Pages

Tuesday, December 13, 2011

INTELLIGENT TUTORING SYSTEM


INTRODUCTION

Education is currently undergoing a major transition in modern societies. Many authorities attribute this transition to serious limitations in the traditional lecturing approach to education, which places the student in a passive role. Recently, a wave of innovations, which is stimulated by the revolution in the Information Technology, promises to revitalize schools and the education system.
GENERATIVE CAI
In the 1960's, researchers created a number of Computer Assisted Instructional (CAI) systems that were generative (Uhr, 1969). These programs generated sets of problems designed to enhance student performance in skill-based domains, primarily arithmetic and vocabulary recall. Essentially, these were automated flash card systems, designed to present the student with a problem, receive and record the student's response, and tabulate the student's overall performance on the task

THE STRUCTURE OF AN ITS SYSTEM

Intelligent tutoring systems consist of four different subsystems or modules: the interface module, the expert module, the student module, and the tutor module. The interface module provides the means for the student to interact with the ITS, usually through a graphical user interface and sometimes through a rich simulation of the task domain the student is learning (e.g., controlling a power plant or performing a medical operation). The expert module references an expert or domain model containing a description of the knowledge or behaviors that represent expertise in the subject-matter domain the ITS is teaching, often an expert system or cognitive model. An example would be the kind of diagnostic and subsequent corrective actions an expert technician takes when confronted with a malfunctioning thermostat. The student module uses a student model containing descriptions of student knowledge or behaviors, including his misconceptions and knowledge gaps. An apprentice technician might, for instance, believe a thermostat also signals too high temperatures to a furnace (misconception) or might not know about thermostats that also gauge the outdoor temperature (knowledge gap). A mismatch between a student's behavior or knowledge and the expert's presumed behavior or knowledge is signaled to the tutor module, which subsequently takes corrective action, such as providing feedback or remedial instruction. To be able to do this, it needs information about what a human tutor in such situations would do: the tutor model.
An intelligent tutoring system is only as effective as the various models it relies on to adequately model expert, student and tutor knowledge and behavior. Thus, building an ITS needs careful preparation in terms of describing the knowledge and possible behaviors of experts, students and tutors. This description needs to be done in a formal language in order that the ITS may process the information and draw inferences in order to generate feedback or instruction. Therefore a mere description is not enough; the knowledge contained in the models should be organized and linked to an inference engine. It is through the latter's interaction with the descriptive data that tutorial feedback is generated.

USE IN PRACTICE

All this is a substantial amount of work, even if authoring tools have become available to ease the task. This means that building an ITS is an option only in situations in which they, in spite of their relatively high development costs, still reduce the overall costs through reducing the need for human instructors or sufficiently boosting overall productivity. Such situations occur when large groups need to be tutored simultaneously or many replicated tutoring efforts are needed. Cases in point are technical training situations such as training of military recruits and high school mathematics. One specific type of intelligent tutoring system, cognitive tutors, has been incorporated into mathematics curricula in a substantial number of United States high schools, producing improved student learning outcomes on final exams and standardized tests. Intelligent tutoring systems have been constructed to help students learn geography, circuits, medical diagnosis, computer programming, mathematics, physics, genetics, chemistry, etc.
CHALLENGES TO ITS
By the mid-1980's, much of enthusiasm in AI for creating "thinking" computers had waned as the field began to mature. Researchers turned to the more prosaic tasks of building expert systems that could function well in constrained domains, such as troubleshooting and diagnostic systems. At the same time, as ITS began to move out of the AI laboratories into classrooms and other instructional settings, they began to attract critical reactions. Some shortcomings of ITS became apparent as researchers realized that the problems associated with creating ITS were more intractable than they had originally anticipated. Rosenberg notes that most papers about ITS make few references to the education literature; the majority are grounded in the computing literature. He asserts that much ITS work suffers from two major flaws:
The systems are not grounded in a substantiated model of learning. Model formulation should be preceded by protocol analysis, but very little analysis is done, almost none of it qualitative. ITS models should be validated by the teachers and students who will use the systems, but ITS researchers do not appear to consult these experts.
Testing is incomplete, inconclusive, or in some cases totally lacking. Data on computerized tutorials are, at best, mixed. The almost universally positive claims for ITS and other computerized instructional systems, most notable in the education literature, are based on results from severely flawed tests.
It was obvious that the basic premises of ITS research needed revision.

