Data Mining Technique
Content
Data Mining Technique
Student evaluations are use to measure the teaching effectiveness of instructor’s are very frequently
applied in higher education for many years. The proposed study will investigates the factors associated
with the assessment of instructors teaching performance using two different data mining techniques;
stepwise regression and decision trees.
Decision Tree
A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their
possible consequences, including chance event outcomes, resource costs, and utility. It is one way to
display an algorithm. Decision trees are commonly used in operations research, specifically in decision
analysis, to help identify a strategy most likely to reach a goal. If in practice decisions have to be taken
online with no recall under incomplete knowledge, a decision tree should be paralleled by
a Probability model as a best choice model or online selection model algorithm. Another use of decision
trees is as a descriptive means for calculating conditional probabilities.
Stepwise Regression
In statistics, stepwise regression includes regression models in which the choice of predictive variables is
carried out by an automatic procedure.[1][2][3] Usually, this takes the form of a sequence ofF-tests, but
other techniques are possible, such as t-tests, adjusted R-square, Akaike information criterion, Bayesian
information criterion, Mallows' Cp, or false discovery rate.
The main approaches are:
Forward selection, which involves starting with no variables in the model, trying out the variables
one by one and including them if they are 'statistically significant'.
Backward elimination, which involves starting with all candidate variables and testing them one by
one for statistical significance, deleting any that are not significant.
Methods that are a combination of the above, testing at each stage for variables to be included or
excluded
The data collected will come anonymously from students’ using the proposed system. Several variables
related to the instructor and course characteristics are also included in the study. The results show that,
a factor summarizing the instructor related questions in the evaluation form, the employment status of
the instructor, the workload of the course, the attendance of the students, and the percentage of the
students filling the form are significant dimensions of instructor’s teaching performance.
In order to obtain useful knowledge from data, KDD (Knowledge Discovery in Database) methodology is
used. The process of KDD has been defined by Fayyad et al. (1996) as “the non-trivial process of
identifying valid, novel, potentially useful, and ultimately understandable patterns in data”. KDD process
covers mainly goal identification, data selection, data cleaning, data integration, data transformation,
data mining, pattern evaluation and knowledge presentation steps. To reduce the data and to obtain a
concise model for explaining the teaching performance two data mining techniques; regression analysis
and decision trees are utilized. For regression analysis the stepwise regression method and for decision
trees CHAID and CART algorithms are applied.
Student evaluations are use to measure the teaching effectiveness of instructor’s are very frequently
applied in higher education for many years. The proposed study will investigates the factors associated
with the assessment of instructors teaching performance using two different data mining techniques;
stepwise regression and decision trees.
Decision Tree
A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their
possible consequences, including chance event outcomes, resource costs, and utility. It is one way to
display an algorithm. Decision trees are commonly used in operations research, specifically in decision
analysis, to help identify a strategy most likely to reach a goal. If in practice decisions have to be taken
online with no recall under incomplete knowledge, a decision tree should be paralleled by
a Probability model as a best choice model or online selection model algorithm. Another use of decision
trees is as a descriptive means for calculating conditional probabilities.
Stepwise Regression
In statistics, stepwise regression includes regression models in which the choice of predictive variables is
carried out by an automatic procedure.[1][2][3] Usually, this takes the form of a sequence ofF-tests, but
other techniques are possible, such as t-tests, adjusted R-square, Akaike information criterion, Bayesian
information criterion, Mallows' Cp, or false discovery rate.
The main approaches are:
Forward selection, which involves starting with no variables in the model, trying out the variables
one by one and including them if they are 'statistically significant'.
Backward elimination, which involves starting with all candidate variables and testing them one by
one for statistical significance, deleting any that are not significant.
Methods that are a combination of the above, testing at each stage for variables to be included or
excluded
The data collected will come anonymously from students’ using the proposed system. Several variables
related to the instructor and course characteristics are also included in the study. The results show that,
a factor summarizing the instructor related questions in the evaluation form, the employment status of
the instructor, the workload of the course, the attendance of the students, and the percentage of the
students filling the form are significant dimensions of instructor’s teaching performance.
In order to obtain useful knowledge from data, KDD (Knowledge Discovery in Database) methodology is
used. The process of KDD has been defined by Fayyad et al. (1996) as “the non-trivial process of
identifying valid, novel, potentially useful, and ultimately understandable patterns in data”. KDD process
covers mainly goal identification, data selection, data cleaning, data integration, data transformation,
data mining, pattern evaluation and knowledge presentation steps. To reduce the data and to obtain a
concise model for explaining the teaching performance two data mining techniques; regression analysis
and decision trees are utilized. For regression analysis the stepwise regression method and for decision
trees CHAID and CART algorithms are applied.
