Model Of Intelligent Tutoring System Research Articles

An Intelligent Mathematics Problem-Solving Tutoring System Framework

This study proposed a novel framework for redesigning problem-solving activities in an intelligent tutoring system (ITS) called the intelligent neural-mechanistic mathematics problem- solving tutoring system (IN-MP-STS). This concept paper presents a new approach to ITS by incorporating elements of neuroscience mechanisms as a learning strategy that focuses on optimizing the brain’s ability through neural mechanisms. It also introduces fuzzy neural networks (FNNs) as a tool for modulating assessment and analyzing outcomes. This framework offers an alternative perspective on delivery methods and learning approaches in the ITS module. By effectively integrating neuroscience mechanistic elements such as motivation, activation, regulation, execution, memorization, and interactivities, deep learning can be achieved, leading to improved student competence. This framework also proposes an adaptive assessment component based on FNNs, which will enhance the measurement and feedback modules in the system. It is necessary to modify the way that ITS and soft computing methods, such as the study of neural networks (NNs), are combined to make learning measurement and assessment more transparent. This innovation has not been fully disclosed, so researchers are encouraged to further test the concepts presented to assess their alignment with the existing system and ethical considerations. This framework enhances the conceptual research findings of FNNs and incorporates neuroscience-based strategies into architecture and autonomous problem-solving skills within an ITS model. It also offers references for the development of problem-solving learning. IN-MP-STS has the potential to significantly enhance students’ competencies and abilities, thereby fostering the development of more comprehensive, holistic, and sustainable ITS. This approach also has the potential to enrich the existing literature on the sustainability of neural networks.

Design and Evaluation of Trustworthy Knowledge Tracing Model for Intelligent Tutoring System

Design and Evaluation of Trustworthy Knowledge Tracing Model for Intelligent Tutoring System

Motivation assessment model for intelligent tutoring system based on mamdani inference system

<span lang="EN-US">Many educators have used the benefit offer by intelligent tutoring system. To become more personalizing and effective tutoring system, student characteristics need to be considered. One of important student characteristic is motivation. Therefore, in this study a motivation assessment model based on self-efficacy theory was proposed. Refer to the theory, effort, choice of activities, performance and persistence were discussed as motivation attributes. Further, time spend, difficulty level, number of correct answers and number of questions skipped are the parameters was defined for each attribute. The model was designed by taking the advantages of Mamdani inference system as fuzzy logic technique to predict students’ motivation level. The model able to inmates like a human tutor does in the traditional classroom to understand students’ motivation level.</span>

Student Modelling on Language Teaching Based on Bayesian Networks

Acquiring a reliable student model is the principal task of an Intelligent Tutoring System (ITS). A precisely defined student model is also the key term for the success of ITSs. In this paper, a study on inferring the accurate student model on language learning is offered by utilizing artificial intelligence. In addition to the general structure of an ITS, a probabilistic model for inference using Bayesian Networks is stated in the paper. Bayesian Networks are acyclic directed graphs in which nodes represent random variables and arcs represent direct probabilistic dependences among them. In this study, graphical models and structures are implemented in a general-purpose decision modelling system SMILE and its Windows user interface, GeNIe, developed at the Decision Systems Laboratory. GeNIe user interface is also used in this study to perform Influence diagrams. Influence diagrams represent decision problems and help to choose a decision alternative with the highest expected gain. Toward the end of the study, an ITS student model which is directly associated with a standard proficiency level is aimed to be developed. This model is also complemented with a domain model which incorporates language components such as grammar, vocabulary, and functions of language in different cases. At the end of the study, an evaluation of the model is performed with an experimental study and the results show that the participants worked with offered ITS model gathered close results when compared to those obtained by the participants who worked with a real tutor.

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

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.