Intelligent tutoring systems, four different subsystems or modules:
1.      The interface module: It provides the means for the student to interact with the ITS, usually through a graphical user interface and sometimes through a rich simulation of the task domain the student is learning (e.g., controlling a power plant or performing a medical operation).

2.      The expert module: It references an expert or domain model containing a description of the knowledge or behaviors that represent expertise in the subject-matter domain the ITS is teaching, often an expert system or cognitive model. An example would be the kind of diagnostic and subsequent corrective actions an expert technician takes when confronted with a malfunctioning thermostat.

3.The student module: It uses a student model containing descriptions of student knowledge or behaviors, including his misconceptions and knowledge gaps. An apprentice technician might, for instance, believe a thermostat also signals too high temperatures to a furnace (misconception) or might not know about thermostats that also gauge the outdoor temperature (knowledge gap)

4. The tutor module: A mismatch between a student's behavior or knowledge and the expert's presumed behavior or knowledge is signaled to the tutor module, which subsequently takes corrective action, such as providing feedback or remedial instruction. To be able to do this, it needs information about what a human tutor in such situations would do: the tutor model.

THE INTELLIGENT TUTORING SYSTEMS CONFERENCE
Ø  in Montréal (Canada) by Claude Frasson and Gilles Gauthier in 1988, 1992, 1996 and 2000;
Ø  in San Antonio (US) by Carol Redfield and Valerie Shute in 1998;
Ø  in Biarritz (France) and San Sebastian (Spain) by Guy Gouardères and Stefano Cerri in 2002;
Ø  in Maceio (Brazil) by Rosa Maria Vicari and Fábio Paraguaçu in 2004;
Ø  in Jhongli (Taiwan) by Tak-Wai Chan in 2006.
Ø  The conference was recently back in Montreal in 2008 (for its 20th anniversary) by Roger Nkambou and Susanne Lajoie.
Ø  ITS'2010 will be held in Pittsburgh (US) by Jack Mostow, Judy Kay, and Vincent Aleven.
SOME INTELLIGENT TUTORING SYSTEMS ARE

Ø  The Love Machine
Ø  Toward Empathetic Agents in Tutoring Systems
Ø  Intelligent Tutoring Systems with Conversational Dialogue.
Ø  Using technology for learning & teaching science. (as astronomy, space research, physics, mathematics and the earth sciences. )
Ø  Pitch-perfect PC
Ø  The F-16 Maintenance Skills Tutor
Ø  City pushes computer tutor for struggling algebra students.
PROSPECTS FOR CREATING AN INTELLIGENT TUTORING SYSTEM

Ø  Modularize the curriculum.
Ø  Customize it for different student populations.
Ø  Individualize the presentation and assessment of the content.
Ø  Collect data which instructors could use to tutor and remediate students.
The first step in this process is to understand what others had done before us and the implications for future developments.
ARTIFICIAL INTELLIGENCE AS TUTOR
In 1998, chemist Benny Johnson founded Quantum Simulations, Inc. with high school mentor Dale Holder and colleague Rebecca Renshaw to create highly interactive tutoring software for the sciences.

Student modeling remains at the core of ITS research (Holt, Dubs, Jones & Greer, 1994). What distinguishes ITS from CAI is the goal of being able to respond to the individual student's learning style to deliver customized instruction.
 DIAGNOSIS
Diagnosis means that an ITS infers information about the learner's state on three levels.
At the behavioral level, ignoring the learner's knowledge and focusing only on the observable behavior.
At the epistemic level, dealing with the learner's knowledge state and attempting to infer that state based on observed behavior.
At the individual level, covering such areas as the learner's personality, motivational style, self-concept in relation to the domain in question, and conceptions the learner has of the ITS. Wenger notes that, up to now, ITS have not been concerned with the individual level. However, he advocates further research in this area as a prerequisite for viewing the student as an active learner, rather than as a passive recipient of knowledge

No comments:

Post a Comment