Knowledge Discovery for Higher Education Student Retention Based on Data Mining: Machine Learning Algorithms and Case Study in Chile
Abstract
:1. Symbology, Introduction, and Bibliographical Review
1.1. Abbreviations, Acronyms, Notations, and Symbols
1.2. Introduction
1.3. Related Works
1.4. Models and Description of Sections
2. Methodology
2.1. Contextualization
 (i)
 Data selection,
 (ii)
 Preprocessing,
 (iii)
 Transformation,
 (iv)
 Data mining/ML algorithms, and
 (v)
 Interpretation/evaluation [43].
2.2. Data Selection
2.3. Preprocessing and Transformation
2.4. Data Mining/ML Algorithms
2.5. Data Mining/ML Algorithms’ Performance
2.6. Interpretation and Evaluation
3. Case Study
3.1. ML Algorithms and Computer Configurations
Algorithm 1: Methodology proposed to predict student retention/dropout in HE institutions similar to the Chilean case. 

3.2. Data Selection
3.3. Preprocessing, Transformation of Data, and Initial Results
3.4. Performance Evaluation of Predictive Models
3.5. Interpretation and Evaluation
4. Conclusions, Results, Limitations, Knowledge Discovery, and Future Work
 (a)
 Implement a new information system that enables different databases to coexist for the quick acquisition of necessary information. Data warehouse compilation requires extensive time to extract the relevant data from university records.
 (b)
 Establish a datamonitoring plan to track the enrollment of all students for further analysis and decisionmaking.
 (c)
 Create a model for predicting students at risk of dropout at different levels of study.
 (d)
 Employ a welcome plan for atrisk students who are identified by the predictive model, in order to assist in improving academic results.
 (e)
 Offer a support program at all grade levels for identifying atrisk students.
 (f)
 In order to increase the innovation of future works, a voting scheme of the machine learning algorithms used can be proposed or the explainability of an examined classifier may be promoted. Voting is an ensemble learning algorithm that, for example in regression, performs a prediction from the mean of several other regressions. In particular, majority voting is used when every model carries out a prediction (votes) for each test instance and the final output prediction obtains more than half of the votes. If none of the predictions reach this majority of votes, the ensemble algorithm is not able to perform a stable prediction for such an instance.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Friedman Test Results and PostHoc Analysis
Friedman Test (Significance Level of 0.05)  

Statistic  pvalue  Result  
360.428080  0.00000  H0 is rejected  
Friedman Value  Algorithm  Ranking  
3.44875  SVM  1  
3.45125  RF  2  
3.48317  LR  3  
3.49475  DT  4  
3.51025  NB  5  
3.61317  KNN  6  
PostHoc Analysis (Significance Level of 0.05)  
Comparison  Statistic  pvalue  Result 
KNN vs. SVM  4.16872  0.00046  H0 is rejected 
KNN vs. RF  4.10534  0.00057  H0 is rejected 
KNN vs. LR  3.29610  0.01274  H0 is rejected 
KNN vs. DT  3.00241  0.03214  H0 is rejected 
KNN vs. NB  2.60941  0.09977  H0 is accepted 
NB vs. SVM  1.55931  1.00000  H0 is accepted 
NB vs. RF  1.49592  1.00000  H0 is accepted 
DT vs. SVM  1.16631  1.00000  H0 is accepted 
RF vs. DT  1.10293  1.00000  H0 is accepted 
LR vs. SVM  0.87262  1.00000  H0 is accepted 
RF vs. LR  0.80924  1.00000  H0 is accepted 
NB vs. LR  0.68669  1.00000  H0 is accepted 
NB vs. DT  0.39300  1.00000  H0 is accepted 
LR vs. DT  0.29369  1.00000  H0 is accepted 
RF vs. SVM  0.06339  1.00000  H0 is accepted 
Friedman Test (Significance Level of 0.05)  

