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University teaching evaluation system design based on data mining

Author: TangSong
Tutor: GaoHui
School: University of Electronic Science and Technology
Course: Computer Software and Theory
Keywords: data mining association rules Apriori teaching evaluation
CLC: TP311.13
Type: Master's thesis
Year: 2010
Downloads: 189
Quote: 0
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Abstract


Data Mining is the information processing technology which is developing very fast in recent years. Information and knowledge can be abstracted from a great deal of data which is incomplete, noisy dark, and random by data mining. The information and knowledge wet get was ignored and had not been known before but potentially useful. Association rules mining is an important sub-branch of data mining, which mining interesting association or correlation relationships are among a large set of data items. Association rules are considered interesting if both a minimum support threshold and a minimum confidence threshold are satisfied.The teaching quality can be improved by teaching quality assessment and the teaching behaviors will be adjusted to demands. In accordance to the related teaching objectives and standards, value appraisals are afforded to teaching activities and achievements by teaching evaluation. It includes two aspects, first is to define standard system of excellent teaching quality; second is to establish a set of classification evaluation standards. Then the rules relating to evaluation standard and result needs to be found so as to improve teaching courses.Firstly, in this dissertation, the concept of data mining and its application are introduced in detail, and the Apriori algorithm and its improvement are analyzed deeply. Secondly, the way to choose the target of college teaching evaluation and the foundation of model on hierarchical and association model are carefully studied. Finally, the application of the conclusions is discussed concretely. In this case, I have accomplished data mining process to analyze teachers’teaching quality evaluation result and students’score results. The teaching evaluation model, analysis of association rules on students’score and association rules mined by Apriori are completed in the system. After that, the rules and results are analyzed and suggestions are concluded to improve teaching evaluation.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Programming > Database theory and systems
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