Técnicas de machine learning aplicadas a la evaluación del rendimiento y a la predicción de la deserción de estudiantes universitarios, una revisión.
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ISSN: 2076-8133
E-ISSN: 2312-637X
Sección
Tecnología I+D
Tecnología I+D
Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0.
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Enviado:
Jun 8, 2021
Publicado: Feb 23, 2022
Publicado: Feb 23, 2022
Resumen
En los últimos años, técnicas de Inteligencia Artificial (IA) como el aprendizaje automático o Machine Learning (ML) y el Aprendizaje profundo o Deep Learning (DL), han impactado de forma positiva el avance de distintos campos del conocimiento entre ellos la educación. La educación es un importante motor de todas las sociedades, permite a los individuos ser más productivos y resolver problemas con mayor efectividad aplicando generalmente enfoques creativos. En la educación se ha utilizado las técnicas de ML antes mencionadas para distintas tareas entre ellas predicción de deserción y ayuda al rendimiento del estudiante. En este estudio analizaremos los trabajos más relevantes en estos campos, otorgando una perspectiva de cómo han influenciado los algoritmos de ML y DL en la educación.
Palabras clave
Inteligencia Artificial, Machine Learning, Deep Learning, deserción estudiantil., mejoramiento estudiantilDescargas
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Cómo citar
Cruz, E., González, M., & Rangel, J. (2022). Técnicas de machine learning aplicadas a la evaluación del rendimiento y a la predicción de la deserción de estudiantes universitarios, una revisión. Prisma Tecnológico, 13(1), 77-87. https://doi.org/10.33412/pri.v13.1.3039
Citas
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predic- ting at-risk students by analyzing clicking behavior in a virtual
learning environment. In Proceedings of the third international conference
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rates and dropout causes concerning the students during informatics of
the hellenic open university. Computers & Education, 39(4):361–377,
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undergraduate student dropout model using principal component analysis
through r package. In 2016 IEEE International Conference on
Computational Intelligence and Computing Research (ICCIC), pages 1–6.
IEEE, 2016.
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to predict student dropout: a case study. In 2018 IEEE 1st Colombian
Conference on Applications in Computational Intelligence (ColCACI),
pages 1–6. IEEE, 2018.
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reducing student’s dropout rates. 2019.
(33) L. Kemper, G. Vorhoff, and B. U. Wigger. Predicting student dropout: A
machine learning approach. European Journal of Higher Education,
(1):28–47, 2020.
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Using machine learning methods to identify significant variables for the
prediction of first-year informatics engineering students’ dropout. In 2020
th International Conference of the Chilean Computer Science Society
(SCCC), pages 1–5. IEEE, 2020.
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Prediction of university dropout using machine learning. In Álvaro Rocha,
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Information Technology and Systems, pages 396–406, Cham, 2021.
Springer International Publishing.
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Fardoun, and S. Ventura. Early dropout prediction using data mining: a
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mining algorithm. Information Sciences, 240:1–20, 2013.
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Automation Control Conference (IAEAC), volume 5, pages 780–784,
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moocs. In Proceedings of the 2nd International Conference on Crowd
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participants’ perspective. 2014.
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prediction using machine learning techniques based on socioeconomic
data. Electronics, 9(10):1613, 2020.
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education system. In 2016 International Conference on Automatic Control
and Dynamic Optimization Techniques (ICACDOT), pages 1110–1114,
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leveraging machine learning models. In 2019 International Conference in
Frontiers in Education: CS and CE, 2019.
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framework for improving early predictions in stem education. In 2019
International Conference on Computational Science and Computational
Intelligence (CSCI), pages 826–831, 2019.
(50) X. Xu, J. Wang, H. Peng, and R. Wu. Prediction of academic performance
associated with internet usage behaviors using machine learning
algorithms. Computers in Human Behavior, 98:166 – 173, 2019.
(51) R. R. Halde, A. Deshpande, and A. Mahajan. Psychology assisted
prediction of academic performance using machine learning. In 2016 IEEE
International Conference on Recent Trends in Electronics, Information
Communication Technology (RTEICT), pages 431–435, 2016.
(52) C. Robinson, M. Yeomans, J. Reich, C. Hulleman, and H. Gehlbach.
Forecasting student achievement in moocs with natural language
processing. In Proceedings of the Sixth International Conference on
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NY, USA, 2016. Association for Computing Machinery.
(53) C.E. Weinstein and D.R. Palmer. LASSI-HS User’s Manual. H & H Pub.,
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student feedback data using supervised learning algorithms. In 2016 3rd
MEC International Conference on Big Data and Smart City (ICBDSC),
pages 1–5, 2016.
