¿Se logra predecir el rendimiento académico? Un análisis desde la tecnología educativa

Autores/as

DOI:

https://doi.org/10.12795/revistafuentes.2021.14278

Palabras clave:

Aprendizaje; educación; educación superior; estudio bibliográfico; evaluación de la educación; informática educativa; rendimiento escolar; tecnología educativa.

Resumen

Predecir el rendimiento académico es un elemento clave en la educación, permitiéndole al profesorado diseñar acciones didácticas preventivas. Diversas disciplinas intervienen en este proceso predictivo, siendo las analíticas de aprendizaje, el aprendizaje automático, la minería de datos educativos las redes neuronales artificiales y las teorías difusas, las de mayor influencia. Se presenta una revisión sistemática a la literatura científica (2010-marzo 2020) presente en Scopus, IEEE Xplore, ACM Digital Library y Springer, con el objetivo valorar el cómo se ha comportado la predicción del rendimiento académico en dos escenarios: (1) modalidades de estudios online (en línea) y semipresencial; y (2) Apoyo tecnológico a la modalidad presencial. Se concluye el artículo con la determinación de las tendencias entre las disciplinas de las tecnologías educativas y las variables del rendimiento académico.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Adán-Coello, J. M., & Tobar, C. M. (2016). Using Collaborative Filtering Algorithms for Predicting Student Performance. Lecture Notes in Computer Science, 9831 LNCS, 206–218. https://doi.org/10.1007/978-3-319-44159-7_15

Adil, M., Tahir, F., & Maqsood, S. (2018). Predictive Analysis for Student Retention by Using Neuro-Fuzzy Algorithm. 2018 10th Computer Science and Electronic Engineering (CEEC) (pp. 41-45). Colchester, United Kingdom, United Kingdom.

Akçapınar, G., Altun, A., & Aşkar, P. (2015). Modeling Students’ Academic Performance Based on Their Interactions in an Online Learning Environment. Elementary Education Online, 14(3).

Altaf, S., Soomro, W., & Rawi, M. I. M. (2019). Student Performance Prediction using Multi-Layers Artificial Neural Networks. En 2019 3rd International Conference on Information System and Data Mining-ICISDM 2019 (pp.59-64). Houston TX USA.

Ameen, A., Alabi, M., & Adewole, K (2019). Students’ academic performance and dropout prediction: A review. Malaysian Journal of Computing 4 (2), 278-303. https://doi.org/10.24191/mjoc.v4i2.6701

Amelia, N., Gafar, A., & Mulyadi, Y (2019). Meta-analysis of student performance assessment using fuzzy logic. Indonesian Journal of Science and Technology (IJoST), 4 (1), 74-88. https://doi.org/10.17509/ijost.v4i1.15804

Amoo, M. A., Alaba, O. B., & Usman, O. L. (2018). Predictive modelling and analysis of academic performance of secondary school students: Artificial Neural Network approach. International Journal of Science and Technology Education Research, 9(1), 1–8. https://doi.org/10.5897/ijster2017.0415

Arsad, P. M., Buniyamin, N., & Ab Manan, J. (2014). Neural Network and Linear Regression methods for prediction of students’ academic achievement. 2014 IEEE Global Engineering Education Conference (EDUCON) (pp. 916-921).

Arsad, P. M., Buniyamin, N., & Manan, J. A. (2013). A neural network students’ performance prediction model (NNSPPM). 2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA).

Benablo, C. I. P., Sarte, E. T., Dormido, J. M. D., & Palaoag, T. (2018). Higher Education Student’s Academic Performance Analysis through Predictive Analytics. En 2018 7th International Conference on Software and Computer Applications - ICSCA 2018 (pp.238-242).

Borgobello, A., & Roselli, N. (2016). Rendimiento académico e interacción sociocognitiva de estudiantes en un entorno virtual. Educação E Pesquisa, 42(2), 359-374. https://doi.org/10.1590/S1517-9702201606143478

Bydžovská, H. (2015). Student Performance Prediction Using Collaborative Filtering Methods. Artificial Intelligence in Education, 550–553. https://doi.org/10.1007/978-3-319-19773-9_59

Cáceres, P., Rodríguez-García, A.M., Gómez, G., & Rodríguez, C. (2020). Learning analytics in higher education: a review of impact scientific literature. IJERI: International Journal of Educational Research and Innovation, (13), 32-46.

Castrillón, O., Sarache, W., & Ruiz-Herrera, S. (2020). Predicción del rendimiento académico por medio de técnicas de inteligencia artificial. Formación universitaria, 13(1),93-102.

