ITS-632
WK 10
University of the Cumberlands
Data Mining (ITS-632-M22)-Full Term
1.What is the definition of data mining that the author mentions? How is this different from
our current understanding of data mining?
The application of data analysis techniques with the goal of extracting hidden knowledge from
data by performing pattern recognition and predictive modeling activities is referred to as data
mining. The application of data mining techniques to educational data from a Croatian higher
education institution is described in this paper. Event logs from a real e-e-learning course's
environment were used to conduct the analysis. Cluster analysis and decision trees were used as
data mining approaches in this study. Cluster analysis was carried out by categorizing patterns
into groups based on similarities in student behavior when using course materials. The method
of interest for developing a representation of decision-making that permitted identifying classes
of items for further investigation of how pupils learned was the decision tree (Tan et. al, 2018).
Data mining is a widely used technique for analyzing big data sets and extracting necessary or
valuable information. The purpose of a data mining program is to find hidden data patterns
and correlations between parameters in large amounts of data. Educational data mining is the
use of data mining tools to explore data in education. Various educational data is recorded in
massive databases (Otey et. al., 2006). This is especially true for online tools that assist
teaching
processes and can record and retain student learning behaviors. The learning management
system is the most popular sort of such information system.
2. What is the premise of the use case and findings?
The educational data mining analysis carried out in this study yielded one model based on
cluster analysis that shows groups of students based on their behavior in the elearning system,
as well as three decision tree models based on the previously done cluster analysis. The
grouping analysis and decision tree results are described in the following section. In addition, a
box plot diagram made up of points from students' mid-term tests is shown to demonstrate the
validation of gained models through student achievement.
The goal of grouping the students was to find groups of students who were similar within the
group yet different from each other. The degree of resemblance is determined by the students'
conduct in an e-learning system over the course of a semester. Behavioral intention is a key
predictor of student conduct, and it varies depending on the student's behavioral, control, and
normative views about the desired behavior. The k-means method was used to data from 185
students in one e-course at a higher education institution, yielding three groups: 84 pupils were
in Group 0. 82 pupils were in Group 1.
Findings
WK 10
University of the Cumberlands
Data Mining (ITS-632-M22)-Full Term
1.What is the definition of data mining that the author mentions? How is this different from
our current understanding of data mining?
The application of data analysis techniques with the goal of extracting hidden knowledge from
data by performing pattern recognition and predictive modeling activities is referred to as data
mining. The application of data mining techniques to educational data from a Croatian higher
education institution is described in this paper. Event logs from a real e-e-learning course's
environment were used to conduct the analysis. Cluster analysis and decision trees were used as
data mining approaches in this study. Cluster analysis was carried out by categorizing patterns
into groups based on similarities in student behavior when using course materials. The method
of interest for developing a representation of decision-making that permitted identifying classes
of items for further investigation of how pupils learned was the decision tree (Tan et. al, 2018).
Data mining is a widely used technique for analyzing big data sets and extracting necessary or
valuable information. The purpose of a data mining program is to find hidden data patterns
and correlations between parameters in large amounts of data. Educational data mining is the
use of data mining tools to explore data in education. Various educational data is recorded in
massive databases (Otey et. al., 2006). This is especially true for online tools that assist
teaching
processes and can record and retain student learning behaviors. The learning management
system is the most popular sort of such information system.
2. What is the premise of the use case and findings?
The educational data mining analysis carried out in this study yielded one model based on
cluster analysis that shows groups of students based on their behavior in the elearning system,
as well as three decision tree models based on the previously done cluster analysis. The
grouping analysis and decision tree results are described in the following section. In addition, a
box plot diagram made up of points from students' mid-term tests is shown to demonstrate the
validation of gained models through student achievement.
The goal of grouping the students was to find groups of students who were similar within the
group yet different from each other. The degree of resemblance is determined by the students'
conduct in an e-learning system over the course of a semester. Behavioral intention is a key
predictor of student conduct, and it varies depending on the student's behavioral, control, and
normative views about the desired behavior. The k-means method was used to data from 185
students in one e-course at a higher education institution, yielding three groups: 84 pupils were
in Group 0. 82 pupils were in Group 1.
Findings
