DOI:

https://doi.org/10.14483/23448393.19514

Published:

2022-11-20

Issue:

Vol. 28 No. 1 (2023): January-April

Section:

Computational Intelligence

Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods

Predicción del rendimiento académico universitario mediante mecanismos de aprendizaje automático y métodos supervisados

Authors

  • Leonardo Emiro Contreras Bravo Universidad Distrital Francisco José de Caldas
  • Nayibe Nieves-Pimiento Universidad ECCI
  • Karolina Gonzalez-Guerrero Universidad Militar Nueva Granada (Bogotá, Colombia)

Keywords:

educational data analysis, Machine Learning, higher education (en).

Keywords:

análisis de datos educativos, Machine Learning, educación superior (es).

Downloads

Abstract (en)

Context:  In the education sector, variables have been identified which considerably affect students’ academic performance. In the last decade, research has been carried out from various fields such as psychology, statistics, and data analytics in order to predict academic performance.

Method: Data analytics, especially through Machine Learning tools, allows predicting academic performance using supervised learning algorithms based on academic, demographic, and sociodemographic variables. In this work, the most influential variables in the course of students’ academic life are selected through wrapping, embedded, filter, and assembler methods, as well as the most important characteristics semester by semester using Machine Learning algorithms (Decision Trees, KNN, SVC, Naive Bayes, LDA), which were implemented using the Python language.

Results: The results of the study show that the KNN is the model that best predicts academic performance for each of the semesters, followed by Decision Trees, with precision values that oscillate around 80 and 78,5% in some semesters.

Conclusions: Regarding the variables, it cannot be said that a student’s per-semester academic average necessarily influences the prediction of academic performance for the next semester. The analysis of these results indicates that the prediction of academic performance using Machine Learning tools is a promising approach that can help improve students’ academic life allow institutions and teachers to take actions that contribute to the teaching-learning process.

Abstract (es)

considerablemente en el rendimiento académico de los estudiantes. En la última década se han llevado a cabo investigaciones desde diversos campos como la psicología, la estadística y el análisis de datos con el fin de predecir el rendimiento académico.

Método: La analítica de datos, especialmente a través de herramientas de Machine Learning, permite predecir el rendimiento académico utilizando algoritmos de aprendizaje supervisado basados ​​en variables académicas, demográficas y sociodemográficas. En este trabajo se seleccionan las variables más influyentes en el transcurso de la vida académica de los estudiantes mediante métodos de filtro, embebidos, y de ensamble, así como las características más importantes semestre a semestre utilizando algoritmos de Machine Learning (árbol de decisión, KNN, SVC, Naive Bayes, LDA), implementados en el lenguaje Python.

Resultados: Los resultados del estudio muestran que el KNN es el modelo que mejor predice el rendimiento académico para cada uno de los semestres, seguido de los árboles de decisión, con valores de precisión que oscilan alrededor del 80 y 78,5 % en algunos semestres.

Conclusiones: Con respecto a las variables, no se puede decir que el promedio académico semestral de un estudiante influya necesariamente en la predicción del rendimiento académico del siguiente semestre. El análisis de estos resultados indica que la predicción del rendimiento académico utilizando herramientas de Machine Learning es un enfoque promisorio que puede ayudar a mejorar la vida académica de los estudiantes y permitir a las instituciones y a los docentes adoptar acciones que ayuden al proceso de enseñanza-aprendizaje.