(55) M. Sivakumar and U. S. Reddy. Aspect based sentiment analysis of
students opinion using machine learning techniques. In 2017 International
Conference on Inventive Computing and Informatics (ICICI), pages 726–
, 2017.
(56) P. X. V. Nguyen, T. T. T. Hong, K. V. Nguyen, and N. L. Nguyen. Deep
learning versus traditional classifiers on Vietnamese students’ feedback
corpus. In 2018 5th NAFOSTED Conference on Information and Computer
Science (NICS), pages 75–80, 2018.
(57) D. Deeksha Dsouza, D. P Nayak Deepika, E. Jenisha Machado, and ND
Adesh. Sentimental analysis of student feedback using machine learning
techniques. IJRTE, ISSN, pages 2277– 3878.
(58) Z. Nasim, Q. Rajput, and S. Haider. Sentiment analysis of student
feedback using machine learning and lexicon-based approaches. In 2017
International Conference on Research and Innovation in Information
Systems (ICRIIS), pages 1–6, 2017.
(59) K. V. Nguyen, V. D. Nguyen, P. X. V. Nguyen, T. T. H. Truong, and N. L.
Nguyen. Uit-vsfc: Vietnamese students’ feedback corpus for sentiment
analysis. In 2018 10th International Conference on Knowledge and
Systems Engineering (KSE), pages 19–24, 2018
(60) S. Katragadda, V. Ravi, P. Kumar, and G. J. Lakshmi. Performance
analysis on student feedback using machine learning algorithms. In 2020
th International Conference on Advanced Computing and Communication
Systems (ICACCS), pages 1161–1163, 2020.
(61) https://aws.amazon.com/es/education/ml-in-education/?nc1=h_ls
(62) https://www.twaweza.org/go/uwezo-datasets
(63) https://www.cs.waikato.ac.nz/ml/weka/
(2) D. M. West and J. R. Allen. How artificial intelligence is transforming the world, Apr 2020.
(3) F. Galbusera, G. Casaroli and T. Bassani Artificial intelligence and machine learning in spine research. JOR SPINE, 2(1), p.e1044. 2019
(4) B. M Kehm, M. Rode Larsen, and H. Bjørnøy Sommersel. Student dropout from universities in europe: A review of empirical literature. Hungarian Educational Research Journal, 9(2):147–164, 2019
(5) J. L Rastrollo-Guerrero, J. A Gómez-Pulido, and A. Durán - Domínguez. Analyzing and predicting students’ performance by means of machine learning: A review. Applied Sciences, 10(3):1042, 2020.
(6) B. Prenkaj, P. Velardi, G. Stilo, D. Distante, and S. Faralli. A survey of machine learning approaches for student dropout prediction in online courses. ACM Computing Surveys (CSUR), 53(3):1–34, 2020.
(7) F. Dalipi, A. Shariq Imran, and Z. Kastrati. Mooc dropout prediction using machine learning techniques: Review and research challenges. In 2018 IEEE Global Engineering Education Conference (EDUCON), pages 1007–IEEE, 2018.
(8) S Thulasi Bharathi. Analysis on massive open online course (mooc) dropout prediction using machine learning techniques-the state of the art.
(9) N. Mduma, K. Kalegele, and D. Machuve. A survey of machine learning approaches and techniques for student dropout prediction. 2019.
(10) M. Alban and D. Mauricio. Predicting university dropout through data mining: A systematic literature. Indian Journal of Science and Technology, (4):1–12, 2019.
(11) S. Ulfa, R. Bringula, C. Kurniawan, and M. Fadhli. Student feedback on online learning by using sentiment analysis: A literature review. In 2020 th International Conference on Education and Technology (ICET), pages –58, 2020.
(12) M. Edalati. The potential of machine learning and nlp for handling students ’feedback (a short survey), 2020.
(13) C. St˘aiculescu and R. Nastase E. Ramona. University dropout. Causes and solution. Mental Health: Global Challenges Journal, 1(1):71–75, 2018.
(14) M. Fei and D. Y. Yeung. Temporal models for predicting student dropout in massive open online courses. In 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pages 256–263. IEEE, 2015.
(15) M. Alban and D. Mauricio. Neural networks to predict dropout at the universities. International Journal of Machine Learning and Computing, (2):149–153, 2019.
(16) A. F Cabrera, A. Nora, and M. B Castaneda. The role of finances in the persistence process: A structural model. Research in higher education,
(5):571–593, 1992.
(17) M. Xenos, C. Pierrakeas, and P. Pintelas. A survey on student dropout
rates and dropout causes concerning the students during informatics of
the hellenic open university. Computers & Education, 39(4):361–377,
(18) A. Wolff, Z. Zdrahal, A. Nikolov, and M. Pantucek. Improving retention:
predic- ting at-risk students by analyzing clicking behavior in a virtual
learning environment. In Proceedings of the third international conference
on learning analytics and knowledge, pages 145–149, 2013
(19) I. El Naqa and M. J Murphy. What is machine learning? In Machine
Learning in Radiation Oncology, pages 3–11. Springer, 2015.