Statistic  pvalue  Result  
361.260066  0.00000  H0 is rejected  
Friedman Value  Algorithm  Ranking  
3.42083  RF  1  
3.44033  DT  2  
3.47883  SVR  3  
3.48183  LR  4  
3.56733  NB  5  
3.61133  KNN  6  
PostHoc Analysis (Significance Level of 0.05)  
Comparison  Statistic  pvalue  Result 
KNN vs. RF  4.83006  0.00002  H0 is rejected 
KNN vs. DT  4.33564  0.00020  H0 is rejected 
NB vs. RF  3.71445  0.00265  H0 is rejected 
KNN vs. SVR  3.35949  0.00937  H0 is rejected 
KNN vs. LR  3.28342  0.01128  H0 is rejected 
NB vs. DT  3.22004  0.01282  H0 is rejected 
NB vs. SVR  2.24388  0.22356  H0 is accepted 
NB vs. LR  2.16782  0.24138  H0 is accepted 
RF vs. LR  1.54663  0.85366  H0 is accepted 
RF vs. SVR  1.47057  0.85366  H0 is accepted 
KNN vs. NB  1.11560  1.00000  H0 is accepted 
LR vs. DT  1.05222  1.00000  H0 is accepted 
DT vs. SVR  0.97615  1.00000  H0 is accepted 
RF vs. DT  0.49442  1.00000  H0 is accepted 
LR vs. SVR  0.07606  1.00000  H0 is accepted 
Friedman Test (Significance Level of 0.05)  

Statistic  pvalue  Result  
362.345869  0.00000  H0 is rejected  
Friedman Value  Algorithm  Ranking  
3.41825  RF  1  
3.45425  SVM  2  
3.45675  DT  3  
3.48625  LR  4  
3.49425  KNN  5  
3.69075  NB  6  
PostHoc Analysis (Significance Level of 0.05)  
Comparison  Statistic  pvalue  Result 
NB vs. RF  6.90914  0.00000  H0 is rejected 
SVM vs. NB  5.99637  0.00000  H0 is rejected 
NB vs. DT  5.93298  0.00000  H0 is rejected 
NB vs. LR  5.18502  0.00000  H0 is rejected 
KNN vs. NB  4.98218  0.00001  H0 is rejected 
KNN vs. RF  1.92695  0.53986  H0 is accepted 
RF vs. LR  1.72411  0.76218  H0 is accepted 
KNN vs. SVM  1.01419  1.00000  H0 is accepted 
RF vs. DT  0.97615  1.00000  H0 is accepted 
KNN vs. DT  0.95080  1.00000  H0 is accepted 
SVM vs. RF  0.91277  1.00000  H0 is accepted 
SVM vs. LR  0.81135  1.00000  H0 is accepted 
LR vs. DT  0.74796  1.00000  H0 is accepted 
KNN vs. LR  0.20284  1.00000  H0 is accepted 
SVM vs. DT  0.06339  1.00000  H0 is accepted 
Friedman Test (Significance Level of 0.05)  

Statistic  pvalue  Result  
360.476685  0.00000  H0 is rejected  
Friedman Value  Algorithm  Ranking  
3.35866  DT  1  
3.38406  RF  2  
3.43132  KNN  3  
3.45825  SVM  4  
3.60561  LR  5  
3.76270  NB  6  
PostHoc Analysis (Significance Level of 0.05)  
Comparison  Statistic  Adjusted pvalue  Result 
KNN vs. NB  8.33467  0.00000  H0 is rejected 
NB vs. RF  9.52320  0.00000  H0 is rejected 
NB vs. DT  10.16220  0.00000  H0 is rejected 
SVM vs. NB  7,65733  0.00000  H0 is rejected 
LR vs. DT  6.21106  0.00000  H0 is rejected 
RF vs. LR  5.57207  0.00000  H0 is rejected 
KNN vs. LR  4.38353  0.00011  H0 is rejected 
NB vs. LR  3.95114  0.00062  H0 is rejected 
SVM vs. LR  3.70619  0.00147  H0 is rejected 
SVM vs. DT  2.50487  0.07350  H0 is accepted 
SVM vs. RF  1.86588  0.31029  H0 is accepted 
KNN vs. DT  1.82754  0.31029  H0 is accepted 
KNN vs. RF  1.18854  0.70387  H0 is accepted 
KNN vs. SVM  0.67734  0.99638  H0 is accepted 
RF vs. DT  0.63900  0.99638  H0 is accepted 
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Abbreviations/Acronyms  Notations/Symbols  

