Affect Recognition for Web 2.0 Intelligent E-Tutoring Systems

2014 ◽  
pp. 188-215 ◽  
Author(s):  
Oryina Kingsley Akputu ◽  
Kah Phooi Seng ◽  
Yun Li Lee
Keyword(s):  
Machine Vision ◽  
Facial Expressions ◽  
Teaching And Learning ◽  
Learning Systems ◽  
Affective States ◽  
Feedback Information ◽  
Tutoring Systems ◽  
Video Lecture ◽  
Significant Performance ◽  
Case Based

This chapter describes how a machine vision approach could be utilized for tracking learning feedback information on emotions for enhanced teaching and learning with Intelligent Tutoring Systems (ITS). The chapter focuses on analyzing learners’ emotions to show how affective states account for personalization or traceability for learning feedback. The chapter achieves this goal in three ways: (1) by presenting a comprehensive review of adaptive educational learning systems, particularly inspired by machine vision approaches; (2) by proposing an affective model for monitoring learners’ emotions and engagement with educational learning systems; (3) by presenting a case-based technique as an experimental prototype for the proposed affective model, where students’ facial expressions are tracked in the course of studying a composite video lecture. Results of the experiments indicate the superiority of such emotion-aware systems over emotion-unaware ones, achieving a significant performance increment of 71.4%.

Affect Recognition for Web 2.0 Intelligent E-Tutoring Systems

2018 ◽  
pp. 338-368
Author(s):  
Oryina Kingsley Akputu ◽  
Kah Phooi Seng ◽  
Yun Li Lee
Keyword(s):  
Machine Vision ◽  
Facial Expressions ◽  
Teaching And Learning ◽  
Learning Systems ◽  
Affective States ◽  
Feedback Information ◽  
Tutoring Systems ◽  
Video Lecture ◽  
Significant Performance ◽  
Case Based

This chapter describes how a machine vision approach could be utilized for tracking learning feedback information on emotions for enhanced teaching and learning with Intelligent Tutoring Systems (ITS). The chapter focuses on analyzing learners' emotions to show how affective states account for personalization or traceability for learning feedback. The chapter achieves this goal in three ways: (1) by presenting a comprehensive review of adaptive educational learning systems, particularly inspired by machine vision approaches; (2) by proposing an affective model for monitoring learners' emotions and engagement with educational learning systems; (3) by presenting a case-based technique as an experimental prototype for the proposed affective model, where students' facial expressions are tracked in the course of studying a composite video lecture. Results of the experiments indicate the superiority of such emotion-aware systems over emotion-unaware ones, achieving a significant performance increment of 71.4%.

Affect Recognition for Web 2.0 Intelligent E-Tutoring Systems

2016 ◽  
pp. 818-848 ◽  
Author(s):  
Oryina Kingsley Akputu ◽  
Kah Phooi Seng ◽  
Yun Li Lee
Keyword(s):  
Machine Vision ◽  
Facial Expressions ◽  
Teaching And Learning ◽  
Learning Systems ◽  
Affective States ◽  
Feedback Information ◽  
Tutoring Systems ◽  
Video Lecture ◽  
Significant Performance ◽  
Case Based

This chapter describes how a machine vision approach could be utilized for tracking learning feedback information on emotions for enhanced teaching and learning with Intelligent Tutoring Systems (ITS). The chapter focuses on analyzing learners' emotions to show how affective states account for personalization or traceability for learning feedback. The chapter achieves this goal in three ways: (1) by presenting a comprehensive review of adaptive educational learning systems, particularly inspired by machine vision approaches; (2) by proposing an affective model for monitoring learners' emotions and engagement with educational learning systems; (3) by presenting a case-based technique as an experimental prototype for the proposed affective model, where students' facial expressions are tracked in the course of studying a composite video lecture. Results of the experiments indicate the superiority of such emotion-aware systems over emotion-unaware ones, achieving a significant performance increment of 71.4%.

Affective Tutoring System for Better Learning

2011 ◽  
pp. 134-150 ◽  
Author(s):  
Abdolhossein Sarrafzadeh ◽  
Samuel T.V. Alexander ◽  
Jamshid Shanbehzadeh
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Recognition System ◽  
Expression Recognition ◽  
Tutoring System ◽  
Tutoring Systems ◽  
System A ◽  
Facial Expression Analysis ◽  
Animated Agent ◽  
Case Based

Intelligent tutoring systems (ITS) are still not as effective as one-on-one human tutoring. The next generation of intelligent tutors are expected to be able to take into account the emotional state of students. This paper presents research on the development of an Affective Tutoring System (ATS). The system called “Easy with Eve” adapts to students via a lifelike animated agent who is able to detect student emotion through facial expression analysis, and can display emotion herself. Eve’s adaptations are guided by a case-based method for adapting to student states; this method uses data that was generated by an observational study of human tutors. This paper presents an analysis of facial expressions of students engaged in learning with human tutors and how a facial expression recognition system, a life like agent and a case based system based on this analysis have been integrated to develop an ATS for mathematics.

Artificial Intelligence Methods in E-Learning

2019 ◽  
pp. 287-307
Author(s):  
Meltem Eryılmaz ◽  
Afaf Muftah Adabashi ◽  
Ali Yazıcı
Keyword(s):  
Artificial Intelligence ◽  
Teaching And Learning ◽  
Intelligent Tutoring ◽  
Learning Systems ◽  
Educational Systems ◽  
Educational Materials ◽  
Tutoring Systems ◽  
Challenges And Opportunities ◽  
E Learning ◽  
Effective Teaching And Learning

Gathering and extracting knowledge from the large amount of data available today is becoming more and more important in our information society, and similarly, learning is an essential important part of our everyday lives. The new requirements of the competing world and the development of more advanced technologies have also changed traditional educational systems, which now employ better and more effective teaching and learning methods. In this regard, the integration of artificial intelligence (AI) technologies in the field of education offers both great challenges and opportunities in building e-learning systems. E-learning systems allow learners to access the educational materials ubiquitously from anywhere at any time. Therefore, these systems have to become adaptive to the needs and preferences of each individual learner. This chapter presents a review of the important concepts and background for research to include introduction and examination of e-learning systems and intelligent tutoring systems (ITSs), available today.