Cea d´Ancona, Mª.A. (2001). Metodología cuantitativa. Estrategias y técnicas de investigación social. Madrid, Síntesis.

Chango, W., Cerezo, R., & Romero, C. (2019). Predicting academic performance of university students from multi-sources data in blended learning. En Second International Conference on Data Science, E-Learning and Information Systems - DATA ’19.

Cheng, J.J., & Do, H. Q (2014). A cooperative Cuckoo Search – hierarchical adaptive neuro-fuzzy inference system approach for predicting student academic performance. Journal of Intelligent y Fuzzy Systems, 27, 2551–2561. https://doi.org/10.3233/IFS-141229

Cuji, B., Gavilanes, W., & Sánchez, R (2017). Modelo predictivo de deserción estudiantil basado en arboles de decisión. Revista Espacios, 38 (55), 1-27.

Del Rio, C., & Pineda, J (2016). Predicting academic performance in traditional environments at higher-education institutions using data mining: A review. ECOS DE LA ACADEMIA,2,(4).185-201.

De-La-Hoz, E., De-La-Hoz, E., & Fontalvo, T. (2019). Metodología de Aprendizaje Automático para la Clasificación y Predicción de Usuarios en Ambientes Virtuales de Educación. Información tecnológica, 30(1), 247-254. https://dx.doi.org/10.4067/S0718-07642019000100247

Dharmasaroja, P., & Kingkaew, N (2016). Application of artificial neural networks for prediction of learning performances. En 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) (pp. 745–751) Changsha, China.

Do, Q. H., & Chen, J.F. (2013). A Neuro-Fuzzy Approach in the Classification of Students’ Academic Performance. Computational Intelligence and Neuroscience, 1–7. https://doi.org/10.1155/2013/179097

Fedriani, E., & Romano, I. (2017). Fuzzy-Set Qualitative Comparative Analysis to Determine Effects from Socio-Economical Factors and University Students Performance. Revista De Métodos Cuantitativos Para La Economía Y La Empresa, 24, Páginas 250 a 269.

Fernández-Mellizo, M., & Constante-Amores, A. (2020). Determinantes del rendimiento académico de los estudiantes de nuevo acceso a la Universidad Complutense de Madrid. Revista de educación, 387, 213-231. https://doi.org/10.4438/1988-592X-RE-2020-387-433

Garcia, E. P. I., & Mora, P. M. (2011). Model Prediction of Academic Performance for First Year Students. En 2011 10th Mexican International Conference on Artificial Intelligence (pp.169-174), Puebla, Mexico.

García-Martín, S., & Cantón-Mayo, I. (2019). Use of technologies and academic performance in adolescent students. Comunicar, 59, 73-81. https://doi.org/10.3916/C59-2019-07

Gutiérrez, F., Seipp, K., Ochoa, X., Chiluiza, K., Laet, T. D., & Verbert, K. (2020). LADA: A learning analytics dashboard for academic advising. Computers in Human Behavior, 107, 1-13. https://doi.org/10.1016/j.chb.2018.12.004

Hai-tao, P., Ming-qu, F., Hong-bin, Z., Bi-zhen, Y., Jin-jiao, L., Chun-Fang, L., Yan-ze, Z., & Rui, S. (2020). Predicting academic performance of students in Chinese-foreign cooperation in running schools with graph convolutional network. Neural Computing and Applications. https://doi.org/10.1007/s00521-020-05045-9

Halpern, D., Piña, M., & Ortega-Gunckel, C. (2020). School performance: New multimedia resources versus traditional notes. Comunicar, 64, 39-48. https://doi.org/10.3916/C64-2020-04

Hamsa, H., Indiradevi, S., & Kizhakkethottam, J. J. (2016). Student Academic Performance Prediction Model Using Decision Tree and Fuzzy Genetic Algorithm. Procedia Technology, 25, 326–332. https://doi.org/10.1016/j.protcy.2016.08.114

Hasan, R., Palaniappan, S., Raziff, A. R. A., Mahmood, S., & Sarker, K. U. (2018). Student Academic Performance Prediction by using Decision Tree Algorithm. En 2018 4th International Conference on Computer and Information Sciences (ICCOINS) (pp.1-5). Kuala Lumpur, Malaysia.

Hellas, A., Liao, S. N., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., Knutas, A., Leinonen, J., Messom, C., & Liao, S. (2018). Predicting academic performance: a systematic literature review. Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education - ITiCSE 2018 (pp. 175-199). Companion. Larnaca Cyprus.

Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., & Hlosta, M. (2019). A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective. Educational Technology Research and Development. https://doi.org/10.1007/s11423-019-09685-0

Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., Hlosta, M., & Naydenova, G. (2017). Implementing predictive learning analytics on a large scale: The teacher’s perspective. En Seventh International learning analytics y knowledge conference (pp.267-271), Vancouver, British Columbia.

Herodotou, C., Rienties, B., Hlosta, M., Boroowa, A., Mangafa, C., & Zdrahal, Z. (2020). The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study. The Internet and Higher Education,45, 100725. https://doi.org/10.1016/j.iheduc.2020.100725

Herodotou, C., Rienties, B., Verdin, B., & Boroowa, A. (2019). Predictive learning analytics ‘at scale’: Guidelines to successful implementation in higher education. Journal of Learning Analytics, 6(1), 85–95.

Hidayah, I., Permanasari, A. E., & Ratwastuti, N. (2013). Student classification for academic performance prediction using neuro fuzzy in a conventional classroom. En 2013 International Conference on Information Technology and Electrical Engineering (ICITEE). Yogyakarta, Indonesia.

Hirokawa, S. (2018). Key attribute for predicting student academic performance. En 10th International Conference on Education Technology and Computers - ICETC ’18 (pp.308-313). Tokyo Japan.

Jafari Petrudi, S. H., Pirouz, M., & Pirouz, B. (2013). Application of fuzzy logic for performance evaluation of academic students. En 2013 13th Iranian Conference on Fuzzy Systems (IFSC).

Jembere, E., Rawatlal, R., & Pillay, A. W. (2017). Matrix Factorisation for Predicting Student Performance. En 2017 7th World Engineering Education Forum (WEEF) (pp.513–518). Kuala Lumpur, Malaysia.

La Red, D. L., & Podestá, C. E.(2014). Metodología de Estudio del Rendimiento Académico Mediante la Minería de Datos. Campus virtuales, 3(1), 56-73. http://www.uajournals.com/campusvirtuales/journal/4/5.pdf

La Red, D. L., Karanik, M., Giovannini, M., & Pinto, N. (2015). Perfiles de Rendimiento Académico: Un Modelo basado en Minería de datos. Campus Virtuales, 4(1). 12-30.

Leon, F., & Popescu, E. (2017). Using Large Margin Nearest Neighbor Regression Algorithm to Predict Student Grades Based on Social Media Traces. In: Vittorini P. et al. (eds) Methodologies and Intelligent Systems for Technology Enhanced Learning. MIS4TEL 2017. Advances in Intelligent Systems and Computing, vol 617. Springer, Cham

Li, Z., Shang, C., & Shen, Q. (2016). Fuzzy-clustering embedded regression for predicting student academic performance. En 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp.344-351), Vancouver, BC, Canada.

Ma, C., Yao, B., Ge, F., Pan, Y., & Guo, Y. (2017). Improving Prediction of Student Performance based on Multiple Feature Selection Approaches. En 2017 International Conference on E-Education, E-Business and E-Technology ICEBT 2017 (pp.38-43). Toronto, Canada.

Maitra, S., Eshrak, S., Bari, A., Al-Sakin, A., Hossain,R., Akter, N., & Haque, Z (2019). Prediction of Academic Performance Applying NNs: A Focus on Statistical Feature-Shedding and Lifestyle. International Journal of Advanced Computer Science and Applications(IJACSA),10(9),561-70. http://dx.doi.org/10.14569/IJACSA.2019.0100974

Maitra, S., Madan, S., & Mahajan, P. (2018). An Adaptive Neural Fuzzy Inference System for prediction of student performance in Higher Education. 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). Greater Noida (UP) (pp.1158-1163), India, India.

Martinez-Rodriguez, R. A., Alvarez-Xochihua, O., Mejia Victoria, O. D., Jordan Aramburo, A., & Gonzalez Fraga, J. A. (2019). Use of Machine Learning to Measure the Influence of Behavioral and Personality Factors on Academic Performance of Higher Education Students. IEEE Latin America Transactions, 17(04), 633–641. https://doi.org/10.1109/TLA.2019.8891928

Matcha, W., Gašević, D., Uzir, N. A., Jovanović, J., & Pardo, A. (2019). Analytics of Learning Strategies. En 9th International Conference on Learning Analytics y Knowledge - LAK19 (pp.461-470). Tempe AZ USA.

Meca, I., Mollá-Campello, N., & Rabasa, A (2019). A new methodology for early warning of critical academic performance, based on discrete predictive models. En Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM’19) (pp.680-685). Association for Computing Machinery, New York, NY, USA.