(20) W. James Murdoch, C. Singh, K. Kumbier, R. Abbasi-Asl, and B. Yu.
Definitions, methods, and applications in interpretable machine learning.
Proceedings of the National Academy of Sciences, 116(44):22071–22080,
Oct 2019.
(21) L. Aulck, N. Velagapudi, J. Blumenstock, and J. West. Predicting student
dropout in higher education. arXiv preprint arXiv:1606.06364, 2016.
(22) S. Sivakumar, S. Venkataraman, and R. Selvaraj. Predictive modeling of
student dropout indicators in educational data mining using improved
decision tree. Indian Journal of Science and Technology, 9(4):1–5, 2016.
(23) J. R. Quinlan. Discovering rules by induction from large collections of
examples. Expert systems in the microelectronics age, 1979.
(24) J. Ross Quinlan. Induction of decision trees. Machine learning, 1(1):81–
, 1986.
(25) A. Renyi et al. On measures of entropy and information. In Proceedings
of the Fourth Berkeley Symposium on Mathematical Statistics and
Probability, Volume 1: Contributions to the Theory of Statistics. The
Regents of the University of California, 1961.
(26) Y. Chen and M. Zhang. Mooc student dropout: Pattern and prevention. In
Proceedings of the ACM Turing 50th Celebration Conference-China,
pages 1–6, 2017.
(27) J. Whitehill, K. Mohan, D. Seaton, Y. Rosen, and D. Tingley. Delving
deeper into mooc student dropout prediction. arXiv preprint
arXiv:1702.06404, 2017.
(28) V. Hegde and PP Prageeth. Higher education student dropout prediction
and analysis through educational data mining. In 2018 2nd International
Conference on Inventive Systems and Control (ICISC), pages 694–699.
IEEE, 2018.
(29) M. Xenos, C. Pierrakeas, and P. Pintelas. A survey on student dropout
rates and dropout causes concerning the students during informatics of
the hellenic open university. Computers & Education, 39(4):361–377,
(30) V. Hegde. Dimensionality reduction technique for developing
undergraduate student dropout model using principal component analysis
through r package. In 2016 IEEE International Conference on
Computational Intelligence and Computing Research (ICCIC), pages 1–6.
IEEE, 2016.
(31) B. Perez, C. Castellanos, and D. Correal. Applying data mining techniques
to predict student dropout: a case study. In 2018 IEEE 1st Colombian
Conference on Applications in Computational Intelligence (ColCACI),
pages 1–6. IEEE, 2018.
(32) N. Mduma, K. Kalegele, and D. Machuve. Machine learning approach for
reducing student’s dropout rates. 2019.
(33) L. Kemper, G. Vorhoff, and B. U. Wigger. Predicting student dropout: A
machine learning approach. European Journal of Higher Education,
(1):28–47, 2020.
(34) F. A Bello, J. K´ohler, K. Hinrechsen, V. Araya, L. Hidalgo, and J. L. Jara.
Using machine learning methods to identify significant variables for the
prediction of first-year informatics engineering students’ dropout. In 2020
th International Conference of the Chilean Computer Science Society
(SCCC), pages 1–5. IEEE, 2020.
(35) A. F. Núñez - Naranjo, M. Ayala-Chauvin, and G. Riba-Sanmart´ı.
Prediction of university dropout using machine learning. In Álvaro Rocha,
Carlos Ferras, Paulo Carlos López- López, and Teresa Guarda, editors,
Information Technology and Systems, pages 396–406, Cham, 2021.
Springer International Publishing.
(36) C. Márquez-Vera, A. Cano, C. Romero, A. Y. M. Noaman, H. Mousa
Fardoun, and S. Ventura. Early dropout prediction using data mining: a
case study with high school students. Expert Systems, 33(1):107–124,
(37) A. Cano, A. Zafra, and S. Ventura. An interpretable classification rule
mining algorithm. Information Sciences, 240:1–20, 2013.
(38) C. Cortes and V. Vapnik. Support-vector networks. Machine Learning,
(3):273–297, September 1995.
(39) J. Platt. Sequential minimal optimization: A fast algorithm for training
support vector machines. 1998.
(40) W. W Cohen. Fast effective rule induction. In Machine learning
proceedings 1995, pages 115–123. Elsevier, 1995.
(41) L. Cai and G. Zhang. Prediction of moocs dropout based on wclsrt model.
