Analítica académica: nuevas herramientas aplicadas a la educación
##plugins.themes.bootstrap3.article.main##
Keywords
Analítica, analítica académica, aprendizaje automático, educación en ingeniería
Resumen
La analítica de datos es un campo nuevo que ha permeado la educación superior mediante la incursión de herramientas matemáticas, la estadística, la minería de datos y el aprendizaje automático. Inicialmente se presenta una fundamentación teórica relacionada con la analítica aplicada a la educación, analítica académica y sus enfoques. Posteriormente se plantea una metodología cuyo propósito es la revisión referencial de los últimos cinco años referente al campo de la analítica en educación y especialmente en lo que concierne a la analítica académica, con el fin de identificar aspectos relativos al crecimiento de este enfoque y sus campos de aplicación, enfocados a la educación superior. Los resultados muestran que los investigadores se han enfocado en los últimos años a trabajar en el desarrollo de modelos que permitan comprender aspectos de la vida académica del estudiante, docentes e instituciones (rendimiento académico, tasa de deserción y tasa de graduación en su respectivo orden) que permitan la elaboración y toma de decisiones acertadas.
Referencias
Adekitan, A. I., & Noma-Osaghae, E. (2019). Data mining approach to predicting the performance of first year student in a university using the admission requirements. Education and Information Technologies, 24(2), 1527–1543. https://doi.org/10.1007/s10639-018-9839-7
Ahamed, A. T. M. S., Mahmood, N. T., & Rahman, R. M. (2016). Prediction of HSC Examination Performance using Socioeconomic , Psychological and Academic Factors. International Conference on Electrical and Computer Engineering, 263–266.
Ajibade, S. M., Ahmad, N. B., & Shamsuddin, S. M. (2019). An Heuristic Feature Selection Algorithm to Evaluate Academic Performance of Students. 10th Control and System Graduate Research Colloquium (ICSGRC, 2–3.
Ajibade, S. S. M., Bahiah Binti Ahmad, N., & Mariyam Shamsuddin, S. (2019). Educational Data Mining: Enhancement of Student Performance model using Ensemble Methods. IOP Conference Series: Materials Science and Engineering, 551(1). https://doi.org/10.1088/1757-899X/551/1/012061
Amazona, M. V., & Hernandez, A. A. (2019). Modelling Student Performance Using Data Mining Techniques. Proceedings of the 2019 5th International Conference on Computing and Data Engineering - ICCDE’ 19, 36–40. https://doi.org/10.1145/3330530.3330544
Argonza, J. (2016). Big data en la educación. Revista Digital Universitaria, 17(1), 1–16. http://www.revista.unam.mx/vol.17/num1/art06/
Avella, J. T., Kebritchi, M., Nunn, S. G., & Kanai, T. (2016). Learning analytics methods, benefits, and challenges in higher education: A systematic literature review. Journal of Asynchronous Learning Network, 20(2). https://doi.org/10.24059/olj.v20i2.790
Baepler, P., & Murdoch, C. (2010a). Academic Analytics and Data Mining in Higher Education. International Journal for the Scholarship of Teaching and Learning, 4(2). https://doi.org/10.20429/ijsotl.2010.040217
Baepler, P., & Murdoch, C. J. (2010b). International Journal for the Scholarship of Teaching and Learning Academic Analytics and Data Mining in Higher Education Academic Analytics and Data Mining in Higher Education. International Journal for the Scholarship of Teaching and Learning, 4(2). https://doi.org/10.20429/ijsotl.2010.040217
Bhutto, S., Siddiqui, I. F., Arain, Q. A., & Anwar, M. (2020). Predicting Students’ Academic Performance Through Supervised Machine Learning. ICISCT 2020 - 2nd International Conference on Information Science and Communication Technology. https://doi.org/10.1109/ICISCT49550.2020.9080033
Brownlee, J. (2020). Machine Learning Mastery. https://machinelearningmastery.com/
Burgos, C., Campanario, M. L., Peña, D. de la, Lara, J. A., Lizcano, D., & Martínez, M. A. (2018). Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout. Computers and Electrical Engineering, 66, 541–556. https://doi.org/10.1016/j.compeleceng.2017.03.005
Burman, I., & Som, S. (2019). Predicting Students Academic Performance Using Support Vector Machine. Proceedings - 2019 Amity International Conference on Artificial Intelligence, AICAI 2019, 756–759. https://doi.org/10.1109/AICAI.2019.8701260
Campbell, J. P., & Oblinger, D. (2007). Learning analytics | Higher Education Academy. https://www.heacademy.ac.uk/enhancement/starter-tools/learning-analytics#snapshot-logo
Castrillón, O. D., Sarache, W., & Ruiz-Herrera, S. (2020). Prediction of academic performance using artificial intelligence techniques. Formacion Universitaria, 13(1), 93–102. https://doi.org/10.4067/S0718-50062020000100093
Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683–695. https://doi.org/10.1080/13562517.2013.827653
Contreras, L. E., Fuentes, H. J., & Rodríguez, I. (2020). ACADEMIC INTERRUPTION MODEL USING AUTOMATIC LEARNING ALGORITHMS. International Journal of Mechanical and Production Engineering Research and Development (IJMPERD), 10(3), 16075–16086. www.tjprc.org
Contreras, L., Fuentes, H., & Rodriguez, J. (2020). Academic performance prediction by machine learning as a success/failure indicator for engineering students. Formacion Universitaria, 13(5), 233–246. https://doi.org/10.4067/S0718-50062020000500233
Contreras, L., & Tarazona, G. (2018). Big Data: An Exploration Toward the Improve of the Academic Performance in Higher Education. Lecture Notes in Computer Science, 10943, 627–637. https://doi.org/10.1007/978-3-319-93803-5
Costa, E. B., Fonseca, B., Santana, M. A., de Araújo, F. F., & Rego, J. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Computers in Human Behavior, 73, 247–256. https://doi.org/10.1016/j.chb.2017.01.047
Daniel, B. (2015a). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46–5(0007–1013), 904–920. https://www.learntechlib.org/p/151804/
Daniel, B. (2015b). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230
Daumiller, M., & Janke, S. (2019). The Impact of Performance Goals on Cheating Depends on How Performance Is Evaluated. AERA Open, 5(4), 1–10. https://doi.org/10.1177/2332858419894276
David, J., & Anastasija, G. (2019). Predicting Academic Performance Based on Students ’ Family Environment : Evidence for Colombia Using Classification Trees. Psychology, Society, & Education, 11(3), 299–311. https://doi.org/10.25115/psye.v10i1.2056
Devasia, M. T., P, M. V. T., & Hegde, V. (2016). Prediction of Students Performance using Educational Data Mining. International Conference on Data Mining and Advanced Computing (SAPIENCE), 91–95.