Mendoza-Macías, M., & Barcia-Briones, M. (2020). Las relaciones familiares y rendimiento académico en estudiantes de educación básica. Dominio de las Ciencias, 6(2), 378-394. http://dx.doi.org/10.23857/dc.v6i3.1223

Merchan Rubiano, S. M., & Duarte Garcia, J. A. (2016). Analysis of Data Mining Techniques for Constructing a Predictive Model for Academic Performance. IEEE Latin America Transactions, 14(6), 2783–2788. https://doi.org/10.1109/TLA.2016.7555255

Mishra, T., Kumar, D., & Gupta, S. (2014). Mining Students’ Data for Prediction Performance. 2014 Fourth International Conference on Advanced Computing y Communication Technologies (pp.255-263).

Moreno-Marcos, P. M., Pong, T.C., Munoz-Merino, P. J., & Delgado Kloos, C. (2020). Analysis of the Factors Influencing Learners’ Performance Prediction With Learning Analytics. IEEE Access, 8, 5264–5282. https://doi.org/10.1109/ACCESS.2019.2963503

Musso, M., Kyndt, E., Cascallar, E., & Dochy, F (2013). Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks. Frontline Learn. Res, 1, 42–71. https://doi.org/10.14786/flr.v1i1.13

Na, K. S., & Tasir, Z. (2017). Identifying at-risk students in online learning by analysing learning behaviour: A systematic review. En 2017 IEEE Conference on Big Data and Analytics (ICBDA) (pp.118-123).

Palominos, F., Díaz, H, Palominos, S., & Cañete, L. (2018). Relación entre los Procedimientos de Selección a la Educación Superior y el Desempeño Académico de los Estudiantes con base en una Clasificación mediante Conjuntos Difusos. Formación universitaria, 11(1), 45-52. https://dx.doi.org/10.4067/S0718-50062018000100045

Pandey, M., & Taruna, S. (2016). Towards the integration of multiple classifier pertaining to the Student’s performance prediction. Perspectives in Science, 8, 364–366. https://doi.org/10.1016/j.pisc.2016.04.076

Pardo, A., Mirriahi, N., Martinez-Maldonado, R., Jovanovic, J., Dawson, S., & Gašević, D. (2016). Generating actionable predictive models of academic performance. En Sixth International Conference on Learning Analytics y Knowledge - LAK ’16 (pp.474-478). Edinburgh United Kingdom.

Popescu, E., & Leon, F. (2018). Predicting Academic Performance Based on Learner Traces in a Social Learning Environment. IEEE Access, 6, 72774-72785 https://doi.org/10.1109/ACCESS.2018.2882297

Quinn, R., & Gray, G. (2020). Prediction of student academic performance using Moodle data from a Further Education setting. Irish Journal of Technology Enhanced Learning, 5(1). https://doi.org/10.22554/ijtel.v5i1.57

Rahul, R., & Katarya, R. (2019). A Review: Predicting the Performance of Students Using Machine learning Classification Techniques. 2019 Third International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)(pp.36-41). Palladam, India, India.

Ranjeeth, S., Latchoumi, T. P., & Paul, P. V. (2020). A Survey on Predictive Models of Learning Analytics. Procedia Computer Science, 167, 37–46. https://doi.org/10.1016/j.procs.2020.03.180

Rastrollo-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

Rienties, B., Herodotou, C., Olney, T., Schencks, M., & Boroowa, A. (2018). Making sense of learning analytics dashboards: A technology acceptance perspective of 95 teachers. The International Review of Research in Open and Distributed Learning, 19 (5) https://doi.org/10.19173/irrodl.v19i5.3493 .

Romero, C., Márquez V., & Ventura, S. (2012). Predicción del Fracaso Escolar Mediante Técnicas de Minería de Datos. Iee-Rita, 7(3), 109–117.

Salam, S. D., Paul, P., Tabassum, R., Mahmud, I., Ullah, M. A., Rahman, A., & Rahman, R. M. (2018). Determination of Academic Performance and Academic Consistency by Fuzzy Logic. 2018 International Conference on Intelligent Systems (IS). Funchal - Madeira, Portugal, Portugal.

Shahiri, A. M., Husain, W., & Rashid, N. A. (2015). A Review on Predicting Student’s Performance Using Data Mining Techniques. Procedia Computer Science, 72, 414–422. https://doi.org/10.1016/j.procs.2015.12.157

Shanthini, A., Vinodhin, G., & Chandrasekaran, R. M. (2018). Predicting Students’ Academic Performance in the University Using Meta Decision Tree Classifiers. Journal of Computer Science, 14(5), 654–662. https://doi.org/10.3844/jcssp.2018.654.662

Shingari, I., Kumar, D., & Khetan, M. (2017). A review of applications of data mining techniques for prediction of students’ performance in higher education. Journal of Statistics and Management Systems, 20(4), 713–722. https://doi.org/10.1080/09720510.2017.1395191

Son, L. H., & Fujita, H. (2018). Neural-fuzzy with representative sets for prediction of student performance. Applied Intelligence, 49, 172–187, https://doi.org/10.1007/s10489-018-1262-7

Stake, R. E. (2005). Investigación con estudio de casos. Madrid, Morata.

