Frown gives game away: affect sensitive systems for elementary mathematics

Sarrafzadeh, Abdolhossein; Fan, Chao; Dadgostar, Farhad; Alexander, S.; Messom, Chris

doi:10.1109/icsmc.2004.1398265

Cited by 12 publications

(15 citation statements)

References 17 publications

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“…This approach has been used for recognizing nonverbal interaction of learners in an affective tutoring system [26][27][28][29]. This method is to be applied in conjunction with a fast pixel-based skin color segmentation algorithm as the level of noise and the quality of the skin detection are not deterministic.…”

Section: Discussionmentioning

confidence: 99%

Gesture-based human–machine interfaces: a novel approach for robust hand and face tracking

Dadgostar¹,

Sarrafzadeh

2018

Iran J Comput Sci

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Nonverbal communication forms a substantial portion of human-human interaction. In recent years, there has been increasing interest in developing gesture-based user interfaces for better human-machine interaction. Hand and face tracking is a central issue in the development of real-time gesture recognition systems. In this article, a new approach for boundary detection in blob tracking based on the mean-shift algorithm is proposed. Our approach is based on continuous sampling of the boundaries of the kernel and changing the size of the kernel using our novel Fuzzy-based algorithm. We compare our approach to the kernel density-based approach which is known as the CAM-shift algorithm in a set of different noise levels and conditions. The results show that the proposed approach is superior in stability against white noise and also provides correct boundary detection for arbitrary hand postures which is not achievable by the CAM-shift algorithm. This algorithm provides the required framework for vision-based real-time gesture recognition and hand and face tracking. It can be applied in scientific and commercial extensions of either vision-based or hybrid gesture recognition systems.

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Section: Discussionmentioning

confidence: 99%

Gesture-based human–machine interfaces: a novel approach for robust hand and face tracking

Dadgostar¹,

Sarrafzadeh

2018

Iran J Comput Sci

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“…ATS is a type of ITS which detects nonverbal behavior in the real time (e.g., using a camera) and makes use of this information for personalized interaction with the student (Sarrafzadeh, Fan, Dadgostar, Alexander, & Messom, 2004). It also adopts the emotional state of the student, emulating the behavior that teachers have (S. T. V. Alexander, 2004).…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is mentioned as the first fully functioning ATS (Ivanova, 2013). Initially, the system was designed as an automated face expression analysis system (S. Alexander, 2005; Sarrafzadeh et al., 2004) and gesture analysis (S. Alexander, 2005), but it ultimately uses real-time face expression analysis (Sarrafzadeh, Fourie, Alexander, Overmyer, & Shanbehzadeh, 2014) for mathematics. In general, the architecture of Easy with Eve consists of the following components: (a) the facial expression analysis unit, (b) the student’s model, (c) the teaching strategy unit, and (d) a set of teaching activities in the form of an animated pedagogical agent.…”

Section: Analysis Of Five Atssmentioning

confidence: 99%

“…The Easy with Eve initially recognized seven emotions (surprise, happiness, sadness, puzzlement, disgust, anger, and neutral; Sarrafzadeh, Alexander, Dadgostar, Fan, & Bigdeli, 2006; Sarrafzadeh et al., 2004) but eventually presented to recognize eight emotional states (neutral, smiling, laughing, surprised, angry, fearful, sad, and disgusted; Sarrafzadeh et al., 2014). A web camera (S. Alexander, 2005; S. T. V. Alexander, 2004; Sarrafzadeh et al., 2004, 2006) and a system consisting of (a) an artificial neural network for face detection, (b) the extraction of facial features, and (c) the fuzzy face expression classifier to detect student’s emotional state (Sarrafzadeh et al., 2006).…”

Section: Analysis Of Five Atssmentioning

confidence: 99%

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Studying Affective Tutoring Systems for Mathematical Concepts

Mastorodimos

Chatzichristofis

2019

Journal of Educational Technology Systems

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Students face difficulties in learning mathematical processes. As a result, they have negative emotions toward mathematics. The use of technology is employed to change the student’s attitude toward mathematics. Some methods utilize intelligent tutoring systems to recognize student’s emotional state and adapt the learning process accordingly. These systems, known as affective tutoring systems (ATSs), sense the emotional state of a student and then intelligently attempt to suggest appropriate strategies that can guide the learning process and ultimately shift the negative attitude of students toward mathematical learning. This article presents a survey of ATSs, which teach mathematic content, and tries to find common elements among them. It examines the kind and the number of student’s emotions that can be recognized and the strategies and methods that these ATSs use to recognize student’s emotional state. There are findings that agree with other studies about the recognized emotions and the methods that are used.

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“…Intelligent tutoring systems (ITS) provide individualised instruction, by being able to adapt to the knowledge, learning abilities and needs of each individual student. Existing ITS build a model of the student's current state of knowledge and individualise instruction based on that model [31,32] . Intelligent tutoring systems offer many advantages over the traditional classroom scenario: they are always available, non judgmental and provide tailored feedback [15] .…”

Section: Introductionmentioning

confidence: 99%

A Study of Mathematics Web-Based Learning in Schools

Al‐A'ali¹

2008

American J. of Applied Sciences

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Teaching Mathematics to intermediate school boys is a challenging process which can greatly benefit from software technology and web-based learning. In this research we study the difficulties and opportunities of using this technology in teaching Mathematics to 13 year old boys studying Mathematics at school. For students' we identify the basic requirements at home, e.g., Mathematics computer software packages, availability of internet access and ability of the parents to provide computers, software and the internet for their sons. We assess the expected improvement in grades, motivation and communication between home and school. For the teaches', we assess the expected improvement in teachers' productivity, teachers' experience, qualifications in computing and Mathematics packages and their anticipated expectations such as improvement in problem solving, grades of students. Our main objective is to determine the expected results resulting from applying computer software and web-based learning in teaching Mathematics

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Frown gives game away: affect sensitive systems for elementary mathematics

Cited by 12 publications

References 17 publications

Gesture-based human–machine interfaces: a novel approach for robust hand and face tracking

Gesture-based human–machine interfaces: a novel approach for robust hand and face tracking

Studying Affective Tutoring Systems for Mathematical Concepts

A Study of Mathematics Web-Based Learning in Schools

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