Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review
Abstract
:1. Introduction
2. Methodology
- Students’ level: Each reference analyzes datasets built from students of a particular level. We consider a classification of wide levels, corresponding to School (S), High School (HS) and University (U).
- Objectives: The objectives are connected to the interests and risks in the students’ learning processes.
- Techniques: The techniques consider the different algorithms, methods and tools that process the data to analyze and predict the above objectives.
- Algorithms and methods: The main algorithms and computational methods applied in each case are detailed in the Table 1. Other algorithms with related names or versions not shown in this table could be also applied. The shadowed cells corresponds with the best algorithms found when several methods were compared for the same purpose.
3. Techniques
3.1. Machine Learning
3.1.1. Supervised Learning
3.1.2. Unsupervised Learning
3.2. Recommender Systems
Collaborative Filtering
3.3. Artificial Neural Networks
3.4. Impact of the Techniques
4. Objectives
4.1. Student Dropout
4.2. Student Performance
4.3. Recommender Activities and Resources
4.4. Students’ Knowledge
5. Discussion
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
AB | AdaBoost |
ANN | Artificial Neural Networks |
AT | Answer Tree |
BART | Bayesian Additive Regressive Trees |
BBN | Bayesian Belief Network |
BKT | Bayesian Knowledge Tracing |
BMF | Biased-Matrix Factorization |
BN | Bayesian Networks |
BSLO | Bipolar Slope One |
CBN | Combination of Multiple Classifiers |
CF | Collaborative Filtering |
DL | Deep Learning |
DM | Data Mining |
DT | Decision Tree |
ELM | Extreme Learning Machine |
EM | Expectation-Maximization |
GBT | Gradient Boosted Tree |
JMLM | Jacobian Matrix-Based Learning Machine |
KNN | K-Nearest Neighbor |
LDA | Latent Dirichlet Allocation |
LR | Logistic Regression |
LRMF | Low Range Matrix Factorization |
LM | Linear Models |
MDP | Markov Decision Process |
MF | Matrix Factorization |
ML | Machine Learning |
MLP | Multilayer Perception |
MLR | Multiple Linear Regression |
NB | Naïves Bayes |
pGPA | Grade Prediction Advisor |
RBN | Radial Basis Networks |
RF | Random Forests |
RS | Recommender Systems |
SA | Survival Analysis |
SFS | Sequential Forward Selection |
SL | Supervised Learning |
SLO | Slope One |
SMOTE | Synthetic Minority Over-Sampling |
SVD | Singular Value Decomposition |
SVM | Support Vector Machine |
UL | Unsupervised Learning |
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Objectives | Techniques | Algorithms and Methods (2)(3) | ||||||||||||||||||||||||||||||||||||||||||||||||||
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Reference | Students’ Level (1) | Students’ Dropout | Students’ Performance | Recommend Activities and Resources | Students’ Knowledge | Supervised Learning | Unsupervised Learning | Recommender Systems (C. Filtering) | Artificial Neural Networks | Data Mining Techniques | AB | ANN | AT | BART | BBN | BKT | BMF | BN | BSLO | C4.5 | CBN | CF | DL | DM | DT | ELM | EM | GBT | JMLM | KNN | LDA | LR | LRMF | LM | MDP | MF | MLP | MLR | NB | One-R | pGPA | RBN | RF | RS | SA | SFS | SL | SLO | SMOTE | SVD | SVM | UL |
[2] | U | × | × | × | × | × | × | |||||||||||||||||||||||||||||||||||||||||||||
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[10] | U | × | × | × | × | × | × | × | × | |||||||||||||||||||||||||||||||||||||||||||
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[14] | S | × | × | × | ||||||||||||||||||||||||||||||||||||||||||||||||
[15] | U | × | × | × | × | × | × | × | ||||||||||||||||||||||||||||||||||||||||||||
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[20] | HS | × | × | × | × | |||||||||||||||||||||||||||||||||||||||||||||||
[21] | U | × | × | × | × | × | ||||||||||||||||||||||||||||||||||||||||||||||
[22] | U | × | × | × | ||||||||||||||||||||||||||||||||||||||||||||||||
[23] | HS | × | × | × | ||||||||||||||||||||||||||||||||||||||||||||||||
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[25] | S | × | × | × | × | × | × | × | ||||||||||||||||||||||||||||||||||||||||||||
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[33] | HS | × | × | × | × | × | × | |||||||||||||||||||||||||||||||||||||||||||||
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[64] | U | × | × | × | ||||||||||||||||||||||||||||||||||||||||||||||||
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Rastrollo-Guerrero, J.L.; Gómez-Pulido, J.A.; Durán-Domínguez, A. Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review. Appl. Sci. 2020, 10, 1042. https://doi.org/10.3390/app10031042
Rastrollo-Guerrero JL, Gómez-Pulido JA, Durán-Domínguez A. Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review. Applied Sciences. 2020; 10(3):1042. https://doi.org/10.3390/app10031042
Chicago/Turabian StyleRastrollo-Guerrero, Juan L., Juan A. Gómez-Pulido, and Arturo Durán-Domínguez. 2020. "Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review" Applied Sciences 10, no. 3: 1042. https://doi.org/10.3390/app10031042
APA StyleRastrollo-Guerrero, J. L., Gómez-Pulido, J. A., & Durán-Domínguez, A. (2020). Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review. Applied Sciences, 10(3), 1042. https://doi.org/10.3390/app10031042