An Intelligent Tutoring System for Health Problems Related To Addiction of Video Game Playing

Lately in the past couple of years, there are an increasing in the normal rate of playing computer games or video games compared to the E-learning content that are introduced for the safety of our children, and the impact of the video game addictiveness that ranges from (Musculoskeletal issues, Vision problems and Obesity). Furthermore, this paper introduce an intelligent tutoring system for both parent and their children for enhancement the experience of gaming and tell us about the health problems and how we can solve them, with an easy user interface that way can our children be happy and excited about the information and their health

Design and Development of an Intelligent Tutoring System for C# Language

Learning programming is thought to be troublesome. One doable reason why students don’t do well in programming is expounded to the very fact that traditional way of learning within the lecture hall adds more stress on students in understanding the Material rather than applying the Material to a true application. For a few students, this teaching model might not catch their interest. As a result, they'll not offer their best effort to grasp the Material given. Seeing however the information is applied to real issues will increase student interest in learning. As a consequence, this may increase their effort to be taught. In the current paper, we try to help students learn C# programming language using Intelligent Tutoring System. This ITS was developed using ITSB authoring tool to be able to help the student learn programming efficiently and make the learning procedure very pleasing. A knowledge base using ITSB authoring tool style was used to represent the student's work and to give customized feedback and support to students

Exploring the visualization of student behavior in interactive learning environments

My research combines Interactive Learning Environments (ILE), Educational Data Mining (EDM) and Information Visualization (Info-Vis) to inform analysts, educators and researchers about user behavior in software, specifically in CBEs, which include intelligent tutoring systems, computer aided instruction tools, and educational games. InVis is a novel visualization technique and tool I created for exploring, navigating, and understanding user interaction data. InVis reads in user-interaction data logged from students using educational systems and constructs an Interaction Network from those logs. Using this data InVis provides an interactive environment to allow instructors and education researchers to navigate and explore to build new insights and discoveries about student learning. I conducted a three-point user study, which included a quantitative task analysis, qualitative feedback, and a validated usability survey. Through this study, I show that creating an Interaction Network and visualizing it with InVis is an effective means of providing information to users about student behavior. In addition to this, I also provide four use-cases describing how InVis has been used to confirm hypotheses and debug software tutors. A major challenge in visualizing and exploring the Interaction Network is network's complexity, there are too many nodes and edges presented to understand the data efficiently. In a typical Interaction Network for twenty students, it is common to have hundreds of nodes, which to make sense of, has proven to be too many. I present a network reduction method, based on edge frequencies, which lowers the number of edges and nodes by roughly 90\\% while maintaining the most important elements of the Interaction Network. Next, I compare the results of this method with three alternative approaches and show our reduction method produces the preferred results. I also present an ordering detection method for identifying solution path redundancy because of student action orders. This method reduces the number of nodes and edges further and advances the resulting network towards the structure of a simple graph. Understanding the successful student solutions is only a portion of the behaviors we are interested in as researchers and educators using computer based educational systems, student difficulties are also important. To address areas of student difficulty, I present three different methods and two visual representations to draw the attention of the user to nodes where students had difficulty. Those methods include presenting the nodes with the highest number of successful students, the nodes with the highest number of failing students, and the expected difficulty of each state. Combined with a visual representation, these methods can draw the focus of users to potentially important nodes, which contain areas of difficulty for students. Lastly, I present the latest version of the InVis tool, which is a platform for investigating student behavior in computer based educational systems. Through the continued use of this tool, new researchers can investigate many new hypotheses, research questions and student behaviors, with the potential to facilitate a wide range of new discoveries

Innovating Pedagogy 2015: Open University Innovation Report 4

This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This fourth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Center for Technology in Learning at SRI International. We proposed a long list of new educational terms, theories, and practices. We then pared these down to ten that have the potential to provoke major shifts in educational practice, particularly in post-school education. Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education. These are summarised below in an approximate order of immediacy and timescale to widespread implementation

Survey of Personalized Learning Software Systems: A Taxonomy of Environments, Learning Content, and User Models

This paper presents a comprehensive systematic review of personalized learning software systems. All the systems under review are designed to aid educational stakeholders by personalizing one or more facets of the learning process. This is achieved by exploring and analyzing the common architectural attributes among personalized learning software systems. A literature-driven taxonomy is recognized and built to categorize and analyze the reviewed literature. Relevant papers are filtered to produce a final set of full systems to be reviewed and analyzed. In this meta-review, a set of 72 selected personalized learning software systems have been reviewed and categorized based on the proposed personalized learning taxonomy. The proposed taxonomy outlines the three main architectural components of any personalized learning software system: learning environment, learner model, and content. It further defines the different realizations and attributions of each component. Surveyed systems have been analyzed under the proposed taxonomy according to their architectural components, usage, strengths, and weaknesses. Then, the role of these systems in the development of the field of personalized learning systems is discussed. This review sheds light on the field’s current challenges that need to be resolved in the upcoming years

ITS for health problems related to addiction of video game playing

Lately in the past couple of years, there are an increasing in the normal rate of playing computer games or video games compared to the E-learning content that are introduced for the safety of our children, and the impact of the video game addictiveness that ranges from (Musculoskeletal issues, Vision problems and Obesity). Furthermore, this paper introduce an intelligent tutoring system for both parent and their children for enhancement the experience of gaming and tell us about the health problems and how we can solve them, with an easy user interface that way can our children be happy and excited about the information and their health

Student Modeling and Analysis in Adaptive Instructional Systems

There is a growing interest in developing and implementing adaptive instructional systems to improve, automate, and personalize student education. A necessary part of any such adaptive instructional system is a student model used to predict or analyze learner behavior and inform adaptation. To help inform researchers in this area, this paper presents a state-of-the-art review of 11 years of research (2010-2021) in student modeling, focusing on learner characteristics, learning indicators, and foundational aspects of dissimilar models. We mainly emphasize increased prediction accuracy when using multidimensional learner data to create multimodal models in real-world adaptive instructional systems. In addition, we discuss challenges inherent in real-world multimodal modeling, such as uncontrolled data collection environments leading to noisy data and data sync issues. Finally, we reinforce our findings and conclusions through an industry case study of an adaptive instructional system. In our study, we verify that adding multiple data modalities increases our model prediction accuracy from 53.3% to 69%. At the same time, the challenges encountered with our real-world case study, including uncontrolled data collection environment with inevitably noisy data, calls for synchronization and noise control strategies for data quality and usability

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

Smart, social, flexible and fun: Escaping the flatlands of virtual learning environments

© 2019, Springer Nature Switzerland AG. This paper describes the development of intelligent, social, flexible and game-based pedagogic approaches and their applications in Virtual Learning Environment based Education. Applications of computer science technologies and techniques can enable, facilitate and change educational approaches, allowing scalable approaches that can address both individual student needs whilst managing large – sometimes-massive - cohort sizes. The benefits of these information systems include supporting the wide range of contexts met in education, in terms of individual needs and specific subject and curriculum requirements. Technologies and approaches that are considered range from the representation of knowledge and the use of intelligent systems, the use of social computing, through to the enabling opportunities of ubicomp and the practical application of game mechanics (gamification). This paper concludes with practical illustrations in the context of undergraduate computer science didactics