### Abstract

Original language | English |
---|---|

Place of Publication | Enschede |

Publisher | University of Twente, Faculty Educational Science and Technology |

Number of pages | 29 |

Publication status | Published - 1994 |

### Publication series

Name | OMD research report |
---|---|

Publisher | University of Twente, Faculty of Educational Science and Technology |

No. | 94-16 |

### Fingerprint

### Keywords

- Test Results
- Test Theory
- Models
- Intelligent Tutoring Systems
- Foreign Countries
- Bayesian Statistics
- Scores
- Psychometrics
- METIS-140146
- IR-104217
- Decision Making

### Cite this

*Applications of Bayesian decision theory to intelligent tutoring systems*. (OMD research report; No. 94-16). Enschede: University of Twente, Faculty Educational Science and Technology.

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*Applications of Bayesian decision theory to intelligent tutoring systems*. OMD research report, no. 94-16, University of Twente, Faculty Educational Science and Technology, Enschede.

**Applications of Bayesian decision theory to intelligent tutoring systems.** / Vos, Hendrik J.

Research output: Book/Report › Report › Professional

TY - BOOK

T1 - Applications of Bayesian decision theory to intelligent tutoring systems

AU - Vos, Hendrik J.

PY - 1994

Y1 - 1994

N2 - Some applications of Bayesian decision theory to intelligent tutoring systems are considered. How the problem of adapting the appropriate amount of instruction to the changing nature of a student's capabilities during the learning process can be situated in the general framework of Bayesian decision theory is discussed in the context of the Minnesota Adaptive Instructional System (MAIS). Two basic elements of this approach are used to improve instructional decision making in intelligent tutoring systems. First, it is argued that in many decision-making situations the linear loss model is a realistic representation of the losses actually incurred. Second, it is shown that the psychometric model relating observed test scores to the true level of functioning can be represented by Kelley's regression line from classical test theory. Optimal decision rules for the MAIS are derived using these two features.

AB - Some applications of Bayesian decision theory to intelligent tutoring systems are considered. How the problem of adapting the appropriate amount of instruction to the changing nature of a student's capabilities during the learning process can be situated in the general framework of Bayesian decision theory is discussed in the context of the Minnesota Adaptive Instructional System (MAIS). Two basic elements of this approach are used to improve instructional decision making in intelligent tutoring systems. First, it is argued that in many decision-making situations the linear loss model is a realistic representation of the losses actually incurred. Second, it is shown that the psychometric model relating observed test scores to the true level of functioning can be represented by Kelley's regression line from classical test theory. Optimal decision rules for the MAIS are derived using these two features.

KW - Test Results

KW - Test Theory

KW - Models

KW - Intelligent Tutoring Systems

KW - Foreign Countries

KW - Bayesian Statistics

KW - Scores

KW - Psychometrics

KW - METIS-140146

KW - IR-104217

KW - Decision Making

M3 - Report

T3 - OMD research report

BT - Applications of Bayesian decision theory to intelligent tutoring systems

PB - University of Twente, Faculty Educational Science and Technology

CY - Enschede

ER -