ANN  artificial neural networks  ∼  distributed as 
CLU  clustering  k  number of nearest neighbors 
CP  community poverty index  n  sample size 
DT  decision trees  $l={\beta}_{0}+{\beta}_{1}x$  logodd 
EDM  educational data mining  $o={b}^{{\beta}_{0}+{\beta}_{1}x}$  odd 
EM  ensemble models  ${\beta}_{0},{\beta}_{1}$  regression coefficients 
FN  false negative  X  independent variable or feature 
FP  false positive  Y  dependent variable or response 
HE  higher education  $p=P(Y=1)$  probability function of LR 
IG  information gain  $\phantom{\rule{7.11317pt}{0ex}}=\frac{exp({\beta}_{0}+{\beta}_{1}x)}{exp({\beta}_{0}+{\beta}_{1}x)+1}$  
KNN  knearest neighbors  $\phantom{\rule{7.11317pt}{0ex}}=\frac{1}{1+exp({\beta}_{0}{\beta}_{1}x)}$  
LR  logistic regression  $P(Y=c\mid \mathit{X}=\mathit{x})$  probability Y given $\mathit{X}$ 
ML  machine learning  $\frac{P(Y=c)P(\mathit{X}=\mathit{x}\mid Y=c)}{P(\mathit{X}=\mathit{x})}$  Bayes conditional probability 
NB  naive Bayes  $\mathit{X}=({X}_{1},\dots ,{X}_{p})$  vector of independent variables 
NEM  secondary educational score  $({x}_{1},{Y}_{1}),\cdots ,({x}_{n},{Y}_{n})$  instances 
(notas enseñanza media)  c  number of classes  
PSU  university selection test  $\parallel x\parallel $  norm of a point x 
(prueba selección universitaria)  s  number of folds in crossvalidation  
RAM  random access memory  $\mathit{w}$  normal vector to the hyperplane 
RF  random forest  TP/(TP + FP)  precision 
SVM  support vector machines  $\kappa =({p}_{a}{p}_{e})/(1{p}_{e})$  $\kappa $statistic 
TF  true negative  ${p}_{a}$  % of agreement classifier/ground truth 
TP  true positive  ${p}_{e}$  agreement chance 
UCM  Catholic University of Maule  $Q=\frac{12n}{c(c+1)}{\displaystyle \sum _{j=1}^{c}}{\left(\right)}^{{\overline{r}}_{\xb7j}}2$  Friedman statistic 
(Universidad Católica del Maule)  ${\left\{{x}_{ij}\right\}}_{n\times c}$  $n\times c$ data matrix  
SMOTE  synthetic minority  ${\left\{{r}_{ij}\right\}}_{n\times c}$  $n\times c$ rank matrix 
oversampling technique  ${\overline{r}}_{\xb7j}=\frac{1}{n}{\displaystyle \sum _{i=1}^{n}}{r}_{ij}$  rank average of column j  
KDD  knowledge discovery  $P\left(\right)open="("\; close=")">{\chi}_{c}^{2}\ge Q$  pvalue 
in databases  ${\chi}_{c}^{2}$  chisquared distribution  
with c degrees of freedom 
Reference  Instances  Technique(s)  Confusion Matrix  Accuracy  Institution  Country 

[7,8]  16,066  ANN, DT, SVM, LR  Yes  87.23%  Oklahoma State  
University  USA  
[23]  713  DT, NB, LR, EM, RF  Yes  80%  Eindhoven University  
of Technology  Netherlands  
[24]  N/A  ANN, SVM, EM  No  N/A  National Technical  
University of Athens  Greece  
[25]  8025  DT, NB  Yes  79%  Kent State  
University  USA  
[26]  452  ANN, DT, KNN  Yes  N/A  University  
of Chile  Chile  
[27]  6078  NN, NB  Yes  N/A  Roma Tre  
University  Italy  
[28]  17,910  RF, DT  Yes  N/A  University  
of Duisburg  Germany  
[29]  N/A  LR, DT, ANN, EM  No  N/A  N/A  
N/A  USA  
[30]  1500  CLU, SVM, RF  No  N/A  University  
of Bologna  Italy  
[31]  6470  DT  No  87%  Mugla Sitki  
Kocman University  Turkey  
[32]  811  EM, NB, KNN, ANN  No  N/A  Mae Fah  
Luang University  Thailand  
[33]  3877  LR, SVM, DT  No  N/A  Purdue  
University  USA  
[34]  456  ANN, DT  No  N/A  University of  
Computer Science  Cuba  
[35]  1359  NB, SVM  Yes  87%  Federal University  
of Rio de Janeiro  Brazil  
[36]  N/A  N/A  No  61%  Unitec Institute  New 
of Technology  Zealand  
[37]  22,099  LR, DT, ANN  No  N/A  several  
universities  USA  
[38]  1055  C45, RF, CART, SVM  No  86.6%  University  
of Oviedo  Spain  
[39]  6500  DT, KNN  No  98.98%  Technical University  
of Izúcar  Mexico  
[40]  N/A  DT  Yes  N/A  N/A  
N/A  India  
[41]  6690  ANN, LR, DT  No  76.95%  Arizona State  
University  USA 
Attributes  Features 

