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.

Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

Abstract: Incorporating substantial, sustainable development issues into teaching and learning is the ultimate task of Education for Sustainable Development (ESD). The purpose of our study was to identify the confused students who had failed to master the skill(s) given by the tutors as homework using the Intelligent Tutoring System (ITS). We have focused ASSISTments, an ITS in this study, and scrutinized the skill-builder data using machine learning techniques and methods. We used seven candidate models including: Naive Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Trees (XGBoost). We trained, validated, and tested learning algorithms, performed stratified cross-validation, and measured the performance of the models through various performance metrics, i.e., ROC (Receiver Operating Characteristic), Accuracy, Precision, Recall, F-Measure, Sensitivity, and Specificity. We found RF, GLM, XGBoost, and DL were high accuracy-achieving classifiers. However, other perceptions such as detecting unexplored features that might be related to the forecasting of outputs can also boost the accuracy of the prediction model. Through machine learning methods, we identified the group of students that were confused when attempting the homework exercise, to help foster their knowledge and talent to play a vital role in environmental development.

An intelligent tutoring system for turbine startup training of electrical power plant operators

Abstract: Training is a critical issue for operators responsible for the efficient operation of complex dynamic systems. Consequently, the number of utilities that have invested in simulator-based programs has grown and the necessity of extending the availability of simulator-based training, beyond the limited use of simulators has emerged. Trying to increase the training effectiveness of simulators, intelligent training systems (ITS) are being developed. The objective of an ITS is to increase the training effectiveness, providing a means to have additional training time. It benefits the utility by cutting down operational costs of a training simulator and improving the training methodology of the simulator. This paper describes an ITS designed for the operators' training of fossil-fuelled electrical power plants. The system consists of a real time simulation of a 300 MW turbine from zero to rated speed, a computer model of a turbine operator expert and other components that allow for evaluation and coaching of a turbine operator engineer. The rationale for such a system is presented. The architectural design, procedure and approach taken in constructing such a system for practical application are described. Instructional content and strategies to teach a novice operator how to supervise and control the turbine startup are included.

Automated Generation of Examples for a Tutorial in Case-Based Argumentation

Abstract: Examples in our domain are argumentation problems involving small sets of legal cases that are related in interesting ways We call these collections of cases Argument Contexts We have identified several types of Argument Contexts that can be used to teach the Issues in our curriculum for case-based argumentation We have developed a program that generates Argument Contexts that address these Issues and have other pedagogically desirable properties, such as being clear and concise In a preliminary feasibility study a human tutor used examples generated by the program with encouraging results Because assembling Argument Contexts by hand is time-consuming, we believe that our program can be a useful tool for law professors who teach reasoning with cases Also, we plan to use it as a component of an intelligent tutoring system for case-based argumentation that we are currently developing

Intelligent Tutoring System for Negotiation Skills Training.

Abstract: Intelligent tutoring systems have proven very effective at teaching hard skills such as math and science, but less research has examined how to teach “soft” skills such as negotiation. In this paper, we introduce an effective approach to teaching negotiation tactics. Prior work showed that students can improve through practice with intelligent negotiation agents. We extend this work by proposing general methods of assessment and feedback that could be applied to a variety of such agents. We evaluate these techniques through a human subject study. Our study demonstrates that personalized feedback improves students’ use of several foundational tactics.

Building Intelligent Tutoring Systems: An Overview

Abstract: This chapter addresses the challenge of building or authoring an Intelligent Tutoring System (ITS), along with the problems that have arisen and been dealt with, and the solutions that have been tested. We begin by clarifying what building an ITS entails, and then position today’s systems in the overall historical context of ITS research. The chapter concludes with a series of open questions and an introduction to the other chapters in this part of the book.