Author Biographies

Nayibe Nieves-Pimiento, Universidad ECCI

Nayive Nieves Pimiento, Ingeniera mecánica,  Máster en Ciencias Ambientales, Facultad de ingeniería, Ingeniería Ambiental, Grupo de investigación Gestión ambiental y desarrollo sostenible, Universidad ECCI, Bogotá, Colombia. Correo electrónico:  nnievesp@ecci.edu.co

Karolina Gonzalez-Guerrero, Universidad Militar Nueva Granada (Bogotá, Colombia)

Karolina Gonzalez Guerrero, Licenciada en Educación, Magister en educación, Doctora en Educación,  Grupo de investigación PYDE, Gestión ambiental y desarrollo sostenible. Docente de la Universidad Militar Nueva Granada. Correo electrónico: karolina.gonzalez@unimilitar.edu.co

References

M. Ferreyra, J. Botero, P. Haimovich, and S. Urzúa, “Momento decisivo La educación superior en América Latina y el Caribe,” Washington, 2017. [Online]. Available: https://openknowledge.worldbank.org/bitstream/handle/10986/26489/211014ovSP.pdf

E. J. de La Hoz, E. J. de La Hoz, and T. J. Fontalvo, “Methodology of Machine Learning for the classification and prediction of users in virtual education environments,” Inf. Tecnol., vol. 30, no. 1, pp. 247-254, Feb. 2019. https://doi.org/10.4067/S0718-07642019000100247

Ministerio de Educación, “Sistema nacional de información de la educación superior,” 2019. [Online]. Available: https://snies.mineducacion.gov.co/portal/

I. A. Khan and J. T. Choi, “An application of educational data mining (EDM) technique for scholarship prediction,” Int. J. Softw. Eng. Its Appl., vol. 8, no. 12, pp. 31-42, 2014. https://doi.org/10.14257/ijseia.2014.8.12.03

H. Lamas, “Sobre el rendimiento escolar,” Prósitos y Represent. Rev. Psicol. Educ., vol. 3, no. 1, pp. 313-386, 2015. https://doi.org/10.20511/pyr2015.v3n1.74

J. Espinosa, J. Hernández, J. Rodríguez, M. Chacín, and V. Bermúdez, “Influencia del estrés sobre el rendimiento académico,” AVFT-Archivos Venez. Farmacol. y Ter., vol. 39, no. 1, 2020. https://doi.org/10.5281/zenodo.4065032

M. G. Jiménez, J. A. I.- Psicothema, and 2000, “La predicción del rendimiento académico: regresión lineal versus regresión logística,” Psicothema, vol. 12, pp. 222-248, 2000. https://www.psicothema.com/pdf/558.pdf

Garbanzo and G. María, “Factores asociados al rendimiento académico en estudiantes universitarios, una reflexión desde la calidad de la educación superior pública,” Rev. Educ., vol. 31, no. 1, pp. 43-63, 2007. https://www.redalyc.org/articulo.oa?id=44031103

L. Rojas, “Validez predictiva de los componentes del promedio de Admisión a la universidad de costa rica utilizando el Género y el tipo de colegio como variables control,” Rev. Elec. Actual. Investig. en Educ., vol. 13, no. 1, pp. 17-25, Jan. 2013. https://revistas.ucr.ac.cr/index.php/aie/article/view/11707/18183

D. García, J. Manuel, and M. Pichardo, “Learning analytics as an analysis factor of university academic performance,” in CEUR Workshop Proceedings, 2019, pp. 42-50. http://ceur-ws.org/Vol-2231/LALA_2018_paper_14.pdf

J. Huamán, “Evaluación del rendimiento académico estudiantil de la cohorte 2011-2015, según áreas de la carrera de estomatología Universidad Peruana Cayetano Heredia”. Título de Cirujano Dentista, Departamento Académico de Odontología Social, Universidad Peruana Cayetano Heredia, 2018. [Online]. Available: https://repositorio.upch.edu.pe/handle/20.500.12866/1429

D. A. Montoya-Arenas, E. M. Bustamante-Zapata, C. M. Díaz-Soto, and D. Pineda, “Factores de la capacidad intelectual y de la función ejecutiva relacionados con el rendimiento académico en estudiantes universitarios,” Rev. la Esc. Cienc. Salud Univ. Pontif. Boliv., vol. 40, no. 1, pp. 10-18, 2021. https://doi.org/10.18566/medupb.v40n1.a03

L. Contreras, J. Rodríguez, and H. Fuentes, “Analítica académica: nuevas herramientas aplicadas a la educación,” Rev. Boletín Redipe, vol. 10, no. 3, pp. 137-158, 2021.

