Knowledge based student academic advising in institutions of higher learning in Kenya

67 Pages·2013·0.79 MB·English

UNIVERSITY OF NAIROBI

SCHOOL OF COMPUTING AND INFORMATICS

KNOWLEDGE BASED STUDENT ACADEMIC ADVISING IN
INSTITUTIONS OF HIGHER LEARNING IN KENYA

BY
MOSES KARANI
P58/63367/2011

SUPERVISOR
CHRISTOPHER MOTURI

SEPTEMBER 2013

Submitted in partial fulfillment of the requirements of the Master of Science in Computer Science
i
DECLARATION
I hereby declare that the research project presented in this report is my original work and has
not been presented in any other institution. Due and clear reference is made to the works of
other researches that have informed this project.

Signature_____________________________________
Date________________________________________
Karani Moses Orupia
P58/63367/2011

The Research Project has been submitted in partial fulfillment of the Requirements of
the Degree of Master of Science in Computer Science at the University of Nairobi with my
approval as the University Supervisor.

Signature_____________________________________
Date________________________________________
Mr. Christopher Moturi
School of Computing and Informatics
University of Nairobi
ii
DEDICATION
To
All my teachers who have taught and counseled me over the years,
My father and late mother,
Daisy and Valerie
Above all to God
iii
ACKNOWLEDGEMENT
The counsel and advice of my supervisor Mr. Christopher Moturi is highly appreciated. I
also appreciate the useful advice I got from my evaluation panel members and fellow
classmates as a whole. Above all to God be the glory.
iv
ABSTRACT
Technological advances in the last two decades have led to reduced costs of computer
hardware and software. This has in turn led to widespread acquisition and use Student
Management Information Systems by institutions of higher learning resulting in huge
databases of student data. Despite these advancements, reporting applications have remained
largely rudimentary employing simple reports such as a student transcript, a consolidated
mark sheet and so on.

Data mining techniques can be used to obtain knowledge from large collections of data by
employing techniques from both computer science and statistics thereby enriching the
reporting applications. These techniques can be used to provide patterns and analyses that
cannot be obtained using rudimentary querying and reporting techniques. Furthermore these
patterns can be used to build models that can be used to perform predictions among many
other applications.

This research proposes to employ data mining techniques to build predictive models that can
be used to predict the degree honors class that a student is likely to get. Using this
knowledge plus that obtained using clustering and classification data mining techniques,
prudent advice can be furnished to students early enough about their likely final degree
honors class. With this knowledge, students can then be advised on how to improve their
performance using the knowledge provided by classification and clustering algorithms.

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TABLE OF CONTENTS
DECLARATION ...................................................................................................................... ii
DEDICATION ........................................................................................................................ iii
ACKNOWLEDGEMENT ...................................................................................................... iv
ABSTRACT ..............................................................................................................................v
LIST OF TABLES .................................................................................................................. ix
LIST OF FIGURES ..................................................................................................................x
ACRONYMS ........................................................................................................................... xi
CHAPTER 1: INTRODUCTION ............................................................................................1
1.0 Background............................................................................................................. 1
1.2 Problem Statement .................................................................................................. 2
1.3 Objectives ............................................................................................................... 2
1.4 Research Questions ................................................................................................. 3
1.5 Scope ...................................................................................................................... 3
1.6 Assumptions and Limitations .................................................................................. 4
CHAPTER 2: LITERATURE REVIEW ................................................................................5
2.0 Introduction ............................................................................................................ 5
2.1 EDM Problems ....................................................................................................... 6
2.2 EDM techniques ..................................................................................................... 9
2.3 Data Mining tools ................................................................................................. 10
2.4 EDM Society and Research ................................................................................... 10
CHAPTER 3: METHODOLOGY ......................................................................................... 11
3.0 Introduction .......................................................................................................... 11
3.1 Data ...................................................................................................................... 16
3.2 Data Mining Tasks ................................................................................................ 17
3.3 Hardware and Software Requirements .................................................................. 17
3.4 Requirement Specification and Analysis ............................................................... 18
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3.5 Data Analysis and Data Mining ............................................................................. 21
3.6 Client Side Interface ............................................................................................. 21
3.7 Non-Functional Requirements ............................................................................... 22
3.8 Design .................................................................................................................. 22
3.9 Evaluation and Testing .......................................................................................... 24
CHAPTER 4: IMPLEMENTATION AND FINDINGS ....................................................... 26
4.0 Introduction .......................................................................................................... 26
4.1 Data Mining Process ............................................................................................. 26
4.2 Business understanding ......................................................................................... 26
4.3 Data Understanding .............................................................................................. 28
4.4 Data Preparation ................................................................................................... 30
4.5 Modeling .............................................................................................................. 33
4.6 Evaluation and Testing .......................................................................................... 34
4.7 User interfaces and Reports ................................................................................... 45
4.8 Results .................................................................................................................. 46
4.9 Review of the Process ........................................................................................... 48
4.10 Resulting Model for EDM ..................................................................................... 49
CHAPTER 5: CONCLUSION AND RECOMMENDATIONS ........................................... 50
5.0 Introduction .......................................................................................................... 50
5.1 Contribution of the Project .................................................................................... 50
5.2 Challenges and Limitations ................................................................................... 51
5.3 Suggestions for Improvement ................................................................................ 51
5.4 Recommendations and further research ................................................................. 52
APPENDICES ......................................................................................................................... 53
I Project Plan .............................................................................................................. 53
II Project Budget .......................................................................................................... 54
vii
REFERENCES ........................................................................................................................ 55

viii
LIST OF TABLES
Table 1: The CRISP-DM Phases, tasks and outputs ............................................................. 14
Table 2: Application of CRISP-DM to the Project ............................................................... 16
Table 3: Fact and Dimension tables ..................................................................................... 20
Table 4: Overall Data Summary .......................................................................................... 29
Table 5: Student Population by Gender ............................................................................... 29
Table 6: Student Population by Degree Programme ............................................................. 29
Table 7: Student Degree by Class ........................................................................................ 30
Table 8: Rational for Inclusion/ Exclusion of Attributes ...................................................... 31
Table 9: Dimension Tables .................................................................................................. 31
Table 10: Data Mining Tasks and Selected Algorithms ....................................................... 33
Table 11: Implemented Data Mining Models ....................................................................... 34
Table 12: Classification Matrices for all the Models ............................................................ 38
Table 13: Cross Validation Results for all the Models ......................................................... 44
Table 14: Knowledge Acquired using Data Mining on the Developed Data Warehouse ...... 47
Table 15: Expert Review of the Prototype ........................................................................... 48
Table 16: Project Plan ......................................................................................................... 53
Table 17: Project Budget ..................................................................................................... 54
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LIST OF FIGURES
Figure 1: The CRISP-DM Process Model ............................................................................ 12
Figure 2: Data Warehouse Conceptual Schema ................................................................... 21
Figure 3: Nguyen, Huang et. al. Conceptual Framework ...................................................... 22
Figure 4: Proposed Conceptual Framework ......................................................................... 23
Figure 5: Lift Charts for Predicting Second Upper ............................................................... 35
Figure 6: Lift Charts for Predicting First Class .................................................................... 36
Figure 7: Mining Legend for Ranking Models in Predicting Pass ........................................ 36
Figure 8: Sample User Interface for Integrating the Proposed Solution in a Web Based
Application ......................................................................................................................... 45
Figure 9: Sample Analytics and Visualization that can be Generated from the Data
Warehouse .......................................................................................................................... 46
Figure 10: Final EDM Model .............................................................................................. 49
x