Assessment of affective state in distance learning based on image detection by using fuzzy fusion

Hwang, Kuo‐An; Yang, Cheng-Hsiung

doi:10.1016/j.knosys.2008.10.004

Cited by 16 publications

(5 citation statements)

References 15 publications

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“…Pattern recognition methods are now used in so many fields such as character recognition [1,23], face affective detect [9], leaf recognition [31] and hyper-spectral image classification [14]. In insect image identification, some of these methods such as the nearest neighbor classifier have been used [19].…”

Section: Pattern Recognition Methodsmentioning

confidence: 99%

A new automatic identification system of insect images at the order level

Wang¹,

Lin²,

Ji³

et al. 2012

Knowledge-Based Systems

165

104

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Section: Pattern Recognition Methodsmentioning

confidence: 99%

A new automatic identification system of insect images at the order level

Wang¹,

Lin²,

Ji³

et al. 2012

Knowledge-Based Systems

165

104

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“…Hwang et al [13] proposed a formative assessment approach using data mining technique which integrates six computational intelligence schemes in order to determine learning behaviors and performance from learners. However, their proposed system was focused on asynchronous e-learning system where there is no interaction between learners and an instructor.…”

Section: Related Workmentioning

confidence: 99%

Detection of Learner’s Concentration in Distance Learning System with Multiple Biological Information

Daniel¹,

Kamioka²

2017

JCC

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The trend of distance learning education has increased year by year because of the rapid advancement of information and communication technologies. Distance learning system can be regarded as one of ubiquitous computing applications since the learners can study anywhere even in mobile environments. However, the instructor cannot know if the learners comprehend the lecture or not since each learner is physically isolated. Therefore, a framework which detects the learners' concentration condition is required. If a distance learning system obtains the information that many learners are not concentrated on the class due to the incomprehensible lecture style, the instructor can perceive it through the system and change the presentation strategy. This is a contextaware technology which is widely used for ubiquitous computing services. In this paper, an efficient distance learning system, which accurately detects learners' concentration condition during a class, is proposed. The proposed system uses multiple biological information which are learners' eye movement metrics, i.e. fixation counts, fixation rate, fixation duration and average saccade length obtained by an eye tracking system. The learners' concentration condition is classified by using machine learning techniques. The proposed system has performed the detection accuracy of 90.7% when Multilayer Perceptron is used as a classifier. In addition, the effectiveness of the proposed eye metrics has been confirmed. Furthermore, it has been clarified that the fixation duration is the most important eye metric among the four metrics based on the investigation of evaluation experiment.

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“…Emotions play a significant role in everyday activities, in turn influencing attitudes, memory, decision making, attention, learning, and learning achievement ( Sionti et al, 2018 ). By assessing the emotions of students, teachers can effectively change their approach to teaching and evaluation, while supporting learning performance ( Moridis and Economides, 2008 ; Hwang and Yang, 2009 ; Petrovica et al, 2017 ; Daouas and Lejmi, 2018 ; Wronowski et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Affective Mobile Language Tutoring System for Supporting Language Learning

Lin

Wang

et al. 2022

Front. Psychol.

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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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Assessment of affective state in distance learning based on image detection by using fuzzy fusion

Cited by 16 publications

References 15 publications

A new automatic identification system of insect images at the order level

A new automatic identification system of insect images at the order level

Detection of Learner’s Concentration in Distance Learning System with Multiple Biological Information

Affective Mobile Language Tutoring System for Supporting Language Learning

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