A computer program listing is submitted in form of compact disk.
The present invention is directed to a computer implemented method and apparatus for simulating an intelligent tutor for interactive adaptive training of learners in any domain (that is, field of learning) under study.
Known computer-based training systems generally may be divided into two groups: classical Computer Based Training (CBT) and advanced Intelligent Tutoring Systems (ITS). Classical CBT systems are based on xe2x80x9cmanualxe2x80x9d design of training xe2x80x9cscriptsxe2x80x9d, which predefine what the CBT system should to do in any learning situation for each learner. Because of the enormous number of possible situations and diversity of learners, it is practically impossible to design quality training systems by manual scripting.
Intelligent Tutoring Systems, on the other hand, include two opposite approaches: Case-Based and Model-Based training systems. Case-Based Training systems are learnable and can be trained by expert-tutors how to teach learners. In the training stage, they memorize the most effective actions of the tutor in current learning situations from examples (cases) given by designer (author). After that xe2x80x9ctrainingxe2x80x9d, in the implementation stage, such ITS can teach learners itself by taking the same teaching actions in the same and close situations by recalling the cases from memory. In practice learners encounter a large number of learning situations, so that the number of cases to train the system must be large as well. In contrast, short and affordable authoring can provide only very rough simulation of tutor""s activity. Case-Based Training systems may be regarded as useful when applied complementary to the following model-based approach.
Model-Based Training systems need neither scripts to enter nor cases to learn. Rather, they are based directly on generalized tutoring knowledge and experience represented with models. Models can be represented in the form of empirical rules, decision tables, and theoretical relations. A part of these models can be reusable for different training paradigms/domains/learners. Reusability allows reducing the authoring labor. Examples are REDEEM, XAIDA, SIMQUEST, MOBIT.
Within Model-Based Training systems it is possible to distinguish empirical and theoretical modeling. Empirical model-based ITSs are designed usually by rule-based authoring. This is similar to rather high-level programming with production systems, which can be supported with some commercial tools for knowledge-based systems design. Such systems provide authors with capability to design and implement reusable rules and save their labor in this manner, but do not provide them with ready-made reusable models. See REDEEM, XAIDA, SIMQUEST.
Theoretical model-based ITSs are based on ready-made models that are specific for a training field and are generic within this field. These models comprise the best pedagogical experience materialized in a form of a reusable model-based Shell, which can be used by different authors to save on authoring for different training paradigms/domains/learners. However, there are at present no ready-made theoretical models, shells and tools for theoretical model based ITS""s. Accordingly, some authors have partially solved this problem using a bottom up approach, for example, providing authors with specific templates within which blanks can be filled in. However, no known system has heretofore used a top=down approach and imprecise tunable modeling, as achieved by the system according to the invention.
Prior art model-based intelligent training systems have included:
a Domain Module for presentation and/or simulation of domain under study;
an expert system for presentation of ideal task performing;
a learner model defining personal knowledge, skills, and traits for training individualization,
an instructional or tutorial module defining objectives, strategy, style, and modes of training, and
an interface for system interaction with learner and author.
These prior intelligent tutoring systems suffer, however, from a number of important deficiencies. First, they are empirical (not theoretical) model-based systems. Moreover, their tutorial module does not comprise a generic and reusable ITS Shell, so that authors need to re-design the ITS tutorial module for different training paradigms/domains/learners from scratch Altogether this means a high cost of ITSs and an absence of their quality guarantee.
Accordingly, a primary goal of the present invention is to provide a reusable, training paradigms/domains/learners-independent, theoretical, actively adaptive, fuzzy, domain/learner/tutor model-based Shell with checking/prognosis/diagnosis-based dynamic planning capability.
Another object of the invention is to provide an ITS of the type described above, which, being filled in with concrete meta and media data by an author, can automatically realize intelligent, high quality, and efficient interactive adaptive training at low cost.
Yet another important objective of the invention is to provide a theoretical model based ITS on the basis of a top down approach and imprecise tunable modeling.
