Explanation Module in an Intelligent Tutoring Shell

ACM SIGCSE Bulletin, 1997

The important characteristics of any intelligent systems are the possibilities of explanation. So, any soflsvare product which intend to be intelligent must provide some kind of explanation, i.e., explanation of some conclusions, explanation of new knowIedge (theorem), etc. As Intelligent Tutoring Systems (ITSS) intend to be intelligent software, the explanation feature must be provided in ITSS, Jn this paper, we will briefly survey how we realized the explanation properties and features in Intelligent Tutoring System (ITS) shell called EduSof. The OBOA (OBject -Oriented Abstraction) model for representing the knowledge, interaction within that knowledge and actions on that knowledge is used for the model of explanation, the transitions and the interactions features in EduSof shell.

The activity of explaining has been studied and modeled by Artificial Intelligence researchers for almost three decades. While no standalone educational applications have resulted, a number of techniques have been developed that, when combined in the right way, can support automated explanation facilities capable of playing a useful role as one educational resource among others. In this paper, we will briefly survey the available techniques and evaluate their utility in generating explanations, and how we realized the explanation properties and features in Intelligent Tutoring System shell called EduSof. We then show how we have combined these techniques in the design of a computer program that provides explanations in response to some questions in some domains and discuss the contribution such a facility could make to the computer-based educational environments of the future.

2019

This work is the first step towards understanding when and if it is necessary for an Intelligent Tutoring System (ITS) to explain its underlying user modeling techniques to students. We conduct an initial pilot study to explore student attitudes towards incorporating explanations to an ITS, by asking participants for suggestions on the type of explanations, if any, that they would like to see. Our results indicate an overall positive sentiment towards wanting explanation and suggest a few design directions for incorporating explanation into an existing ITS.

2000

The problem of judging the effectiveness of a course in general, and of explanations in particular, is certainly one of the most sensible areas in intelligent tutoring systems. In this paper, we present an explanation agent, whose aim is to evaluate the quality of explanations presented to learners. He has two objectives: discovering the source of learner’s misunderstandings by taking into account his student model, and helping the course designer to adapt his explanations according to these observations. We use the conceptual graph theory to structure our explanations into a formal representation. This representation is used by the explanation agent to make his deductions about learners misconceptions

The difficulty of designing and developing intelligent tutoring systems (ITSs) has caused a recent increase in the interest of the AI researchers in realization of some new approaches in that field. Our starting point and perspective on developing of ITS shell is motivated by issues of pragmatics and usability. Considering commercially available and widely used authoring systems for traditional computer-based teaching, we try to give the next step, the next paradigm shift that is needed to enable some of the ITS advantages. The educational community became interested in taking an active role in designing educational software, also. However, the advancement of AI methods and techniques makes understanding of ITS more difficult, so that the teachers are less and less prepared to accept and understand these systems. The paper describes an object-oriented model of ITS shell in which the end-user (teacher) could make their own ITS lesson, alone. It was developed while trying to redesign a previously developed ITS called EduSof. The model enables the developing of more flexible software environment for building of the ITS, significantly increasing their reusability. The processes of computer-based tutoring and learning based on this model are much closer to humanbased instruction. The model can be easily extended to cover the needs of particular tutoring systems.

2001

We are engaged in a research project to create a tutorial dialogue system that helps students to explain the reasons behind their problem-solving actions, in order to help them learn with greater understanding. Currently, we are pilottesting a prototype system that is able to analyze student explanations, stated in their own words, recognize the types of omissions that we typically see in these explanations, and provide feedback. The system takes a knowledge-based approach to natural language understanding and uses a statistical text classifier as a backup. The main features are: robust parsing, logic-based representation of semantic content, representation of pedagogical content knowledge in the form of a hierarchy of partial and complete explanations, and reactive dialogue management. A preliminary evaluation study indicates that the knowledge-based natural language component correctly classifies 80% of explanations and produces a reasonable classification for all but 6% of explanations.

2006

Intelligent Tutoring Systems (ITS) have assisted engineering students in several domains. The domains considered ideal for ITS contain easily represented issues in computational form and allow the interaction type between student and ITS be limited to a restricted set of words, symbols, and numbers. It is proposed to exploit intelligent system technology to support an explanation process in the context of ITS. A system was developed to support explanations of examples to assist the learning process of basic programming. Examples of C programs, previously elaborated by a teacher, are presented to a student from who are expected explanations to source-code regions. Using techniques of approximate natural language understanding, the system tries to recognize explanation contents to send the result to a module that classifies explanations as correct, incorrect, or incomplete according to the context of the proposed activity. The context can be configured by the teacher. After explanation processing, an ITS could determine the subsequent stages according to its educational strategy.

Lecture Notes in Computer Science, 2000

We present further results on the educational effectiveness of an intelligent computer tutor that helps students learn effectively from examples by coaching self-explanation -the process of explaining to oneself an example worked-out solution. An earlier analysis of the results from a formative evaluation of the system provided suggestive evidence that it could improve students' learning. In this paper, we present additional results derived from a more comprehensive analysis of the experimental data. They provide a stronger indication of the system's effectiveness and suggest general guidelines for effective support of self-explanation during example studying.

