Architecture Of Intelligent Tutoring System Research Articles

A simple model of smart learning environment

ABSTRACT In this article, smart learning environment (SLE) is defined as a hybrid learning system that provides learners and other stakeholders with a joyful learning process while achieving learning outcomes as a result of the employed intelligent tools and techniques. From literature study, existing SLE models and frameworks are difficult to understand and to use as guidance, both for implementation and maturity measurement. This research proposes a simple model of SLE as a guideline that is easy to understand and to be used for developing learning environments that support effective, efficient, and engaging learning processes. The proposed model adopted the intelligent tutoring system architecture as a foundation. The components of the model were generated by mapping of 12 existing SLE models, frameworks, and best practices. Model formation and validation followed the system dynamics (SD) modeling process. In addition, indirect model validation process was also performed by interviewing educational technology practitioners and experts, based on assessment results of two newly developed SLE instances. For measuring maturity of existing SLE instances, this research also derived SLE maturity model. It is concluded that the simple model is usable for easy understanding, for establishing new SLE instances, and also for assessing the maturity level of existing SLE instances.

An intelligent tutoring system architecture based on fuzzy neural network (FNN) for special education of learning disabled learners

An intelligent tutoring system architecture based on fuzzy neural network (FNN) for special education of learning disabled learners

Intelligent Tutoring Supported Collaborative Learning (ITSCL): A Hybrid Framework

Recently Intelligent Tutoring Systems (ITS) and Computer-Supported Collaborative Learning (CSCL) have got much attention in the field of computer science, artificial intelligence, cognitive psychology, and educational technologies. An ITS is a technologically intelligent system that provides an adaptive learning paradigm for an individual learner only, while CSCL is also a technology-driven learning paradigm that supports groups of learners in pertaining knowledge by collaboration. In a multidisciplinary research field—the Learning Sciences, both individual and collaborative learning have their own significance. This research aims to extend ITS for collaborative constructivist view of learning using CSCL. Integrating both design architecture of CSCL and ITS, this research model propose a new conceptual framework underpinning “Intelligent Tutoring Supported Collaborative Learning (ITSCL)”. ITSCL extend ITS by supporting multiple learners interacting system. ITSCL support three different types of interaction levels. The first level of interaction supports individual learning by learner-tutor interaction. The second and third level of interaction support collaborative learning, by learner-learner interaction and tutor-group of collaborative learners’ interactions, respectively. To evaluate ITSCL, a prototype model was implemented to conduct few experiments. The statistical results extrapolate the learning gains, measured from Paired T-Test and frequency analysis, contend a significant learning gain and improvement in the learning process with enhanced learning performance.

An agent-based intelligent tutoring systems review

This paper presents the current state of research in the fields of intelligent tutoring systems and intelligent agents. The architecture of intelligent tutoring systems typically consists of four major components: the domain knowledge module, the learner diagnosis module, the tutoring module, and the communication module. An agent-based approach to intelligent tutoring systems research and development is applied as the set of agents that constitute the general architecture, and as animated characters - pedagogical agents. Therefore, the paper focuses on the components of intelligent tutoring systems that can be implemented as agents, as well as on functions that these intelligent agents can perform in each intelligent tutoring system component. Also, we present the recent endeavours in the intelligent tutoring systems community that moves from standalone systems focused on a single instructional domain to adaptive instructional systems that comprise learners, intelligent tutoring systems, and external (non-adaptive) environment.

Building Intelligent Tutoring Systems Immersed in Repositories of E-learning Content

The article presents the principles for creating intelligent tutoring systems, which are active in dynamically growing repositories of e-learning contents. To make such a system work it is necessary to give the contents appropriate structure to enable it to be used in numerous educational contexts. The architecture of intelligent tutoring system is based on wordnet based ontology with expert knowledge, which has been used for repository resource indexing, and which is a basic component of domain model. The solution in its entirety has been supplemented with software agent, responsible for linking the most relevant content in repository to predefined educational strategy.