Dixson, D. D., Worrell, F. C., Olszewski-Kubilius, P., & Subotnik, R. F. (2016). Beyond perceived ability: the contribution of psychosocial factors to academic performance. Annals of the New York Academy of Sciences, 1377(1), 67–77. https://doi.org/10.1111/nyas.13210
Dyckhoff, A. L., Zielke, D., Bültmann, M., Chatti, M. A., & Schroeder, U. (2012). Design and implementation of a learning analytics toolkit for teachers. Educational Technology and Society, 15(3), 58–76.
Echegaray-calderon, O. A., & Barrios-aranibar, D. (2015). Optimal selection of factors using Genetic Algorithms and Neural Networks for the prediction of students ’ academic performance. Congreso Latinoamericano de Inteligencia Computacional, 1–6.
Estrada, J., & Quintero, R. (2015). Bajo rendimiento académico en la Universidad Distrital Francisco José de Caldas (Universidad Distrital (ed.); Primera). Editorial UD.
Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning Int. J. Technology Enhanced Learning Int. J. Technology Enhanced Learning, 4(x), 304–317. https://doi.org/10.1504/IJTEL.2012.051816
Fournier, H. ;, Kop, R. ;, & Sitlia, H. (2011). The Value of Learning Analytics to Networked Learning on a Personal Learning Environment. LAK ’11 Proceedings of the 1st International Conference on Learning Analytics and Knowledge, 104–109. https://doi.org/10.1145/2090116.2090131
Francis, B. K., & Babu, S. S. (2019). Predicting Academic Performance of Students Using a Hybrid Data Mining Approach. Journal of Medical Systems, 43(6). https://doi.org/10.1007/s10916-019-1295-4
García-González, J. R., Sánchez-Sánchez, P. A., Orozco, M., & Obredor, S. (2019). Extracción de Conocimiento para la Predicción y Análisis de los Resultados de la Prueba de Calidad de la Educación Superior en Colombia Knowledge Capture for the Prediction and Analysis of Results of the Quality Test of Higher Education in Colombia. Revista Formación Universitaria, 12(4), 55–62. https://doi.org/10.4067/S0718-50062019000400055
García Tinisaray, D. K. (2015). CONSTRUCCIÓN DE UN MODELO PARA DETERMINAR EL RENDIMIENTO ACADÉMICO DE LOS ESTUDIANTES BASADO EN LEARNING ANALYTICS (ANÁLISIS DEL APRENDIZAJE), MEDIANTE EL USO DE TÉCNICAS MULTIVARIANTES. Universidad de Sevilla.
Goldstein, P. J., & Katz, R. N. (2005). Academic Analytics : The Uses Of Management Information And Technology In Higher Education. EDUCAUSE Quarterly, 8(December), 113. https://library.educause.edu/resources/2005/12/academic-analytics-the-uses-of-management-information-and-technology-in-higher-education
Gómez, D. (2015). Analítica visual en eLearning [Universdiad de Salamanca]. https://gredos.usal.es/handle/10366/125950
Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology and Society, 15(3), 42–57.
Hasan, R., Palaniappan, S., Mahmood, S., Abbas, A., Sarker, K. U., & Sattar, M. U. (2020). Predicting student performance in higher educational institutions using video learning analytics and data mining techniques. Applied Sciences , 10(11). https://doi.org/10.3390/app10113894
Hasbun, T., Araya, A., & Villalon, J. (2016). Extracurricular activities as dropout prediction factors in higher education using decision trees. 16th International Conference on Advanced Learning Technologies Extracurricular, 3–5. https://doi.org/10.1109/ICALT.2016.66
Hassan, H., Ahmad, N. B., & Anuar, S. (2020). Improved students’ performance prediction for multi-class imbalanced problems using hybrid and ensemble approach in educational data mining. Journal of Physics, 1529(5). https://doi.org/10.1088/1742-6596/1529/5/052041
Hernández, A. G., Meléndez, R. A., Morales, L. A., García, A., Tecpanecatl, J. L., & Algredo, I. (2016). Comparative Study of Algorithms to Predict the Desertion in the Students at the ITSM-Mexico. LATIN AMERICA TRANSACTIONS, 14(11), 4573–4578.
Herodotou, C., Rienties, B., Boroowa, A., & Zdrahal, Z. (2019). A large ‑ scale implementation of predictive learning analytics in higher education : the teachers ’ role and perspective. In Educational Technology Research and Development (Vol. 67, Issue 5). Springer US. https://doi.org/10.1007/s11423-019-09685-0
Hirokawa, S. (2018). Key attribute for predicting student academic performance. ACM International Conference Proceeding Series, 308–313. https://doi.org/10.1145/3290511.3290576
Iten, L., Arnold, K., & Pistilli, M. (2008). Mining Real-Time Data to Improve Student Success in a Gateway Course. Eleventh Annual TLT Conference.