Tanabe, Y., Kagari, K., Kitanaka, Y., Takeuchi, K., & Hirokawa, S. (2017). Finding Key Integer Values in Many Features for Learners’ Academic Performance Prediction. En 2017 9th International Conference on Education Technology and Computers-ICETC 2017 (pp.167-171), Barcelona Spain.

Taylan, O., & Karagözoğlu, B. (2009). An adaptive neuro-fuzzy model for prediction of student’s academic performance. Computers y Industrial Engineering, 57(3), 732–741. https://doi.org/10.1016/j.cie.2009.01.019

Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning analytics in a data-rich context. Computers in Human Behavior, 47, 157–167. https://doi.org/10.1016/j.chb.2014.05.038 .

Torres, P.V., González, C.S., Aciar, S.V., & Morales, G.R. (2018). Methodology for systematic literature review applied to engineering and education. En 2018 IEEE Global Engineering Education Conference (EDUCON), (pp. 1364-1373). Canary, Island, Spain.

Urrútia, G., & Bonfill, X (2010). PRISMA declaration: A proposal to improve the publication of systematic reviews and meta-analyses. Med. Clín, 135 (11), 507–511, 2010. https://bit.ly/3bZ3X0R

Urteaga, I., Siri, L., & Garófalo, G. (2020). Predicción temprana de deserción mediante aprendizaje automático en cursos profesionales en línea. RIED. Revista Iberoamericana de Educación a Distancia, 23(2), 147-167. https://doi.org/10.5944/ried.23.2.26356

Van Leeuwen, A., Janssen, J., Erkens, G., & Brekelmans, M. (2015). Teacher regulation of cognitive activities during student collaboration: Effects of learning analytics. Computers y Education, 90, 80–94. https://doi.org/10.1016/j.compedu.2015.09.006

Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98–110. https://doi.org/10.1016/j.chb.2018.07.027

Waheed, H., Hassan, S.U., Aljohani, N. R., Hardman, J., & Nawaz, R. (2020). Predicting Academic Performance of Students from VLE Big Data using Deep Learning Models. Computers in Human Behavior, 106189. https://doi.org/10.1016/j.chb.2019.106189

Yadav, R. S., Soni, A. K., & Pal, S. (2014). A study of academic performance evaluation using Fuzzy Logic techniques. En 2014 International Conference on Computing for Sustainable Global Development (INDIACom) (pp.48-53).

Yao, H., Lian, D., Cao, Y., Wu., & Zhou, T (2019). Predicting Academic Performance for College Students: A Campus Behavior Perspective. ACM Trans. Intell. Syst. Technol. 10(3), 1-21. https://doi.org/10.1145/3299087

Yildiz, O., Bal, A., & Gulsecen, S. (2013). Improved fuzzy modelling to predict the academic performance of distance education students. The International Review of Research in Open and Distributed Learning, 14(5), 143-165 https://doi.org/10.19173/irrodl.v14i5.1595

Zaldumbide, J. P., & Párraga, V. C (2018). Systematic Mapping Study of Literature on Educational Data Mining to determine factors that affect school performance. En 2018 International Conference on Information Systems and Computer Science (INCISCOS) (pp.239-245). Quito, Ecuador

Zambrano, C, Urrutia, A., & Varas, M. (2017). Análisis de rendimiento académico estudiantil usando Data Warehouse Difuso. Ingeniare. Revista chilena de ingeniería, 25(2), 242-254. https://dx.doi.org/10.4067/S0718-33052017000200242

Zuviria, N. M., Mary, S. L., & Kuppammal, V. (2012). SAPM: ANFIS based prediction of student academic performance metric. En 2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT’12). Coimbatore, India.

Publicado

2021-09-15

Cómo citar

Estrada Molina, O., & Fuentes Cancell, D. (2021). ¿Se logra predecir el rendimiento académico? Un análisis desde la tecnología educativa. Revista Fuentes, 23(3), 363–375. https://doi.org/10.12795/revistafuentes.2021.14278

Número

Sección

Investigaciones
Recibido 2020-12-07
Aceptado 2021-05-11
Publicado 2021-09-15
Visualizaciones
  • Resumen 844
  • PDF 480
  • HTML 166
  • ePUB 66