P. Murnion and M. Helfert, “Academic analytics in quality assurance using organisational analytical capabilities,” in Annual Conf. UK Acad. Info. Sys. (UKAIS), 2013. [Online]. Availavle: https://doi.org/10.13140/2.1.3368.1600

G. Hackeling, Mastering machine learning with scikit-learn: Learn to implement and evaluate machine learning solutions with scikit-learn, 2nd ed., vol. 1., Bigmingham, UK: Packt Publishing Ltd., 2014.

L. Contreras, H. Fuentes, and J. Rodríguez, “Predicción del rendimiento académico como indicador de éxito/fracaso de los estudiantes de ingeniería, mediante aprendizaje automático,” Form. Univ., vol. 13, no. 5, pp. 233-246, 2020. https://doi.org/10.4067/S0718-50062020000500233

T. C. Hakyemez and S. Mardikyan, “The interplay between institutional integration and self-efficacy in the academic performance of first-year university students: A multigroup approach,” Int. J. Manag. Educ., vol. 19, no. 1, 2021. https://doi.org/10.1016/j.ijme.2020.100430

G. Guizado, M. Valenzuela, and P. Vallejo, “Desempeño docente y el rendimiento académico de los estudiantes de la Facultad de Tecnología en la Universidad Nacional de Educación de Perú,” Rev. Conrado, vol. 16, no. 72, 200-203, 2020. https://conrado.ucf.edu.cu/index.php/conrado/article/view/1231

E. Zárate, B. Lavado, and W. Pomahuacre, “Competecia comunicativa intercultural y rendimiento académico en lenguas extranjeras,” Rev. Conrado, vol. 16, no. 74, 30-37, 2020. https://conrado.ucf.edu.cu/index.php/conrado/article/view/1330

T. Icekson, O. Kaplan, and O. Slobodin, “Does optimism predict academic performance? Exploring the moderating roles of conscientiousness and gender,” Stud. High. Educ., vol. 45, no. 3, pp. 635-647, 2020. https://doi.org/10.1080/03075079.2018.1564257

A. M. Pavelea and O. Moldovan, “Why some fail and others succeed: Explaining the academic performance of PA undergraduate students,” NISPAcee J. Public Adm. Policy, vol. 13, no. 1, pp. 109-132, 2020. https://doi.org/10.2478/nispa-2020-0005

H. Vargas, L. Solórzano, and W. Chanini, “Modelo matemático entre el puntaje de examen de ingreso y el rendimiento académico de los estudiantes ingresantes a la Universidad Nacional Jorge Basadre Grohmann, año académico 2018,” Ciencias, vol. 3, no. 3, 45-51, 2019. https://doi.org/10.33326/27066320.2019.3.949

A. Lenskiy, R. Shariat, and S. Seol, “The effect of academic breaks on undergraduate academic performance,” Int. J. Electr. Eng. Educ., 2020. [Online]. Available: https://doi.org/10.1177/0020720920922518

M. Oladejo, “A path-analytic study of socio-psychological variables and academic performance of distance learners in nigerian universities,” Doctoral thesis, Univ. Lagos, 2010. [Online]. Available: https://doi.org/10.13140/RG.2.2.19443.73762

M. Kotzé; Niemann, “Psychological resources as predictors of academic performance of first-year students in higher education,” Acta académica., vol. 45, no. 2, pp. 85-121, 2013. https://journals.ufs.ac.za/index.php/aa/article/view/1399

E. Alyahyan and D. Düştegör, “Predicting academic success in higher education: Literature review and best practices,” Int. J. Educ. Technol. High. Educ., vol. 17, no. 1, pp. 1-21, Dec. 2020. https://doi.org/10.1186/S41239-020-0177-7/TABLES/15

