# A Learning Analytics Approach to Identify Students at Risk of Dropout: A Case Study with a Technical Distance Education Course

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## Abstract

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## 1. Introduction

**RQ1.**- Does the GA approach to hyperparameter optimization outperform traditional techniques?
**RQ2.**- Does the resulting predictive models generated by the use of the GA approach for hyperparameter optimization perform better than models with default hyperparameters?

## 2. Theoretical Background

## 3. Proposed Approach

#### 3.1. Case Study

#### 3.2. Fine Tuning with Proposed Genetic Algorithm

- (a)
- Epoch: One complete cycle execution of the GA (from Steps 1 to 5). The proposed approach works with 50 epochs;
- (b)
- Individual (or candidate): A machine learning algorithm/classifier (DT, RF, MLP, LG, and ADA) together with its hyperparameters;
- (c)
- Chromosome: A vector of hyperparameters for a given individual (machine learning algorithm). As different machine learning algorithms have different hyperparameters, the chromosomes in our study have different sizes and meaning according to the machine learning algorithm to which they are referring.

- Step 1 (generate population): The GA generates 100 individuals (candidates) for each machine learning algorithm (DT, RF, MLP, LG, and ADA) with hyperparameters (chromosomes) randomly defined considering the available list of options. The classifiers are trained and tested using 10-fold cross-validation and their performances are measured by using the area under the receiver operating characteristic curve metric (AUROC) [49] and as conducted by Gašević et al. [50].
- Step 2 (fitness function): The performance obtained by each of the 100 individuals of each machine learning algorithm are then compared by the fitness function.
- Step 3 (selection): The 25 individuals with the highest AUC for each machine learning algorithm are selected for the next step.
- Step 4 (crossover): The crossover is conducte using the concept based on the genetic inheritance of sexual reproductions, where each descendant receives a part of the genetic code (chromosome) of the father and part of the mother, as exemplified in Figure 5. Thus, the configurations of the fittest individuals of the last step are combined, one being the father and the other the mother. In the implemented algorithm, the individuals who will assign part of their genetic code to form a new member are chosen randomly from among the 25 best placed of that classifier in the last generation. This step results in 25 new individuals for each machine learning algorithm.
- Step 5 (mutation): This step randomly alters the chromosome (hyperparameter) of the 25 best individuals. In other words, a certain characteristic of an individual selected in the previous step receives a randomly generated configuration. As shown in Figure 5, an individual of the MLP type with hyperparameter “Active” set to “RELU” was changed to “TAHN”. The mutation is set to change only one hyperparameter of the chromosome.

- 25 individuals selected from the previous generation from the fitness function (Steps 2 and 3);
- 25 individuals formed by crossover (Step 4);
- 25 individuals formed by mutations (Step 5); and
- 25 new individuals randomly generated (Step 1).

#### 3.3. Experiments

- AUC ≤ 0.50: Bad discrimination;
- 0.50 < AUC ≤ 0.70: Acceptable discrimination;
- 0.70 < AUC ≤ 0.90: Excellent discrimination; and
- AUC > 0.90: Outstanding discrimination.

## 4. Results and Discussion

## 5. Final Remarks

**RQ1.**- Does the approach for hyperparameter optimization with a GA outperform traditional techniques?

**RQ2.**- Does the resulting predictive models generated by the use of the GA approach for hyperparameter optimization perform better than models with default hyperparameters?

