Topic
Intelligent tutoring system
About: Intelligent tutoring system is a research topic. Over the lifetime, 3472 publications have been published within this topic receiving 58217 citations. The topic is also known as: ITS.
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TL;DR: The design of a web based intelligent tutoring system for teaching Java objects to students to overcome the difficulties they face and provides explicit support for adaptive presentation constructs is described.
Abstract: The paper describes the design of a web based intelligent tutoring system for teaching Java objects to students to overcome the difficulties they face. The basic idea of this system is a systematic introduction into the concept of Java objects. The system presents the topic of Java objects and administers automatically generated problems for the students to solve. The system is dynamically adapted at run time to the student’s individual progress. The system provides explicit support for adaptive presentation constructs. An initial evaluation study was done to investigate the effect of using the intelligent tutoring system on the performance of students enrolled in computer science III in the Faculty of Engineering and Information technology at Al-Azhar University, Gaza. The results showed a positive impact on the evaluators.
63 citations
Proceedings Article•
01 Jan 2017
TL;DR: This work investigates how to recover concept prerequisite relations from course dependencies and proposes an optimization based framework to address the problem and creates the first real dataset, which consists of the listings of computer science courses from 11 U.S. universities and their concept pairs with prerequisite labels.
Abstract: Prerequisite relations among concepts play an important role in many educational applications such as intelligent tutoring system and curriculum planning. With the increasing amount of educational data available, automatic discovery of concept prerequisite relations has become both an emerging research opportunity and an open challenge. Here, we investigate how to recover concept prerequisite relations from course dependencies and propose an optimization based framework to address the problem. We create the first real dataset for empirically studying this problem, which consists of the listings of computer science courses from 11 U.S. universities and their concept pairs with prerequisite labels. Experiment results on a synthetic dataset and the real course dataset both show that our method outperforms existing baselines.
63 citations
10 Sep 2001
TL;DR: The main pedagogical advantages of animated agents in virtual worlds are reviewed, two key research challenges are discussed, and an ambitious new project is outlined addressing those challenge.
Abstract: Interactive virtual worlds provide a powerful medium for experiential learning. Intelligent virtual agents can cohabit virtual worlds with people and facilitate such learning as guides, mentors, and teammates. This paper reviews the main pedagogical advantages of animated agents in virtual worlds, discusses two key research challenges, and outlines an ambitious new project addressing those challenge.
63 citations
TL;DR: This paper describes the expert system (ES) module of an Algebra ITS, called PAT2Math, and describes how it was reduced from O(nd) to O(d), where n is the number of rules in the knowledge base, by implementing some meta-rules that aim at inferring the operations students applied in order to produce a step.
Abstract: In order for an Intelligent Tutoring System (ITS) to correct students’ exercises, it must know how to solve the same type of problems that students do and the related knowledge components. It can, thereby, compare the desirable solution with the student’s answer. This task can be accomplished by an expert system. However, it has some drawbacks, such as an exponential complexity time, which impairs the desirable real-time response. In this paper we describe the expert system (ES) module of an Algebra ITS, called PAT2Math. The ES is responsible for correcting student steps and modeling student knowledge components during equations problem solving. Another important function of this module is to demonstrate to students how to solve a problem. In this paper, we focus mainly on the implementation of this module as a rule-based expert system. We also describe how we reduced the complexity of this module from O(nd) to O(d), where n is the number of rules in the knowledge base, by implementing some meta-rules that aim at inferring the operations students applied in order to produce a step. We evaluated our approach through a user study with forty-three seventh grade students. The students who interacted with our tool showed statistically higher scores on equation solving tests, after solving algebra exercises with PAT2Math during an approximately two-hour session, than students who solved the same exercises using only paper and pencil.
63 citations
16 Aug 1998
TL;DR: A vision for learning environments, called Science Learning Spaces, that are rich in engaging content and activities, provide constructive experiences in scientific process skills, and are as instructionally effective as a personal tutor is presented.
Abstract: We present a vision for learning environments, called Science Learning Spaces, that are rich in engaging content and activities, provide constructive experiences in scientific process skills, and are as instructionally effective as a personal tutor. A Science Learning Space combines three independent software systems: 1) lab/field simulations in which experiments are run and data is collected, 2) modeling/construction tools in which data representations are created, analyzed and presented, and 3) tutor agents that provide just-in-time assistance in higher order skills like experimental strategy, representational tool choice, conjecturing, and argument. We believe that achieving this ambitious vision will require collaborative efforts facilitated by a component-based software architecture. We have created a feasibility demonstration that serves as an example and a call for further work toward achieving this vision. In our demonstration, we combined 1) the Active Illustrations lab simulation environment, 2) the Belvedere argumentation environment, and 3) a modeltracing Experimentation Tutor Agent. We illustrate student interaction in this Learning Space and discuss the requirements, advantages, and challenges in creating one.
62 citations