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STUDENT DROP OUT FACTOR ANALYSIS AND
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TREND PREDICTION USING DECISION TREE
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Running head: Student Drop out Factor Analysis and Trend Prediction
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Using Decision Tree
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Jeeranan Chareonrat
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Department of Business Computer, Faculty of Management Science,
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Sakon Nakhon Rajabhat University, Sakon Nakhon 47000, Thailand. Tel. 0-4297-0028;
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Fax. 0-4297-0028; E-mail: [email protected]
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Abstract
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Issues relating to increases in student drop-out rates are becoming a top
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priority in many educational institutions. This paper aims to identify and
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explore the factors influencing this growing phenomenon focusing on a
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university in provincial Thailand. Research conducted between 2010 and
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2014 targeted Management Science students attending Sakon Nakhon
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Rajabhat University. Survey database on 14 attributes of 4,163 current
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students. Data analysis was undertaken using algorithm J48 Data Mining
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techniques with a decision- tree classification and Weka's 10-fold cross
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validation program. The findings of the research indicated that the four most
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significant factors that induced student drop-out were low GPA results,
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studying loans, earlier educational attainment, and parents' monthly
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incomes. Further analysis indicated that in the 2010-2011 year low GPA
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attainment was the most significant factor, and added with studying loans in
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2012 to 2013 then plused parents' incomes in 2014. This suggests a trend in
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line with the Classification Rule that may predict drop-out rates in the
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current year 2015.
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Keyword: Data mining, Classification, Prediction, Student drop out
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Introduction
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It is widely known that Information Technology (IT) which brings about efficient
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working process and decision making has been sky-rocketed developing these
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days. It plays a vital role in most organization for manipulating and collecting data
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of huge databases. Educational institutions, for example, have stored many
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aspects of information including, students’ data, and lectures’ websites and e-
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learning administration. Despite, obtaining a large amount of data, they seldom
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made use of those data for other benefits especially for prediction analysis. Data
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mining is the process of discovering interesting patterns and knowledge from
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large amount of data (Han et al., 2011). Gulati (2015) predicted student’ drop out
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by using data mining technique, Yukselturk et al. (2014) predicted students’ drop
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out of the On-line program using K-Nearest Neighbour(K-NN), Decision
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Tree(DT), Naive Bayes (NB) and Neural Network (NN). Omkar and Parag (2015)
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use Data mining J48, Random Forest, Rep Tree and BF Tree of Decision Tree and
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JRip rule.
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The Management Science faculty has also been facing with students’ drop
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out which is considered to be an important problem in the educational system as it
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directly affects organization’s budget management. Some data from the Education
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promotion Department revealed that the drop out rate was 20.68 percent in
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between year 2010-2014 which considered “high”. This study aims to form a
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model that can predict factors affecting annual students’ drop out by using data
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mining technique. This suggests a trend that contributes administrators to help
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prevent students from dropping out.
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Materials and Methods
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This research was conducted according to Cross-Industry Standard Process for
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Data Mining (CRIPS-DM) (Chapman et al., 2000). The process of study was as
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followings :
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1. Business Understanding: By gathering student data from the educational
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Promotion department and studying related research, there were 14 significant
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attributes. The study was started by Attribute-Class relationship analyzing.
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Attributes that suitable to analyze the drop out rate shown in Table1
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2. Data Understanding: Researcher selected 4,163 datasets of the
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management science students stored in the department of academic promotion’s
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databases system during the year 2010-2014 then classified by year as shown in
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Table 2
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3. Data preparation: Steps in preparing data to use with the WEKA
program (Bouckaert et al., 2013) shown below.
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1) Data Cleansing: Initial data with missing value, error or noisy
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data were dropped at first step of cleaning up data. This has left 4,163 data sets
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out of the total 4,366.
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2) Data Adjusting: Raw data consisted of both numerical and
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alphabetic aspects so they were needed to adjust into a common from that can be
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analyzed.
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4. Modeling and selecting the right technique: Classification Data mining
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technique was used to form a model and used Decision Tree algorithm J48 to
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predict trends then used WEKA’s 10-fold Cross Validation to specify results test
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form.
