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

Developing an Affordable Authoring Tool For Intelligent Tutoring Systems

Intelligent tutoring systems (ITSs) are computer based tutoring systems that provide individualized tutoring to the students. Building an ITS is recognized to be expensive task in terms of cost and resources. Authoring tools provide a framework and an environment for building the ITSs that help to reduce the resources like skills, time and cost required to build an intelligent tutoring system. In this thesis we have implemented the Cognitive Tutor Authoring Tools (CTAT) and performed experiments to empirically determine the common programming errors that authors tend to make while building an ITS and study what is hard in authoring an ITS. The CTAT were used in a graduate class at Worcester Polytechnic Institute and also at the 4th Summer school organized at the Carnegie Mellon University. Based on the analysis of the experiments we suggest future work to reduce the debugging time and thereby reduce the time required to author an ITS. We also implemented the model tracing algorithm in JESS, evaluated its performance and compared to that of the model tracing algorithm in TDK. This research is funded by the Office of Naval Research (Grant # N00014-0301-0221)

The development and analysis of extended architecture model for intelligent tutoring systems

Intelligent Tutoring Systems (ITS) are computer programs that use leamers" knowledge level to providing indívidualized education. ITS research has successfully delivered systems efficiently supporting one-to-one tutoring. Most of these systems are actively used in real-worid settings and have even contributed to changing traditional education curricula. Instructional activities, learning examples, exploring interactive simulations and playing educational games can benefit from individualized computer-based assistance. To enhance ongoing research related to the improvement of tutoring, we present an extended knowledge mode! including besides the standard modules a common shared database and knowledge-based background, too. The external databases can improve the guality of the behavior models both in tutor and student models. The Python programming language and OWL are efficient tools to combine the ontology management and machine leaming functions to develop ITS systems. In this Paper, we survey ITS technologies andpresent a novel extended architecture model for Intelligent e-Tutoring Systems

Sharing Learners' Behavior to Enhance a Metacognition-oriented Intelligent Tutoring System

International audienceLiterature shows that Intelligent Tutoring Systems (ITS) are growing in acceptance and popularity because they increase performances of students, leverage cognitive development, but also significantly reduce time to acquire knowledge and competencies. Moreover, monitoring metacognitive skills enables learners to assess performance and select appropriate fix-up: individuals unable to ensure self-monitoring cannot detect errors and as a consequence, they process information less efficiently than skilled monitors. Thus, we present an ITS offering the opportunity of evaluating various metacognitive indicators and able to share this information with others learning tools. Our online tutor is based on an existing ITS authoring tool that we extended to support metacognition and share learners’ profiles and activities into a standardized, distributed and open tracking repository. This framework, validated by an experimentation, thus helps to correlate metadata experiences with real performanc

BeSocratic: An Intelligent Tutoring System for the Recognition, Evaluation, and Analysis of Free-form Student Input

This dissertation describes a novel intelligent tutoring system, BeSocratic, which aims to help fill the gap between simple multiple-choice systems and free-response systems. BeSocratic focuses on targeting questions that are free-form in nature yet defined to the point which allows for automatic evaluation and analysis. The system includes a set of modules which provide instructors with tools to assess student performance. Beyond text boxes and multiple-choice questions, BeSocratic contains several modules that recognize, evaluate, provide feedback, and analyze student-drawn structures, including Euclidean graphs, chemistry molecules, computer science graphs, and simple drawings. Our system uses a visual, rule-based authoring system which enables the creation of activities for use within science, technology, engineering, and mathematics classrooms. BeSocratic records each action that students make within the system. Using a set of post-analysis tools, teachers have the ability to examine both individual and group performances. We accomplish this using hidden Markov model-based clustering techniques and visualizations. These visualizations can help teachers quickly identify common strategies and errors for large groups of students. Furthermore, analysis results can be used directly to improve activities through advanced detection of student errors and refined feedback. BeSocratic activities have been created and tested at several universities. We report specific results from several activities, and discuss how BeSocratic\u27s analysis tools are being used with data from other systems. We specifically detail two chemistry activities and one computer science activity: (1) an activity focused on improving mechanism use, (2) an activity which assesses student understanding of Gibbs energy, and (3) an activity which teaches students the fundamentals of splay trees. In addition to analyzing data collected from students within BeSocratic, we share our visualizations and results from analyzing data gathered with another educational system, PhET

Un modèle pour la génération d'indices par une plateforme de tuteurs par traçage de modèle

La présente thèse décrit des travaux de recherche effectués dans le domaine des systèmes tutoriels intelligents (STI). Plus particulièrement, elle s'intéresse aux tuteurs par traçage de modèle (MTT). Les MTTs ont montré leur efficacité pour le tutorat de la résolution de tâches bien définies. Par contre, les interventions pédagogiques qu'ils produisent doivent être incluses, par l'auteur du tuteur, dans le modèle de la tâche enseignée. La recherche effectuée répond à cette limite en proposant des méthodes et algorithmes permettant la génération automatique d'interventions pédagogiques. Une méthode a été développée afin de permettre à la plateforme Astus de générer des indices par rapport à la prochaine étape en examinant le contenu du modèle de la tâche enseignée. De plus, un algorithme a été conçu afin de diagnostiquer les erreurs des apprenants en fonction des actions hors trace qu'ils commettent. Ce diagnostic permet à Astus d'offrir une rétroaction par rapport aux erreurs sans que l'auteur du tuteur ait à explicitement modéliser les erreurs. Cinq expérimentations ont été effectuées lors de cours enseignés au département d'informatique de l'Université de Sherbrooke afin de valider de façon empirique les interventions générées par Astus. Le résultat de ces expérimentations montre que 1) il est possible de générer des indices par rapport à la prochaine étape qui sont aussi efficaces et aussi appréciés que ceux conçus par un enseignant et que 2) la plateforme Astus est en mesure de diagnostiquer un grand nombre d'actions hors trace des apprenants afin de fournir une rétroaction par rapport aux erreurs

Developing a Cognitive Rule-Based Tutor for the ASSISTment System

The ASSISTment system is a web-based tutor that is currently being used as an eighth and tenth-grade mathematics in both Massachusetts and Pennsylvania. This system represents its tutors as state-based pseudo-tutors which mimic a more complex cognitive tutor based on a set of production rules. It has been shown that building pseudo-tutors significantly decreases the time spent authoring content. This is an advantage for authoring systems such as the ASSITment builder, though it sacrifices greater expressive power and flexibility. A cognitive tutor models a student\u27s behavior with general logical rules. Through model-tracing of a cognitive tutor\u27s rule space, a system can find the reasons behind a student action and give better tutoring. In addition, these cognitive rules are general and can be used for many different tutors. It is the goal of this thesis to provide the architecture for using cognitive rule-based tutors in the ASSITment system. A final requirement is that running these computationally intensive model-tracing tutors do not slow down students using the pseudo-tutors, which represents the majority of ASSISTment usage. This can be achieved with remote computation, realized with SOAP web services. The system was further extended to allow the creation and implementation of user-level experiments within the system. These experiments allow the testing of pedagogical choices. We implemented a hint dissuasion experiment to test this experimental framework and provide those results