Sustainable e-Learning by Data Mining—Successful Results in a Chilean University
Abstract
:1. Introduction
Problem Statement, Goal, and Contributions
- First, this article identifies potential variables for success or failure in e-learning programs, not only academic factors, through a systematic literature review.
- Second, this article defines a repeatable data mining application for identifying students’ success patterns in e-learning environments using a large set of data. This analysis was not feasible with other methods.
- Third, this article provides a utilization example of multi-year historical data starting when e-learning programs began being a phenomenon in Chile and other countries (the year 2000). Other institutions in the region could repeat this application.
2. e-Learning and Data Mining
2.1. CRISP-DM
- Business Understanding Phase: The first phase analysis of the problem includes understanding the project’s objectives and requirements from a business or institutional perspective.
- Data Comprehension Phase: The second phase of data analysis includes the initial data collection, identifying the quality of the data.
- Data Preparation Phase: This phase includes general data selection tasks for applying modeling techniques (variables and samples), data cleaning, generation of additional variables, integration of different data sources, and format changes.
- Modeling Phase: In this phase, selecting the most appropriate modeling techniques takes place to generate and evaluate the model. The parameters used in the model generation depend on the characteristics of the data.
- Evaluation Phase: In the evaluation phase, the model is evaluated, not from the data point of view, but for fulfilling the problem’s success criteria. If the generated model is valid based on the success established in the first phase, the model is exploited.
- Implementation Phase: At this stage, in addition to the implementation of the model, the results must be presented and documented understandably, to achieve an increase in knowledge.
2.2. Data Mining Techniques
- Naive Bayes: It is based on Bayes theorem with an assumption of independence among predictors. It assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. Naïve Bayes mainly targets the text classification industry. It is mainly used for clustering and classification purposes depending on the conditional probability of happening [36].
- Random Forest: This classifier combines prediction trees in which a hierarchical division of the underlying data space is sustained. In the hierarchical division of the data space, comment partitions are created that are more skewed in terms of their distribution of terms [37].
- AdaBoost: The name is an acronym for Adaptive Boosting and it is a meta-algorithm. This algorithm supports a distribution or set of weights over the training set. Initially, all weights equally set, but on each round, the weights of incorrectly classified samples are increased then the weak learner focuses on these samples. AdaBoost originally ability to minimize the error, and maximize the margin with respect to features [38].
- Decision Trees (J48): The J48 algorithm builds a decision tree that classifies the class attribute based on the input attributes. The algorithm is based on the C4.5 algorithm developed by Quinlan [39]. The algorithm uses a greedy search method to create decision trees and allows changing different parameters to obtain a better classification accuracy [40].
- Neural Networks: The development of the neural network uses a non-linear optimization model. Unlike other analyses, it is not easy to interpret clearly, unlike the results and parameters provided by other analyses. For the construction of the classifier used in this research, the multilayer perceptron neural network was used, which builds a neural network in the form of a waterfall, which has one or more hidden layers [41].
2.3. Data Mining in Education
3. Methodology
3.1. Institution Background
3.2. Selection and Understanding of Data
3.3. Data Preparation
- Data selection and cleaning: first, we selected the data attributes, considering the objective and data quality problems. As a result, we selected the following tables and attributes: (i) STUDENT (RUT of the student, sex, date of birth, profession, nationality, marital status, education); (ii) STUDENT_ADDRESS (RUT of the student, commune, city); (iii) PROGRAM (name of the program, date of registration, type of program); (iv) STUDENT_PROGRAM (RUT of the student, academic situation, final grade of the program). For the analysis, we followed the next filter: Students who enrolled in the programs between 2000 and 2018 (enrollment date 2000) and (enrollment date 2018)).
- Data Quality: a problem presented by the data is the amount of missing data in some of them, such as the program’s attribute name. We decided to keep the records with unknown values because their elimination results in excluding rows with valid values in the objective set. Moreover, we faced data categorization because the applied techniques for the analysis (classification) mainly use categorical data to facilitate their construction and interpretation.
- Construction of the Data: to fulfill the project’s objective, an attribute called region was created, which derives from the attribute commune and city. The region attribute takes the value corresponding to the region that the commune and city belong to. When the data selection and construction process was complete, the changes were saved to the files for their use later in the modeling stage. The new files are in SPSS format.
3.4. Modeling
4. Results
5. Discussion
6. Conclusions, Recommendation, and Future Work
- The use of educational data mining, particularly the CRISP-DM methodology, greatly contributes to systematization and efficiency in identifying patterns in the data of distance education. The study allowed us to systematize the data in various sources and formats of the distance education platform in the institution under study (DEC-UCN) and provide valuable information for future analyses in this context.
- Data mining tools can present more significant advantages than purely statistical tools since they are exploratory, allowing working with different dimensions of the same problem. It is also essential to highlight the possibility and flexibility of these analysis tools to allow us to work with categorical and numerical variables in the same analysis.