Student Modeling Based on Problem Solving Times

Student modeling in intelligent tutoring systems is mostly concerned with modeling correctness of students’ answers. As interactive problem solving activities become increasingly common in educational systems, it is useful to focus also on timing information associated with problem solving. We argue that the focus on timing is natural for certain types of educational problems and we describe a simple model of problem solving times which assumes a linear relationship between a latent problem solving skill and a logarithm of a time to solve a problem. The model is closely related to models from two different areas: the item response theory and collaborative filtering. We describe two parameter estimation techniques for the model and several extensions – models with multidimensional skill, learning, or variability of performance. We describe an application of the proposed models in a widely used computerized practice system. Using both simulated data and real data from the system we evaluate the model, analyse its parameter values, and discuss the insight into problem difficulty which the model brings.

A problem solving oriented intelligent tutoring system to improve students' acquisition of basic computer skills

Personalization and intelligent tutor are two key factors in the research on learning environment. Intelligent tutoring system (ITS), which can imitate the human teachers' actions to implement one-to-one personalized teaching to some extent, is an effective tool for training the ability of problem solving. This research firstly discusses the concepts and methods of designing problem solving oriented ITS, and then develops the current iTutor based on the extended model of ITS. At last, the research adopts a quasi-experimental design to investigate the effectiveness of iTutor in skills acquisition. The results indicate that students in iTutor group experience better learning effectiveness than those in the control group. iTutor is found to be effective in improving the learning effectiveness of students with low-level prior knowledge.

Design of Intelligent Tutoring System for Mastery Learning

Intelligent Tutoring System (ITS) is an interactive e-learning tool to help students learn independently. Currently, the implementation ITS is still in laboratory scale, means ITS has not been used in large scale using internet technology. One of the difficulties is ITS has not fulfill yet the learning needs. The main requirement of current learning based-competency is the high number of students in mastery learning, on the other hand current learning activities could not overcome the problems of student characteristics and different learning speed in each student as well. The concept of mastery learning is an approach used in competency-based learning, which consists of repetition of learning for slow students, mastering for good progress students, and enrichment for fast students. In this paper, the model of ITS is designed to accommodate the competency-based mastery learning needs and be able to follow learning speed of each student. The design of ITS has learning sequences and gradually test student`s cognitive level. It could measure the position of student`s cognitive knowledge, be able to guide to reach mastery level, and give challenges for smart students

The Study on the Multiple Agent-Based Independent and Collaborative Intelligent Tutoring System Model

Over the years, the traditional computer-assisted teaching can not meet the needs of university teaching, the traditional teaching system software, most of the existence of low intelligence, lack of teaching strategies and other shortcomings.Multi-AGENT technology and intelligent tutoring systems is the current research focus in computer intelligence education. Integrating multi-Agent features and multi-Agent application theories in ITS, this paper proposes a multiple Agent-based intelligent network tutoring system design model, detailedly analyzes the functions of each layer in the system, and presents system database category design and system model features.

Reserch on Intelligent Tutoring System Model Based on Multi-AGENT in Mobile Environment

Along with the mobile network techniques become more progressive and perfect, the mobile terminal is as a kind of tool, way and assisting power.Its role and status has become more and more significant in the field of intelligent teaching system development. It combines the traditional intelligent teaching system and mobile learning system and make full use of them, in order to adapt to the intelligent teaching system development needs in mobile environment.It is a hot research direction of the current intelligent teaching system.Based on the analysis of the development history of intelligent teaching system, Multi-AGENT system key technology and mobile environment requirement on intelligent teaching system, this paper puts forward a intelligent teaching system model under four layer structure of the mobile environment based on Multi-AGENT.

Survey on Understanding the Tutorial Actions based on Students' Affect

We have developed an affective behavior model for intelligent tutoring systems that considers both the affective and knowledge state of the student to generate tutorial actions. The affective behavior model was designed based on teachers' expertise obtained through a survey which 11 math teachers participated. The study focused in knowing how teachers manage the affective state of the students in order the students learn. During the survey, teachers watched a video of a student interacting with an educational game with an animated pedagogical agent. We asked them which agent's animation and which pedagogical actions are suitable for affect and knowledge of the student in each student's movement.

