An Evaluation of Mouse and Keyboard Interaction Indicators towards Non-intrusive and Low Cost Affective Modeling in an Educational Context☆
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TLDR
A series of indicators, which derive from user's interactions with mouse and keyboard, are proposed, to evaluate their use in identifying affective states and behavior changes in an e-learning platform by means of non-intrusive and low cost methods.About:
This article is published in Procedia Computer Science.The article was published on 2014-01-01 and is currently open access. It has received 62 citations till now. The article focuses on the topics: Affective computing & User modeling.read more
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Journal ArticleDOI
Towards an automatic early stress recognition system for office environments based on multimodal measurements
TL;DR: This work reviews and brings together the recent works carried out in the automatic stress detection looking over the measurements executed along the three main modalities, namely, psychological, physiological and behaviouralmodalities, in order to give hints about the most appropriate techniques to be used and thereby, to facilitate the development of such a holistic system.
Journal ArticleDOI
Affective computing in education: A systematic review and future research
Elaheh Yadegaridehkordi,Nurul Fazmidar Mohd Noor,Mohamad Nizam Ayub,Hannyzzura Affal,Nornazlita Hussin +4 more
TL;DR: A review of the literature on affective computing in education by selecting articles published from 2010 to 2017 is presented and recommendations for future research directions are provided to help researchers, policymakers and practitioners in the education sector to apply Affective computing technology more effectively and to expand educational practices.
Book ChapterDOI
Improving Sensor-Free Affect Detection Using Deep Learning
TL;DR: This work constructs new “deep” sensor-free affect detectors and reports significant improvements over previously reported models, including RNNs, which have only recently been attempted in educational contexts.
Journal ArticleDOI
Recent developments in affective recommender systems
Rahul Katarya,Om Prakash Verma +1 more
TL;DR: This article categorizes, synthesizes, and discusses the research and development in ARS, and classified and managed ARS papers according to different perspectives: research gaps, nature, algorithm or method adopted, datasets, the platform on executed, types of information and evaluation techniques applied.
Journal ArticleDOI
Reading Emotion From Mouse Cursor Motions: Affective Computing Approach.
Takashi Yamauchi,Kunchen Xiao +1 more
TL;DR: 10-fold cross-validation analysis shows that statistical models formed from "known" participants (training data) could predict nearly 10%-20% of the variance of positive affect and attentiveness ratings of "unknown" participants, suggesting that cursor movement patterns such as the area under curve and direction change help infer emotions of computer users.
References
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Journal ArticleDOI
Measuring emotion: The self-assessment manikin and the semantic differential
Margaret M. Bradley,Peter Lang +1 more
TL;DR: Reports of affective experience obtained using SAM are compared to the Semantic Differential scale devised by Mehrabian and Russell (An approach to environmental psychology, 1974), which requires 18 different ratings.
Book
Human-Computer Interaction
TL;DR: The human and the design of interactive systems: The myth of the infinitely fast machine, a guide to designing for diversity and the process of design.
Journal ArticleDOI
What are emotions? And how can they be measured?:
TL;DR: In this article, the authors define emotion as a phenomenon to be studied, without consensual conceptualization and operationalization of exactly what phenomenon is to be investigated. But progress in theory and research is difficult to a...
Journal ArticleDOI
Intelligent tutoring systems
TL;DR: Computer tutors based on a set of pedagogical principles derived from the ACT theory of cognition have been developed for teaching students to do proofs in geometry and to write computer programs in the language LISP.
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Measuring emotion: The self-assessment manikin and the semantic differential
Margaret M. Bradley,Peter Lang +1 more