G. Tarazona, L. Contreras, and H. Fuentes, “Machine Learning variables and algorithms that influence academic performance: A review,” Int. J. Mech. Prod. Eng. Res. Dev., vol. 10, no. 3, pp. 16011-16028, 2020. http://www.tjprc.org/view_paper.php?id=14467

L. Contreras, H. Fuentes, and J. Rodríguez, “Academic Interruption Model using Automatic Learning Algorithms” Sylwan J., vol. 10, no. 3, pp 16075-16086 ,2020. http://www.tjprc.org/view_paper.php?id=14480

L. Contreras, H. Fuentes, and J. Molano, “Analítica académica: nuevas herramientas aplicadas a la educación,” Rev. Bol. Redipe, vol. 10, no. 3, pp. 137-158, 2021. https://doi.org/10.36260/rbr.v10i3.1225

A. Rico, N. Gaytán, and D. Sánchez, “Construcción e implementación de un modelo para predecir el rendimiento académico de estudiantes universitarios mediante el algoritmo Naïve Bayes,” Diálogos sobre Educ., vol. 19, art. 509, 2019. https://doi.org/10.32870/dse.v0i19.509

Y. Widyaningsih, N. Fitriani, and D. Sarwinda, “A semi-supervised learning approach for predicting student's performance: First-year,” 2019 12th International Conference on Information & Communication Technology and System (ICTS), pp. 291–295, 2019. https://doi.org/10.1109/ICTS.2019.8850950

F. Otálora, “Modelo para la identificación de patrones de desempeño académico estudiantil para fortalecer el acompañamiento académico en la Universidad Nacional de Colombia,” MSc. dissertation, Dept. Elect. Eng., Universidad Nacional de Colombia, 2019. [Online]. Available: https://repositorio.unal.edu.co/handle/unal/77758.

R. Istvan and V. Lasagna, “Sistema informático para la detección temprana de deserción estudiantil universitaria,” Innovación y Desarro. Tecnológico y Soc., vol. 1, no. 2, pp. 1-15, 2019. https://doi.org/10.24215/26838559e006

S. S. M. Ajibade, N. Bahiah Binti Ahmad, and S. Mariyam Shamsuddin, “Educational data mining: Enhancement of

student performance model using ensemble methods,” IOP Conf. Ser. Mater. Sci. Eng., vol. 551, no. 1, art. 012061, 2019. https://doi.org/10.1088/1757-899X/551/1/012061

C. Jalota and R. Agrawal, “Analysis of educational data mining using classification,” in Proc. Int. Conf. Mach. Learn. Big Data, Cloud Parallel Comput. Trends, Prespectives Prospect. Com. 2019, 2019, pp. 243-247. https://doi.org/10.1109/COMITCon.2019.8862214

O. Castrillón, W. Sarache, and S. Ruiz, “Predicción del rendimiento académico por medio de técnicas de inteligencia artificial,” Rev. Form. Univ., vol. 13, no. 1, pp. 93-102, 2020. https://doi.org/10.4067/S0718-50062020000100093

A. Das and E. Rodríguez, “A predictive analytics system for forecasting student academic performance: Insights from a pilot project at eastern Washington university,” 2019 Jt. 8th Int. Conf. Informatics, Electron. Vision, ICIEV, 2019, pp. 255-262. https://doi.org/10.1109/ICIEV.2019.8858523

I. Burman and S. Som, “Predicting Students Academic Performance Using Support Vector Machine,” in Proc. 2019 Amity Int. Conf. Artif. Int., AICAI 2019, Apr. 2019, pp. 756-759. https://doi.org/10.1109/AICAI.2019.8701260

M. V. Amazona and A. A. Hernández, “Modelling student performance using data mining techniques,” in Proc. 2019 5th Int. Conf. Comp. Data Eng., ICCDE’ 19, May 2019, pp. 36-40. https://doi.org/10.1145/3330530.3330544

A. I. Adekitan and E. Noma-Osaghae, “Data mining approach to predicting the performance of first year student in a university using the admission requirements,” Educ. Inf. Technol., vol. 24, no. 2, pp. 1527-1543, 2019. https://doi.org/10.1007/s10639-018-9839-7