Demographic background  Name, age, gender. 
Geographic origin  Place of origin, province. 
Socioeconomic index  CP index. 
School performance  High school grades, secondary educational score (NEM), PSU score. 
University performance  Number of approved courses, failed courses, approved credits, failed credits. 
Financial indicators  Economic quintile, family income. 
Others  Readmissions, program, application preference, selected/waiting list, health insurance. 
Attributes  

Age Application preference Approved credits 1th semester Approved credits 2nd semester Approved credits 3rd semester Approved credits 4th semester Approved courses 1th semester Approved courses 2nd semester Approved courses 3rd semester Approved courses 4th semester CP index Dependent group Educational area Entered credits 1th semester  Entered credits 2nd semester Entered credits 3rd semester Entered credits 4th semester Family income Gender Graduate/nongraduate Health insurance Marks 1th semester Marks 2nd semester Marks 3rd semester Marks 4th semester NEM Program Province  PSU averaged score in language/maths PSU score of language PSU score of maths PSU score of specific topic PSU weighted score Quintile Readmissions Registered courses 1th semester Registered courses 2nd semester Registered courses 3rd semester Registered courses 4th semester School Selected/waiting list 
Global  First Level  Second Level  Third Level  

Rank  IG  Variable  IG  Variable  IG  Variable  IG  Variable 
1  0.430  NEM  0.511  NEM  0.357  NEM  0.098  Marks 3rd semester 
2  0.385  CP index  0.468  CP index  0.220  CP index  0.087  Marks 4th semester 
3  0.209  Program  0.286  School  0.211  School  0.084  Approved courses 3rd semester 
4  0.204  School  0.190  Program  0.211  Approved courses 2nd semester  0.083  Approved courses 2nd semester 
5  0.105  PSU specific topic  0.112  PSU specific topic  0.195  Approved credits 2nd semester  0.074  School 
6  0.068  Quintile  0.110  PSU language  0.183  Approved credits 1st semester  0.069  Marks 1st semester 
7  0.059  Gender  0.098  Quintile  0.176  Approved courses 1st semester  0.067  Approved courses 4th semester 
8  0.051  Family income  0.056  Age  0.163  Marks 1st semester  0.066  Approved courses 1st semester 
9  0.041  Age  0.053  Educational area  0.149  Program  0.063  Marks 2nd semester 
10  0.037  Educational area  0.047  PSU weighted score  0.141  Marks 2nd semester  0.059  Approved credits 1st semester 
11  0.034  PSU language  0.043  Graduate/nongraduate  0.130  Entered credits 2nd semester  0.059  Entered credits 2nd semester 
12  0.030  Province  0.037  Family income  0.103  Entered credits 1st semester  0.056  Approved credits 2nd semester 
13  0.027  Application preference  0.034  Province  0.079  Registered courses 2nd semester  0.051  Approved credits 4th semester 
14  0.026  Health insurance  0.033  Gender  0.058  Gender  0.049  Entered credits 3rd semester 
15  0.025  Readmissions  0.030  PSU math  0.038  Registered courses 1st semester  0.049  Program 
16  0.025  PSU weighted score  0.029  Readmissions  0.032  Province  0.048  Entered credits 4th semester 
17  0.019  PSU math  0.028  Health insurance  0.030  Family income  0.044  Approved credits 3rd semester 
18  0.015  Graduate/nongraduate  0.025  PSU language/math  0.029  Quintile  0.042  Registered courses 1st semester 
19  0.014  PSU language/math  0.022  Application preference  0.025  Age  0.030  Registered courses 3rd semester 
20  0.001  Dependent group  0.001  Dependent group  0.024  Educational area  0.030  Registered courses 4th semester 
ML Algorithm  Accuracy  Precision  TP Rate  FP Rate  FMeasure  RMSE  $\mathit{\kappa}$Statistic 

DT  82.75%  0.840  0.973  0.806  0.902  0.365  0.227 
KNN  81.36%  0.822  0.984  0.929  0.896  0.390  0.082 
LR  82.42%  0.849  0.954  0.739  0.898  0.373  0.271 
NB  79.63%  0.860  0.894  0.631  0.877  0.387  0.283 
RF  81.82%  0.829  0.979  0.879  0.897  0.370  0.143 
SVM  81.67%  0.828  0.977  0.881  0.897  0.428  0.138 
Algorithm  Accuracy  Precision  TP Rate  FP Rate  FMeasure  RMSE  $\mathit{\kappa}$Statistic  Friedman Value (Ranking) 

