virtual agents in social coaching has increased rapidly in the
last decade. In order to train the user in different situations than can occur
in real life, the virtual agent should be able to express different social
attitudes. In this paper, we propose a model of social attitudes that enables a
virtual agent to reason on the appropriate social attitude to express during
the interaction with a user given the course of the interaction, but also the
emotions, mood and personality of the agent. Moreover, the model enables the
virtual agent to display its social attitude through its non-verbal behaviour.
The proposed model has been developed in the context of job interview
simulation. The methodology used to develop such a model combined a theoretical
and an empirical approach. Indeed, the model is based both on the literature in
Human and Social Sciences on social attitudes but also on the analysis of an
audiovisual corpus of job interviews and on post-hoc interviews with the
recruiters on their expressed attitudes during the job interview
How to Ask for Technical Help? Evidence-based Guidelines for Writing Questions on Stack Overflow
Context: The success of Stack Overflow and other community-based
question-and-answer (Q&A) sites depends mainly on the will of their members to
answer others' questions. In fact, when formulating requests on Q&A sites, we
are not simply seeking for information. Instead, we are also asking for other
people's help and feedback. Understanding the dynamics of the participation in
Q&A communities is essential to improve the value of crowdsourced knowledge.
Objective: In this paper, we investigate how information seekers can increase
the chance of eliciting a successful answer to their questions on Stack
Overflow by focusing on the following actionable factors: affect, presentation
quality, and time.
Method: We develop a conceptual framework of factors potentially influencing
the success of questions in Stack Overflow. We quantitatively analyze a set of
over 87K questions from the official Stack Overflow dump to assess the impact
of actionable factors on the success of technical requests. The information
seeker reputation is included as a control factor. Furthermore, to understand
the role played by affective states in the success of questions, we
qualitatively analyze questions containing positive and negative emotions.
Finally, a survey is conducted to understand how Stack Overflow users perceive
the guideline suggestions for writing questions.
Results: We found that regardless of user reputation, successful questions
are short, contain code snippets, and do not abuse with uppercase characters.
As regards affect, successful questions adopt a neutral emotional style.
Conclusion: We provide evidence-based guidelines for writing effective
questions on Stack Overflow that software engineers can follow to increase the
chance of getting technical help. As for the role of affect, we empirically
confirmed community guidelines that suggest avoiding rudeness in question
writing.Comment: Preprint, to appear in Information and Software Technolog
Identifying Emotions in Social Media: Comparison of Word-emotion lexica
In recent years, emotions expressed in social media messages have become a vivid research topic due to their influence on the spread of misinformation and online radicalization over online social networks. Thus, it is important to correctly identify emotions in order to make inferences from social media messages. In this paper, we report on the performance of three publicly available word-emotion lexicons (NRC, DepecheMood, EmoSenticNet) over a set of Facebook and Twitter messages. To this end, we designed and implemented an algorithm that applies natural language processing (NLP) techniques along with a number of heuristics that reflect the way humans naturally assess emotions in written texts. In order to evaluate the appropriateness of the obtained emotion scores, we conducted a questionnaire-based survey with human raters. Our results show that there are noticeable differences between the performance of the lexicons as well as with respect to emotion scores the human raters provided in our surve
Generating Music from Literature
We present a system, TransProse, that automatically generates musical pieces
from text. TransProse uses known relations between elements of music such as
tempo and scale, and the emotions they evoke. Further, it uses a novel
mechanism to determine sequences of notes that capture the emotional activity
in the text. The work has applications in information visualization, in
creating audio-visual e-books, and in developing music apps
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