M. Hussain, W. Zhu, W. Zhang, S. M. R. Abidi, and S. Ali, “Using machine learning to predict student difficulties from learning session data,” Artif. Intell. Rev., vol. 52, no. 1, pp. 381-407, 2019. https://doi.org/10.1007/s10462-018-9620-8

X. Xu, J. Wang, H. Peng, and R. Wu, “Prediction of academic performance associated with internet usage behaviors using machine learning algorithms,” Comput. Human Behav., vol. 98, pp. 166-173, Apr. 2019. https://doi.org/10.1016/j.chb.2019.04.015

Bendangnuksung, “Students’ performance prediction using deep neural network,” Int. J. Appl. Eng. Res., vol. 13, no. 02, pp. 1171-1176, 2018. https://www.ripublication.com/ijaer18/ijaerv13n2_46.pdf

Y. Nieto, V. García-Díaz, C. Montenegro, and R. G. Crespo, “Supporting academic decision making at higher educational institutions using machine learning-based algorithms,” Soft Comput., vol. 23, no. 12, pp. 4145-4153, 2018. https://doi.org/10.1007/s00500-018-3064-6

L. Wang and Y. Yuan, “A prediction strategy for academic records based on classification algorithm in online learning environment,” Proc. - IEEE 19th Int. Conf. Adv. Learn. Technol. ICALT 2019, vol. 2161-377X, pp. 1-5, 2019. https://doi.org/10.1109/ICALT.2019.00007

Y. K. Salal, S. M. Abdullaev, and M. Kumar, “Educational data mining: Student performance prediction in academic,” Int. J. Eng. Adv. Technol., vol. 8, no. 4C, pp. 54-59, 2019. https://www.semanticscholar.org/paper/Educational-Data-Mining-%3A-Student-Performance-in-Salal-Abdullaev/b21fa7245581c3baad2d468cb9d706940de7e010

S. Hirokawa, “Key attribute for predicting student academic performance,” in ICETC '18: 10th Int. Conf. Ed. Tech. Comp, 2018, pp. 308-313. https://doi.org/10.1145/3290511.3290576

A. B. Nassif, I. Shahin, I. Attili, M. Azzeh, and K. Shaalan, “Speech recognition using deep neural networks: A systematic review,” IEEE Access, vol. 7, pp. 19143-19165, 2019. https://doi.org/10.1109/ACCESS.2019.2896880

J. Sotomonte, C. Rodríguez, C. Montenegro, P. Gaona, and J. Castellanos, “Hacia la construcción de un modelo predictivo de deserción académica basado en técnicas de minería de datos,” Rev. Científica, vol. 3, no. 26, p. 35, 2016. https://doi.org/10.14483/23448350.11089

M. Alloghani, D. Al-Jumeily, A. Hussain, A. J. Aljaaf, J. Mustafina, and E. Petrov, “Application of machine learning on student data for the appraisal of academic performance,” Proc. - Int. Conf. Dev. eSystems Eng. DeSE, vol. 2018, pp. 157-162, Sep. 2019. https://doi.org/10.1109/DeSE.2018.00038

M. Mohammadi, M. Dawodi, W. Tomohisa, and N. Ahmadi, “Comparative study of supervised learning algorithms for student performance prediction,” in 1st Int. Conf. Artif. Intell. Inf. Commun. ICAIIC 2019, 2019, pp. 124-127. https://doi.org/ 10.1109/ICAIIC.2019.8669085

H. Anderson, B. Afshan, and R. Baker, “Predicting Graduation at a Public R1 University,” 2019. [Online]. Available: https://learninganalytics.upenn.edu/ryanbaker/paper323.pdf

J. Hou and Y. Wen, “Prediction of learners’ academic performance using factorization machine and decision tree,” in 2019 IEEE Int. Congr. Cybermatics, 2019, pp. 1-8. https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00024