Additional objectives of the invention are:
to support learner-driven training;
to provide reuse of ready-made CBT, simulators, and games learning materials;
to supervise (control over) functioning of traditional ready-made CBT, simulators, games, and other training systems and applications;
to support plug in of existing web, CBT, simulation, game, and other applications authoring tools for learning/training content media design;
to support plug in of existing pre-authoring tools for job/task cognitive analysis;
to realize the system""s self-improvement capability.
These and other objects and advantages are achieved by the intelligent tutoring system according to the invention which includes some of the same standard modules as other ITS""s (Domain Module, Tutor Module, and interface) but with new properties, functions, structure, modes and in a new interrelation among them. In this regard, an important feature of the invention resides in the methodology used to structure and represent items to be learned and their pre-requisite and other dependency relationships (in any subject domain for any category of learners, and on the basis of any training paradigm and theory) in a (fuzzy) graph, together with a (fuzzy logic) computational engine (learner model) which dynamically adapts the available sequence of training actions (such as presentations/explanations, simulations, exercises, tasks/questions) to a current assessment of the learner""s knowledge/skills, the level of difficulty of the presented material, and preferences and learning style of the individual learner. According to the invention, fuzzy logic is used as the basis of arc weightings and the computations, but the general methodology is applicable to other approaches to weighting and computation. Details of the programming functionality for important features of the invention are set forth in Table I (including a glossary) at the conclusion of this specification, of which it is a part.
The Intelligent Tutoring Systems Shell and Authoring Tool according to the invention is implemented in the form of a computer program for the following purposes:
For easy design of cost-effective Intelligent Tutoring Systems by filling in training paradigm/domain/learnerxe2x80x94specific data into blanks of training paradigm/domain/learnerxe2x80x94generic system Shell;
For executing cost-effective adaptive training of learners tailored to individual learner""s current knowledge/skills, difficulty level, control level, preferences, and learning style in accordance with training objectives, strategy, and style of training pre-defined by the author.
The main system properties are:
The system can deliver realistic domain content in interactive multimedia, simulation, VR media.
It can be used any time and in any place, through the Internet as well.
It is a mixed initiative system providing a learner with capability to learn on his own and to use instructional support of an embedded tutor when it is necessary to resolve learning problems and/or achieve learning objectives by the most effective way,
The system provides a complete set of basic instructional modes: pre-test, instruction, practice, remediation, post-test.
The system makes learning/training logic transparent for learners by displaying the current assessment of learner""s knowledge/skills mastery and corresponding system""s training action.
High quality and effectiveness of training that can be expressed in terms of meeting objectives and learners"" satisfaction in shortest time.
Low cost of system design, that can be expressed in terms of effort, time, and recourses of authors for course design and development.
Low cost of training that can be expressed in terms of consuming time and resource for achieving learning/training objectives.
The system simulates the best tutor that adapts to a learner.
The system pre-tests student to ensure that he/she is ready to learn the course unit selected.
The system dynamically plans and executes an individual training process in one-on-one interaction with learner.
The system gives to learner new knowledge/skills by providing him/her with necessary learning materials and/or domain situations for knowledge/skills construction.
The system realizes the optimal teaching strategy and style for the courseware available.
The system adapts to learner""s personal knowledge, skills, preferences, and learning style.
The system can check learner""s knowledge/skills tracking his/her progress and detecting errors.
The system assists learner by finding the causes of his/her errors and correcting them by appropriate re-training.
The system lets a learner control the pace and progress of his/her own learning.
The system post-tests a learner to make sure he/she successfully achieved the course or unit""s training objectives.
The system supports the following author""s operations:
(1) Login;
(2) Create a new course or open an existing course;
(3) Specification of a customer order for course design in terms of a target audience, particular domain, job/tasks, and training objectives;
(4) Pre-authoring session for an analysis of target audience, particular domain, job/tasks, and training objectives;
(5) Authoring session of the course synthesis (design and development) for target audience, particular domain, job/tasks, and training objectives;
(6) Course release, testing, and debugging;
(7) Implementation results analysis. Course correction.
(8) Close a course;
(9) Logout.
The system supports the following learner""s operations:
Login;
Open course and unit;
Start unit
Learning/Training session;
Stop unit;
Close unit and course;
Logout.
USPTO Backgrounds
USPTO Backgrounds