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

ADA | ADABoost |

ANOVA | Analysis of Variance |

AUC | Area Under the Curve |

AUROC | Area Under the Receiver Operating Characteristic Curve |

DT | Decision Tree |

EDA | Exploratory Data Analysis |

EDM | Educational Data Mining |

GA | Genetic Algorithm |

GBGP | Grammar-Based Genetic Programming |

GRID | Grid SearchCV |

IBK | Instance-Based Lazy Learning |

ICRM | Interpretable Classification Rule Mining Algorithm |

IFSul | Instituto Federal Sul Rio-grandense |

INN | I-nearest neighbor |

kNN | k-Nearest Neighbor |

LA | Learning Analytics |

LG | Logistic Regression |

LMS | Learning Management Systems |

ML | Machine Learning |

MAE | Mean Absolute Error |

MLP | Multilayer Perceptron |

NDS | Number of Dropout Students |

PCA | Principal Component Analysis |

RQ | Research Question |

RF | Random Forest |

SAT | Scholastic Aptitude Test |

SMOTE | Synthetic Minority Over-Sampling Technique |

SVM | Support Vector Machine |

TNR | True Negative Rate |

TPR | True Positive Rate |

VLE | Virtual Learning Environment |

## Appendix A

Alg. | Hyperparameters | Possibi-Lities | Numberof Ind. | Grid | Eval. |

DT | criterion: [gini, entropy], max_depth: range (0, 32), min_samples_split: range (1, 15), min_samples_leaf: range (1, 20) | 19.200 | 5.100 | criterion: [gini, entropy], max_depth: [0, 1, 2, 3, 5, 7, 10, 12, 15, 17, 20, 23, 25, 30], min_samples_split: [0, 1, 2, 3, 5, 7, 10, 12, 15] min_samples_leaf: [0, 1, 2, 3, 4, 5, 7, 9, 10, 12, 15, 17, 20] | 3.726 |

RF | n_estimators: range (1, 200), criterion: [gini, entropy], max_features [1, 2, 3, 4], min_samples_split: range (2, 21), min_samples_leaf: range (1, 2), bootstrap: [True, False] | 128.000 | 5.100 | n_estimators: [1, 10, 20, 30, 40, 50, 70, 100, 120, 130, 150, 170, 190, 200], criterion: [gini, entropy], max_features [1, 2, 3, 4], min_samples_split: [2, 3, 4, 5, 7, 9, 10, 12, 15, 17, 20], min_samples_leaf: [1, 2], bootstrap: [True, False] | 4.928 |

ADA | algorithm: [SAMME, SAMME.R], n_estimators: range (1, 200), random_state: range (None, 50), learning_rate: range (1e-2, 1) | 2 KK | 5.100 | algorithm: [SAMME, SAMME.R], n_estimators: [1, 10, 20, 30, 40, 50, 70, 100, 120, 130, 150, 170, 190, 200], random_state: [None, 1, 5, 10, 15, 20, 25, 30, 40, 50], learning_rate: [1e-2, 5e-2, 7e-2, 1e-1, 3e-1, 5e-1, 7e-1, 1] | 2.240 |

MLP | hidden_layer_sizes: range (1, 200), activation: [identity, logistic, tanh, relu], solver: [lbfgs, sgd, adam], max_iter: range (50, 200), alpha: range (1e-4, 1e-1], warm_start: [True, False] | 720 KK | 5.100 | hidden_layer_sizes: [(50, 50, 50), (50, 100, 50), (100), (50), (10), (1), (5)], activation: [identity, logistic, tanh, relu], solver: [lbfgs, sgd, adam], max_iter: [1, 2, 5, 10, 30, 50], alpha: [1e-4, 1e-3, 1e-2, 5e-2, 1e-1], warm_start: [True, False] | 5.040 |

RL | penalty: [l1, l2, elasticnet], C: [1e-4, 1e-3, 1e-2, 1e-1, 5e-1, 1, 5, 10, 15, 20, 25], dual: [True, False], solver: [newton-cg, lbfgs, lbfgs, sag, saga], multi_class: [ovr, auto], max_iter: range (50, 200) | 99.000 | 5.100 | penalty: [l1, l2, elasticnet], C: [1e-4, 1e-1, 5e-1, 1, 5, 15, 25], dual: [True, False], solver: [newton-cg, lbfgs, lbfgs, sag, saga], multi_class: [ovr, auto], max_iter: [1, 10, 20, 30, 40, 50, 70, 100, 120, 130, 150, 170, 190, 200] | 5.800 |

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**Figure 5.**Genetic algorithm flow: An example of crossover and mutation with a multilayer perceptrons (MLP) chromosome.