Janani, P., Verma, S., Natarajan, S., & Sinha, A. K. (2019). Communication Using IoT. In Information and Communication Technology for Sustainable Development, Advances in Intelligent Systems and Computing. Springer Singapore. https://doi.org/10.1007/978-981-13-7166-0
Kauffman, C., Derazin, M., Asmar, A., & Kibble, J. (2019). Patterns of medical student engagement in a second-year pathophysiology course: relationship to USMLE Step 1 performance. Advances in Physiology Education, 43(01), 512–518. https://doi.org/10.1152/advan.00082.2019
Kaunang, F. J., & Rotikan, R. (2018). Students’ academic performance prediction using data mining. Proceedings of the 3rd International Conference on Informatics and Computing, ICIC 2018, 1–5. https://doi.org/10.1109/IAC.2018.8780547
Khalil, M. K., Hawkins, H. G., Crespo, L. M., & Buggy, J. (2018). The Design and Development of Prediction Models for Maximizing Students ’ Academic Achievement. Medical Science Educator, 111–117.
Khalil, M., Khalil, M., & Ebner, M. (2015). Learning Analytics: Principles and Constraints. EdMedia: World Conference on Educational Media and Technology, 2015(1), 1789–1799. https://www.learntechlib.org/p/151455/
Kwankajonwong, N., Ongprakobkul, C., Qureshi, S. P., Watanatada, P., Thanprasertsuk, S., & Bongsebandhu-phubhakdi, S. (2019). Attitude, but Not Self-Evaluated Knowledge, Correlates with Academic Performance in Physiology in Thai Medical Students. Advances in Physiology Education, 43(3), p324-331.
Lasarte, O. F., Díaz, E. R., Palacios, E. G., & Fernández, A. R. (2020). The role of social support in school adjustment during Secondary Education. 32(1), 100–107. https://doi.org/10.7334/psicothema2019.125
Lauría, E., Moody, E., Jayaprakash, S., Jonnalagadda, N., & Baron, J. (2013). Open academic analytics initiative: initial research findings. Proceedings of the Third International Conference on Learning Analytics and Knowledge, 150–154.
Lima, J., Alves, P., Pereira, M., & Almeida, S. (2018). Using Academic Analytics to Predict Dropout Risk in Engineering Courses. European Conference on E-Learning.
Limsathitwong, K., Tiwatthanont, K., & Yatsungnoen, T. (2018). Dropout Prediction System to Reduce Discontinue Study Rate of Information Technology Students. International Conference on Business and Industrial Research (ICBIR), 110–114.
Liu, H., Wang, Z., Benachour, P., & Tubman, P. (2018). A Time Series Classification Method for Behaviour-Based Dropout Prediction. 18th International Conference on Advanced Learning Technologies A, 191–195. https://doi.org/10.1109/ICALT.2018.00052
Long, P., Siemens, G., Conole, G., & Gašević, D. (2011). LAK ’11. Proceedings of the 1st International Conference on Learning Analytics and Knowledge, 195. https://iucat.iu.edu/iub/13609333
Mahroeian, H., Daniel, B., & Butson, R. (2017). The perceptions of the meaning and value of analytics in New Zealand higher education institutions. International Journal of Educational Technology in Higher Education, 14(1), 1–17. https://doi.org/10.1186/s41239-017-0073-y
Martín, A. N. (2019). Variables motivacionales y cognitivas predictivas del rendimiento en estudiantes universitarios de ciencias de la salud Cognitive and motivational predictive variables associated with performance in University Students of Health Sciences. 33(1), 1–30.
Medina, E. C., Chunga, C. B., Armas-aguirre, J., Grandón, E. E., & Bío-bío, U. (2020). Modelo predictivo para reducir el índice de deserción de estudiantes universitarios en el Perú : Redes Bayesianas vs . Árboles de Decisión Predictive model to reduce the dropout rate of
Decision Trees. 15th Iberian Conference on Information Systems and Technologies (CISTI), June, 24–27.
Mengash, H. A. (2020a). Using data mining techniques to predict student performance to support decision making in university admission systems. IEEE Access, 8, 55462–55470. https://doi.org/10.1109/ACCESS.2020.2981905
Mengash, H. A. (2020b). Using data mining techniques to predict student performance to support decision making in university admission systems. IEEE Access, 8, 55462–55470. https://doi.org/10.1109/ACCESS.2020.2981905
Merchán-rubiano, S., Beltrán-gómez, A., & Duarte-garcía, J. (2017). Engineering Students´ Academic Performance Prediction using ICFES Test Scores and Demo- graphic Data. Ingeniería Solidaria, 13(21).
Miguéis, V. L., Freitas, A., Garcia, P. J. V., & Silva, A. (2018). Early segmentation of students according to their academic performance: A predictive modelling approach. Decision Support Systems, 115, 36–51. https://doi.org/10.1016/j.dss.2018.09.001
Mohammadi, M., Dawodi, M., Tomohisa, W., & Ahmadi, N. (2019). Comparative study of supervised learning algorithms for student performance prediction. 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019, 124–127. https://doi.org/10.1109/ICAIIC.2019.8669085
Murnion, P., & Helfert, M. (2013). Academic Analytics in quality assurance using organisational analytical capabilities A User-level Usage Analytics in Cloud Based Applications View project Insight View project. In U. Oxford (Ed.), Annual Conference of the UK Academy of Information Systems (UKAIS). https://doi.org/10.13140/2.1.3368.1600
Nabizadeh, S., Hajian, S., Sheikhan, Z., & Rafiei, F. (2019). Prediction of academic achievement based on learning strategies and outcome expectations among medical students. BMC Medical Education, 19(1), 1–11. https://doi.org/10.1186/s12909-019-1527-9
Nieto, Y., Garcia, V., Montenegro, C., Gonzalez, C., & Gonzalez, R. (2019). Usage of Machine Learning for Strategic Decision Making at Higher Educational Institutions. IEEE Access, 7, 75007–75017. https://doi.org/10.1109/ACCESS.2019.2919343
Norris, D. M., & Lefrere, P. (2011). Transformation through expeditionary change using online learning and competence-building technologies. Research in Learning Technology, 19(1), 1741–1629. https://doi.org/10.3402/rlt.v19i1.17105
Oblinger, G., Campbell, J. P., Deblois, P. B., & Oblinger, D. G. (2007). Academic Analytics: A New Tool for a New Era. Research in Higher Education, 1(2), 727–742. http://er.educause.edu/~/media/files/article-downloads/erm0742.pdf
Pradeep, A., Das, S., & Kizhekkethottam, J. J. (2015). Students Dropout Factor Prediction Using EDM Techniques. International Conference on Soft-Computing and Network Security.
