Intelligent Tutoring System Authoring Tools for Non-Programmers

An intelligent tutoring system (ITS) is a software application that tries to replicate the performance of a human tutor by supporting the theory of learning by doing . ITSs have been shown to improve the performance of a student in wide range of domains. Despite their benefits, ITSs have not seen widespread use due to the complexity involved in their development. Developing an ITS from scratch requires expertise in several fields including computer science, cognitive psychology and artificial intelligence. In order to decrease the skill threshold required to build ITSs, several authoring tools have been developed. In this thesis, I document several contributions to the field of intelligent tutoring in the form of extensions to an existing ITS authoring tool, research studies on authoring tool paradigms and the design of authoring tools for non-programmers in two complex domains - natural language processing and 3D game environments. The Extensible Problem Specific Tutor (xPST) is an authoring tool that helps rapidly develop model-tracing like tutors on existing interfaces such as webpages. xPST\u27s language was made more expressive with the introduction of new checktypes required for answer checking in problems belonging to domains such as geometry and statistics. A web-based authoring (WAT) tool was developed for the purpose of tutor management and deployment and to promote non-programmer authoring of ITSs. The WAT was used in a comparison study between two authoring tool paradigms - GUI based and text based, in two different problem domains - statistics and geometry. User-programming of natural language processing (NLP) in ITSs is not common with authoring toolkits. Existing NLP techniques do not offer sufficient power to non-programmers and the NLP is left to expert developers or machine learning algorithms. We attempted to address this challenge by developing a domain-independent authoring tool, ConceptGrid that is intended to help non-programmers develop ITSs that perform natural language processing. ConceptGrid has been integrated into xPST. When templates created using ConceptGrid were tested, they approached the accuracy of human instructors in scoring student responses. 3D game environments belong to another domain for which authoring tools are uncommon. Authoring game-based tutors is challenging due to the inherent domain complexity and dynamic nature of the environment. We attempt to address this challenge through the design of authoring tool that is intended to help non-programmers develop game-based ITSs

Using ConceptGrid as an easy authoring technique to check natural language responses

ConceptGrid provides a template-style approach to check natural language responses by students using a model-tracing style intelligent tutoring system. The tutor-author creates, using a web-based authoring system, a latticestyle structure that contains the set of required concepts that need to be in a student response. The author can also create just-in-time feedback based on the concepts present or absent in the student\u27s response. ConceptGrid is integrated within the xPST authoring tool and was tested in two experiments, both of which show the efficacy of the technique to check student answers. The first study tested the tutor\u27s effectiveness overall in the domain of statistics. The second study investigated ConceptGrid\u27s use by non-programmers and non-cognitive scientists. ConceptGrid extends existing capabilities for authoring of intelligent tutors by using this template-based approach for checking sentence-length natural language input

The effect of multiple knowledge sources on learning and teaching

Current paradigms for machine-based learning and teaching tend to perform their task in isolation from a rich context of existing knowledge. In contrast, the research project presented here takes the view that bringing multiple sources of knowledge to bear is of central importance to learning in complex domains. As a consequence teaching must both take advantage of and beware of interactions between new and existing knowledge. The central process which connects learning to its context is reasoning by analogy, a primary concern of this research. In teaching, the connection is provided by the explicit use of a learning model to reason about the choice of teaching actions. In this learning paradigm, new concepts are incrementally refined and integrated into a body of expertise, rather than being evaluated against a static notion of correctness. The domain chosen for this experimentation is that of learning to solve "algebra story problems." A model of acquiring problem solving skills in this domain is described, including: representational structures for background knowledge, a problem solving architecture, learning mechanisms, and the role of analogies in applying existing problem solving abilities to novel problems. Examples of learning are given for representative instances of algebra story problems. After relating our views to the psychological literature, we outline the design of a teaching system. Finally, we insist on the interdependence of learning and teaching and on the synergistic effects of conducting both research efforts in parallel

Authoring knowledge based tutors: ‘tools for content, instructional strategy, student model, and interface design

Abstract While intelligent tutoring systems (ITSs), also called knowledge based tutors, are becoming more common and proving to be increasingly effective, each one must still be built from scratch at a significant cost. We have developed domain independent tools for authoring all aspects of a knowledge based tutor: the domain model, the teaching strategies, the student model, and the learning environment. In this paper we describe these tools, discuss a number of design issues and design tradeoffs that are involved in building ITS authoring tools, and discuss knowledge acquisition and representation issues encountered in our work. We also describe how we plan to use these tools (collectively called Eon), including "ontology objects," as a meta-authoring tool for designing special purpose authoring tools tailored for specific domain types

Participating in Instructional Dialogues: Finding and Exploiting Relevant Prior Explanations

In this paper we present our research on identifying and modeling the strategies that human tutors use for integrating previous explanations into current explanations. We have used this work to develop a computational model that has been partially implemented in an explanation facility for an existing tutoring system known as SHERLOCK. We are implementing a system that uses case-based reasoning to identify previous situations and explanations that could potentially affect the explanation being constructed. We have identified heuristics for constructing explanations that exploit this information in ways similar to what we have observed in instructional dialogues produced by human tutors

Towards designing a knowledge-based tutoring system : SQL-tutor as an example

A Knowledge-Based Tutoring System, also sometimes called an Intelligent Tutoring System, is a computer based instructional system that uses artificial intelligence techniques to help people learn some subject. The goal of the system is to provide private tutoring to its students based on their different backgrounds, requests, and interests. The system knows what subject materials it should teach, when and how to teach them, and can diagnose the mistakes made by the students and help them correct the mistakes. The major objective of this dissertation is to investigate and develop a generic framework upon which we can build a Knowledge-Based Tutoring System effectively. As an example, we have focused on developing SQL-TUTOR, a tutoring system for teaching SQL concepts and programming skills. The generic architecture of the system is rooted at the popular view that a tutoring process between a tutor (either a human being or a machine) and a student is a knowledge communication process. This process can be divided into a series of communication cycles and each communication cycle consists of four phases, namely, planning, discussing, evaluating, and remedying phases. One major feature of the architecture proposed by us in this dissertation is its curriculum knowledge base which contains the knowledge about the course curriculum, We have developed a representation schema for describing the goal structure of the course, the prerequisite relationships among the course materials, and the multiple views to organize these materials. The inclusion of the curriculum knowledge in a KBTS allows the system to create different curricula for each individual student and to diagnose the student\u27s errors more effectively. The system also provides a group of operators for the student to hand-tailor his/her curricula when he/she starts learning the course. The student can use these operators to select a specific path to go through the course materials, to pick a specific topic from the curricula to study, or to remove a particular topic from the curricula. Since the student can construct his/her own learning plans by these operators, he/she is relatively free to determine how to study the course materials and, as a result, he/she can become more active in the tutoring process. The knowledge about a subject domain is stored in a set of topics and a sample database. The content of a topic consists of a set of related domain concepts. Each concept is described by both natural and formal forms. The relationships among the concepts are modeled a type of semantic network called the context network. The sample database contains a set of sample tables and an enhanced system catalog which contains the knowledge about the name, semantic meanings of the database objects. The built-in Problem Solver of the system allows the system to reason over the networks and the sample database and answer various kinds of questions raised by the student about the domain concepts and their relationships. The knowledge of writing SQL queries is embodied in a set of examples attached to the topics. Each of such an example is carefully designed for one category of SQL query problems. An example in SQL-TUTOR is a packed knowledge chunk which can serve several important teaching purposes, including generating problem descriptions with different levels of details, formulating various SQL solutions for the given problem, explaining these solutions to the student, and evaluating SQL queries written by the student