17-03-2014, 08:44 PM
Abstract- With the advance of information technology, it is
highlighted on how to extract valuable information from mass
data. Through data mining technology we can automatically
analyze data, make inductive reasoning, mine potential
network learning behavior classification and characteristics of
students, quantify the effect and evaluation of network
learning to continuously improve the overall quality and
learning ability of them.
Keywords- data mining; learning behavior; genetic algorithm
Network learning behavior is considered to be the key
point to resolve network learning resources development
and, learning result assessment, which receives high
attention from experts in education and academy and the
vast majority of teachers and students. The research on the
application analysis about data mining technology on the
college students learning behavior can help us understand
the current network learning situation on campus, and the
major factor affected network learning result, so that it is
conducive to the development of campus network
learning resources and the organization and guidance for
college teachers curriculum, as well as the effective
evaluation on learning resource and learners, which can be
used to guide the construction and improvement of network
teaching system, to improve the network learning
effectiveness of college students.
1 The function structure of data mining of college
students’ network learning behavior
1.1 Data Mining Technology
Data mining (data mining, DM) is the process of
extracting the implicit, potential and useful information
unknown by people in advance from a large number of
incomplete, noisy, ambiguous, and random practically
applied data. The function of DM is for the model type
found in the assigned data mining task. Data mining task
can generally be divided into two categories: description
and prediction. Descriptive mining task mainly portrays the
general characteristics of data in data base. Predictable
mining task is to deduce for prediction on the current data
[1].
The general techniques are as follows: statistical
techniques, association rules, analyses on history: MBR
(Memory-based Reasoning), GA(Genetic Algorithm,
gathered detection, Link analysis, decision tree, Neural Net,
Rough Set, and Regression Analysis [2].
With the advance of information technology, it is
highlighted on how to extract valuable information from
mass data. Through data mining technology we can
automatically analyze data, make inductive reasoning, mine
potential network learning behavior classification and
characteristics of students, quantify the effect and
evaluation of network learning to continuously improve the
overall quality and learning ability of them.
highlighted on how to extract valuable information from mass
data. Through data mining technology we can automatically
analyze data, make inductive reasoning, mine potential
network learning behavior classification and characteristics of
students, quantify the effect and evaluation of network
learning to continuously improve the overall quality and
learning ability of them.
Keywords- data mining; learning behavior; genetic algorithm
Network learning behavior is considered to be the key
point to resolve network learning resources development
and, learning result assessment, which receives high
attention from experts in education and academy and the
vast majority of teachers and students. The research on the
application analysis about data mining technology on the
college students learning behavior can help us understand
the current network learning situation on campus, and the
major factor affected network learning result, so that it is
conducive to the development of campus network
learning resources and the organization and guidance for
college teachers curriculum, as well as the effective
evaluation on learning resource and learners, which can be
used to guide the construction and improvement of network
teaching system, to improve the network learning
effectiveness of college students.
1 The function structure of data mining of college
students’ network learning behavior
1.1 Data Mining Technology
Data mining (data mining, DM) is the process of
extracting the implicit, potential and useful information
unknown by people in advance from a large number of
incomplete, noisy, ambiguous, and random practically
applied data. The function of DM is for the model type
found in the assigned data mining task. Data mining task
can generally be divided into two categories: description
and prediction. Descriptive mining task mainly portrays the
general characteristics of data in data base. Predictable
mining task is to deduce for prediction on the current data
[1].
The general techniques are as follows: statistical
techniques, association rules, analyses on history: MBR
(Memory-based Reasoning), GA(Genetic Algorithm,
gathered detection, Link analysis, decision tree, Neural Net,
Rough Set, and Regression Analysis [2].
With the advance of information technology, it is
highlighted on how to extract valuable information from
mass data. Through data mining technology we can
automatically analyze data, make inductive reasoning, mine
potential network learning behavior classification and
characteristics of students, quantify the effect and
evaluation of network learning to continuously improve the
overall quality and learning ability of them.