Number of Log Rows | Number of Students | Dropouts (%) | Success (%) |
---|---|---|---|

1,051,012 | 752 | 354 (47%) | 398 (53%) |

Column | Comment |
---|---|

Course | Name of the virtual classroom accessed |

Time | Day and time of the access |

IP Address | IP Address of the machine |

Full name | User (student) name |

Action Event Name | The action represents the type of interaction that the student performed in the classroom. For instance: (1) Visualization and participation on chats; (2) Visualization and inclusion of posts in forums; (3) Visualization of resources; and (4) Visualization of the course. |

Description | Detailed description of the event. Example: Download the .pdf file. |

Variable | Description |
---|---|

Daily interactions | Count of interactions of a given day (from 1 to 350 days) |

Weekly interactions | Count of interactions of a given week (from 1 to 50 weeks) |

Mean of the week | Average of the count of interactions of a given week |

Standard deviation of the week | Standard deviation of the count of interactions of a given week |

Student final status | Dependent variable representing the student final status: Dropout or success |

Year | Period | Number of Students in Course | Number of Dropout Students (NDS) | NDS Rate | Accumulated NDS | Accmulated NDS Rate |
---|---|---|---|---|---|---|

Year 1 | Week 10 | 752 | 87 | 11.56 | 87 | 11.56 |

Week 20 | 665 | 71 | 10.67 | 158 | 21.01 | |

Week 30 | 594 | 21 | 3.5 | 179 | 23.27 | |

Week 40 | 573 | 1 | 0.17 | 180 | 23.4 | |

Week 50 | 572 | 2 | 0.34 | 182 | 24.20 | |

Total of First 50 Weeks | 752 | 182 | 24.20 | 182 | 24.20 | |

Year 2 | Total after 50 Weeks | 572 | 172 | 22.87 | 354 | 47.07 |

Final Values | Total | 752 | 354 | 47.07 | 354 | 47.07 |

Approach or Machine Learning Algorithm | Hyperparameter Optimization | AUC Mean | AUC Median | AUC Standard Deviation |
---|---|---|---|---|

GA | Yes | 0.8454 | 0.8498 | 0.0637 |

GRID | 0.7939 | 0.8288 | 0.1056 | |

ADA | No | 0.7509 | 0.8062 | 0.1342 |

DT | 0.6771 | 0.7065 | 0.1008 | |

LG | 0.6943 | 0.7198 | 0.1110 | |

MLP | 0.7353 | 0.7946 | 0.1277 | |

RF | 0.7752 | 0.8243 | 0.1150 |

Hyperparameter Optimization | Hidden Layer Sizes | Activation | Solver | Alpha | Max Iter | Warm Start | AUC |
---|---|---|---|---|---|---|---|

yes | 30 | logistic | sgd | 0.2855486101 | 353 | False | 0.9154 |

no | 100 | relu | adam | 0.0001 | 200 | False | 0.849 |

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## Share and Cite

**MDPI and ACS Style**

Queiroga, E.M.; Lopes, J.L.; Kappel, K.; Aguiar, M.; Araújo, R.M.; Munoz, R.; Villarroel, R.; Cechinel, C.
A Learning Analytics Approach to Identify Students at Risk of Dropout: A Case Study with a Technical Distance Education Course. *Appl. Sci.* **2020**, *10*, 3998.
https://doi.org/10.3390/app10113998

**AMA Style**

Queiroga EM, Lopes JL, Kappel K, Aguiar M, Araújo RM, Munoz R, Villarroel R, Cechinel C.
A Learning Analytics Approach to Identify Students at Risk of Dropout: A Case Study with a Technical Distance Education Course. *Applied Sciences*. 2020; 10(11):3998.
https://doi.org/10.3390/app10113998

**Chicago/Turabian Style**

Queiroga, Emanuel Marques, João Ladislau Lopes, Kristofer Kappel, Marilton Aguiar, Ricardo Matsumura Araújo, Roberto Munoz, Rodolfo Villarroel, and Cristian Cechinel.
2020. "A Learning Analytics Approach to Identify Students at Risk of Dropout: A Case Study with a Technical Distance Education Course" *Applied Sciences* 10, no. 11: 3998.
https://doi.org/10.3390/app10113998