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Improving quality of graduate
students by data mining
Asst. Prof. Kitsana Waiyamai, Ph.D.
Dept. of Computer Engineering
Faculty of Engineering, Kasetsart University
Bangkok, Thailand
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Content
PART I



Introduction to data mining
Data mining technique: association rule
discovery
Data mining technique: data classification
PART II

Improving quality of graduate students by
data mining
Conclusion
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What Is Data Mining ?
Knowledge Discovery from Data: KDD (Data Mining):
The process of nontrivial extraction of patterns
from data. Patterns that are:
•implicit,
•previously unknown, and
•potentially useful
Patterns must be comprehensible for human users.
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Mining
Objective
Knowledge Discovery Process:
Iterative & Interactive Process
Take actions
based on findings
Data sources
Databases, flat files,
Complex data
Data
Warehouses
Interpret results
Preprocessing data
Gathering, cleaning
and selecting data
Search for patterns: Data Mining
Neural nets, machine learning,
statistics and others
Report findings
Analyst reviews output
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What kind of data can be mined?
Databases
Relational databases
Data warehouses
Data
Warehouse
Transactional databases and Flat files
Advanced DB systems and information repositories

Object-oriented and object-relational databases

Spatial databases

Time-series data and temporal data

Text databases, multimedia databases

Heterogeneous and legacy databases

World Wide Web

Bioinformatic data
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Two modes of data mining
Predictive data mining



Predict behavior based on historic data
Use data with known results to build a model that
can be later used to explicitly predict values for
different data
Methods: classification, prediction, … etc.
Descriptive data mining


Describe patterns in existing data that may be
used to guide decisions
Methods: Associations rule discovery, Sequence
pattern discovery, Clustering, … etc.
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Data Mining Techniques
Data Clustering
Association rule discovery
Data Classification
Outlier detection
Data regression
Etc.
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Data Classification
Classification is the process of assigning new objects to
predefined categories or classes
 Given a set of labeled records
 Build a model
 Predict labels for future unlabeled records
Example:


Age, Educational background, Annual income, Current
debts, Housing location => Making Decision
Degree=“Master” and Income=7500 =>
Credit=“Excellent”
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Three-Step Process of
Classification
Training
Data
Model construction
Classifier Model
Testing
Data
Model Evaluation
Classifier Model
Unseen
Data
Classification
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Data Mining Tools
ANGOSS KnowledgeStudio
IBM Intelligent Miner
Metaputer PolyAnalyst
SAS Enterprise Miner
SGI Mineset
SPSS Clementine
Many others
More at http://www.kdnuggets.com/software
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Data Mining Projects
Checklist:


Start with well-defined questions
Define measures of success and failure
Main difficulty: No automation




Understanding the problem
Data preparation
Selection of the right mining methods
Interpretation
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Using Data Mining for Improving Quality
of Engineering Graduates
Objective:
Discover knowledge from large databases of
engineering student records.
Discovered knowledge are useful in:
- Assisting in development of new curricula,
- Improvement of existing curricula,
- Helping students to select the
appropriate major
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Using a data mining technique to help
students in selecting their majors
Motivation:
- Student major selection is very important factor for
his/her success.
- Lack of experience and information on each major.
Solution:
- Find out the profiles of good students for each
major using student profile database and course
enrollment student databases (10 years)
- Determine the most appropriate major for each
student
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A Data Mining based Approach for Improving
Quality of Engineering Graduates
Data Mining Tool
student
profile
database
SQL Server
course
enrollment
student
databases
User
DB2
Java
Servlet
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Data for Data Mining
Stu_code
Sex
Address
Sch_GPA
.....
GPA
37058063
male
Bangkok
2.5
.....
2.3
37058167
male
Songkla
3.4
.....
3.2
...........
....
.......
......
....
....
Stu_code
Sub_code
Term
Year
Grade
37058063
204111
1
2537
C+
37058063
403111
1
2537
D
37058063
208111
1
2537
B+
Student profile
database
course enrollment
student databases
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Data preparation a classification model
Stu_code
37058063
37058167
Sex
Sch_GPA .... GPA
.
male Bangkok
2.5
.... 2.3
.
male Songkla
3.4
.... 3.2
.
.......... ....
.
Address
+
Stu_code
Sub_code
37058063
204111
1
2537
C+
37058063
403111
1
2537
D
37058063
208111
1
2537
B+
.......
...... .... ....
Stu_code Sex 204111
403111
37058063 male Medium Low
37058167 male High
High
.......
.... ...... .......
.
…
....
.....
.....
Term Year Grade
GPA
2.3
3.2
......
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Global Classification Model
Global Decision Tree
which determines which
majors should be appropriate
to which students.
Each internal node represents
a test on student’s profile.
Each leaf node represents an
appropriate major to be
selected
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Drawbacks of Global Classification
Model
- Low Precision ~ 50% due to the large
number of majors
- Number of students is different in each
department => the model cannot predict
correctly the best major to be selected.
- The model proposes a unique major to be
selected, a set of possible majors ordered by
appropriateness score would be preferred.
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Classification Model for Each Major
-
-
-
Decision tree
predicts whether a
student is likely to
be a good student
in a given major.
Good students are
those that
graduate within 4
years and are at
the first 40%
ranking in a given
major.
Leaf nodes
represent two
class: Good and
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Bad
Advantage of Major’s Classification Model
 Good precision 80%
 The model predicts the best major to be
selected even if number of students in each
major is different
 Its proposes a set of possible majors to be
selected ordered by appropriateness score.
Encountered problems
 Database size

Other factors that could affect student’s decision:
 Teacher Preference, etc.
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Presentation of Discovered
Knowledge
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Applying Association rule discovery
for Grade prediction
Basket Analysis
Education
204111
403111
417167
417168
Medium
High
Medium
Medium
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Grade Prediction for the Coming Term
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Presentation of Discovered Knowledge
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Conclusion & Future works
Application of data mining in Education
Use data mining techniques for improving
quality of engineering students
Apply data mining techniques to several other
educational domains.
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