QIN, F., ZHU, L., CHENG, Z., & ZHANG, Q. (2017). Research on Student Performance Evaluation Based on Random Forest. DEStech Transactions on Engineering and Technology Research, eeta, 387–393. https://doi.org/10.12783/dtetr/eeta2017/7762
Qowidho, T., Zarlis, M., Nababan, E. B., Agusnady, A., & Sembiring, B. S. (2019). Analysis of c4.5 and ID3 Methods in Determining Student Graduation. Journal of Physics: Conference Series, 1255(1). https://doi.org/10.1088/1742-6596/1255/1/012025
Razaque, F., Soomro, N., Ahmed, S., Soomro, S., Samo, J. A., & Dharejo, H. (2017). Using Naïve Bayes Algorithm to Students ’ bachelor Academic Performances Analysis. Conferencia Internacional IEEE Sobre Tecnologías de Ingeniería y Ciencias Aplicadas (ICETAS), 1–5.
Salal, Y. K., Abdullaev, S. M., & Kumar, M. (2019). Educational data mining: Student performance prediction in academic. International Journal of Engineering and Advanced Technology, 8(4C), 54–59.
Salcedo, A. (2010). Desertion in Colombian Universities. Revista Academia y Virtualidad, 3(1), 50–60. http://www.alfaguia.org/alfaguia/files/1319043663_03.pdf
Schroeder, U. (2009). Web-Based Learning – Yes We Can! (pp. 25–33). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03426-8_3
Siemens, G. (2013). Learning Analytics. American Behavioral Scientist, 57(10), 1380–1400. https://doi.org/10.1177/0002764213498851
Stanko, T., Zhirosh, O., Johnston, D., & Gartsev, S. (2017). On possibility of prediction of academic performance and potential improvements of admission campaign at IT university. IEEE Global Engineering Education Conference, 862–866.
Tan, T. Y., Jain, M., Obaid, T., & Nesbit, J. C. (2020). What can completion time of quizzes tell us about students ’ motivations and learning strategies ? Journal of Computing in Higher Education, 32(2), 389–405. https://doi.org/10.1007/s12528-019-09239-6
Tasnim, N., Paul, M. K., & Sarowar Sattar, A. H. M. (2019). Performance Analysis of Different Decision Tree Based Methods for Identifying Drop out Students. 1st International Conference on Advances in Science, Engineering and Robotics Technology 2019, ICASERT 2019, 2019(Icasert), 1–6. https://doi.org/10.1109/ICASERT.2019.8934518
Tenpipat, W., & Akkarajitsakul, K. (2020). Student Dropout Prediction : A KMUTT Case Study. International Conference on Big Data Analytics and Practices (IBDAP) Student.
Tomasevic, N., Gvozdenovic, N., & Vranes, S. (2020). An overview and comparison of supervised data mining techniques for student exam performance prediction. Computers and Education, 143(August 2019), 103676. https://doi.org/10.1016/j.compedu.2019.103676
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. https://doi.org/10.5944/ried.23.2.26356
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89(October 2017), 98–110. https://doi.org/10.1016/j.chb.2018.07.027
Viloria, A., García Guliany, J., Niebles Núñez, W., Palma, H. H., & Niebles Núñez, L. (2020). Data Mining Applied in School Dropout Prediction. Journal of Physics, 1432, 12092. https://doi.org/10.1088/1742-6596/1432/1/012092
Vora, D. R., & Rajamani, K. (2019). A hybrid classification model for prediction of academic performance of students : a big data application. Evolutionary Intelligence, 0123456789. https://doi.org/10.1007/s12065-019-00303-9
Wang, Q., Wu, W., & Qi, Y. (2018). A Learning Analytic Model for Smart Classroom. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 11268 LNCS. Springer International Publishing. https://doi.org/10.1007/978-3-030-01298-4_19
Wati, M., Indrawan, W., Widians, J. A., & Puspitasari, N. (2018). Data mining for predicting students’ learning result. Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017, 2018-Janua, 1–4. https://doi.org/10.1109/CAIPT.2017.8320666
Wheeler, E., Finucane, S., Soper, S., & Mayhew, T. (2018). Perceived Value of Preadmission Physical Therapy Observation Hours and Their Ability to Predict Academic Performance. Journal of Physical Therapy Education:, 32(1), 17–25.
Zacharias, D., & Athanasios, A. (2020). Monitoring of the Results through a Survey Concerning the Socio-Economic Characteristics of the Elderly Using Geographic Information Systems (GIS): A Case Study in Greece. International Journal of Innovation and Economic Development, 6(3), 36–45. https://doi.org/10.18775/ijied.1849-7551-7020.2015.64.2004
Ahamed, A. T. M. S., Mahmood, N. T., & Rahman, R. M. (2016). Prediction of HSC Examination Performance using Socioeconomic , Psychological and Academic Factors. International Conference on Electrical and Computer Engineering, 263–266.
