The types of data utilized during the mining process include population, employment, tuition and fees, household income, high school graduates, and historical enrollment data. Support vector machines produce the initial predictive results, which are then used by a tool called Cubist to generate easy-to-understand rule-based predictive models. Data Mining Your Enrollment Data: Enrollment Analytics 101 Presenter: Michael Laracy Founder and President, Rapid Insight Inc. [3] J. Han and M. Kamber, “Data Mining: Concepts and Techniques,” Morgan Kaufmann, 2000. [4] Romero C and Ventura S, “Educational Data Mining: A survey from 1995 to 2005” expert system with Application 33(2007) 135-146. [5] J. R. Quinlan, “Introduction of decision tree: Machine learn”, 1: pp. 86-106, 1986. This paper described on Data Mining methods that could be applied by higher education institution to predict the possible areas on student enrollment. 38 Higher Education Students’ Enrolment Forecasting System Using Data Mining Application in Ethiopia The methodological root of this research that are.

#### Transcript of Data Mining Application in Enrollment Management

Content:

1. Introduction: - Background;

- Problem identification;

- Goals and objectives

2. Data preparation

3. Methodology

4. Model construction and results

5. Strategies and recommendations

6. References

Strategies:

Data Mining can be

used in educational field

to enhance our understanding of learning process to focus on identifying, extracting and evaluating variables related to the learning process of students

The core of the

ID3 algorithm

is to select properties on nodes that all levels in the decision tree.

Introduction：

-Quality education is main promising to his students of a university.

-Quality education is to provide an efficient way to students

-Application of computer，using educational data mining

-EDM

-Solving the problem

Introduction:

Problem：

High competiton for students getting admission to these Higher Education Institutions（HEI）

Objective：

Using data mining methodologies to select student to take a particular courese（MCA）

Methodology:

The basic idea is to construct the decision tree by employing a top-down, greedy search through the given sets to test each attribute at every tree node.

Node Splitting

---if all examples are positive, return the single-node tree root, with label = +;

---if all examples are negative, return the single-node tree root, with label = -;

---if number of predicting attributes is empty, then return the single node tree root, with label = most common value of the target *enrollment data mining* in the examples.

Rules for Classification:

The satoshi mining represented by decision tree can be extracted and represented in the form of

IF-THEN rules

in Table 2. **Data Mining Application in Enrollment Management**

Thank you!

Data preparation

Data size:

432 students of MCA

Data source:

through enrolment form filled by the student at the time of admission

Methodology:

Decision trees are used for gaining information, such as stream, marks in graduation and students performance, for the purpose of predicting the suitable student for enrollment in a particular course

Rule Induction---

ID3 is chosen

ID3 algorithm is a greedy algorithm that selects the next attributes based on the information gain associated with the attributes. The attribute with the highest information gain or greatest entropy reduction is chosen as the test attribute for the current node

Model Construction:

The Weka Knowledge Explorer is an easy to use graphical user interface that harnesses the power of the Weka software,

**enrollment data mining**. The major Weka packages are Filters, Classifiers, Clusters, Associations, and Attribute Selection is represented in the Explorer along with a Visualization tool, which allows datasets and the predictions of Classifiers and Clusters to be visualized in two dimensions.

Weka supports several standard data mining tasks like data clustering, classification, regression, preprocessing, visualization and feature selection. And there are 16 decision tree algorithms like ID3, J48, Simple CART etc. implemented in WEKA.

The Weka‘s workbench contains a collection of visualization tools and algorithms for data analysis and predictive modeling together with graphical user interfaces for easy access to this functionality.

Variables and Possible value:

Related Work examples:

1.) Data mining can be used in educational field to enhance our understanding of learning process to focus on identifying, extracting and evaluating variables related to the mining news crypto process of students as described by Alaa el-halees.

2.) Bharadwaj and Pal obtained the university students

*enrollment data mining*like attendance, class test, seminar and assignment marks from the students’ previous database to predict the performance at the end of semester.

3.) Pandey and Pal conducted study on the student performance based by selecting 60 students from a degree college of a university. By means of association rule they find the interestingness of student in opting class teaching language.

Model Process:

Results:

The classification matrix

has been presented in Table 3, which compared the actual and predicted classifications. In addition, the classification accuracy for the four-class outcome categories was presented.

Why choose these data?

How these data can be used?

Weka

**Enrollment data mining**class wise accuracy

is shown in Table 4

Results:

1.The accuracy of the model is

60.46 %

. That is out of 430 instances 260 instances are correctly classified.

2.The most important attribute in predicting student’s enrollment is found to be

GS

.

3.The social attributes like category, Medium, College Location and Admission Type are not appearing in the decision tree indicating less relevance of the prediction with such attributes.