A survey of components for intelligent tutoring pedagogical aspects of GET-BITS model

The concept of software components has received relatively little attention so far by the researchers in the field of Artificial Intelligence in Education (AIED). This paper is an attempt to bring more light on this important concept and to describe the benefits that component-based Intelligent Tutoring Systems (ITSs) can bring to the field. Surprisingly enough, there is still no consensus on the notion of component among AIED researchers. There are many open questions and unclear issues. By carefully structuring the description of software components in general, as well as by showing some examples of components for ITSs design, the paper presents several important related issues, like functionality of components, their granularity, generality, interoperability, and reusability. Special attention is given to the architectural and communication considerations, as well as to the relation between components and ontology's for ITSs design. The paper describes pedagogical aspects and the control knowledge of the component based model of intelligent tutoring systems, called GET-BITS . The focus is on class hierarchies and design of classes for representing the pedagogical and the control knowledge in the GET-BITS model. Distinction has been made between different modes of operation of the pedagogical module in an intelligent tutoring system (teaching, examination, and consulting), resulting in the necessity for the class hierarchies to reflect specific pedagogical knowledge structure.

Neural networks applied to knowledge acquisition in the student model

Knowledge acquisition for the student model of intelligent tutoring systems (ITSs) remains a difficult problem, partly because of the complexity associated with understanding both how people learn and how it is best to tutor, much of which relates to metacognition and problem-solving skills. The bottleneck associated with this area significantly increases the development times of ITSs. Neural networks have made a marked impact in many artificial intelligence areas such as pattern recognition, speech learning, speech understanding, and hand-written character recognition. Neural networks are noted for their ability to handle noise and approximate data, to generalize over situations they have not handled before, and to be represented in a way amenable to parallel processing. In addition, they have the ability to learn, a characteristic which should prove very useful in the development of ITSs. In this paper, we show that neural networks can address the knowledge acquisition bottleneck associated with the student model. We demonstrate that incomplete knowledge obtained from the expert can be refined and expanded by a neural network to provide a more complete, and hence more accurate, student model.

Interactive reconstructive student modeling: A machine‐learning approach

Reconstructive bug modeling is a well‐known approach to student modeling in intelligent tutoring systems, suitable for modeling procedural tasks. Domain knowledge is decomposed into the set of primitive operators and the set of conditions of their applicability. Reconstructive modeling is capable of describing errors that come from irregular application of correct operators. The main obstacle to successfulness of this approach is such decomposition of domain knowledge to primitive operators with a very low level of abstraction so that bugs could never occur within them. The other drawback of this modeling scheme is its efficiency because it is usually done offline, due to vast search spaces involved. This article reports a novel approach to reconstructive modeling based on machine‐learning techniques for inducing procedures from traces. The approach overcomes the problems of reconstructive modeling by its interactive nature. It allows online model generation by using domain knowledge and knowledge about t...

Student Modeling in Intelligent Tutoring Systems: Acquisition of Cognitive Skill and Tutorial Interventions

In intelligent tutoring systems (ITS) student models are used for diagnosis resulting in didactical choices. Among others, didactical choices concern at a global level decisions about the sequencing of learning materials and at a local level decisions about presenting additional material and remediation. In ITS didactical choices are often implicit. In some cases choices are derived from models of human tutoring behavior. As far as ITS are based on student (and expert) models, which are plausible from the cognitive psychological point of view, implicit didactical choices or human tutor models can be avoided. When a student model is constructed according to the principles of ACT*, predictions of the effects of didactical interventions on learning outcomes can be made. It can be shown that learning outcomes as speed of processing and probability of correct response are mediated by the storage-and-strengthening parameters of the control structure. Storage and strengthemng in turn are induced by specific types of instruction, such as outcome feedback, working through examples, and providing the correct answer. A provided theoretical analysis explains why the buggy rule approach is, as such, of limited benefit. The results of this analysis are further illustrated by empirical research. The comparison of our predictions about the effects of feedback with the results obtained in empirical research in the domain of concept learning shows that optimal provisions for feedback (direct or postponed) depend on the type of learning: deterministic or probabilistic. This factor should be taken into account when designing didactical modules. Keywords: ITS, intelligent tutoring systems, student models, didactical interventions.