- The performance analysis of the different classification algorithms indicated that the random forest and decision tree algorithms were the ones that allowed a better prediction of results and, therefore, identified the variables that could better explain the performance of students in e-learning programs. The decision tree proved to be a beneficial tool to find relationships between variables unidentified by previously used analysis tools, mainly because a decision tree uses techniques less restrictive than statistics. Those techniques do not require, for example, conditions of data normality and are tolerant of noise in the data.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Metric | Definition |
---|---|
Precision | It is used to measure the positive patterns that are correctly predicted from the total predicted patterns in a positive class [62]. |
Recall | It permits to measure the fraction of positive patterns that are correctly classified [62]. |
Accuracy | It measures the ratio of correct predictions over the total number of instances evaluated [63]. |
F1 | Metric that represents the harmonic mean between recall and precision values [41]. |
Matthew’s correlation coefficient (MCC) | Measure that is not affected by the dataset problem of being unbalanced. MCC is a correlation coefficient between observed and predicted binary rankings; returns a value between and . A coefficient of represents a perfect prediction, 0 is no better than a random prediction, and indicates complete disagreement between prediction and observation [64]. |
ROC curve | It is a graphic representation of the relationship between the true-positive and false-positive ratios of the classifier. The area under the ROC curve provides an approach to evaluate which model is better on average. A model will be considered to discriminate better than chance if the curve lies above the diagonal of no discrimination, i.e., if the AUC is higher than [65]. |
Program | Frequency | Percentage |
---|---|---|
Human Resources Management | 1996 | 10.7 |
Environmental Management | 1617 | 8,7 |
Family Mediation | 1506 | 8.1 |
Psychopedagogy | 1499 | 8.0 |
Total Quality Management Total | 1169 | 6.3 |
Integrated Management: Quality. Environment. and Safety | 1009 | 5.4 |
Educational Orientation | 894 | 4.8 |
Higher Education | 648 | 3.5 |
Education and Professional Technical High School Teacher | 545 | 2.9 |
Primary Education Teacher with a Minor in NB1 and NB2 | 509 | 2.7 |
Family Counseling | 507 | 2.7 |
Behavioral Management Techniques Applied to Children and Adolescents | 499 | 2.7 |
Education and Primary Education Teacher | 467 | 2.5 |
Criminal Procedural Law: “Accusatory System or Oral Trial” | 446 | 2.4 |
Communication and Language Disorder | 418 | 2.2 |
Educational Administration | 402 | 2.2 |
Administration of Technical-Pedagogical Units | 388 | 2.1 |
Preparation and Evaluation of Investment Projects | 326 | 1.7 |
Minor in Language and Communication for Teachers of the Second Cycle of Language and Communication | 292 | 1.6 |
Minor in Education in Mathematics for Teachers of the Second Cycle of Basic General Education | 247 | 1.3 |
Degree in Education and Primary Education Teacher | 237 | 1.3 |
Management in Corporate Communication | 179 | 1.0 |
Continuous Improvement | 173 | 0.9 |
Higher level in Executive Secretariat | 154 | 0.8 |
Mathematics Education for Primary Education Teachers | 145 | 0.8 |
Pedagogical Management for Higher Level Technical Training | 129 | 0.7 |
Formulation and evaluation of projects | 98 | 0.5 |
Others | 2180 | 11.6 |
Sample | Predicted | |||||||
---|---|---|---|---|---|---|---|---|
Act | Rem | Abn | Trans | Grad | Cert | Correct % | ||
Training | Active (Act) | 0 | 2 | 0 | 0 | 0 | 34 | 0.0% |
Removed (Rem) | 0 | 6102 | 0 | 0 | 0 | 1140 | 84.3% | |
Abnegated (Abn) | 0 | 426 | 0 | 0 | 0 | 20 | 0.0% | |
Transferred (Trans) | 0 | 17 | 0 | 0 | 0 | 3 | 0.0% | |
Graduated (Grad) | 0 | 302 | 0 | 0 | 0 | 201 | 0.0% | |
Certified (Cert) | 0 | 2906 | 0 | 0 | 0 | 1720 | 37.2% | |
Overall % | 0.0% | 75.8% | 0.0% | 0.0% | 0.0% | 24.2% | 60.8% | |
Testing | Active (Act) | 0 | 2 | 0 | 0 | 0 | 16 | 0.0% |
Removed (Rem) | 0 | 2565 | 0 | 0 | 0 | 479 | 84.3% | |
Abnegated (Abn) | 0 | 181 | 0 | 0 | 0 | 8 | 0.0% | |
Transferred (Trans) | 0 | 13 | 0 | 0 | 0 | 0 | 0.0% | |
Graduated (Grad) | 0 | 103 | 0 | 0 | 0 | 97 | 0.0% | |
Certified (Cert) | 0 | 1300 | 0 | 0 | 0 | 753 | 36.7% | |
Overall % | 0.0% | 75.0% | 0.0% | 0.0% | 0.0% | 24.5% | 60.2% |
TP Rate | FP Rate | Precision | Recall | F-Measure | MCC | ROC Area | PRC Area | |
---|---|---|---|---|---|---|---|---|
AdaBoostM1 | 0.622 | 0.532 | 0.593 | 0.622 | 0.574 | 0.118 | 0.543 | 0.559 |
Naïve Bayes | 0.617 | 0.616 | 0.548 | 0.617 | 0.475 | 0.007 | 0.535 | 0.554 |
Random Forest | 0.645 | 0.435 | 0.634 | 0.645 | 0.636 | 0.222 | 0.652 | 0.658 |
TREE J.48 | 0.643 | 0.494 | 0.625 | 0.643 | 0.607 | 0.184 | 0.604 | 0.609 |
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Sánchez, A.; Vidal-Silva, C.; Mancilla, G.; Tupac-Yupanqui, M.; Rubio, J.M. Sustainable e-Learning by Data Mining—Successful Results in a Chilean University. Sustainability 2023, 15, 895. https://doi.org/10.3390/su15020895
Sánchez A, Vidal-Silva C, Mancilla G, Tupac-Yupanqui M, Rubio JM. Sustainable e-Learning by Data Mining—Successful Results in a Chilean University. Sustainability. 2023; 15(2):895. https://doi.org/10.3390/su15020895
Chicago/Turabian StyleSánchez, Aurora, Cristian Vidal-Silva, Gabriela Mancilla, Miguel Tupac-Yupanqui, and José M. Rubio. 2023. "Sustainable e-Learning by Data Mining—Successful Results in a Chilean University" Sustainability 15, no. 2: 895. https://doi.org/10.3390/su15020895