DT  82.19%  0.814  0.837  0.194  0.825  0.368  0.644  3.49475 (4) 
KNN  83.93%  0.859  0.814  0.135  0.836  0.363  0.679  3.61317 (6) 
LR  83.45%  0.825  0.851  0.182  0.838  0.351  0.669  3.48317 (3) 
NB  79.14%  0.791  0.796  0.213  0.793  0.399  0.583  3.51025 (5) 
RF  88.43%  0.860  0.920  0.151  0.889  0.301  0.769  3.45125 (1) 
SVM  83.97%  0.822  0.869  0.190  0.845  0.400  0.679  3.44875 (1) 
Algorithm  Accuracy  Precision  TP Rate  FP Rate  FMeasure  RMSE  $\mathit{\kappa}$Statistic  Friedman Value (Ranking) 

DT  89.21%  0.888  0.933  0.166  0.910  0.294  0.775  3.44033 (1) 
KNN  89.43%  0.929  0.887  0.096  0.908  0.298  0.784  3.61133 (6) 
LR  87.70%  0.885  0.908  0.166  0.896  0.309  0.745  3.48183 (4) 
NB  83.95%  0.869  0.854  0.181  0.862  0.349  0.671  3.56733 (5) 
RF  93.65%  0.921  0.976  0.119  0.947  0.238  0.868  3.42083 (1) 
SVM  88.30%  0.889  0.914  0.160  0.901  0.342  0.758  3.47883 (3) 
ML Algorithm  Accuracy  Precision  TP Rate  FP Rate  FMeasure  RMSE  $\mathit{\kappa}$Statistic  Friedman Value (Ranking) 

DT  91.06%  0.938  0.954  0.288  0.946  0.278  0.687  3.45675 (3) 
KNN  94.41%  0.965  0.967  0.161  0.966  0.222  0.809  3.49425 (5) 
LR  93.57%  0.958  0.964  0.193  0.961  0.232  0.779  3.48625 (4) 
NB  86.69%  0.954  0.880  0.194  0.916  0.347  0.603  3.69075 (6) 
RF  95.76%  0.959  0.99  0.196  0.975  0.193  0.847  3.41825 (1) 
SVM  94.40%  0.958  0.975  0.196  0.966  0.237  0.804  3.45425 (2) 
ML Algorithm  Accuracy  Precision  TP Rate  FP Rate  FMeasure  RMSE  $\mathit{\kappa}$Statistic  Friedman Value (Ranking) 

DT  94.99%  0.955  0.993  0.739  0.974  0.208  0.360  3.35866 (1) 
KNN  96.90%  0.977  0.990  0.371  0.984  0.168  0.689  3.43132 (3) 
LR  90.58%  0.973  0.926  0.414  0.949  0.305  0.376  3.60561 (5) 
NB  88.09%  0.987  0.885  0.181  0.933  0.331  0.396  3.76270 (6) 
RF  96.92%  0.969  0.999  0.503  0.984  0.160  0.641  3.38406 (1) 
SVM  96.17%  0.978  0.982  0.356  0.980  0.196  0.644  3.45825 (4) 
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Palacios, C.A.; ReyesSuárez, J.A.; Bearzotti, L.A.; Leiva, V.; Marchant, C. Knowledge Discovery for Higher Education Student Retention Based on Data Mining: Machine Learning Algorithms and Case Study in Chile. Entropy 2021, 23, 485. https://doi.org/10.3390/e23040485
Palacios CA, ReyesSuárez JA, Bearzotti LA, Leiva V, Marchant C. Knowledge Discovery for Higher Education Student Retention Based on Data Mining: Machine Learning Algorithms and Case Study in Chile. Entropy. 2021; 23(4):485. https://doi.org/10.3390/e23040485
Chicago/Turabian StylePalacios, Carlos A., José A. ReyesSuárez, Lorena A. Bearzotti, Víctor Leiva, and Carolina Marchant. 2021. "Knowledge Discovery for Higher Education Student Retention Based on Data Mining: Machine Learning Algorithms and Case Study in Chile" Entropy 23, no. 4: 485. https://doi.org/10.3390/e23040485