A MENTOR SYSTEM INCORPORATING EXPERTISE TO GUIDE AND TEACH STATISTICAL DECISION-MAKING.
| dc.contributor.author | ATHEY, SUSAN. | |
| dc.creator | ATHEY, SUSAN. | en_US |
| dc.date.accessioned | 2011-10-31T16:56:15Z | en |
| dc.date.available | 2011-10-31T16:56:15Z | en |
| dc.date.issued | 1987 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10150/184011 | en |
| dc.description.abstract | The statistical mentor system incorporates a knowledge base into an educational tool for novices in statistical decision making to use in choosing a statistical technique. The novices are students in a business school curriculum who are expected to learn the basic statistical processes in business applications. The purpose of the system is to stimulate learning of the data analysis process on the part of the novice, usually a difficult task. The system acts as a consultant to the novice and approaches the task using a top-down problem solving strategy rather than the traditional bottom-up strategy used by novices. The heart of the system is the rule base for differentiating between statistics. These rules were built by gathering expertise from two experts in statistical analysis. The rules are based on five questions which the data can answer, as well as the type of data, the number of variables, and any dependent/independent relationships which exist between the variables. The knowledge base consists of five rule sets and can be represented either by condition/conclusion rules or by a set of multi-dimensional tables. Twenty-nine statistics and the rules for choosing them are in the rules sets. The knowledge base was used to define the logic incorporated in the consultant system in order to aid the user in selecting a correct technique. A dialogue mode is employed in the consultant to determine which conditions are true for the problem and data set. The rule sets are then checked to find the conclusion satisfying the conditions. The computer mentor was tested against the usual textbook mentor method (search through a textbook until one finds a statistic that looks promising) with two different groups of subjects, 25 undergraduates and 19 doctoral students. The results were that the computer-assisted students in both samples correctly solved a larger proportion of problems and had a higher average number of problems correct than did the textbook assisted groups. | |
| dc.language.iso | en | en_US |
| dc.publisher | The University of Arizona. | en_US |
| dc.rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. | en_US |
| dc.subject | Decision making -- Statistical methods. | en_US |
| dc.subject | Computer-assisted instruction. | en_US |
| dc.title | A MENTOR SYSTEM INCORPORATING EXPERTISE TO GUIDE AND TEACH STATISTICAL DECISION-MAKING. | en_US |
| dc.type | text | en_US |
| dc.type | Dissertation-Reproduction (electronic) | en_US |
| dc.contributor.chair | Nunamaker, Jay | en_US |
| dc.identifier.oclc | 698375754 | en_US |
| thesis.degree.grantor | University of Arizona | en_US |
| thesis.degree.level | doctoral | en_US |
| dc.contributor.committeemember | Summers, George | en_US |
| dc.contributor.committeemember | Weber, Sue | en_US |
| dc.contributor.committeemember | Ram, Sudha | en_US |
| dc.contributor.committeemember | Wagner, Gary | en_US |
| dc.identifier.proquest | 8711623 | en_US |
| thesis.degree.discipline | Business Administration | en_US |
| thesis.degree.discipline | Graduate College | en_US |
| thesis.degree.name | Ph.D. | en_US |
| refterms.dateFOA | 2018-05-18T06:13:26Z | |
| html.description.abstract | The statistical mentor system incorporates a knowledge base into an educational tool for novices in statistical decision making to use in choosing a statistical technique. The novices are students in a business school curriculum who are expected to learn the basic statistical processes in business applications. The purpose of the system is to stimulate learning of the data analysis process on the part of the novice, usually a difficult task. The system acts as a consultant to the novice and approaches the task using a top-down problem solving strategy rather than the traditional bottom-up strategy used by novices. The heart of the system is the rule base for differentiating between statistics. These rules were built by gathering expertise from two experts in statistical analysis. The rules are based on five questions which the data can answer, as well as the type of data, the number of variables, and any dependent/independent relationships which exist between the variables. The knowledge base consists of five rule sets and can be represented either by condition/conclusion rules or by a set of multi-dimensional tables. Twenty-nine statistics and the rules for choosing them are in the rules sets. The knowledge base was used to define the logic incorporated in the consultant system in order to aid the user in selecting a correct technique. A dialogue mode is employed in the consultant to determine which conditions are true for the problem and data set. The rule sets are then checked to find the conclusion satisfying the conditions. The computer mentor was tested against the usual textbook mentor method (search through a textbook until one finds a statistic that looks promising) with two different groups of subjects, 25 undergraduates and 19 doctoral students. The results were that the computer-assisted students in both samples correctly solved a larger proportion of problems and had a higher average number of problems correct than did the textbook assisted groups. |