Does supporting multiple student strategies lead to greater learning and motivation? Investigating a source of complexity in the architecture of intelligent tutoring systems

Intelligent tutoring systems (ITS) support students in learning a complex problem-solving skill. One feature that makes an ITS architecturally complex, and hard to build, is support for strategy freedom, that is, the ability to let students pursue multiple solution strategies within a given problem. But does greater freedom mean that students learn more robustly? We developed three versions of the same ITS for solving linear algebraic equations that differed only in the amount of freedom given to students. One condition required students to strictly adhere to a standard strategy, the other two allowed minor and major variations, respectively. We conducted a study in two US middle schools with 57 students in grades 7 and 8. Overall, students' algebra skills improved. Contrary to our hypotheses, the amount of freedom offered by the system did not affect students' learning outcomes, nor did if affect their intrinsic motivation. Students tended to use only the standard strategy and its minor variations. Thus, the study suggests that in the early stages of problem-solving practice within a complex domain, an ITS should allow at least a small amount of freedom, validating, albeit to a limited degree, one source of complexity in ITS architectures. To help students develop strategic flexibility, a desirable outcome in many domains, more is needed than letting students chose their own solution strategy within a given problem.

Employing UMLS for generating hints in a tutoring system for medical problem-based learning

While problem-based learning has become widely popular for imparting clinical reasoning skills, the dynamics of medical PBL require close attention to a small group of students, placing a burden on medical faculty, whose time is over taxed. Intelligent tutoring systems (ITSs) offer an attractive means to increase the amount of facilitated PBL training the students receive. But typical intelligent tutoring system architectures make use of a domain model that provides a limited set of approved solutions to problems presented to students. Student solutions that do not match the approved ones, but are otherwise partially correct, receive little acknowledgement as feedback, stifling broader reasoning. Allowing students to creatively explore the space of possible solutions is exactly one of the attractive features of PBL. This paper provides an alternative to the traditional ITS architecture by using a hint generation strategy that leverages a domain ontology to provide effective feedback. The concept hierarchy and co-occurrence between concepts in the domain ontology are drawn upon to ascertain partial correctness of a solution and guide student reasoning towards a correct solution. We describe the strategy incorporated in METEOR, a tutoring system for medical PBL, wherein the widely available UMLS is deployed and represented as the domain ontology. Evaluation of expert agreement with system generated hints on a 5-point likert scale resulted in an average score of 4.44 (Spearman’s ρ=0.80, p<0.01). Hints containing partial correctness feedback scored significantly higher than those without it (Mann Whitney, p<0.001). Hints produced by a human expert received an average score of 4.2 (Spearman’s ρ=0.80, p<0.01).

NOTE FOR EDITOR: Design And Development Of An Intelligent Instructive System: (Scholastic Tutor (St*)

Intelligent Tutoring Systems (ITS) is an act of impacting knowledge while computer teaches or acts as the tutors which is a supplement to human teachers. The ability to teach each student based on their individual abilities a major advantage posed by ITS and that is why it is being embraced in this work. This work describes the design of an Intelligent Tutoring System that was tagged Scholastic tutor (St*), which has the individual learning and collaborative problem-solving modules. The individual tutoring module was designed to provide appropriate lessons to individuals based on his/her background knowledge level, interest, and learning style and assimilation rate prior to using the tutoring system. A software agent is used to monitor and process these parameters, arrange the learning topic, and exercises, for each individual. The collaborative problem-based tutoring module was designed to present tutorial problems and provides facilities to assist learners with some useful information and advice for problem solving. This is because the present lecturing methodology which is the conventional teaching methodology provides an interactive classroom setting that promotes the open exchange of ideas and allows for the lecturer to communicate directly with the students but has a great disadvantage of not teaching all the students according to their own learning rate and pace. The intelligent tutor solves this problem by providing individualised learning for each student where they can learn according to their own pace and learning abilities it will provide remedy and advice when learners encounter difficulties during learning session. The classical model of ITS architecture has four main modules; domain model, student model, tutoring model and the user interface model.