Ajibade, S. M., Ahmad, N. B., & Shamsuddin, S. M. (2019). An Heuristic Feature Selection Algorithm to Evaluate Academic Performance of Students. 10th Control and System Graduate Research Colloquium (ICSGRC, 2–3.
Ajibade, S. S. M., Bahiah Binti Ahmad, N., & Mariyam Shamsuddin, S. (2019). Educational Data Mining: Enhancement of Student Performance model using Ensemble Methods. IOP Conference Series: Materials Science and Engineering, 551(1). https://doi.org/10.1088/1757-899X/551/1/012061
Amazona, M. V., & Hernandez, A. A. (2019). Modelling Student Performance Using Data Mining Techniques. Proceedings of the 2019 5th International Conference on Computing and Data Engineering - ICCDE’ 19, 36–40. https://doi.org/10.1145/3330530.3330544
Argonza, J. (2016). Big data en la educación. Revista Digital Universitaria, 17(1), 1–16. http://www.revista.unam.mx/vol.17/num1/art06/
Avella, J. T., Kebritchi, M., Nunn, S. G., & Kanai, T. (2016). Learning analytics methods, benefits, and challenges in higher education: A systematic literature review. Journal of Asynchronous Learning Network, 20(2). https://doi.org/10.24059/olj.v20i2.790
Baepler, P., & Murdoch, C. (2010a). Academic Analytics and Data Mining in Higher Education. International Journal for the Scholarship of Teaching and Learning, 4(2). https://doi.org/10.20429/ijsotl.2010.040217
Baepler, P., & Murdoch, C. J. (2010b). International Journal for the Scholarship of Teaching and Learning Academic Analytics and Data Mining in Higher Education Academic Analytics and Data Mining in Higher Education. International Journal for the Scholarship of Teaching and Learning, 4(2). https://doi.org/10.20429/ijsotl.2010.040217
Bhutto, S., Siddiqui, I. F., Arain, Q. A., & Anwar, M. (2020). Predicting Students’ Academic Performance Through Supervised Machine Learning. ICISCT 2020 - 2nd International Conference on Information Science and Communication Technology. https://doi.org/10.1109/ICISCT49550.2020.9080033
Brownlee, J. (2020). Machine Learning Mastery. https://machinelearningmastery.com/
Burgos, C., Campanario, M. L., Peña, D. de la, Lara, J. A., Lizcano, D., & Martínez, M. A. (2018). Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout. Computers and Electrical Engineering, 66, 541–556. https://doi.org/10.1016/j.compeleceng.2017.03.005
Burman, I., & Som, S. (2019). Predicting Students Academic Performance Using Support Vector Machine. Proceedings - 2019 Amity International Conference on Artificial Intelligence, AICAI 2019, 756–759. https://doi.org/10.1109/AICAI.2019.8701260
Campbell, J. P., & Oblinger, D. (2007). Learning analytics | Higher Education Academy. https://www.heacademy.ac.uk/enhancement/starter-tools/learning-analytics#snapshot-logo
Castrillón, O. D., Sarache, W., & Ruiz-Herrera, S. (2020). Prediction of academic performance using artificial intelligence techniques. Formacion Universitaria, 13(1), 93–102. https://doi.org/10.4067/S0718-50062020000100093
Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683–695. https://doi.org/10.1080/13562517.2013.827653
Contreras, L. E., Fuentes, H. J., & Rodríguez, I. (2020). ACADEMIC INTERRUPTION MODEL USING AUTOMATIC LEARNING ALGORITHMS. International Journal of Mechanical and Production Engineering Research and Development (IJMPERD), 10(3), 16075–16086. www.tjprc.org
Contreras, L., Fuentes, H., & Rodriguez, J. (2020). Academic performance prediction by machine learning as a success/failure indicator for engineering students. Formacion Universitaria, 13(5), 233–246. https://doi.org/10.4067/S0718-50062020000500233
Contreras, L., & Tarazona, G. (2018). Big Data: An Exploration Toward the Improve of the Academic Performance in Higher Education. Lecture Notes in Computer Science, 10943, 627–637. https://doi.org/10.1007/978-3-319-93803-5
Costa, E. B., Fonseca, B., Santana, M. A., de Araújo, F. F., & Rego, J. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Computers in Human Behavior, 73, 247–256. https://doi.org/10.1016/j.chb.2017.01.047
Daniel, B. (2015a). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46–5(0007–1013), 904–920. https://www.learntechlib.org/p/151804/
Daniel, B. (2015b). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230
Daumiller, M., & Janke, S. (2019). The Impact of Performance Goals on Cheating Depends on How Performance Is Evaluated. AERA Open, 5(4), 1–10. https://doi.org/10.1177/2332858419894276
David, J., & Anastasija, G. (2019). Predicting Academic Performance Based on Students ’ Family Environment : Evidence for Colombia Using Classification Trees. Psychology, Society, & Education, 11(3), 299–311. https://doi.org/10.25115/psye.v10i1.2056
Devasia, M. T., P, M. V. T., & Hegde, V. (2016). Prediction of Students Performance using Educational Data Mining. International Conference on Data Mining and Advanced Computing (SAPIENCE), 91–95.
Dixson, D. D., Worrell, F. C., Olszewski-Kubilius, P., & Subotnik, R. F. (2016). Beyond perceived ability: the contribution of psychosocial factors to academic performance. Annals of the New York Academy of Sciences, 1377(1), 67–77. https://doi.org/10.1111/nyas.13210
Dyckhoff, A. L., Zielke, D., Bültmann, M., Chatti, M. A., & Schroeder, U. (2012). Design and implementation of a learning analytics toolkit for teachers. Educational Technology and Society, 15(3), 58–76.
Echegaray-calderon, O. A., & Barrios-aranibar, D. (2015). Optimal selection of factors using Genetic Algorithms and Neural Networks for the prediction of students ’ academic performance. Congreso Latinoamericano de Inteligencia Computacional, 1–6.
