Affective Tutoring System for Android Mobiles

Zataraín-Cabada, Ramón; Barrón-Estrada, M. L.; Camacho, José; Reyes-García, Carlos A.

doi:10.1007/978-3-319-09339-0_1

Cited by 8 publications

(10 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the ATSs objectives is to change the emotional state of student (Zatarain-Cabada, Barrón-Estrada, Alor-Hernández, et al., 2014). ATSs have implemented different approaches to change the emotional state of students.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The system includes three basic units: (a) expert, later renamed domain (Zatarain-Cabada, Barrón-Estrada, Alor-Hernández, et al., 2014); (b) student; and (c) tutor, which also has an affective subunit. A typical student profile contains (a) a static profile with personal and academic information and (b) a dynamic profile that contains information through the interaction with the system and recognition of the student’s feelings (Cabada et al., 2012).…”

Section: Analysis Of Five Atssmentioning

confidence: 99%

“…The diagnostic test shows what student knows and what to teach (Barrón-Estrada, Zatarain-Cabada, & Pérez, 2012). The emotional states which recognized by Fermat were originally considering seven kind of emotions (anger, disgust, fear, happiness, sadness, surprise, and neutral; Cabada et al., 2012) and finally adapted to recognize five kind of emotions (anger, happiness, sadness, surprise, and neutral; Zatarain-Cabada, Barrón-Estrada, Alor-Hernández, et al., 2014). A detailed description for extracting facial features and recognizing emotional status through efficient techniques is described in Zatarain-Cabada, Barrón-Estrada, Camacho, et al.…”

Section: Analysis Of Five Atssmentioning

confidence: 99%

“…However, the operation of multiplication (Zatarain-Cabada, Barrón-Estrada, and Zatarain-Cabada, 2013) and division (Zatarain-Cabada, Barrón-Estrada, Alor-Hernández, et al., 2014) has been evaluated. Fermat has been applied to third-grade primary school students in multiplication (Cabada et al., 2012) and natural numbers (Barrón-Estrada, Zatarain-Cabada, Aranda-Ortega, Pérez, & Mejía-Arredondo, 2014) and primary school students in multiplication and division (Zatarain-Cabada, Barrón-Estrada, Alor-Hernández, et al., 2014). In addition, it is addressed to elementary school students (second grade) for learning mathematics (Cabada, Estrada, Hernández, & Bustillos, 2014).…”

Section: Analysis Of Five Atssmentioning

confidence: 99%

See 3 more Smart Citations

Studying Affective Tutoring Systems for Mathematical Concepts

Mastorodimos

Chatzichristofis

2019

Journal of Educational Technology Systems

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Analysis Of Five Atssmentioning

confidence: 99%

Section: Analysis Of Five Atssmentioning

confidence: 99%

Section: Analysis Of Five Atssmentioning

confidence: 99%

See 2 more Smart Citations

Studying Affective Tutoring Systems for Mathematical Concepts

Mastorodimos

Chatzichristofis

2019

Journal of Educational Technology Systems

View full text Add to dashboard Cite

show abstract

“…Considering the effect of affect in the ability of learners to learn, Affective Tutoring Systems utilise affect detection methods, such as facial expressions [8], facial features combined with neural networks [13], facial and voice features [14], etc., in order to detect the affective state of the learner and adapt the learning process accordingly. A field of research that can potentially provide solutions for the detection of affect in ITS is the field of Affective Computing.…”

Section: Introductionmentioning

confidence: 99%

On the use of ECG and EMG Signals for Question Difficulty Level Prediction in the Context of Intelligent Tutoring Systems

Alqahtani

Katsigiannis

Ramzan

2019

2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)

View full text Add to dashboard Cite

A fundamental drawback of traditional Intelligent Tutoring Systems (ITS) is that, unlike human tutors, they are not able to understand the emotional state of their users and adapt the learning process accordingly. This work explores the potential use of affective computing techniques for providing an affect detection mechanism for ITS. Electrocardiography (ECG) and electromyography (EMG) signals were recorded from 45 individuals that undertook a computerised English language test and provided feedback on the difficulty of the test's questions. Features extracted from the ECG and EMG signals were then used in order to train machine learning models for the task of predicting the self-perceived difficulty level of the questions. The conducted supervised classification experiments provided promising results for the suitability of this approach for enhancing ITS with information relating to the affective state of the learners, reaching an average classification F1-score of 75.49% for the personalised single-participant models and a classification F1score of 64.10% for the global models.

show abstract

Measuring and Integrating Facial Expressions and Head Pose as Indicators of Engagement and Affect in Tutoring Systems

Gupta

Lee

et al. 2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Affective Tutoring System for Android Mobiles

Cited by 8 publications

References 6 publications

Studying Affective Tutoring Systems for Mathematical Concepts

Studying Affective Tutoring Systems for Mathematical Concepts

On the use of ECG and EMG Signals for Question Difficulty Level Prediction in the Context of Intelligent Tutoring Systems

Measuring and Integrating Facial Expressions and Head Pose as Indicators of Engagement and Affect in Tutoring Systems

Contact Info

Product

Resources

About