Multi-Agent and Service Oriented Architectures for Intelligent Tutoring System Development

Multi-Agent and Service Oriented Architectures for Intelligent Tutoring System DevelopmentTraditional modular architecture of Intelligent Tutoring Systems (ITSs) does not provide sufficient modularity of complex ITSs. Distributed technologies like services and agents are used to increase modularity of ITSs by implementing traditional modules as sets of services or agents. The paper describes holonic agent architecture that implements each module as one or more holonic agents. It uses the lessons learned from the multi-agent architecture to propose service oriented ITS architecture.

Requirements analysis of Multi-Agent Based Intelligent Tutoring Systems

Requirements analysis of Multi-Agent Based Intelligent Tutoring SystemsThe agent oriented software engineering research proposes general assumptions for agent oriented software development, while intelligent tutoring system (ITS) research proposes specific ITS architecture and other specific knowledge for ITS development. Both of these views should be taken into consideration while developing multi-agent based ITSs. Thus there is a need for specific approaches for all phases of agent based ITS development which take into consideration main ideas from both agent oriented software engineering and ITS research. In this paper we propose a requirements analysis approach for multi-agent based ITSs. A case study of a simple ITS is included, too. Requirements analysis in the proposed approach consist of two main steps, namely goal modelling and use case modelling. During the goal modelling the main goals of the system are identified and a goal hierarchy for the system is created. During the use case modelling use cases needed to achieve each lower level goal and their descriptions are created. The proposed approach of the requirements analysis is intended to be a part of the full life cycle methodology for multi-agent based ITS development. The developed use case model (especially use case scenarios) is used during the agent interaction design and task definition. Goal hierarchy during the design phase is mainly used for checking, if the results of design achieve all system's goals.

Designing reusable rule-based architectures with design patterns

Abstract Rule-based systems or production systems still have great importance in the construction of knowledge systems. In these systems, the domain expertise to solve a problem is encoded in the form of ‘if–then’ rules, enabling a modular description of the knowledge, thus facilitating its maintenance and updating. Although they have been extensively described in the Artificial Intelligence literature, their design process is at times repeated because of the lack of common software architecture and the restrictions offered by some off-the-shelf libraries and systems. This paper proposes a reusable architecture for rule-based systems described through design patterns. The aim of these patterns is to constitute a design catalog that can be used by designers to understand and create new rule-based systems, thus promoting reuse in these systems. Additionally, the use of the described patterns in the design of an intelligent tutoring system architecture is exemplified.

Developing Knowledge-Based Intelligent Multimedia TutoringSystems Using Semantic Content-Based Modelling

The complex nature of semantic video and audio content has made multimedia difficult to use within the traditional architecture of a knowledge-based intelligent tutoring system (ITS). This paper investigates how multimedia syntax and semantics may be integrated into the confines of the knowledge-based ITS architecture so as to develop a knowledge-based intelligent multimedia tutoring system (IMTS). The paper contributes a semantic content-based model that can be used for this purpose. Multimedia frames (m-frames) are used as the framework by which the syntactic and semantic information is represented within the model. The paper discusses how IMTSs may be developed using the model, and then demonstrates the use of our approach in the development of ARISTOTLE, an IMTS for teaching young children basic zoology.

Conversation rebuilding: From the foreign language classroom to implementation in an intelligent tutoring system