In 2021 IEEE 5th Advanced Information Technology, Electronic and
Automation Control Conference (IAEAC), volume 5, pages 780–784,
(42) W. Wang, H. Yu, and C. Miao. Deep model for dropout prediction in
moocs. In Proceedings of the 2nd International Conference on Crowd
Science and Engineering, pages 26–32, 2017.
(43) KDD Cup. Kdd cup 2015: Predicting dropouts in mooc, 2015.
(44) Y. Zheng, Z. Gao, Y. Wang, and Q. Fu. Mooc dropout prediction using
fwts-cnn model based on fused feature weighting and time series. IEEE
Access, 8:225324–225335, 2020.
(45) T. R Liyanagunawardena, P. Parslow, and S. Williams. Dropout: Mooc
participants’ perspective. 2014.
(46) F. A da S Freitas, F. FX Vasconcelos, S. A Peixoto, M. Mehedi Hassan, M
Dewan, V. Hugo C de Albuquerque, et al. Iot system for school dropout
prediction using machine learning techniques based on socioeconomic
data. Electronics, 9(10):1613, 2020.
(47) R. R. Halde. Application of machine learning algorithms for betterment in
education system. In 2016 International Conference on Automatic Control
and Dynamic Optimization Techniques (ICACDOT), pages 1110–1114,
(48) M. Aly and M. Rashedul Hasan. Improving stem performance by
leveraging machine learning models. In 2019 International Conference in
Frontiers in Education: CS and CE, 2019.
(49) M. Hasan and M. Aly. Get more from less: A hybrid machine learning
framework for improving early predictions in stem education. In 2019
International Conference on Computational Science and Computational
Intelligence (CSCI), pages 826–831, 2019.
(50) X. Xu, J. Wang, H. Peng, and R. Wu. Prediction of academic performance
associated with internet usage behaviors using machine learning
algorithms. Computers in Human Behavior, 98:166 – 173, 2019.
(51) R. R. Halde, A. Deshpande, and A. Mahajan. Psychology assisted
prediction of academic performance using machine learning. In 2016 IEEE
International Conference on Recent Trends in Electronics, Information
Communication Technology (RTEICT), pages 431–435, 2016.
(52) C. Robinson, M. Yeomans, J. Reich, C. Hulleman, and H. Gehlbach.
Forecasting student achievement in moocs with natural language
processing. In Proceedings of the Sixth International Conference on
Learning Analytics and Knowledge, LAK 16, page 383–387, New York,
NY, USA, 2016. Association for Computing Machinery.
(53) C.E. Weinstein and D.R. Palmer. LASSI-HS User’s Manual. H & H Pub.,
(54) V. Dhanalakshmi, D. Bino, and A. M. Saravanan. Opinion mining from
student feedback data using supervised learning algorithms. In 2016 3rd
MEC International Conference on Big Data and Smart City (ICBDSC),
pages 1–5, 2016.
(55) M. Sivakumar and U. S. Reddy. Aspect based sentiment analysis of
students opinion using machine learning techniques. In 2017 International
Conference on Inventive Computing and Informatics (ICICI), pages 726–
, 2017.
(56) P. X. V. Nguyen, T. T. T. Hong, K. V. Nguyen, and N. L. Nguyen. Deep
learning versus traditional classifiers on Vietnamese students’ feedback
corpus. In 2018 5th NAFOSTED Conference on Information and Computer
Science (NICS), pages 75–80, 2018.
(57) D. Deeksha Dsouza, D. P Nayak Deepika, E. Jenisha Machado, and ND
Adesh. Sentimental analysis of student feedback using machine learning
techniques. IJRTE, ISSN, pages 2277– 3878.
(58) Z. Nasim, Q. Rajput, and S. Haider. Sentiment analysis of student
feedback using machine learning and lexicon-based approaches. In 2017
International Conference on Research and Innovation in Information
Systems (ICRIIS), pages 1–6, 2017.
(59) K. V. Nguyen, V. D. Nguyen, P. X. V. Nguyen, T. T. H. Truong, and N. L.
Nguyen. Uit-vsfc: Vietnamese students’ feedback corpus for sentiment
analysis. In 2018 10th International Conference on Knowledge and
Systems Engineering (KSE), pages 19–24, 2018
(60) S. Katragadda, V. Ravi, P. Kumar, and G. J. Lakshmi. Performance
analysis on student feedback using machine learning algorithms. In 2020
th International Conference on Advanced Computing and Communication
Systems (ICACCS), pages 1161–1163, 2020.
(61) https://aws.amazon.com/es/education/ml-in-education/?nc1=h_ls
(62) https://www.twaweza.org/go/uwezo-datasets
(63) https://www.cs.waikato.ac.nz/ml/weka/