Calina, Christi Florence C

English

Valparaiso University

Valparaiso UniversityValpoScholarGraduate Academic Symposium Graduate SchoolSpring 5-4-2017Educational Data Mining to Identify Risk Factorsand Predictive Models of Student Retention atValparaiso UniversityChristi Florence C. CalinaValparaiso University, christiflorence.calina@valpo.eduFollow this and additional works at: http://scholar.valpo.edu/gasThis Oral Presentation is brought to you for free and open access by the Graduate School at ValpoScholar. It has been accepted for inclusion inGraduate Academic Symposium by an authorized administrator of ValpoScholar. For more information, please contact a ValpoScholar staff member atscholar@valpo.edu.Recommended CitationCalina, Christi Florence C., "Educational Data Mining to Identify Risk Factors and Predictive Models of Student Retention atValparaiso University" (2017). Graduate Academic Symposium. 34.http://scholar.valpo.edu/gas/34Educational Data Mining to Identify Risk Factors and Predictive Models of Student Retention at Valparaiso University Abstract: It has always been a challenge for higher education institution to retain students. Many factors can impact retention, with the most commonly considered being demographic and socio-economic issues. The 2014 report by U.S. Department of Higher Education provides students drop out rate based on demographic factors such as student’s age, race/ethnicity, nativity, financial status, marital status, and gender (“The Condition of Education 2015”). Determining the unique risk factors impacting dropout at a specific institution can help administrators, parents, and students understand what factors may significantly affect a student’s success.              Since factors that affect student retention vary from one institution to another, the above-mentioned factors might not be applicable to all institutions. At Valparaiso University administrators are working to improve retention across all years. This makes an in-depth study important for designing and implementing data-driven educational and programmatic interventions to increase the institution’s retention rate. This study aims to determine what factors contribute to students having a higher dropout risk specifically at Valparaiso University. To determine these risk factors, standard data mining techniques such as clustering and classification models were used. These methods were applied to regularly collected student census data from Valparaiso University.  Standard statistical methods were applied to report on the characteristics of students who did not complete their degrees. Descriptive statistics were computed for the population and validated against data reported to the Department of Education. Hypothesis testing was used to determine if there were statistically significance differences in dropout rates between different population segments. This statistical analysis was also used to identify key indicator attributes before applying data mining techniques.  Data mining methods were used to create predictive models for student dropout and identify trends within the data. Data mining classification such as decision trees, Naive Bayes, K-nearest neighbor (KNN), random forest and neural networks were compared for creating the predictive models. Cluster analysis methods such as K-means and hierarchical were used to identify if new groupings or collections of characteristics better describe students at risk of dropout. This talk will present some of the models that were generated, with a comparison of their performance metrics. We will also compare the characteristics identified as most important from each model. We will test our models with students for whom our original data-set did not include full information, but who have since completed another year (or dropped out).                Reference: “The Condition of Education 2015”. National Center for Education Statistics. U.S. Department of Education, 2015. Web.  Keywords: Educational Data Mining (EDM), retention, student drop out, data mining, classification algorithms, clustering algorithms  

Educational Data Mining to Identify Risk Factors and Predictive Models of Student Retention at Valparaiso University

ValpoScholar

Valparaiso University
ValpoScholar
Graduate Academic Symposium Graduate School
Spring 5-4-2017
Educational Data Mining to Identify Risk Factors
and Predictive Models of Student Retention at
Valparaiso University
Christi Florence C. Calina
Valparaiso University, christiflorence.calina@valpo.edu
Follow this and additional works at: http://scholar.valpo.edu/gas
This Oral Presentation is brought to you for free and open access by the Graduate School at ValpoScholar. It has been accepted for inclusion in
Graduate Academic Symposium by an authorized administrator of ValpoScholar. For more information, please contact a ValpoScholar staff member at
scholar@valpo.edu.
Recommended Citation
Calina, Christi Florence C., "Educational Data Mining to Identify Risk Factors and Predictive Models of Student Retention at
Valparaiso University" (2017). Graduate Academic Symposium. 34.
http://scholar.valpo.edu/gas/34
Educational Data Mining to Identify Risk Factors and Predictive Models of Student 
Retention at Valparaiso University 
Abstract: 
It has always been a challenge for higher education institution to retain students. Many 
factors can impact retention, with the most commonly considered being demographic and socio-
economic issues. The 2014 report by U.S. Department of Higher Education provides students 
drop out rate based on demographic factors such as student’s age, race/ethnicity, nativity, 
financial status, marital status, and gender (“The Condition of Education 2015”). Determining 
the unique risk factors impacting dropout at a specific institution can help administrators, 
parents, and students understand what factors may significantly affect a student’s success.  
            Since factors that affect student retention vary from one institution to another, the above-
mentioned factors might not be applicable to all institutions. At Valparaiso University 
administrators are working to improve retention across all years. This makes an in-depth study 
important for designing and implementing data-driven educational and programmatic 
interventions to increase the institution’s retention rate. This study aims to determine what 
factors contribute to students having a higher dropout risk specifically at Valparaiso University. 
To determine these risk factors, standard data mining techniques such as clustering and 
classification models were used. These methods were applied to regularly collected student 
census data from Valparaiso University.  
Standard statistical methods were applied to report on the characteristics of students who 
did not complete their degrees. Descriptive statistics were computed for the population and 
validated against data reported to the Department of Education. Hypothesis testing was used to 
determine if there were statistically significance differences in dropout rates between different 
population segments. This statistical analysis was also used to identify key indicator attributes 
before applying data mining techniques.  
Data mining methods were used to create predictive models for student dropout and 
identify trends within the data. Data mining classification such as decision trees, Naive Bayes, 
K-nearest neighbor (KNN), random forest and neural networks were compared for creating the 
predictive models. Cluster analysis methods such as K-means and hierarchical were used to 
identify if new groupings or collections of characteristics better describe students at risk of 
dropout. 
This talk will present some of the models that were generated, with a comparison of their 
performance metrics. We will also compare the characteristics identified as most important from 
each model. We will test our models with students for whom our original data-set did not include 
full information, but who have since completed another year (or dropped out). 
             
  
Reference: 
“The Condition of Education 2015”. National Center for Education Statistics. U.S. Department 
of Education, 2015. Web. 
 
Keywords: Educational Data Mining (EDM), retention, student drop out, data mining, 
classification algorithms, clustering algorithms 
 


https://scholar.valpo.edu/cgi/viewcontent.cgi?article=1062&amp;context=gas

Educational Data Mining to Identify Risk Factors and Predictive Models of Student Retention at Valparaiso University

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