Affect can significantly influence education/learning. Thus, understanding a learner's affect throughout the learning process is crucial for understanding motivation. In conventional education/learning research, learner motivation can be known through postevent self-reported questionnaires. With the advance of affective computing technology, researchers are able to objectively identify and measure a learner's affective status during the entire learning process in a real-time manner, and then they are able to understand the interrelationship between emotion, motivation and learning performance. There are over 100 papers in the ScienceDirect database with the keywords "affective computing in education" or "affective computing in learning," which reveals that this emerging technology has been applied to education/learning. This study intends to categorize and summarize those measurements so as to realize their applicability, feasibility and trends. Finally, some challenges and suggestions are then raised for helping educational researchers when applying affective computing technology.The need for AC in learning/education Since AC was proposed by Picard in 1994, there has been a burst of research focused on creating technologies to monitor and appropriately respond to affective states (Picard, 1997a). Through Advancements and trends of affective computing research 1305 V C 2015 British Educational Research AssociationTwo researchers with AC experience were asked to use the key phrases "affective computing in education" and "affective computing in learning" to obtain relevant studies from the SDOS Advancements and trends of affective computing research 3 Advancements and trends of affective computing research 1311 V C 2015 British Educational Research Association 4 AC, affective computing; EEG, electroencephalography; EMG, electromyography; SCR, skin conductance response. Advancements and trends of affective computing research 9 Advancements and trends of affective computing research 1313 V C 2015 British Educational Research Association Advancements and trends of affective computing research 1319 V C 2015 British Educational Research Association Advancements and trends of affective computing research 17 Advancements and trends of affective computing research 1321 V C 2015 British Educational Research Association https://en.wikipedia.org/wiki /Electrodermal_activity Advancements and trends of affective computing research 19
Several studies were conducted in past years which used the evolutionary process of Genetic Algorithms for optimizing the Support Vector Regression parameter values although, however, few of them were devoted to the simultaneously optimization of the type of kernel function involved in the established model. The present work introduces a new hybrid genetic-based Support Vector Regression approach, whose statistical quality and predictive capability is afterward analyzed and compared to other standard chemometric techniques, such as Partial Least Squares, Back-Propagation Artificial Neural Networks, and Support Vector Machines based on Cross-Validation. For this purpose, we employ a data set of experimentally determined binding affinity constants toward the benzodiazepine binding site of the GABA (A) receptor complex on 78 flavonoid ligands.
Students often face difficulties and experience negative emotions toward second language learning. The affective tutoring system (ATS) is a next-generation learning approach that can detect the affective status of learning to increase performance. Therefore, for the purposes of this study, an innovative affective mobile language tutoring system (AMLTS) was designed to support Japanese language learning. The effects of AMLTS, along with asynchronous discussion, that were intended to improve performance, were examined using a triangulation method. To investigate the effect on emotion, the proposed AMLTS provides a virtual emotion agent that can interact with users and record emotional events, learning assessments, and the results of the interaction into a database. Learning effectiveness evaluations were conducted via two experiments: prototype evaluation and final evaluation. Sixty-three students, all beginners, were invited to use the AMLTS to learn Japanese. The research results show that the proposed AMLTS affective interaction design significantly improves learner engagement and performance. In the emotion feedback analysis and learning process, AMLTS helped students deepen their understanding of the content, enabled them to clearly understand the content, and to engage in peer interaction and experience positive emotions. In the evaluation of system usability, AMLTS reveals good usability for foreign language acquisition.
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