01-10-2012, 05:24 PM
Intelligent tutoring system (ITS)
INTELLIGENT TUTORING SYSTEMS.ppt (Size: 965.5 KB / Downloads: 26)
Some Selected Definitions of the ITSs
‘ITSs are computer software systems that seek to mimic the methods and dialog of natural human tutors, to generate instructional interactions in real time and on demand, as required by individual students. Implementations of ITSs incorporate computational mechanisms and knowledge representations in the fields of artificial intelligence, computational linguistics, and cognitive science.’
‘Broadly defined, an intelligent tutoring system is educational software containing an artificial intelligence component. The software tracks students' work, tailoring feedback and hints along the way. By collecting information on a particular student's performance, the software can make inferences about strengths and weaknesses, and can suggest additional work.’
‘In particular, ITSs are computer-based learning systems which attempt to adapt to the needs of learners and are therefore the only such systems which attempt to ’care’ about learners in that sense. Also, ITS research is the only part of the general IT and education field which has as its scientific goal to make computationally precise and explicit forms of educational, psychological and social knowledge which are often left implicit.’
Theoretical Foundations for Intelligent Tutoring Systems
It is generally accepted to refer to an ITS if the system is able to:
build a more or less sophisticated model of cognitive (concerned with acquisition of knowledge: relating to the process of acquiring knowledge by the use of reasoning, intuition, or perception ) processes.
adapt these processes consecutively and control a question-answer-interaction.
Goal of ITS
To modularize the curriculum
To engage the students in sustained reasoning activity
To interact with the student based on a deep understanding of the students behavior
Collect data which instructors could use to tutor and remediate students
If ITS could realize even half the impact of human tutors, the payoff for society promised to be substantial.
Types of ITSs
we will concentrate on two dimensions: abstraction of the learning environment and the knowledge type of the instruction.
Abstraction of the learning environment
Many systems attempt to provide instruction by simulating a realistic working environment in which the student can learn the task. There are many reasons for developing such systems, including the possible danger of training using the actual equipment and the lack of domain experts who can devote their expensive time to training novices. Therefore, a realistic simulated learning environment can reduce both the cost and the risks of training. An example of a simulation-based ITS is the Advanced Cardiac Life Support (ACLS) Tutor in which a student takes the role of team leader in providing emergency life support for patients who have had heart attacks. The system not only monitors student actions, but runs a realistic simulation of the patient's condition and maintains an environment that is reasonably faithful to the ``real life'' situation. Thus, the goal is not only to test the student's knowledge about the correct emergency procedures, but also to allow him to experience practicing those procedures in a more realistic manner than is possible in a traditional classroom.
Emphasis of Instruction
For ease of development, systems tend to concentrate on teaching one type of knowledge. The most common type of ITS teaches procedural skills; the goal is for students to learn how to perform a particular task. There has been substantial research in cognitive psychology about human skill acquisition, so analyzing the domain knowledge in this framework can prove beneficial to instruction. Systems that are designed according to these principles are often called cognitive tutors. The most common result of this analysis is a set of rules that are part of a runnable expert model. This set of expert rules often serves double duty as a knowledge of the domain and as the pedagogical module. If a student encounters difficulty, the specific remediation required can be determined from the expert model.
INTELLIGENT TUTORING SYSTEMS.ppt (Size: 965.5 KB / Downloads: 26)
Some Selected Definitions of the ITSs
‘ITSs are computer software systems that seek to mimic the methods and dialog of natural human tutors, to generate instructional interactions in real time and on demand, as required by individual students. Implementations of ITSs incorporate computational mechanisms and knowledge representations in the fields of artificial intelligence, computational linguistics, and cognitive science.’
‘Broadly defined, an intelligent tutoring system is educational software containing an artificial intelligence component. The software tracks students' work, tailoring feedback and hints along the way. By collecting information on a particular student's performance, the software can make inferences about strengths and weaknesses, and can suggest additional work.’
‘In particular, ITSs are computer-based learning systems which attempt to adapt to the needs of learners and are therefore the only such systems which attempt to ’care’ about learners in that sense. Also, ITS research is the only part of the general IT and education field which has as its scientific goal to make computationally precise and explicit forms of educational, psychological and social knowledge which are often left implicit.’
Theoretical Foundations for Intelligent Tutoring Systems
It is generally accepted to refer to an ITS if the system is able to:
build a more or less sophisticated model of cognitive (concerned with acquisition of knowledge: relating to the process of acquiring knowledge by the use of reasoning, intuition, or perception ) processes.
adapt these processes consecutively and control a question-answer-interaction.
Goal of ITS
To modularize the curriculum
To engage the students in sustained reasoning activity
To interact with the student based on a deep understanding of the students behavior
Collect data which instructors could use to tutor and remediate students
If ITS could realize even half the impact of human tutors, the payoff for society promised to be substantial.
Types of ITSs
we will concentrate on two dimensions: abstraction of the learning environment and the knowledge type of the instruction.
Abstraction of the learning environment
Many systems attempt to provide instruction by simulating a realistic working environment in which the student can learn the task. There are many reasons for developing such systems, including the possible danger of training using the actual equipment and the lack of domain experts who can devote their expensive time to training novices. Therefore, a realistic simulated learning environment can reduce both the cost and the risks of training. An example of a simulation-based ITS is the Advanced Cardiac Life Support (ACLS) Tutor in which a student takes the role of team leader in providing emergency life support for patients who have had heart attacks. The system not only monitors student actions, but runs a realistic simulation of the patient's condition and maintains an environment that is reasonably faithful to the ``real life'' situation. Thus, the goal is not only to test the student's knowledge about the correct emergency procedures, but also to allow him to experience practicing those procedures in a more realistic manner than is possible in a traditional classroom.
Emphasis of Instruction
For ease of development, systems tend to concentrate on teaching one type of knowledge. The most common type of ITS teaches procedural skills; the goal is for students to learn how to perform a particular task. There has been substantial research in cognitive psychology about human skill acquisition, so analyzing the domain knowledge in this framework can prove beneficial to instruction. Systems that are designed according to these principles are often called cognitive tutors. The most common result of this analysis is a set of rules that are part of a runnable expert model. This set of expert rules often serves double duty as a knowledge of the domain and as the pedagogical module. If a student encounters difficulty, the specific remediation required can be determined from the expert model.