This paper shows how an innovative “communicative” technique in teaching foreign languages—Conversation Rebuilding (CR)—readily lends itself to implementation in an Intelligent Tutoring System (ITS). Classroom language teachers using CR get students to formulate acceptable utterances in a foreign idiom by starting from rough approximations (using words the students know) and gradually zeroing in on the utterance which a native speaker of that idiom might produce in a similar setting. The ITS presented here helps students do the “zeroing in” optimally. It lets them express themselves temporarily in an “interlingua” (i.e., in their own kind of French or English or whatever they are studying), as long as they make something of their communicative intent clear, that is, as long as the System can find a semantic starting point on which to build. The ITS then prods the students to express themselves more intelligibly, starting from the “key” elements (determined by a heuristic based on how expert classroom teachers proceed) and taking into consideration the students' past successful or unsuccessful attempts at communication. To simplify system design and programming, however, conversations are “constrained”: students playact characters in set dialogs and aim at coming up with what the characters actually say (not what they could possibly say). While most Intelligent Computer Assisted Language Learning (ICALL) focuses the attention of students on norms to acquire, the ICALL implementation of CR presented in this paper focuses the attention of students on saying something—indeed, almost anything—to keep the conversation going and get some kind of meaning across to the other party. It sees successful language acquisition primarily as the association of forms with intent, not simply as the conditioning of appropriate reflexes or the elaboration/recall of conceptualized rules (which are the by-products of successful communication). Thus, in espousing this hard-line communicative approach, the present paper makes a first, non-trivial point: ICALL researchers might usefully begin by investigating what the more able teachers are doing in the classroom, rather than by building elaborate computer simulations of out-dated practices, as happens all too often. The paper then goes on to describe the architecture of a prototype ITS based on CR—one that the authors have actually implemented and tested—for the acquisition of English as a foreign language. A sample learning session is transcribed to illustrate the man-machine interaction. Concluding remarks show how the present-day limits of ICALL (and Artificial Intelligence in general) can be partially circumvented by the strategy implemented in the program, i.e. by making the students feel they are creatively piloting an interaction rather than being tested by an unimaginative machine.

Using the operator function model and OFMspert as the basis for an intelligent tutoring system: towards a tutor/aid paradigm for operators of supervisory control systems

Training is a critical issue for operators responsible for the safe and efficient operation of large-scale complex dynamic systems. This paper proposes and articulates a set of requirements for an intelligent tutoring system. The requirements specify what (instructional content) and how (instructional strategies) to teach a novice operator to supervise and control a complex dynamic system. The instructional content teaches system structure and behavior (i.e., declarative knowledge), system procedures (i.e., procedural knowledge), and how to use this declarative and procedural knowledge to manage a complex dynamic system in real time (i.e., operational skill). Using the underlying representations of the operator function model (OFM) and OFMspert, the OFM's computational implementation. GT-VITA (Georgia Tech Visual and Inspectable Tutor and Assistant) realizes these requirements. As a proof-of-concept demonstration, an instance of the generic GT-VITA tutoring architecture was implemented for satellite ground controllers. The empirical evaluation, utilizing NASA satellite ground control personnel, showed that GT-VITA was a flexible and useful training system. In fact, NASA has adopted VITA as the foundation for required training for all satellite ground control personnel. In addition to an intelligent tutoring system architecture, by using and extending the operator function model and OFMspert, GT-VITA demonstrates a robust methodology for conceptualizing the tutor-aid paradigm. The tutor-aid paradigm defines a conceptual framework in which learning with a intelligent tutor gradually becomes collaboration with an intelligent associate. Using the same structures (i.e., OFM and OFMspert) and the same domain knowledge, GT-VITA specifies a tutor and GT-MOCA (Jones and Mitchell, 1995) specifies an aid.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

An intelligent tutor for normal form determination

In this paper an intelligent computer-aided instruction system for teaching normal form determination is described. The general architecture of intelligent tutoring systems is also discussed, as are the results of experience using the tool in an undergraduate database course.

An object-oriented architecture for intelligent tutoring systems

We describe an object-oriented architecture for intelligent tutoring systems. The architecture is oriented around objects that represent the various knowledge elements that are to be taught by the tutor. Each of these knowledge elements, called bites , inherits both a knowledge organization describing the kind of knowledge represented and tutoring components that provide the functionality to accomplish standard tutoring tasks like diagnosis, student modeling, and task selection. We illustrate the approach with several tutors implemented in our lab.