Y. S. Alsalman, N. Khamees Abu Halemah, E. S. Alnagi, and W. Salameh, “Using decision tree and artificial neural network to predict students academic performance,” in 2019 10th Int. Conf. Inf. Commun. Syst. ICICS 2019, 2019, pp. 104-109. https://doi.org/ 10.1109/IACS.2019.8809106

T. Icekson, O. Kaplan, and O. Slobodin, “Does optimism predict academic performance? Exploring the moderating roles of conscientiousness and gender,” Stud. High. Educ., vol. 45, no. 3, pp. 635-647, Mar. 2020. https://doi.org/10.1080/03075079.2018.1564257

R. C. Céspedes, A. Vara-Horna, D. López-Odar, I. Santi-Huaranca, A. Díaz-Rosillo, and Z. Asencios-González, “Ausentismo, presentismo y rendimiento académico en estudiantes de universidades peruanas,” Rev. Psicol. Educ., vol. 6, no. 1, pp. 83-133, Jan. 2018. https://doi.org/10.20511/PYR2018.V6N1.177

P. Luján, L. Trelles, and M. Mogollón, “Asertividad y rendimiento académico en estudiantes de la facultad de ciencias administrativas de la Universidad Nacional de Piura,” UCV - Sci., vol. 11, no. 1, 13-20, 2019. https://revistas.ucv.edu.pe/index.php/ucv-scientia/article/view/1170

Y.-W. Liang, D. Jones, and R. A. Robles-Pina, “Ethnic and gender stereotypes on college students’ academic performance,” Res. High. Educ. J., vol. 35, 2018. https://www.aabri.com/manuscripts/182858.pdf

C. Durán and A. Rosado, “La comprensión lectora y el rendimiento académico en estudiantes de ingeniería,” Rev. Colomb. Tecnol. Av., vol. 1, no. 33, pp. 9-15, Mar. 2019, https://doi.org/10.24054/16927257.V33.N33.2019.3317

B. Kitchenham, O. Pearl Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman, “Systematic literature reviews in software engineering – A systematic literature review,” Inf. Softw. Technol., vol. 51, no. 1, pp. 7-15, Jan. 2009. https://doi.org/ 10.1016/j.infsof.2008.09.009.

K. Gonzalez, J. Rodríguez, and L. Contreras, “Academic performance and alternatives with prediction- oriented machine learning: A review of the state of the art,” Int. J. Mech. Prod. Eng. Res. Dev., vol. 10, no. 3, pp. 16329-16340, 2020. http://www.tjprc.org/view_paper.php?id=14520

K. C. Santosh, “AI-driven tools for coronavirus outbreak: Need of active learning and cross-population train/test models on multitudinal/multimodal data,” J. Med. Syst., vol. 44, no. 5, pp. 1-5, May 2020. https://doi.org/10.1007/s10916-020-01562-1

J. García, P. Sánchez, M. Orozco, and S. Obredor, “Extracción de conocimiento para la predicción y análisis de los resultados de la prueba de calidad de la educación superior en Colombia,” Rev. Form. Univ., vol. 12, no. 4, pp. 55-62, 2019. https://doi.org/ 10.4067/S0718-50062019000400055

M. Zaffar, M. A. Hashmani, K. S. Savita, and S. S. H. Rizvi, “A study of feature selection algorithms for predicting students' academic performance,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 5, pp. 541-549, 2018. https://doi.org/10.14569/IJACSA.2018.090569

A. K. Das and E. Rodriguez-Marek, “A Predictive Analytics System for Forecasting Student Academic Performance: Insights from a Pilot Project at Eastern Washington University,” in 2019 Joint 8th Int. Conf. Informatics Elec. Vision (ICIEV) and 2019 3rd Int. Conf. Imaging, 2019, pp. 255-262. https://doi.org/10.1109/ICIEV.2019.8858523

V. L. Uskov, J. P. Bakken, A. Byerly, and A. Shah, “Machine Learning-based predictive analytics of student academic performance in STEM education,” in 2019 IEEE Global Eng. Educ. Conf. (EDUCON), 2019, pp. 1370-1376. https://doi.org/10.1109/EDUCON.2019.8725237