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5. Evaluation:
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1) K-fold cross validation (Hastie et al., 2008)
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divide datasets into K equal parts
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use K-1 parts to form train set
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use the rest datasets to be a test set
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repeat the process until every dataset brought to test
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2) Accuracy (Mohammed and Wagner Meira, 2014)
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The accuracy of a data can be calculated into percentage by using
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formula as following
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𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = (𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁)
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𝑇𝑃 𝑅𝑎𝑡𝑒 = (𝑇𝑃+𝐹𝑁)
(𝑇𝑃+𝑇𝑁)
𝑇𝑃
(1)
(2)
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𝑇𝑁 𝑅𝑎𝑡𝑒 = (𝑇𝑁+𝐹𝑃)
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𝐹𝑃 𝑅𝑎𝑡𝑒 = (𝐹𝑃+𝑇𝑁)
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𝐹𝑁 𝑅𝑎𝑡𝑒 = (𝑇𝑃+𝐹𝑁)
𝑇𝑁
𝐹𝑃
𝐹𝑁
(3)
(4)
(5)
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Where as :
TP is a True Positive value
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TN is a True Negative value
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FP is a False Positive value
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FN is a False Negative value
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6. Development: Bringing a qualified model to modify and use as a
predicting tool for the year 2015.
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Results and Discussion
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The researcher brought datasets of each year to from a model using classification
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data mining technique with algorithm J48 Decision Tree to predict the data and
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The WEKA 3.7.5 10-fold Cross Validation to specify result testing form. This
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model was employed to seek factors affecting students drop-out and to predict
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changing trends of those factors in each year. figures 1 to 5 identified results and
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Classification Rule and Table3 expressed True values.
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According to figure 1 Rules of Decision from Decision Tree in year 2010 were:
IF GPA = Weak THEN student Drop Out
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IF GPA = Medium THEN student not Drop Out
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IF GPA = Good THEN student not Drop Out
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IF GPA = Best THEN student not Drop Out
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IF GPA = Excellent THEN student not Drop Out
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The important Decision Rule in year 2010 indicated the average GPA of less than
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2.00 as the affecting factor to students’ drop out.
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According to figure 2 Rules of Decision from Decision Tree in year 2011 were:
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IF GPA = Weak THEN student Drop Out
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IF GPA = Medium THEN student not Drop Out
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IF GPA = Good THEN student not Drop Out
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IF GPA = Best THEN student not Drop Out
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IF GPA = Excellent THEN student not Drop Out
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The important Decision Rule in year 2011 indicated the average GPA of less than
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2.00 as the affecting factor to students’ drop out.
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According to figure 3 Rules of Decision from Decision Tree in year 2012 were:
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IF GPA = Weak AND Loan = No THEN student Drop Out
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IF GPA = Weak AND Loan = Yes THEN student not Drop Out
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IF GPA = Medium THEN student not Drop Out
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IF GPA = Good THEN student not Drop Out
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IF GPA = Best THEN student not Drop Out
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IF GPA = Excellent THEN student not Drop Out
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The important Decision Rule in year 2012 indicated the average GPA of less than
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2.00 and not being allowed to receive student loans as factors affecting students’
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drop out.
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According to figure 4 Rules of Decision from Decision Tree in year 2013 were:
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IF GPA = Weak AND Loan = No THEN student Drop Out
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IF GPA = Weak AND Loan = Yes THEN student not Drop Out
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IF GPA = Medium THEN student not Drop Out
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IF GPA = Good THEN student not Drop Out
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IF GPA = Best THEN student not Drop Out
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IF GPA = Excellent THEN student not Drop Out
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The important Decision Rule in year 2013 indicated the average GPA of less than
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2.00 and not being allowed to receive student loans as factors affecting students’
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drop out.
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According to figure 5 Rules of Decision from Decision Tree in year 2014 were:
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IF GPA = Weak AND Loan = No THEN student Drop Out
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IF GPA = Weak AND Loan = Yes AND Old_Edu = Old_Edu1 AND
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revenue_far =Rev_far1 THEN student Drop Out
IF GPA = Weak AND Loan = Yes AND Old_Edu = Old_Edu1 AND
revenue_far =Rev_far2 THEN student not Drop Out
IF GPA = Weak AND Loan = Yes AND Old_Edu = Old_Edu1 AND
revenue_far =Rev_far3 THEN student not Drop Out
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IF GPA = Weak AND Loan = Yes AND Old_Edu = Old_Edu1 AND
revenue_far =Rev_far4 THEN student not Drop Out
IF GPA = Weak AND Loan = Yes AND Old_Edu = Old_Edu1 AND
revenue_far =Rev_far5 THEN student not Drop Out
IF GPA = Weak AND Loan = Yes AND Old_Edu = Old_Edu2 THEN
student not Drop Out
IF GPA = Weak AND Loan = Yes AND Old_Edu = Old_Edu3 THEN
student not Drop Out
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IF GPA = Medium THEN student not Drop Out
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IF GPA = Good THEN student not Drop Out
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IF GPA = Best THEN student not Drop Out
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IF GPA = Excellent THEN student not Drop Out
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The important Decision Rule in year 2014 indicated the average GPA of less than
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2.00, being allowed to receive student loans, high school graduation, and father’s
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monthly incomes of less than 12,500 bath as factors affecting students’ drop out.