Estrada, J., & Quintero, R. (2015). Bajo rendimiento académico en la Universidad Distrital Francisco José de Caldas (Universidad Distrital (ed.); Primera). Editorial UD.
Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning Int. J. Technology Enhanced Learning Int. J. Technology Enhanced Learning, 4(x), 304–317. https://doi.org/10.1504/IJTEL.2012.051816
Fournier, H. ;, Kop, R. ;, & Sitlia, H. (2011). The Value of Learning Analytics to Networked Learning on a Personal Learning Environment. LAK ’11 Proceedings of the 1st International Conference on Learning Analytics and Knowledge, 104–109. https://doi.org/10.1145/2090116.2090131
Francis, B. K., & Babu, S. S. (2019). Predicting Academic Performance of Students Using a Hybrid Data Mining Approach. Journal of Medical Systems, 43(6). https://doi.org/10.1007/s10916-019-1295-4
García-González, J. R., Sánchez-Sánchez, P. A., Orozco, M., & Obredor, S. (2019). Extracción de Conocimiento para la Predicción y Análisis de los Resultados de la Prueba de Calidad de la Educación Superior en Colombia Knowledge Capture for the Prediction and Analysis of Results of the Quality Test of Higher Education in Colombia. Revista Formación Universitaria, 12(4), 55–62. https://doi.org/10.4067/S0718-50062019000400055
García Tinisaray, D. K. (2015). CONSTRUCCIÓN DE UN MODELO PARA DETERMINAR EL RENDIMIENTO ACADÉMICO DE LOS ESTUDIANTES BASADO EN LEARNING ANALYTICS (ANÁLISIS DEL APRENDIZAJE), MEDIANTE EL USO DE TÉCNICAS MULTIVARIANTES. Universidad de Sevilla.
Goldstein, P. J., & Katz, R. N. (2005). Academic Analytics : The Uses Of Management Information And Technology In Higher Education. EDUCAUSE Quarterly, 8(December), 113. https://library.educause.edu/resources/2005/12/academic-analytics-the-uses-of-management-information-and-technology-in-higher-education
Gómez, D. (2015). Analítica visual en eLearning [Universdiad de Salamanca]. https://gredos.usal.es/handle/10366/125950
Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology and Society, 15(3), 42–57.
Hasan, R., Palaniappan, S., Mahmood, S., Abbas, A., Sarker, K. U., & Sattar, M. U. (2020). Predicting student performance in higher educational institutions using video learning analytics and data mining techniques. Applied Sciences , 10(11). https://doi.org/10.3390/app10113894
Hasbun, T., Araya, A., & Villalon, J. (2016). Extracurricular activities as dropout prediction factors in higher education using decision trees. 16th International Conference on Advanced Learning Technologies Extracurricular, 3–5. https://doi.org/10.1109/ICALT.2016.66
Hassan, H., Ahmad, N. B., & Anuar, S. (2020). Improved students’ performance prediction for multi-class imbalanced problems using hybrid and ensemble approach in educational data mining. Journal of Physics, 1529(5). https://doi.org/10.1088/1742-6596/1529/5/052041
Hernández, A. G., Meléndez, R. A., Morales, L. A., García, A., Tecpanecatl, J. L., & Algredo, I. (2016). Comparative Study of Algorithms to Predict the Desertion in the Students at the ITSM-Mexico. LATIN AMERICA TRANSACTIONS, 14(11), 4573–4578.
Herodotou, C., Rienties, B., Boroowa, A., & Zdrahal, Z. (2019). A large ‑ scale implementation of predictive learning analytics in higher education : the teachers ’ role and perspective. In Educational Technology Research and Development (Vol. 67, Issue 5). Springer US. https://doi.org/10.1007/s11423-019-09685-0
Hirokawa, S. (2018). Key attribute for predicting student academic performance. ACM International Conference Proceeding Series, 308–313. https://doi.org/10.1145/3290511.3290576
Iten, L., Arnold, K., & Pistilli, M. (2008). Mining Real-Time Data to Improve Student Success in a Gateway Course. Eleventh Annual TLT Conference.
Janani, P., Verma, S., Natarajan, S., & Sinha, A. K. (2019). Communication Using IoT. In Information and Communication Technology for Sustainable Development, Advances in Intelligent Systems and Computing. Springer Singapore. https://doi.org/10.1007/978-981-13-7166-0
Kauffman, C., Derazin, M., Asmar, A., & Kibble, J. (2019). Patterns of medical student engagement in a second-year pathophysiology course: relationship to USMLE Step 1 performance. Advances in Physiology Education, 43(01), 512–518. https://doi.org/10.1152/advan.00082.2019
Kaunang, F. J., & Rotikan, R. (2018). Students’ academic performance prediction using data mining. Proceedings of the 3rd International Conference on Informatics and Computing, ICIC 2018, 1–5. https://doi.org/10.1109/IAC.2018.8780547
Khalil, M. K., Hawkins, H. G., Crespo, L. M., & Buggy, J. (2018). The Design and Development of Prediction Models for Maximizing Students ’ Academic Achievement. Medical Science Educator, 111–117.
Khalil, M., Khalil, M., & Ebner, M. (2015). Learning Analytics: Principles and Constraints. EdMedia: World Conference on Educational Media and Technology, 2015(1), 1789–1799. https://www.learntechlib.org/p/151455/
Kwankajonwong, N., Ongprakobkul, C., Qureshi, S. P., Watanatada, P., Thanprasertsuk, S., & Bongsebandhu-phubhakdi, S. (2019). Attitude, but Not Self-Evaluated Knowledge, Correlates with Academic Performance in Physiology in Thai Medical Students. Advances in Physiology Education, 43(3), p324-331.
