Authoring Example-based Tutors for Procedural Tasks

Researchers who have worked on authoring systems for intelligent tutoring systems (ITSs) have examined how examples may form the basis for authoring. In this chapter, we describe several such systems, consider their commonalities and differences, and reflect on the merit of such an approach. It is not surprising perhaps that several tutor developers have explored how examples can be used in the authoring process. In a broader context, educators and researchers have long known the power of examples in learning new material. Students can gather much information by poring over a worked example, applying what they learn to novel problems. Often these worked examples prove more powerful than direct instruction in the domain. For example, Reed and Bolstad (1991) found that students learning solely by worked examples exhibited much greater learning than those learning instruction based on procedures. By extension then, since tutor authoring can be considered to be teaching a tabula rasa tutor, tutor authoring by use of examples may be as powerful as directly programming the instruction, while being easier to do

Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments

Authoring Effective Embedded Tutors: An Overview of the Extensible Problem Specific Tutor (xPST) System

The Extensible Problem Specific Tutor (xPST) allows authors who are not cognitive scientists and not programmers to quickly create an intelligent tutoring system that provides instruction akin to a model-tracing tutor. Furthermore, this instruction is overlaid on existing software, so that the learner’s interface does not have to be made from scratch. The xPST architecture allows for extending its capabilities by the addition of plug-ins that communicate with additional third-party software. After reviewing this general architecture, we describe three major implementations that we have created using the xPST system, each using different third-party software as the learner’s interface. We have conducted three evaluations of authors using xPST to create tutoring content, and these are considered in turn. These evaluations show that xPST authors can quickly learn the system, and can efficiently produce successful embedded instruction

Technology-enhanced Personalised Learning: Untangling the Evidence

Technology-enhanced personalised learning is not yet common in Germany, which is why we have tasked scientists with summarising the current status of international research on the matter. This study demonstrates the great potential of technology in implementing effective personalised learning. Nevertheless, it has not been assessed yet whether the practical implementation actually works: Even in countries such as the U.S., which lead the way in using techology in classroom settings, hardly any evaluation studies have been done to prove the effectiveness of technology-enhanced personalised learning. In the light of the above, the authors make recommendations for actions to be taken in Germany to make best use of the potential of technology in providing individual support and guidance to students

Towards an Intelligent Tutor for Mathematical Proofs

Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453

Facilitating teacher participation in intelligent computer tutor design : tools and design methods.

This work addresses the widening gap between research in intelligent tutoring systems (ITSs) and practical use of this technology by the educational community. In order to ensure that ITSs are effective, teachers must be involved in their design and evaluation. We have followed a user participatory design process to build a set of ITS knowledge acquisition tools that facilitate rapid prototyping and testing of curriculum, and are tailored for usability by teachers. The system (called KAFITS) also serves as a test-bed for experimentation with multiple tutoring strategies. The design includes novel methodologies for tutoring strategy representation (Parameterized Action Networks) and overlay student modeling (a layered student model), and incorporates considerations from instructional design theory. It also allows for considerable student control over the content and style of the information presented. Highly interactive graphics-based tools were built to facilitate design, inspection, and modification of curriculum and tutoring strategies, and to monitor the progress of the tutoring session. Evaluation of the system includes a sixteen-month case study of three educators (one being the domain expert) using the system to build a tutor for statics (forty topics representing about four hours of on-line instruction), testing the tutor on a dozen students, and using test results to iteratively improve the tutor. Detailed throughput analysis indicates that the amount of effort to build the statics tutor was, surprisingly, comparable to similar figures for building (non-intelligent) conventional computer aided instructional systems. Few ITS projects focus on educator participation and this work is the first to empirically study knowledge acquisition for ITSs. Results of the study also include: a recommended design process for building ITSs with educator participation; guidelines for training educators; recommendations for conducting knowledge acquisition sessions; and design tradeoffs for knowledge representation architectures and knowledge acquisition interfaces

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