Machine Learning Approach for an Advanced Agent-based Intelligent Tutoring System

Machine Learning Approach for an Advanced Agent-based Intelligent Tutoring System Roya Aminikia Learning Management Systems (LMSs) are digital frameworks that provide curriculum, training materials, and corresponding assessments to guarantee an effective learning process. Although these systems are capable of distributing the learning content, they do not support dynamic learning processes and do not have the capability to communicate with human learners who are required to interact in a dynamic environment during the learning process. To create this process and support the interaction feature, LMSs are equipped with Intelligent Tutoring Systems (ITSs). The main objective of an ITS is to facilitate students’ movement towards their learning goals through virtual tutoring. When equipped with ITSs, LMSs operate as dynamic systems to provide students with access to a tutor who is available anytime during the learning session. The crucial issues we address in this thesis are how to set up a dynamic LMS, and how to design the logical structure behind an ITS. Artificial intelligence, multi-agent technology and machine learning provide powerful theories and foundations that we leverage to tackle these issues. We designed and implemented the new concept of Pedagogical Agent (PA) as the main part of our ITS. This agent uses an evaluation procedure to compare each particular student, in terms of performance, with their peers to develop a worthwhile guidance. The agent captures global knowledge of students’ feature measurements during students’ guiding process. Therefore, the PA retains an updated status, called image, of each specific student at any moment. The agent uses this image for the purpose of diagnosing students’ skills to implement a specific correct instruction. To develop the infrastructure of the agent decision making algorithm, we laid out a protocol (decision tree) to select the best individual direction. The significant capability of the agent is the ability to update its functionality by looking at a student’s image at run time. We also applied two supervised machine learning methods to improve the decision making protocol performance in order to maximize the effect of the collaborating mechanism between students and the ITS. Through these methods, we made the necessary modifications to the decision making structure to promote students’ performance by offering prompts during the learning sessions. The conducted experiments showed that the proposed system is able to efficiently classify students into learners with high versus low performance. Deployment of such a model enabled the PA to use different decision trees while interacting with students of different learning skills. The performance of the system has been shown by ROC curves and details regarding combination of different attributes used in the two machine learning algorithms are discussed, along with the correlation of key attributes that contribute to the accuracy and performance of the decision maker components

A group learning management method for intelligent tutoring systems

In this paper we propose a group management specification and execution method that seeks a compromise between simple course design and complex adaptive group interaction. This is achieved through an authoring method that proposes predefined scenarios to the author. These scenarios already include complex learning interaction protocols in which student and group models use and update are automatically included. The method adopts ontologies to represent domain and student models, and object Petri nets to specify the group interaction protocols. During execution, the method is supported by a multi-agent architecture

Designing intelligent computer‐based simulations: A pragmatic approach

This paper examines the design of intelligent multimedia simulations. A case study is presented which uses an approach based in part on intelligent tutoring system design to integrate formative assessment into the learning of clinical decision‐making skills for nursing students. The approach advocated uses a modular design with an integrated intelligent agent within a multimedia simulation. The application was created using an object‐orientated programming language for the multimedia interface (Delphi) and a logic‐based interpreted language (Prolog) to create an expert assessment system. Domain knowledge is also encoded in a Windows help file reducing some of the complexity of the expert system. This approach offers a method for simplifying the production of an intelligent simulation system. The problems developing intelligent tutoring systems are examined and an argument is made for a practical approach to developing intelligent multimedia simulation systems

Development and Evaluation of the Oracle Intelligent Tutoring System (OITS)

This paper presents the design and development of intelligent tutoring system for teaching Oracle. The Oracle Intelligent Tutoring System (OITS) examined the power of a new methodology to supporting students in Oracle programming. The system presents the topic of Introduction to Oracle with automatically generated problems for the students to solve. The system is dynamically adapted at run time to the student’s individual progress. An initial evaluation study was done to investigate the effect of using the intelligent tutoring system on the performance of students

Multimodal Interaction in a Haptic Environment

In this paper we investigate the introduction of haptics in a multimodal tutoring environment. In this environment a haptic device is used to control a virtual piece of sterile cotton and a virtual injection needle. Speech input and output is provided to interact with a virtual tutor, available as a talking head, and a virtual patient. We introduce the haptic tasks and how different agents in the multi-agent system are made responsible for them. Notes are provided about the way we introduce an affective model in the tutor agent

ITS for Teaching TOEFL

Abstract: An e-learning system is increasingly gaining popularity in the academic community because of several benefits of learning anywhere anyplace and anytime. An Intelligent Tutoring System (ITS) is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher.(ITSB) is the tutoring system Builder Which designed and improved to help teachers in building intelligent tutoring system in many fields .In this paper we have an example and an evaluating are presented of building an intelligent tutoring system for teaching TOEFL using ITSB tool

ITS for Teaching French

Abstract: The paper depicts the blueprint of an electronic wise indicating system for demonstrating learning French to understudies to overcome the inconveniences they go up against. The fundamental idea of this structure is a proficient introduction into learning French. The system shows the purpose of learning French and coordinates thusly made issues for the understudies to clarify. The system is logically balanced at run time to the understudy’s individual progress. The system gives unequivocal help to adaptable presentation to learners

ITS Teaching ASP Dot Net

Abstract: ASP dot net is one of the most widely used languages in web developing of its many advantages, so there are many lessons that explain its basics, so it should be an intelligent tutoring system that offers lessons and exercises for this language.why tutoring system? Simply because it is one-one teacher, adapts with all the individual differences of students, begins gradually with students from easier to harder level, save time for teacher and student, the student is not ashamed to make mistakes, and more. Therefore, in this paper, we describe the design of an Intelligent Tutoring System for teaching ASP dot net to help students learn ASP dot net easily and smoothly. Tutor provides beginner level in ASP dot net. Finally, we evaluated our tutor and the results were excellent by students and teacher

An Intelligent Tutoring System for Learning TOEFL

An e-learning system is increasingly gaining popularity in the academic community because of several benefits of learning anywhere anyplace and anytime. An Intelligent Tutoring System (ITS) is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher.(ITSB) is the tutoring system Builder Which designed and improved to help teachers in building intelligent tutoring system in many fields. In this paper, we have an example and an evaluating are presented of building an intelligent tutoring system for teaching TOEFL using ITSB tool