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According to Table 3 the predicting model obtained an accuracy value of
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more than 90 percent (92.57% - 96.90%). Therefore, the year 2013 obtained the
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highest accuracy value of 96.90% Figure 1 to 5 can be concluded factors affecting
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students’ drop out by year shown in Table 4.
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The table 4 showed the average GPA of less than 2.00 displayed the
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mutual factor though out 5 years that went in accordance with Thinsungnoen et al.
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(2012) and the team' s study. Study loans was the factor added in the year 2012
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and 2013 and the 2 new factors i.e. students' background educational attainment
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and father's monthly incomes of less than 12,500 Bht. were found in the year 2015
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in line with the study of Pahannarat et al. (2009) and team which had found that
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students from poor families trended to leave classroom to make ends meet for
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families.
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Conclusions
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The results of this study can be concluded as follow
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1. There were 4 important factors affecting students’ drop-out including
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the average GPA, student loans, earlier educational attainment and father’s
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monthly incomes. Administrators and advisors can use these factors to plan and
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encourage students to be able to finish their studying.
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2. The changing trends of factors from year 2010 to 2011 was the average
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GPA, from year 2012 to 2013 were the average GPA and student loans and for the
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student loans and plused with earlier educational attainment, and father’s monthly
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incomes in 2014.
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This suggests a classification rule to be developed for the year 2015
prediction.
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Acknowledgments
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This research was funded by the Faculty of Management Science, Sakon Nakhon
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Rajabhat University in fiscal year 2015. The author acknowledges the Office of
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Academic Promotion and Registrarion, Sakon Nakhon Rajabhat University for
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providing the data used in this research. The author also acknowledges Assoc.
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Prof. Dr. Kittisak Kerdprasop and Assoc. Prof. Dr. Nittaya Kredprosob from the
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Suranaree University of Technology for their invaluable guidance and consulting
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about the research on data mining techniques. The author would like to express
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the appreciation and gratitude for all supports and assistances.
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References
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Bouckaert, R., Frank, E., Hall, M., Kirkby, R., Reutemann, P., Seewald, A.,
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Scuse, D. (2013). WEKA Manual for Version 3-7-8, University of
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Waikato, Hamilton, New Zealand, 327p.
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Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C.,
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Wirth, R. (2000). CRISP-DM 1.0 Step-by-step data mining guide,
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Technical report, SPSS inc, USA, 78p.
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Gulati, H. (2015). IEEE Conference Publications; March 11-13, 2015; New Delhi,
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Han, J., Kamber, M., Pei, J. (2011). Data mining concepts and techniques. 3rd ed.
Elsevier, USA, 703p.
Hastie ,T., Tibshirani, R., Friedman, J. (2008). The Elements of Statistical
Learning Data Mining, Inference, and Prediction. 2nd ed. Springer, 739p.
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Mohammed, J., Wagner Meira, JR. (2014). Data Mining and Analysis
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Fundamental Concepts and Algorithms. Cambridge University Press, NY,
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USA, 593p.
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Omkar, S., Parag, M. (2015). Predicting Dropout Students Using Data-Mining
Techniques. IJR J., 2(1):365-375.
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Pahannarat, N., Wangrangsimakul, K., Pipatpen M. (2009). The Problems of
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Withdrawal of the Youths Receiving Scholarship from World Vision
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Foundation of Thailand in Songkhla Province. PNU J., 1(3)(130-144).
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Thinsungnoen, T., Kulnawin, K., Thinsungnoen, M. (2012). Payap University
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Research Symposium 2012; February 17, 2012; Chiang Mai, Thailand,
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p.34-42.
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Yukselturk, E., Ozekes, S., Türel, Y. (2014). Predicting Dropout Student: An
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Application of Data Mining Methods in an Online Education Program.
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EURODL J., 17(1):118-133.
