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.

The Andes Physics Tutoring System: An Experiment in Freedom

Abstract: The Andes physics tutoring system is an experiment in student freedom. It allows students to solve a physics problem in virtually any legal way. This means that Andes must recognize an extremely large number of possible steps occurring in an extraordinarily large number of possible orders. Such freedom raises several research questions. (1) How can Andes solve the technical challenge of understanding student’s behavior in such a wide-open context? (2) How can Andes give pedagogically useful help and guidance? In particular, how can it guide students who are floundering without curtailing the freedom of students who are not floundering? (3) Will Andes be effective in getting students in real classrooms to learn physics? (4) What does it take to scale up Andes and disseminate it widely? The Andes project, which began in the mid 1990’s, has achieved workable solutions to the first three goals: Andes can understand student behavior; It provides pedagogical help similar to that of human experts; Most importantly, Andes causes large, reliable learning gains compared to control classes taught with convention, paper-based instruction. This chapter summarizes the first three results and discusses our progress on the fourth goal, scale-up.

A Prediction Model Uses the Sequence of Attempts and Hints to Better Predict Knowledge: Better to Attempt the Problem First, Rather Than Ask for a Hint

Abstract: Intelligent Tutoring Systems (ITS) have been proven to be efficient in providing students assistance and assessing their performance when they do their homework. Many research projects have been done to analyze how students’ knowledge grows and to predict their performance from within intelligent tutoring system. Most of them focus on using correctness of the previous question or the number of hints and attempts students need to predict their future performance, but ignore how they ask for hints and make attempts. In this paper, we build a Sequence of Actions (SOA) model taking advantage of the sequence of hints and attempts a student needed for previous question to predict students’ performance. We used an ASSISTments dataset of 66 students answering a total of 34,973 problems generated from 5010 questions over the course of two years. The experimental results showed that the Sequence of Action model has reliable predictive accuracy than Knowledge Tracing.

The Transition From Intelligent to Affective Tutoring System: A Review and Open Issues

Abstract: The swelling use of computerized learning, accompanied by the rapid growth of information technology has become a surge of interest in the research community. Consequently, several technologies have been developed to maintain and promote computerized learning. In this study, we provided an in-depth analysis of two of the prominent computerized learning systems i.e., Intelligent Tutoring System (ITS) and Affective Tutoring System (ATS). An ITS is one of the training software systems, which use intelligent technologies to provide personalized learning content to students based on their learning needs with the aim of enhancing the individualized learning experience. Recently, researchers have demonstrated that the affect or emotional states of a student have an impact on the overall performance of his/her learning, which introduces a new trend of ITS development termed as ATS, which is the extended research of the previous one. Although there have been several studies on these tutoring systems, however, none of them has comprehensively analyzed both systems, particularly the transition from ITS to ATS. Therefore, this study examines these two tutoring systems more inclusively with regards to their architectures, models, and techniques and approaches used by taking into consideration the related researches conducted between 2014 to 2019. A crucial finding from the study is that ATS can be a promising tutoring system for the next generation learning environment by affiliating proper emotion recognition channels, along with computational intelligence approaches. Finally, this study concludes with research challenges and possible future directions and trends.

Intelligent tutoring system model based on fuzzy logic and constraint-based student model

Abstract: A model for an intelligent tutoring system (ITS) that uses fuzzy logic and a constraint-based student model (CBM) is proposed. The goal of the ITS is to teach the use of punctuation in Turkish. The proposed ITS includes two student models, i.e., an overlay student model and a CBM. The student modeler in the CBM records each mistake a student make when answering questions in the system. Immediate feedback and hints are provided based on the recorded mistakes. In addition, moreover the level of students’ learning of the usage of punctuation marks is determined and overlay student model is updated according to the mistakes. If the student cannot provide the correct answer relative to the desired learning level after a specified number of attempts, this information is recorded by the overlay student model. Students can study the pages and attempt to answer the questions again. For determining the level of learning MYCIN certainty factor, the number of times the student takes for answering the question and fuzzy logic decision system are used. Crowded classes make it difficult for teachers to evaluate all student answers and provide individual feedback. The proposed ITS identifies student mistakes and provides feedback immediately. In addition, the ITS analyzes mistakes to determine the student’s learning gaps relative to specific topics and concepts. Learning to use punctuation correctly is valuable; thus, the proposed ITS model is important and worthwhile.

A Framework for Developing Intelligent Tutoring Systems Incorporating Reusability

Abstract: The need for effective tutoring and training is mounting, especially in industry and engineering fields, which demand the learning of complex tasks and knowledge. Intelligent tutoring systems are being employed for this purpose, thus creating a need for cost-effective means of developing tutoring systems. We discuss a novel approach to developing an Intelligent Tutoring System shell that can generate tutoring systems for a wide range of domains. Our focus is to develop an ITS shell framework for the class of Generic Task expert systems. We describe the development of an ITS for an existing expert system, which serves as an evaluation test-bed for our approach.