R. Asif, A. Merceron, S. A. Ali, and N. G. Haider, “Analyzing undergraduate students’ performance using educational data mining,” Comput. Educ., vol. 113, pp. 177-194, 2017. https://doi.org/10.1016/j.compedu.2017.05.007

J. Horak, J. Vrbka, and P. Suler, “Support vector machine methods and artificial neural networks used for the development of bankruptcy prediction models and their comparison,” J. Risk Financ. Manag., vol. 13, no. 3, p. 80, Mar. 2020. https://doi.org/10.3390/JRFM13030060

F. Ofori, E. Maina, and R. Gitonga, “Using machine learning algorithms to predict students’ performance and improve learning outcome: A literature based review,” J. Inf. Technol., vol. 4, no. 1, pp. 33-55, 2020. https://ir-library.ku.ac.ke/handle/123456789/20243?show=full

J. Brownlee, “Machine Learning Mastery,” 2020. https://machinelearningmastery.com/ (accessed Dec. 21, 2020).

F. J. Kaunang and R. Rotikan, “Students’ academic performance prediction using data mining,” in 3rd Int. Conf. Informatics Comput. ICIC 2018, 2018, pp. 1-5. https://doi.org/10.1109/IAC.2018.8780547

Pandas.org, “pandas.DataFrame.transform,” 2021. https://pandas.pydata.org/

R. M. Aguilar, J. M. Torres, and C. A. Martín, “Automatic learning for the system identification. A case study in the prediction of power generation in a wind farm,” RIAI - Rev. Iberoam. Autom. e Inform. Ind., vol. 16, no. 1, pp. 114-127, 2019. https://doi.org/10.4995/riai.2018.9421

L. E. Contreras, H. J. Fuentes, and J. I. Rodríguez, “Predicción del rendimiento académico como indicador de éxito/fracaso de los estudiantes de ingeniería, mediante aprendizaje automático,” Form. Univ., vol. 13, no. 5, pp. 233-246, 2020. http://dx.doi.org/10.4067/S0718-50062020000500233.

H. Almarabeh, “Analysis of students’ performance by using different data mining classifiers,” Int. J. Mod. Educ. Comput. Sci., vol. 8, pp. 9-15, 2017. https://doi.org/10.5815/ijmecs.2017.08.02

X. J. Lin et al., “Stress and its association with academic performance among dental undergraduate students in Fujian, China: A cross-sectional online questionnair survey,” BMC Med. Educ., vol. 20, art. 181, 2020. https://doi.org/10.1186/s12909-020-02095-4

T. Deliens, P. Clarys, I. de Bourdeaudhuij, and B. Deforche, “Weight, socio-demographics, and health behaviour related correlates of academic performance in first year university students,” Nutr. J., vol. 12, art. 162, 2013. https://doi.org/10.1186/1475-2891-12-162

E. T. Ortlieb and E. H. Cheek, “How geographic location plays a role within instruction: Venturing into both rural and urban elementary schools,” Educ. Res. Q., vol. 31, no. 2, pp. 48-64, 2008. https://www.proquest.com/docview/215932925

J. Cresswell and C. Underwood, “Location, location, location: Implications of geographic situation on australian student performance in PISA 2000,” 2004. https://research.acer.edu.au/acer_monographs/2

A. Porto and L. Di Gresia, “Performance of University students and their determinants,” 2005. [Online]. Available: http://sedici.unlp.edu.ar/bitstream/handle/10915/54674/Documento_completo__.pdf-PDFA.pdf?sequence=1

R. Garzón, M. O. Rojas, L. Del Riesgo, M. Pinzón, and A. L. Salamanca, “Factores que pueden influir en el rendimiento académico de estudiantes de bioquímica que ingresan en el programa de medicina de la Universidad del Rosario-Colombia,” Educ. Médica, vol. 13, no. 2, pp. 85-96, 2010. https://scielo.isciii.es/scielo.php?script=sci_abstract&pid=S1575-18132010000200005

