Showing papers in "Learning and analytics in intelligent systems in 2022"
4 citations
TL;DR: Gamification and the Internet of Things can provide real-world data visualization in near real-time as discussed by the authors , and it becomes possible to develop realistic and safe simulation environments with real life data or triggering systems taking the student learning experience in whole new levels.
Abstract: Gamification and the Internet of Things will impact education in a manner previously impossible. Gamified tools built upon game engines will offer opportunities in the development of innovative virtual labs and other interactive experiences for students. The Internet of Things can provide real-world data visualization in near real-time. Combining these fields, it becomes possible to develop realistic and safe simulation environments with real life data or triggering systems taking the student learning experience in whole new levels.
3 citations
TL;DR: In this article , the authors presented the application of educational computer games aiming to improve emotional understanding in children with ASD, and designed a game based on the ability model of emotional intelligence, which suggests that emotional intelligence is based on perceived emotions, use of emotions to facilitate thought, understanding emotions and managing emotions.
Abstract: This chapter presents the application of educational computer games aiming to improve emotional understanding in children with ASD. A game, designed specifically for children with ASD and based on the ability model of emotional intelligence [22], is introduced. This model suggests that emotional intelligence is based on our four focus areas: perceived emotions, use of emotions to facilitate thought, understanding emotions and managing emotions. We selected this model because it draws a specific focus to understanding emotions through verbal and non-verbal interventions. The game consists of seven modules: The game offers participants a specific combination of visual representation and auditory perception of nominated emotions. In addition, it offers the opportunity for the emotion to be depicted and accessible on target pictures—facial expressions and adapted situational images. The Chapter discusses the principles of game development and its seven components. Data collected from the pilot testing with two groups of children—typically developing and children with ASD—are systematized. Enhancement of social skills and emotional intelligence and its key components, namely, emotion perception and understanding, is a fundamental part of direct and indirect interventions that support the development of children with ASD. Such skills would widen the opportunities for the children to establish and build relationships with peers, be better accepted in society and reach an improved functional level.
2 citations
2 citations
TL;DR: In this paper , a brief overview of current state-of-the-art of developing intelligent educational systems is provided, with particular attention paid on the application of artificial intelligence methods in teaching and learning processes.
Abstract: Artificial intelligence (AI) is a rapidly developing research area, with immense influence on different areas of modern society and with numerous applications in real-life systems and environments. In this chapter we provide a brief overview of current state-of-the-art of developing intelligent educational systems. Particular attention is paid on the application of artificial intelligence methods in teaching and learning processes. Accordingly, we consider recent works that put light on different roles of AI in developing educational systems. Also, an overview of key educational domains that AI techniques influence, i.e.: adaptive personalization systems and intelligent tutoring systems, assessment and evaluation of students’ outcomes and learning performances, and benefits and challenges of educational data mining and learning analytics in educational processes is presented.
2 citations
1 citations
TL;DR: In this article , a convolutional neural network (CNN) was trained with programs with a label of with defects or without defects and the training data were images of program fragments.
Abstract: In system development, code reviews are conducted to improve the quality of programs. The purpose of this study is to support code reviews and improve the quality of the program. In supporting code review, we use a convolutional neural network (CNN) that was trained with programs with a label of “withDefects” or “withoutDefects”. The training data are images of program fragments. In this paper, we call the trained CNN the CNN-BI (CNN based Bug Inference) system. The CNN-BI system infers whether or not there is a defect in the image of a program fragment. In order to validate the effectiveness of the CNN-BI system for review, we conducted experiments. In the experiments, we provided a list of the inferred results by CNN-BI for each program fragment to reviewers. We analyzed the results of their reviews. As a result of the analysis, the effectiveness was observed in some examples. In this paper, we describe a method for making the training data and the results of the experiments.