Lasarte, O. F., Díaz, E. R., Palacios, E. G., & Fernández, A. R. (2020). The role of social support in school adjustment during Secondary Education. 32(1), 100–107. https://doi.org/10.7334/psicothema2019.125
Lauría, E., Moody, E., Jayaprakash, S., Jonnalagadda, N., & Baron, J. (2013). Open academic analytics initiative: initial research findings. Proceedings of the Third International Conference on Learning Analytics and Knowledge, 150–154.
Lima, J., Alves, P., Pereira, M., & Almeida, S. (2018). Using Academic Analytics to Predict Dropout Risk in Engineering Courses. European Conference on E-Learning.
Limsathitwong, K., Tiwatthanont, K., & Yatsungnoen, T. (2018). Dropout Prediction System to Reduce Discontinue Study Rate of Information Technology Students. International Conference on Business and Industrial Research (ICBIR), 110–114.
Liu, H., Wang, Z., Benachour, P., & Tubman, P. (2018). A Time Series Classification Method for Behaviour-Based Dropout Prediction. 18th International Conference on Advanced Learning Technologies A, 191–195. https://doi.org/10.1109/ICALT.2018.00052
Long, P., Siemens, G., Conole, G., & Gašević, D. (2011). LAK ’11. Proceedings of the 1st International Conference on Learning Analytics and Knowledge, 195. https://iucat.iu.edu/iub/13609333
Mahroeian, H., Daniel, B., & Butson, R. (2017). The perceptions of the meaning and value of analytics in New Zealand higher education institutions. International Journal of Educational Technology in Higher Education, 14(1), 1–17. https://doi.org/10.1186/s41239-017-0073-y
Martín, A. N. (2019). Variables motivacionales y cognitivas predictivas del rendimiento en estudiantes universitarios de ciencias de la salud Cognitive and motivational predictive variables associated with performance in University Students of Health Sciences. 33(1), 1–30.
Medina, E. C., Chunga, C. B., Armas-aguirre, J., Grandón, E. E., & Bío-bío, U. (2020). Modelo predictivo para reducir el índice de deserción de estudiantes universitarios en el Perú : Redes Bayesianas vs . Árboles de Decisión Predictive model to reduce the dropout rate of
Decision Trees. 15th Iberian Conference on Information Systems and Technologies (CISTI), June, 24–27.
Mengash, H. A. (2020a). Using data mining techniques to predict student performance to support decision making in university admission systems. IEEE Access, 8, 55462–55470. https://doi.org/10.1109/ACCESS.2020.2981905
Mengash, H. A. (2020b). Using data mining techniques to predict student performance to support decision making in university admission systems. IEEE Access, 8, 55462–55470. https://doi.org/10.1109/ACCESS.2020.2981905
Merchán-rubiano, S., Beltrán-gómez, A., & Duarte-garcía, J. (2017). Engineering Students´ Academic Performance Prediction using ICFES Test Scores and Demo- graphic Data. Ingeniería Solidaria, 13(21).
Miguéis, V. L., Freitas, A., Garcia, P. J. V., & Silva, A. (2018). Early segmentation of students according to their academic performance: A predictive modelling approach. Decision Support Systems, 115, 36–51. https://doi.org/10.1016/j.dss.2018.09.001
Mohammadi, M., Dawodi, M., Tomohisa, W., & Ahmadi, N. (2019). Comparative study of supervised learning algorithms for student performance prediction. 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019, 124–127. https://doi.org/10.1109/ICAIIC.2019.8669085
Murnion, P., & Helfert, M. (2013). Academic Analytics in quality assurance using organisational analytical capabilities A User-level Usage Analytics in Cloud Based Applications View project Insight View project. In U. Oxford (Ed.), Annual Conference of the UK Academy of Information Systems (UKAIS). https://doi.org/10.13140/2.1.3368.1600
Nabizadeh, S., Hajian, S., Sheikhan, Z., & Rafiei, F. (2019). Prediction of academic achievement based on learning strategies and outcome expectations among medical students. BMC Medical Education, 19(1), 1–11. https://doi.org/10.1186/s12909-019-1527-9
Nieto, Y., Garcia, V., Montenegro, C., Gonzalez, C., & Gonzalez, R. (2019). Usage of Machine Learning for Strategic Decision Making at Higher Educational Institutions. IEEE Access, 7, 75007–75017. https://doi.org/10.1109/ACCESS.2019.2919343
Norris, D. M., & Lefrere, P. (2011). Transformation through expeditionary change using online learning and competence-building technologies. Research in Learning Technology, 19(1), 1741–1629. https://doi.org/10.3402/rlt.v19i1.17105
Oblinger, G., Campbell, J. P., Deblois, P. B., & Oblinger, D. G. (2007). Academic Analytics: A New Tool for a New Era. Research in Higher Education, 1(2), 727–742. http://er.educause.edu/~/media/files/article-downloads/erm0742.pdf
Pradeep, A., Das, S., & Kizhekkethottam, J. J. (2015). Students Dropout Factor Prediction Using EDM Techniques. International Conference on Soft-Computing and Network Security.
QIN, F., ZHU, L., CHENG, Z., & ZHANG, Q. (2017). Research on Student Performance Evaluation Based on Random Forest. DEStech Transactions on Engineering and Technology Research, eeta, 387–393. https://doi.org/10.12783/dtetr/eeta2017/7762
Qowidho, T., Zarlis, M., Nababan, E. B., Agusnady, A., & Sembiring, B. S. (2019). Analysis of c4.5 and ID3 Methods in Determining Student Graduation. Journal of Physics: Conference Series, 1255(1). https://doi.org/10.1088/1742-6596/1255/1/012025
Razaque, F., Soomro, N., Ahmed, S., Soomro, S., Samo, J. A., & Dharejo, H. (2017). Using Naïve Bayes Algorithm to Students ’ bachelor Academic Performances Analysis. Conferencia Internacional IEEE Sobre Tecnologías de Ingeniería y Ciencias Aplicadas (ICETAS), 1–5.