Modeling of Adaptive Tutoring Processes

2006 ◽  
pp. 193-215 ◽  
Author(s):  
Alke Martens
Keyword(s):  
Teaching And Learning ◽  
Process Model ◽  
Learning Strategy ◽  
Learning Material ◽  
Formal Approach ◽  
Tutoring Systems ◽  
Reusable Components ◽  
Case Based Learning ◽  
Adaptive Tutoring ◽  
Case Based

In this chapter, a formal, adaptive tutoring process model for case-based Intelligent Tutoring Systems (ITSs) is described. Combining methods of Artificial Intelligence and Cognitive Science led to the development of ITSs more than 30 years ago. In contrast to the common agreement about the ITSs’ architecture, components of ITSs are rarely reusable. Reusability in ITSs is intimately connected with the application domain, that is, with the contents that should be learned and with the teaching and learning strategy. An example of a learning strategy is case-based learning, where the adaptation of the learning material to the learner plays a major role. Adaptation should take place automatically at runtime, and thus should be part of the ITS’s functionality. To support the development of ITSs with reusable components and the communication about and the evaluation of similar ITSs, a formal approach has been chosen. This approach is called the tutoring process model.

scholarly journals Pain assessment in horses using automatic facial expression recognition through deep learning-based modeling

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258672
Author(s):  
Gabriel Carreira Lencioni ◽  
Rafael Vieira de Sousa ◽  
Edson José de Souza Sardinha ◽  
Rodrigo Romero Corrêa ◽  
Adroaldo José Zanella
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
Facial Expressions ◽  
Evaluation System ◽  
Imaging System ◽  
Daily Routine ◽  
Human Observer ◽  
Expression Recognition ◽  
Efficient Treatment ◽  
Vision Algorithm

The aim of this study was to develop and evaluate a machine vision algorithm to assess the pain level in horses, using an automatic computational classifier based on the Horse Grimace Scale (HGS) and trained by machine learning method. The use of the Horse Grimace Scale is dependent on a human observer, who most of the time does not have availability to evaluate the animal for long periods and must also be well trained in order to apply the evaluation system correctly. In addition, even with adequate training, the presence of an unknown person near an animal in pain can result in behavioral changes, making the evaluation more complex. As a possible solution, the automatic video-imaging system will be able to monitor pain responses in horses more accurately and in real-time, and thus allow an earlier diagnosis and more efficient treatment for the affected animals. This study is based on assessment of facial expressions of 7 horses that underwent castration, collected through a video system positioned on the top of the feeder station, capturing images at 4 distinct timepoints daily for two days before and four days after surgical castration. A labeling process was applied to build a pain facial image database and machine learning methods were used to train the computational pain classifier. The machine vision algorithm was developed through the training of a Convolutional Neural Network (CNN) that resulted in an overall accuracy of 75.8% while classifying pain on three levels: not present, moderately present, and obviously present. While classifying between two categories (pain not present and pain present) the overall accuracy reached 88.3%. Although there are some improvements to be made in order to use the system in a daily routine, the model appears promising and capable of measuring pain on images of horses automatically through facial expressions, collected from video images.

scholarly journals The Application of the Learning Sciences to the Design of Business Education Cases

2017 ◽  
pp. 141-168
Author(s):  
Michael D. Hamlin
Keyword(s):  
Situated Learning ◽  
Business Education ◽  
Teaching And Learning ◽  
Learning Sciences ◽  
Integrative Approach ◽  
Theoretical Perspectives ◽  
Business Practitioners ◽  
Based Instruction ◽  
Learning And Cognition ◽  
Case Based

Business education is education for practice and thus, requires a systematic and integrative approach that will guide students toward becoming reflective practitioners. Case-based education is an important tool that can provide the educational experiences that produce effective practitioners but only if its use is guided by a sound theoretical and research based framework. Research and theory from the learning sciences can guide case-based instructional practices. This chapter will provide a framework for the design of case-based instruction that incorporates teaching and learning affordances derived from the theory of situated learning and cognition. If the educational goal is to produce business practitioners with the skills and knowledge necessary to operate successfully in today's global business environment, business education needs to be prepared to incorporate theoretical perspectives derived from learning sciences research into case-based education.

scholarly journals The Application of the Learning Sciences to the Design of Business Education Cases

2018 ◽  
pp. 563-590
Author(s):  
Michael D. Hamlin
Keyword(s):  
Situated Learning ◽  
Business Education ◽  
Teaching And Learning ◽  
Learning Sciences ◽  
Integrative Approach ◽  
Theoretical Perspectives ◽  
Business Practitioners ◽  
Based Instruction ◽  
Learning And Cognition ◽  
Case Based

Business education is education for practice and thus, requires a systematic and integrative approach that will guide students toward becoming reflective practitioners. Case-based education is an important tool that can provide the educational experiences that produce effective practitioners but only if its use is guided by a sound theoretical and research based framework. Research and theory from the learning sciences can guide case-based instructional practices. This chapter will provide a framework for the design of case-based instruction that incorporates teaching and learning affordances derived from the theory of situated learning and cognition. If the educational goal is to produce business practitioners with the skills and knowledge necessary to operate successfully in today's global business environment, business education needs to be prepared to incorporate theoretical perspectives derived from learning sciences research into case-based education.

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