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Table 1. Student related Variables
Variable
Description
Possible Values
Sex
gender
{F,M}
Province
habitation
{Sakon, Nakon, Mukda,Out_sanok}
Occup_farther
Occupation of father
{Occ_far0, Occ_far1, Occ_far2, Occ_far3, cc_far4,
Occ_far5, Occ_far6, Occ_far7 Occ_far8, Occ_far9}
Revenue_far
Incomes of father
{Rev_far1, Rev_far2, Rev_far3, Rev_far4,
Rev_far5}
occup_mother
Occupation of mother
{Occ_mom0, Occ_mom1, Occ_mom2,
Occ_mom3, Occ_mom4, Occ_mom5, Occ_mom6,
Occ_mom7, Occ_mom8, Occ_mom9}
Revenue_mom
Incomes of mother
{Rev_mom1, Rev_mom2, Rev_mom3, Rev_mom4
, Rev_mom5}
Parent_status
Status of parents
{Par_st1, Par_st2, Par_st3, Par_st4, Par_st5,
Par_s61, Par_st7, Par_st8, Par_st9, Par_st10}
GPA_school
Average GPA from
{Weak, Medium, Good, Best, Excellent}
secondary school
Old_Edu
Educational attainment
{Old_Edu1, Old_Edu2, Old_Edu3, Old_Edu4}
from secondary school
curriculum
Learning program
{Curriculum2, Curriculum4}
Major
Subject oriented
{Major1, Major2, Major3, Major4, Major5,
Major6, Major7, Major8, Major9, Major10}
GPA
Average GPA
{Weak, Medium, Good, Best, Excellent}
Loan
Studying loans
{Yes, No}
Drop Out
Learning abandon
Class
{Yes, No}
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Table 2. datasets by year
Academic years
Data sets
2010
715
2011
902
2012
874
2013
775
2014
897
Total
4,163
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Table 3 accuracy comparison by year
Classifier J48
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Year
2010
2011
2012
2013
2014
Accuracy
92.59%
96.23%
94.39%
96.90%
95.42%
TP Rate
0.998
0.994
0.984
0.976
0.967
FP Rate
0.366
0.136
0.198
0.052
0.135
TN Rate
0.634
0.864
0.802
0.748
0.868
FN Rate
0.002
0.006
0.016
0.024
0.033
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Table 4 the affecting factors by year
Year
Affecting factors
2010
The average GPA of less than 2.00
2011
The average GPA of less than 2.00
2012
The average GPA of less than 2.00, not being allowed to receive student
loans
2013
The average GPA of less than 2.00, not being allowed to receive student
loans
2014
The average GPA of less than 2.00, being allowed to receive student loans,
high school graduation, and father’s monthly incomes of less than 12,500
Baht.
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GPA
‘=Good’
‘No(269.0/17.0)’
‘=Medium’
‘No(130.0/24.0)’
‘=Weak’
‘Yes(91.0/1.0)’
‘=Excellent’
‘=Best’
‘No(48.0/2.0)’
Figure 1 Rule model produced by J48 Decision Tree Year 2010
‘No(177.0/9.0)’
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GPA
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‘=Good’
‘No(263.0/9.0)’
‘=Medium’
‘No(101.0/15.0)’
‘=Weak’
‘Yes(195.0/4.0)’
‘=Excellent’
‘No(64.0/2.0)’
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‘=Best’
Figure 2 Rule model produced by J48 Decision Tree Year 2011
‘No(279.0/4.0)’
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GPA
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‘=Good’
‘No(229.0/10.0)’
‘=Medium’
‘No(74.0/13.0)’
‘=Weak’
‘=Excellent’
‘No(90.0/2.0)’
Loan
‘=Yes’
‘No(3.0/1.0)’
‘=No’
‘Yes(162.0/9.0)’
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‘=Best’
Figure 3 Rule model produced by J48 Decision Tree Year 2012
‘No(316.0/13.0)’
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GPA
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‘=Good’
‘No(234.0/4.0)’
‘=Medium’
‘No(83.0/3.0)’
‘=Weak’
‘=Excellent’
‘No(67.0)’
Loan
365
366
‘=Yes’
‘=No’
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‘No(3.0/1.0)’
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Figure 4 Rule model produced by J48 Decision Tree Year 2013
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‘=Best’
‘Yes(194.0/12.0)’
‘No(194.0/2.0)’
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GPA
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‘=Good’
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‘No(310.0/2.0)’
‘=Weak’
‘No(147.0/5.0)’
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‘=Old_edu2’ ‘=Old_edu1’
‘No(0.0)’
‘No(91.0/3.0)’
‘=No’
‘Yes(119.0/20.0)’
Old_Edu
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‘=Best’
‘=Excellent’
Loan
‘=Yes’
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‘=Medium’
‘=Old_edu3’
Revenue_far
‘No(2.0)’
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‘=Rev_far5’ ‘=Rev_far2’ ‘=Rev_far3’ ‘=Rev_far4’
‘No(7.0/2.0)’
‘No(0.0)’
‘No(0.0)’
‘=Rev_far1’
‘No(0.0)’
‘Yes(2.0)’
Figure 5 Rule model produced by J48 Decision Tree Year 2014
‘No(219.0/1.0)’