The paper describes an application of the object-oriented software design methodology for realizing and arranging designing the knowledge bases and manipulating their contents for intelligent tutoring. Such an approach makes the building process for intelligent tutoring systems (ITSs) more natural and more user friendly, and also makes easier to extend the tool set used to build knowledge bases. An object-oriented model of ITSs knowledge bases has been developed, and its two aspects are presented in the paper. First, the design and organization of knowledge bases, and then, the communication between the knowledge base and some other modules of an ITSs.

Progress in Artificial …, 1997

The difficulty of designing and developing intelligent tutoring systems (ITSs) has caused a recent increase in the interest of the AI researchers in realization of some new approaches in that field. Our starting point and perspective on developing ITSs shell is motivated by issues of pragmatics and usability. Considering commercially available and widely used authoring systems for traditional computer-based teaching, we try to give the next step, the next paradigm shift that is needed to enable some of the advantages of ITSs. The paper describes an object-oriented model of ITS shell in which the enduser (teacher) could make their own ITS lessons, alone. The model enables the developing of more flexible software environments for building of the ITS, significantly increasing their reusability.

One of the invitations and challenges for the application of artificial intelligence (AI) to education is the introduction of AI tools and intelligent tutoring systems (ITSs) into everyday classroom use in schools. The difficulty of designing and developing ITSs has caused a recent increase in the interest of the AI researchers in realization some new approaches in that field. On the other hand, the educational communities have become also interested in taking an active role in designing educational software. However, the advancement of AI methods and techniques makes understanding of ITSs more difficult, so that the teachers are less and less prepared to accept these systems. As a result, the gap between the researchers in the field of ITSs and the educational staff is constantly widening. This paper describes our efforts toward developing uniform data and control structures that can be used by a wide circle of authors, e.g., domain experts, teachers, curriculum developers, etc., who are involved in the building of ITSs. One of our goals is to build tools that will enable teachers to use the computer directly in designing their own ITSs. These tools are some models of an application of object-oriented software design methodology for designing ITS knowledge bases and manipulating their contents.

In the development of e-Learning systems, there is a recognizable trend towards more and more adaptive systems aiming to become assistants for the indivi- dual learner. This has led to systems making a lot of decisions and suggestions without asking the lear- ner, e.g., presenting "best-fitting" content, by using methods and techniques from the field of Artificial Intelligence (AI). The

Proceedings of the II International Conference on …, 1995

Building an intelligent tutoring system (ITS) requires the ability to model and reason the domain knowledge, human thinking and learning processes, and the teaching process. Acquiring and encoding this large amount of knowledge is difficult and time-consuming. We have been searching for efficient ways to do these knowledge engineering tasks. This paper describes our efforts toward developing uniform data and control structures that can be used by a wide circle of authors, e.g., domain experts, teachers, curriculum developers, etc., who are involved in the ITS building process. One of our goals is to build tools that will enable experts (teachers) to use the computer directly. These tools are a model of an application of object-oriented software design methodology for designing ITS knowledge bases and manipulating their contents. Such approach makes the ITS building process more natural and more user friendly, and makes it easier to extend the set of tools used to build knowledge bases. An object-oriented model of ITS knowledge bases has been developed, and its two aspects: the design and organization of knowledge bases, and communication between the knowledge base and another module of an ITS or an ITS building tool, are presented in the paper.

Proceedings of the 8th International PEG Conference PEG’97 “Meeting The Challenge Of The New Technologies”, 1997

The difficulty of designing and developing intelligent tutoring systems (ITSs) has caused a recent increase in the interest of the AI researchers in realisation some new approaches in that field. On the other hand, the educational communities have become also interested in taking an active role in designing educational software. However, the advancement of AI methods and techniques makes understanding of ITSs more difficult, so that the teachers are less and less prepared to accept and understand these systems. The paper describes an object-oriented model of ITSs shell, called EduSof, in which the enduser (teacher) could alone make their own ITSs lessons. The model enables the developing more flexible software environments for building ITSs, significantly increasing their reusability. The processes of computer-based tutoring and learning based on this model are much closer to human-based instruction. The model can be easily extended to cover the needs of particular tutoring systems. Jerinić, Lj. and Devedžić, V., Object-Oriented Knowledge Primitives For Intelligent Tutoring Shell. In P. Brna and D. Dicheva (Eds.) Proceedings of the 8th International PEG Conference PEG’97 “Meeting The Challenge Of The New Technologies”, (30th May - 1st June, Sozopol, Bulgaria). VARITECH Ltd., Sofia, Bulgaria, 1997, pp. 348-354. http://www-it.fmi.uni-sofia.bg/PEG97/index.html http://www-it.fmi.uni-sofia.bg/PEG97/accepted.html"

2006

We describe the WHY2-ATLAS intelligent tutoring system for qualitative physics that interacts with students via natural language dialogue. We focus on the issue of analyzing and responding to multisentential explanations. We explore an approach that combines a statistical classifier, multiple semantic parsers and a formal reasoner for achieving a deeper understanding of these explanations in order to provide appropriate feedback on them.