Salal, Y. K., Abdullaev, S. M., & Kumar, M. (2019). Educational data mining: Student performance prediction in academic. International Journal of Engineering and Advanced Technology, 8(4C), 54–59.
Salcedo, A. (2010). Desertion in Colombian Universities. Revista Academia y Virtualidad, 3(1), 50–60. http://www.alfaguia.org/alfaguia/files/1319043663_03.pdf
Schroeder, U. (2009). Web-Based Learning – Yes We Can! (pp. 25–33). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03426-8_3
Siemens, G. (2013). Learning Analytics. American Behavioral Scientist, 57(10), 1380–1400. https://doi.org/10.1177/0002764213498851
Stanko, T., Zhirosh, O., Johnston, D., & Gartsev, S. (2017). On possibility of prediction of academic performance and potential improvements of admission campaign at IT university. IEEE Global Engineering Education Conference, 862–866.
Tan, T. Y., Jain, M., Obaid, T., & Nesbit, J. C. (2020). What can completion time of quizzes tell us about students ’ motivations and learning strategies ? Journal of Computing in Higher Education, 32(2), 389–405. https://doi.org/10.1007/s12528-019-09239-6
Tasnim, N., Paul, M. K., & Sarowar Sattar, A. H. M. (2019). Performance Analysis of Different Decision Tree Based Methods for Identifying Drop out Students. 1st International Conference on Advances in Science, Engineering and Robotics Technology 2019, ICASERT 2019, 2019(Icasert), 1–6. https://doi.org/10.1109/ICASERT.2019.8934518
Tenpipat, W., & Akkarajitsakul, K. (2020). Student Dropout Prediction : A KMUTT Case Study. International Conference on Big Data Analytics and Practices (IBDAP) Student.
Tomasevic, N., Gvozdenovic, N., & Vranes, S. (2020). An overview and comparison of supervised data mining techniques for student exam performance prediction. Computers and Education, 143(August 2019), 103676. https://doi.org/10.1016/j.compedu.2019.103676
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. https://doi.org/10.5944/ried.23.2.26356
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89(October 2017), 98–110. https://doi.org/10.1016/j.chb.2018.07.027
Viloria, A., García Guliany, J., Niebles Núñez, W., Palma, H. H., & Niebles Núñez, L. (2020). Data Mining Applied in School Dropout Prediction. Journal of Physics, 1432, 12092. https://doi.org/10.1088/1742-6596/1432/1/012092
Vora, D. R., & Rajamani, K. (2019). A hybrid classification model for prediction of academic performance of students : a big data application. Evolutionary Intelligence, 0123456789. https://doi.org/10.1007/s12065-019-00303-9
Wang, Q., Wu, W., & Qi, Y. (2018). A Learning Analytic Model for Smart Classroom. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 11268 LNCS. Springer International Publishing. https://doi.org/10.1007/978-3-030-01298-4_19
Wati, M., Indrawan, W., Widians, J. A., & Puspitasari, N. (2018). Data mining for predicting students’ learning result. Proceedings of the 2017 4th International Conference on Computer Applications and Information Processing Technology, CAIPT 2017, 2018-Janua, 1–4. https://doi.org/10.1109/CAIPT.2017.8320666
Wheeler, E., Finucane, S., Soper, S., & Mayhew, T. (2018). Perceived Value of Preadmission Physical Therapy Observation Hours and Their Ability to Predict Academic Performance. Journal of Physical Therapy Education:, 32(1), 17–25.
Zacharias, D., & Athanasios, A. (2020). Monitoring of the Results through a Survey Concerning the Socio-Economic Characteristics of the Elderly Using Geographic Information Systems (GIS): A Case Study in Greece. International Journal of Innovation and Economic Development, 6(3), 36–45. https://doi.org/10.18775/ijied.1849-7551-7020.2015.64.2004
##plugins.themes.bootstrap3.article.sidebar##
Publicado
Mar 1, 2021
##plugins.themes.bootstrap3.article.details##
Cómo citar
1.
Analítica académica: nuevas herramientas aplicadas a la educación. bol.redipe [Internet]. 2021 Mar. 1 [cited 2024 May 19];10(3):137-58. Available from: https://revista.redipe.org/index.php/1/article/view/1225
Sección
Artículos
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Los autores/as conservarán sus derechos de autor y garantizarán a la revista el derecho de primera publicación de su obra, el cuál estará simultáneamente sujeto a la Licencia de reconocimiento de Creative Commons que permite a terceros compartir la obra siempre que se indique su autor y su primera publicación esta revista.
Revista Boletín Redipe por Red Iberoamericana de Pedagogía se distribuye bajo una Licencia Creative Commons Atribución-CompartirIgual 4.0 Internacional.
Biografía del autor/a
Leonardo Emiro Contreras Bravo, Universidad Distrital Francisco José de Caldas, Colombia
Ingeniero, Estudiante de doctorado en ingeniería, Docente de planta. Facultad de ingeniería. Universidad Distrital Francisco José de Caldas (Colombia).
José Ignacio Rodríguez Molano , Universidad Distrital Francisco José de Caldas, Colombia
Ingeniero, Doctor en Ingeniería. Docente de planta. Facultad de ingeniería. Universidad Distrital Francisco José de Caldas (Colombia).
Héctor Javier Fuentes López, Universidad Distrital Francisco José de Caldas, Colombia
Economista, Estudiante de doctorado en Economía. Docente de planta. Facultad de ingeniería. Universidad Distrital Francisco José de Caldas (Colombia).
Cómo citar
1.
Analítica académica: nuevas herramientas aplicadas a la educación. bol.redipe [Internet]. 2021 Mar. 1 [cited 2024 May 19];10(3):137-58. Available from: https://revista.redipe.org/index.php/1/article/view/1225