E. Fernandes, M. Holanda, M. Victorino, V. Borges, R. Carvalho, and G. van Erven, “Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil,” J. Bus. Res., vol. 94, no. 2018, pp. 335-343, Feb. 2019. https://doi.org/10.1016/j.jbusres.2018.02.012

A. Rico and D. Sánchez, “Diseño de un modelo para automatizar la predicción del rendimiento académico en estudiantes del IPN/Design of a model to automate the prediction of academic performance in students of IPN,” RIDE Rev. Iberoam. para la Investig. y el Desarro. Educ., vol. 8, no. 16, pp. 246-266, 2018. https://doi.org/10.23913/ride.v8i16.340

S. Bhutto, I. F. Siddiqui, Q. A. Arain, and M. Anwar, “Predicting students’ academic performance through supervised Machine Learning,” in ICISCT 2020 - 2nd Int. Conf. Inf. Sci. Commun. Technol., Feb. 2020. [Online]. Available: https://doi.org/10.1109/ICISCT49550.2020.9080033

How to Cite

APA

Contreras Bravo, L. E., Nieves-Pimiento, N., & Gonzalez-Guerrero, K. (2022). Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods. Ingeniería, 28(1), e19514. https://doi.org/10.14483/23448393.19514

ACM

[1]
Contreras Bravo, L.E., Nieves-Pimiento, N. and Gonzalez-Guerrero, K. 2022. Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods. Ingeniería. 28, 1 (Nov. 2022), e19514. DOI:https://doi.org/10.14483/23448393.19514.

ACS

(1)
Contreras Bravo, L. E.; Nieves-Pimiento, N.; Gonzalez-Guerrero, K. Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods. Ing. 2022, 28, e19514.

ABNT

CONTRERAS BRAVO, L. E.; NIEVES-PIMIENTO, N.; GONZALEZ-GUERRERO, K. Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods. Ingeniería, [S. l.], v. 28, n. 1, p. e19514, 2022. DOI: 10.14483/23448393.19514. Disponível em: https://revistas.udistrital.edu.co/index.php/reving/article/view/19514. Acesso em: 7 dec. 2022.

Chicago

Contreras Bravo, Leonardo Emiro, Nayibe Nieves-Pimiento, and Karolina Gonzalez-Guerrero. 2022. “Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods”. Ingeniería 28 (1):e19514. https://doi.org/10.14483/23448393.19514.

Harvard

Contreras Bravo, L. E., Nieves-Pimiento, N. and Gonzalez-Guerrero, K. (2022) “Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods”, Ingeniería, 28(1), p. e19514. doi: 10.14483/23448393.19514.

IEEE

[1]
L. E. Contreras Bravo, N. Nieves-Pimiento, and K. Gonzalez-Guerrero, “Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods”, Ing., vol. 28, no. 1, p. e19514, Nov. 2022.

MLA

Contreras Bravo, L. E., N. Nieves-Pimiento, and K. Gonzalez-Guerrero. “Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods”. Ingeniería, vol. 28, no. 1, Nov. 2022, p. e19514, doi:10.14483/23448393.19514.

Turabian

Contreras Bravo, Leonardo Emiro, Nayibe Nieves-Pimiento, and Karolina Gonzalez-Guerrero. “Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods”. Ingeniería 28, no. 1 (November 20, 2022): e19514. Accessed December 7, 2022. https://revistas.udistrital.edu.co/index.php/reving/article/view/19514.

Vancouver

1.
Contreras Bravo LE, Nieves-Pimiento N, Gonzalez-Guerrero K. Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods. Ing. [Internet]. 2022Nov.20 [cited 2022Dec.7];28(1):e19514. Available from: https://revistas.udistrital.edu.co/index.php/reving/article/view/19514

Download Citation

Visitas

23

Dimensions


PlumX


Downloads

Download data is not yet available.