5 research outputs found
Cognitive emotions in e-learning processes and their potential relationship with studentsâ academic adjustment
In times of growing importance and emphasis on improving academic outcomes for
young people, their academic selves/lives are increasingly becoming more central to
their understanding of their own wellbeing. How they experience and perceive their
academic successes or failures, can influence their perceived self-efficacy and eventual
academic achievement. To this end, âcognitive emotionsâ, elicited to acquire or develop
new skills/knowledges, can play a crucial role as they indicate the state or the âflowâ of
a studentâs emotions, when facing challenging tasks. Within innovative teaching models,
measuring the affective components of learning have been mainly based on self-reports
and scales which have neglected the real-time detection of emotions, through for
example, recording or measuring facial expressions. The aim of the present study is to
test the reliability of an ad hoc software trained to detect and classify cognitive emotions
from facial expressions across two different environments, namely a video-lecture and a
chat with teacher, and to explore cognitive emotions in relation to academic e-selfefficacy
and academic adjustment. To pursue these goals, we used video-recordings of
ten psychology students from an online university engaging in online learning tasks, and
employed software to automatically detect eleven cognitive emotions. Preliminary
results support and extend prior studies, illustrating how exploring cognitive emotions in
real time can inform the development and success of academic e-learning interventions
aimed at monitoring and promoting studentsâ wellbeing.peer-reviewe
Sentiment Analysis in Social Streams
In this chapter, we review and discuss the state of the art on sentiment
analysis in social streamsâsuch as web forums, microblogging systems, and social
networks, aiming to clarify how user opinions, affective states, and intended emo tional effects are extracted from user generated content, how they are modeled, and
howthey could be finally exploited.We explainwhy sentiment analysistasks aremore
difficult for social streams than for other textual sources, and entail going beyond
classic text-based opinion mining techniques. We show, for example, that social
streams may use vocabularies and expressions that exist outside the mainstream of
standard, formal languages, and may reflect complex dynamics in the opinions and
sentiments expressed by individuals and communities
Interpretation of userâs feedback in human-robot interaction
In this paper we will propose the use of social robots as interface between users and services in a Smart Environment. We will focus on the need for a robot to recognize the userâs feedback, in order to respond and revise its behaviour according to userâs needs. As we believe speech is a natural and immediate input channel in human-robot interaction, we will discuss the importance of recognising, besides the linguistic content of the spoken sentence, the attitude of the user towards the robot and the environment. In this way, the meaning of the user dialog will be made clear when hardly recognisable by the analysis of the utterance structure. Then, we will present the results of the application of a potential approach used for integrating the linguistic analysis with the recognition of the valence and arousal of the userâs utterance. In order to achieve this goal, we collected and analysed a corpus of data to build an interpretation model based on a Bayesian network. Then we tested the accuracy of the model using a test dataset. Results will show that the integration of the linguistic content with the recognition of some acoustic features of spoken sentences perform better in recognising the key aspects of userâs feedback
Cognitive emotions in E-learning processes and their potential relationship with students' academic adjustment
In times of growing importance and emphasis on improving academic outcomes for young people, their academic selves/lives are increasingly becoming more central to their understanding of their own wellbeing. How they experience and perceive their academic successes or failures, can influence their perceived self-efficacy and eventual academic achievement. To this end, 'cognitive emotions', elicited to acquire or develop new skills/knowledges, can play a crucial role as they indicate the state or the "flow" of a student's emotions, when facing challenging tasks. Within innovative teaching models, measuring the affective components of learning have been mainly based on self-reports and scales which have neglected the real-time detection of emotions, through for example, recording or measuring facial expressions. The aim of the present study is to test the reliability of an ad hoc software trained to detect and classify cognitive emotions from facial expressions across two different environments, namely a video-lecture and a chat with teacher, and to explore cognitive emotions in relation to academic e-selfefficacy and academic adjustment. To pursue these goals, we used video-recordings of ten psychology students from an online university engaging in online learning tasks, and employed software to automatically detect eleven cognitive emotions. Preliminary results support and extend prior studies, illustrating how exploring cognitive emotions in real time can inform the development and success of academic e-learning interventions aimed at monitoring and promoting students' wellbeing
Cognitive Emotions in E-Learning Processes and Their Potential Relationship with Studentsâ Academic Adjustment
In times of growing importance and emphasis on improving academic outcomes for young people, their academic selves/lives are increasingly becoming more central to their understanding of their own wellbeing. How they experience and perceive their
academic successes or failures, can influence their perceived self-efficacy and eventual academic achievement. To this end, âcognitive emotionsâ, elicited to acquire or develop new skills/knowledges, can play a crucial role as they indicate the state or the âflowâ of a studentâs emotions, when facing challenging tasks. Within innovative teaching models, measuring the affective components of learning have been mainly based on self-reports
and scales which have neglected the real-time detection of emotions, through for example, recording or measuring facial expressions. The aim of the present study is to
test the reliability of an ad hoc software trained to detect and classify cognitive emotions from facial expressions across two different environments, namely a video-lecture and a
chat with teacher, and to explore cognitive emotions in relation to academic e-self efficacy and academic adjustment. To pursue these goals, we used video-recordings of ten psychology students from an online university engaging in online learning tasks, and
employed software to automatically detect eleven cognitive emotions. Preliminary results support and extend prior studies, illustrating how exploring cognitive emotions in real time can inform the development and success of academic e-learning interventions aimed at monitoring and promoting studentsâ wellbeing