scholarly journals The Acquisition of Context Data of Study Process and their Application in Classroom and Intelligent Tutoring Systems

2015 ◽  
Vol 18 (1) ◽  
pp. 27-32 ◽  
Author(s):  
Janis Bicans
Keyword(s):  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Contextual Information ◽  
Sensor Data ◽  
Tutoring Systems ◽  
Handheld Device ◽  
Other Information ◽  
E Learning ◽  
Educational Paradigm ◽  
Study Process

Abstract Over the last decade, researchers are investigating the potential of the educational paradigm shift from the traditional “one-size-fits all” teaching approach to an adaptive and more personalized study process. Availability of fast mobile connections along with the portative handheld device evolution, like phones and tablets, enable teachers and learners to communicate and interact with each other in a completely different way and speed. The mentioned devices not only deliver tutoring material to the learner, but might also serve as sensors to provide data about the learning process itself, e.g., learning conditions, location, detailed information on learning of tutoring material and other information. This sensor data put into the context of the study process can be widely used to improve student experience in the classroom and e-learning by providing more precise and detailed information to the teacher and/or an intelligent tutoring system for the selection of an appropriate tutoring strategy. This paper analyses and discusses acquisition, processing, and application scenarios of contextual information.

scholarly journals Intelligent Tutoring Systems

2017 ◽  
Vol 26 (4) ◽  
pp. 717-727 ◽  
Author(s):  
Vladimír Bradáč ◽  
Kateřina Kostolányová
Keyword(s):  
Intelligent Tutoring Systems ◽  
Technological Development ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Point Of View ◽  
Tertiary Sector ◽  
Tutoring Systems ◽  
Number Of Ends ◽  
E Learning ◽  
Education Of Students

AbstractThe importance of intelligent tutoring systems has rapidly increased in past decades. There has been an exponential growth in the number of ends users that can be addressed as well as in technological development of the environments, which makes it more sophisticated and easily implementable. In the introduction, the paper offers a brief overview of intelligent tutoring systems. It then focuses on two types that have been designed for education of students in the tertiary sector. The systems use elements of adaptivity in order to accommodate as many users as possible. They serve both as a support of presence lessons and, primarily, as the main educational environment for students in the distance form of studies – e-learning. The systems are described from the point of view of their functionalities and typical features that show their differences. The authors conclude with an attempt to choose the best features of each system, which would lead to creation of an even more sophisticated intelligent tutoring system for e-learning.

scholarly journals Increasing the Adaptivity of an Intelligent Tutoring System with Educational Data Mining: A System Overview

2016 ◽  
Vol 11 (03) ◽  
pp. 67 ◽  
Author(s):  
Igor Jugo ◽  
Božidar Kovačić ◽  
Vanja Slavuj
Keyword(s):  
Data Mining ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Educational Data Mining ◽  
Knowledge Domain ◽  
Systems Architecture ◽  
Tutoring Systems ◽  
E Learning ◽  
Mining Tools

Intelligent Tutoring Systems (ITSs) are inherently adaptive e-learning systems usually created for teaching well-defined domains (e.g., mathematics). Their objective is to guide the student towards a predefined goal such as completing a lesson, task, or mastering a skill. Defining goals and guiding students is more complex in ill-defined domains where the expert defines the model of the knowledge domain or the students have freedom to follow their own path through it. In this paper we present an overview of our systems architecture that integrates the ITS with data mining tools and performs a number of educational data mining processes to increase the adaptivity and, consequently, the efficiency of the ITS.

scholarly journals A Knowledge Testing Production Model Based on a Cognitive Map for SWI-Prolog Applications

2015 ◽  
Vol 10 (6) ◽  
pp. 62
Author(s):  
Dmitry Ivanovich Popov ◽  
Olga Yurievna Lasareva
Keyword(s):  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Cognitive Map ◽  
Educational Process ◽  
Production Model ◽  
Student Knowledge ◽  
Tutoring Systems ◽  
E Learning ◽  
Electronic Education

In this paper, we describe the ways of improving knowledge assessment with the help of cognitive maps, made during the process of working with intelligent tutoring system “E-learning center — High-edu”. Cognitive map consists of didactic units — minimal units of knowledge about some discipline’s domain. This article shows, how they can be used in intelligent tutoring systems. The example of algorithm realization of forming the didactic units list from discipline cognitive map for consistency of test task presentation determining is shown. The operator model are presented in this paper, which can be helpful for researchers and engineers for using Prolog language for expert systems and knowledge management systems development as well as for support of educational process and the control of student knowledge that will allow achieving improvement of quality of electronic education.