Strategies

The true positive rate for obtaining the Third Class and Fail Class is 33.33% and 50% respectively

The students going to take admission in MCA course can be considered for proper counseling so as to improve their result or choose any other course like MBA

B.Sc. Students with mathematics and BCA students are performed better in MCA course but the student of B.A. without mathematics did not perform well in MCA course

Recommendations:

Main disadvantages

of ID3 algorithm

The calculations based on the mutural information is in favor of a larger number of attributes values, while the attributes with more property values is not always optimal attribute.

ID3 is more sensitive to the noise

Improve traditional ID3 algorithm by introducing “rough set theory” and “fuzzy clustering algorithm”

References:

[1] Heikki, Mannila, Data mining: machine learning, statistics, and databases, IEEE, 1996.

[2] U. Fayadd, Piatesky, G,

**enrollment data mining**. Shapiro, and P. Smyth, From data mining to knowledge discovery in databases, AAAI Press / The MIT Press, Massachusetts Institute Of Technology. ISBN 0–262 56097–6, 1996.

[3] J. Han and M. Kamber, “Data Mining: Concepts and Techniques,” Morgan Kaufmann, 2000.

[4] Romero C and Ventura S, “Educational Data Mining: A survey from 1995 to 2005” expert system with Application 33(2007) 135-146.

[5] J. R. Quinlan, “Introduction of decision tree: Machine learn”, 1: pp. 86-106, 1986.

[6] B.K. Bharadwaj and S. Pal. “Mining Educational Data to Analyze Students‟ Performance”, International Journal of Advance Computer Science and Applications (IJACSA), Vol. 2,

*enrollment data mining*, No. 6, pp. 63-69, 2011.

[7] Alaa el-Halees, “Mining students data to analyze e-Learning behavior: A Case Study”, 2009.

[8] Oladipupo and Oyelade O J, “Knowledge Discovery from student’s result repository: Association rule mining Approach”, IJCSS vol. (4) Issue(2).

[9] S. T. Hijazi, and R,

*enrollment data mining*. S. M. M. Naqvi, “Factors affecting student‟s performance: A Case of Private Colleges”, Bangladesh e-Journal of Sociology, Vol. 3, No. 1, 2006.

[10] Z. N. Khan, “Scholastic achievement of higher secondary students in science stream”, Journal of Social Sciences, Vol. 1, No. 2, pp. 84-87, 2005.

**CHEN Lulu (53283408)**

JIAO Xupeng (53116919)

RUAN Fei (53234543)

SONG Liang (53220041)

ZHANG Fangfei (53283943)

JIAO Xupeng (53116919)

RUAN Fei (53234543)

SONG Liang (53220041)

ZHANG Fangfei (53283943)

Measuring Homogeneity

Splitting Criteria

Using “Information Gain” to determine the best attribute for a particular node in the tree

### Data Mining Application in Enrollment Management by ZHANG Fangfei on Prezi

38 Higher Education Students’ Enrolment Forecasting System Using Data Mining Application in Ethiopia The methodological root of this research that are. Mining Enrolment Data Using Predictive and Descriptive Approaches 55 or numeric value. For example, given a prediction model of credit card transactions, the. Educational Data Mining The purpose of the Predictive Analytics for Student Success factors associated with re-enrollment and retention, and the use of data. DATA MINING IN HIGHER EDUCATION STRATEGIC ENROLLMENT MANAGEMENT 2 Introduction Data mining is a process for extracting useful information from large . Colleges use data to predict who they should target as they hunt for students. A Case Study: Data Mining Applied to Student Enrollment César Vialardi 1, Jorge Chue, Alfredo Barrientos, Daniel Victoria 1, Jhonny Estrella, Juan Pablo Peche1.### This paper described on Data Mining methods that could be applied by higher education institution to predict the possible areas on student enrollment. Data Mining Your Enrollment Data: Enrollment Analytics 101 Presenter: Michael Laracy Founder and President, Rapid Insight Inc. [3] J. Han and M. Kamber, “Data Mining: Concepts and Techniques,” Morgan Kaufmann, 2000. [4] Romero C and Ventura S, “Educational Data Mining: A survey from 1995 to 2005” expert system with Application 33(2007) 135-146. [5] J. R. Quinlan, “Introduction of decision tree: Machine learn”, 1: pp. 86-106, 1986.

**Abstract**

In this paper, we describe our study on enrollment prediction using support vector machines and rule-based predictive models. The goal is to predict the total enrollment headcount that is composed of new (freshman and transfer), continued and returned students. The proposed approach builds predictive models for new, continued and returned students, respectively first, and then aggregates their predictive results from which the model for the total headcount is generated. The types of data utilized during the mining process include population, employment, tuition and fees, household income, high school graduates, and historical enrollment data. Support vector machines produce the initial predictive results, which are then used by a tool called Cubist to generate easy-to-understand rule-based predictive models. Finally we present some empirical results on enrollment prediction for computer science students at California State University, Sacramento

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