The Q-matrix Method of Fault-Tolerant Teaching in Knowledge Assessment and Data Mining

Show simple item record

dc.contributor.advisor Dr. Donald L. Bitzer, Committee Chair en_US
dc.contributor.advisor Dr. Mladen Vouk, Committee Co-Chair en_US
dc.contributor.advisor Dr. R. Michael Young, Committee Member en_US
dc.contributor.advisor Dr. Rob St. Amant, Committee Member en_US
dc.contributor.advisor Dr. Brad Mehlenbacher, Committee Member en_US
dc.contributor.author Barnes, Tiffany Michelle en_US
dc.date.accessioned 2010-04-02T18:57:12Z
dc.date.available 2010-04-02T18:57:12Z
dc.date.issued 2003-11-28 en_US
dc.identifier.other etd-11242003-194840 en_US
dc.identifier.uri http://www.lib.ncsu.edu/resolver/1840.16/4612
dc.description.abstract A major challenge for today's schools is to create individualized instruction in inexpensive, expandable ways. To achieve this goal, educational systems must adapt to each student, mining student data to determine what a student knows and does not know. Using this knowledge, teaching systems can guide students in the learning process. This dissertation investigates the q-matrix method of fault tolerant teaching (FTT). FTT systems are adaptive teaching systems that tolerate student, teacher, and system errors in diagnosing student misconceptions. The q-matrix method is easily applied to any tutorial system data, independent of topic, and can be used to model student behavior and guide student knowledge remediation. We found that, in addition, the model is easily interpretable and can be used to understand large sets of student data, pinpointing problem areas for student learning. In this work, we applied the q-matrix method in three computer tutorials, presenting the first experiment to use the method on a large group of students. Overall, students felt that these tutorials were beneficial. For each tutorial, the q-matrix method was used to create a student knowledge model. When compared with other data mining/knowledge discovery methods, including factor analysis and cluster analysis, the q-matrix method was superior in that it was able to fit the observed data well, while still offering the interpretability needed to devise remediation methods. Our results indicate that the q-matrix model may predict student misconceptions at least as well as students were able to predict themselves, and students generally felt that the tutorial knew which concepts each student least understood. For our logic proofs tutorial, we devised q-matrices as data mining tools, used to extract the axioms needed to solve a proof. In this analysis, we were able to isolate sets of students using similar strategies, and to interpret the strategies of these groups using the q-matrix model. We also compared extracted q-matrix models to expert models, and found that the extracted and expert q-matrices were not a good match, but that extracted q-matrix models were quite useful in understanding student data. This shows that expert models do not necessarily predict student behavior and more accurate student knowledge models, such as q-matrices, are needed to understand student knowledge. An extracted q-matrix can reveal student behavior that might not be predicted by an expert's understanding. This work resulted in the construction of a fully automated, fault tolerant, intelligent tutoring system, which can diagnose and correct student misconceptions. This system also provides an interpretable model for each topic that relates each tutorial question to its underlying concepts. The experimental analysis provides valuable insight into the factors that influence the extraction and interpretability of these models, as well as their value in automatically assessing student knowledge. In addition, the q-matrix method is used as a general data mining tool in one tutorial where a traditional application of the q-matrix method would not be appropriate. This application and its favorable comparison with other data mining tools mark the q-matrix method as a viable data clustering and interpretation tool for data mining and knowledge discovery. en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.subject q-matrix en_US
dc.subject teaching effectiveness en_US
dc.subject knowledge modeling en_US
dc.subject knowledge discovery en_US
dc.subject data mining en_US
dc.subject computer-based education en_US
dc.subject adaptive tutoring system en_US
dc.title The Q-matrix Method of Fault-Tolerant Teaching in Knowledge Assessment and Data Mining en_US
dc.degree.name PhD en_US
dc.degree.level dissertation en_US
dc.degree.discipline Computer Science en_US


Files in this item

Files Size Format View
etd.pdf 940.2Kb PDF View/Open

This item appears in the following Collection(s)

Show simple item record