Using a Knapsack Model to Optimize Continuous Building of a Hybrid Intelligent Tutoring System

2016 ◽  
Vol 7 (2) ◽  
pp. 1-18 ◽  
Author(s):  
Maha Khemaja
Keyword(s):  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Time Budget ◽  
Core Competencies ◽  
Portable Devices ◽  
Dynamic Learning ◽  
Tutoring Systems ◽  
Fine Grained ◽  
Intelligent Decision Making ◽  
Knapsack Model

Intelligent Tutoring Systems (ITS) provide an alternative to the traditional “one size fits all” approach. Their main aim is to adapt learning content, activities and paths to support learners. Meanwhile, during the last decades, advances in lightweight, portable devices and wireless technologies had drastically impacted Mobile and Ubiquitous environments' development which has driven opportunities towards more personalized, context-aware and dynamic learning processes. Moreover, mobile and hand held devices could be advantageous to incremental learning, based on very short and fine grained activities and resources delivery. However, measuring efficiency and providing the most relevant combination/orchestration of learning activities, resources and paths remains and open and challenging problem especially for enterprises where choices and decisions face several constraints as time, budget, targeted core competencies, etc. This paper, attempts to provide a knapsack based model and solution in order to implement ITS's intelligent decision making about best combination and delivery of e-training activities and resources especially in the context of fast changing Information and Communication Technology (ICT) domain and its required skills. An android and OSGi based prototype is implemented to validate the proposal through some realistic use cases.

scholarly journals Intelligent Tutoring System

2020 ◽  
pp. 121-144
Author(s):  
Abhishek Singh Rathore ◽  
Siddhartha Kumar Arjaria
Keyword(s):  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
The Internet ◽  
Tutoring System ◽  
Time Data ◽  
Self Regulated Learning ◽  
Formal And Informal Learning ◽  
Real Time Data ◽  
E Learning ◽  
Teaching Learning

With digitization, a rapid growth is seen in educational technology. Different formal and informal learning contents are available on the internet. Intelligent tutoring system provides personalized e-learning to the learners. Different attributes like historical data, real-time data, behavioral, and cognitive are usually used for personalization. Based on the personalization, the intelligent tutoring system aims to provide easy and effective understanding. Recent research highlights the effect of learner's behavior and emotions on effective teaching-learning process. This chapter provides a brief description of the intelligent tutoring system, current developments, instructional techniques, proposed solution, and future recommendations. The emphasis of the study is to provide insights on self-regulated learning.

Building and Assessing Intelligent Tutoring Systems with an e-Learning 2.0 Authoring System

2010 ◽  
pp. 1-9
Author(s):  
M. L. Barrón-Estrada ◽  
Ramón Zatarain-Cabada ◽  
Rosalío Zatarain-Cabada ◽  
Hector Barbosa-León ◽  
Carlos A. Reyes-García
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Authoring System ◽  
Tutoring Systems ◽  
E Learning

Agent-Based Distributed Intelligent Tutoring System Using Case-Based Reasoning

2018 ◽  
pp. 901-928
Author(s):  
Shweta ◽  
Praveen Dhyani ◽  
O. P. Rishi
Keyword(s):  
Intelligent System ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Domain Model ◽  
Student Modeling ◽  
Specific Situation ◽  
Pedagogical Strategies ◽  
Tutoring Systems ◽  
Agent Based ◽  
Case Based

Intelligent Tutoring Systems have proven their worth in multiple ways and in multiple domains in education. In this chapter, the proposed Agent-Based Distributed ITS using CBR for enhancing the intelligent learning environment is introduced. The general architecture of the ABDITS is formed by the three components that generally characterize an ITS: the Student Model, the Domain Model, and the Pedagogical Model. In addition, a Tutor Model has been added to the ITS, which provides the functionality that the teacher of the system needs. Pedagogical strategies are stored in cases, each dictating, given a specific situation, which tutoring action to make next. Reinforcement learning is used to improve various aspects of the CBR module: cases are learned and retrieval and adaptation are improved, thus modifying the pedagogical strategies based on empirical feedback on each tutoring session. The student modeling is a core component in the development of proposed ITS. In this chapter, the authors describe how a Multi-Agent Intelligent system can provide effective learning using Case-Based Student Modeling.

Dynamic Decision Networks Applications in Active Learning Simulators

2012 ◽  
pp. 248-270
Author(s):  
Julieta Noguez ◽  
Karla Muñoz ◽  
Luis Neri ◽  
Víctor Robledo-Rella ◽  
Gerardo Aguilar
Keyword(s):  
Active Learning ◽  
Intelligent Tutoring ◽  
Learning Experience ◽  
Intelligent Tutoring System ◽  
Cognitive State ◽  
Tutoring Systems ◽  
Decision Networks ◽  
Probabilistic Relational Models ◽  
Entity Relationship ◽  
Definition Of

Active learning simulators (ALSs) allow students to practice and carry out experiments in a safe environment – anytime, anywhere. Well-designed simulations may enhance learning, and provide the bridge from concept to practical understanding. Nevertheless, learning with ALS depends largely on the student’s ability to explore and interpret the performed experiments. By adding an Intelligent Tutoring System (ITS), it is possible to provide individualized personal guidance to students. The challenges are how an ITS properly assesses the cognitive state of the student based on the results of experiments and the student’s interaction, and how it provides adaptive feedback to the student. In this chapter we describe how an ITS based on Dynamic Decision Networks (DDNs) is applied in an undergraduate Physics scenario where the aim is to adapt the learning experience to suit the learners’ needs. We propose employing Probabilistic Relational Models (PRMs) to facilitate the construction of the model. These are frameworks that enable the definition of Probabilistic Graphical and Entity Relationship Models, starting from a domain, and in this case, environments of ALSs. With this representation, the tutor can be easily adapted to different experiments, domains, and student levels, thereby minimizing the development effort for building and integrating Intelligent Tutoring Systems (ITS) for ALSs. A discussion of the methodology is addressed, and preliminary results are presented.

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