Productividad educativa y algoritmos de aprendizaje automático en escuela de ingeniería de sistemas e informática, Universidad Nacional de Moquegua- 2023
Palabras clave:
Productividad académica, algoritmos de aprendizaje automático, resultados de aprendizaje, confiabilidadSinopsis
La presente investigacion tiene como propósito determinar el nivel de relación entre la productividad educativa y los algoritmos de aprendizaje automático en estudiantes EPISI de UNAM, 2023, con un tipo de investigación básica, de nivel descriptivo correlacional, transversal; siendo de diseño no experimental, método hipotético deductivo con enfoque cuantitativo.
La muestra estuvo conformada por 90 estudiantes de un total de 116, se realizó un muestreo aleatorio simple, como técnica se empleó la encuesta para la variable productividad educativa y la observación para la variable algoritmos de aprendizaje automático, con instrumento de cuestionario y lista de cotejo respectivamente con escala de Likert para las variables productividad educativa y algoritmos de aprendizaje automático, los mismos que fueron validados previamente demostrando la eficacia y confiabilidad mediante la técnica de opinión de expertos y alfa de Cronbach cuyo resultado fue 0.777 y 0.974 con significado de confiabilidad muy bueno y excelente.
Los resultados que se obtuvieron evidenciaron, que la productividad educativa y los algoritmos de aprendizaje automático tiene correlación positiva muy alta, debido a que el coeficiente de correlación fue 0,940 en la Escuela Profesional de Ingeniería de Sistemas e Informática de la Universidad Nacional de Moquegua, 2023, con un nivel de significancia es 0,000 < 0,05, de acuerdo a la encuesta se determinó el nivel de relación que existe entre las variables productividad educativa y los algoritmos de aprendizaje automático fue 113.3% (12) Insuficiente, 71.1% (64) Aprobado y 15.6% (14) Bueno.
Capítulos
Referencias
Arias Gonzáles, J. L. (2021). Diseño y metodología de la investigación. Enfoques Consulting E.I.R.L. https://apps.utel.edu.mx/recursos/files/r161r/w26022w/Arias_S2.pdf
Ghasemi, A., y Zahediasl, S. (2012). Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486–489. https://doi.org/10.5812/ijem.3505
Quiñones, L. y Quiñones, Y. L. (2020). Rendimiento académico empleando minería de datos. Espacios, 41(44), 277–285. https://doi.org/10.48082/espacios-a20v41n44p17
Martínez Rebollar, A. y Campos Francisco, W. (2015). The Correlation Among Social Interaction Activities Registered Through New Technologies and Elderly’s Social Isolation Level. Revista Mexicana de Ingeniería Biomédica, 36(3), 177–188. https://doi.org/10.17488/RMIB.36.3.4
Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Application of Student’s t-test, analysis of variance, and covariance in research. Annals of Cardiac Anaesthesia, 22(4), 407–411. https://doi.org/10.4103/aca.ACA_94_19
Morales Hernández, M. Á. y González Camacho, J. M. y Robles Vásquez, H. y Del Valle Paniagua, D. H. y Durán Moreno, J. R. (2022). Algoritmos de aprendizaje automático para la predicción del logro académico. RIDE Revista Iberoamericana Para La Investigación y El Desarrollo Educativo, 12(24). https://doi.org/10.23913/ride.v12i24.1180
Parhizkar, A. y Tejeddin, G. y Khatibi, T. (2023). Student performance prediction using datamining classification algorithms: Evaluating generalizability of models from geographical aspect. Education and Information Technologies
Yağcı, M. (2022). Educational data mining: prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9(1), 11. https://doi.org/10.1186/s40561-022-00192-z
Referencias generales.
Abid, N. y Ali, R. y Akhter, M. (2021). Exploring gender‐based difference towards academic enablers scales among secondary school students of Pakistan. Psychology in the Schools, 58(7), 1380–1398. https://doi.org/10.1002/pits.22538
Ahmed Khan, Z. y Adnan, J. y Adnan Raza, S. (2023). Cognitive Learning Theory and Development: Higher Education Case Study. https://doi.org/10.5772/intechopen.110629
Al-Ababneh M. (2020). Linking Ontology, Epistemology and Research Methodology. Science & Philosophy.
Al-Ababneh, M. M. (2020). Linking Ontology, Epistemology and Research Methodology. Science & Philosophy, 75–91.
Al-Ani, O. y Das, S. (2022). Reinforcement Learning: Theory and Applications in hems. Mathematics & computer science, Artificial Intelligence & Robotics .
Alban, J. y Calero J. L. (2017). El rendimiento académico: aproximación necesaria a un problema pedagógico actual. Revista Conrado.
Alkin, M. C. y King, J. A. (2016). The Historical Development of Evaluation Use. American Journal of Evaluation, 37(4), 568–579. https://doi.org/10.1177/1098214016665164
Andrade, M. S. y Freitas, J. C. de. (2023). Analysis of Performance Metrics on the Conjuncture of Intrusions in IEEE 802.11 Networks with Machine Learning at Hospital N.S.C. In connecting expertise multidisciplinary development for the future. Seven Editora. https://doi.org/10.56238/Connexpemultidisdevolpfut-116
Anyoha, R. (2017). La historia de la inteligencia artificial - Ciencia en las noticias. https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/
Auffarth, B. (2021). Machine Learning for Time-Series with Python. https://books.google.com.pe/books?id=a7tLEAAAQBAJ&pg=PA99&lpg=PA99&dq=author+of+nearest+neighbors+algorithm++Evelyn+Fix+y+Joseph+Hodges&source=bl&ots=D19ck8A07W&sig=ACfU3U0vGNfCvt_kI7tpjETbQgZO0GuyAA&hl=es&sa=X&ved=2ahUKEwiTt474wcj4AhVVAdQKHUSABNUQ6AF6BAg0EAM#v=onepage&q=author%20of%20nearest%20neighbors%20algorithm%20%20Evelyn%20Fix%20y%20Joseph%20Hodges&f=false
Avila, Á. (2022). La motivación en los estudiantes: 3 Claves para aumentarla. https://www.enriccorberainstitute.com/blog/motivacion-en-los-estudiantes/
Bagnato Juan Ignacio. (2020). Aprende Machine Learning en Español Teoría + Práctica Python. 1–184.
Bernal Torres, C. A. (2016). Metodología de la investigación: Administración, economía, humanidades y ciencias sociales (4ª ed.). Pearson Educación. https://drive.google.com/file/d/1-3wqx7vGGCn6O4FxMPkzKwl5E4tByYXX/view?pli=1
Barus, S. P. (2021). Implementation of Naïve Bayes Classifier-based Machine Learning to Predict and Classify New Students at Matana University. Journal of Physics: Conference Series, 1842(1), 012008. https://doi.org/10.1088/1742-6596/1842/1/012008
BBVA. (2019). “Machine learning”: ¿qué es y cómo funciona?
Biblioteca Nacional de Chile. (2021). La Filosofía Positivista. http://www.memoriachilena.gob.cl/602/w3-article-93966.html
Brew, E. A. y Nketiah, B. y Koranteng, R. (2021). A Literature Review of Academic Performance, an Insight into Factors and their Influences on Academic Outcomes of Students at Senior High Schools. OALib, 08(06), 1–14. https://doi.org/10.4236/oalib.1107423
Brownlee, J. (2018). Machine Learning Algorithms From Scratch (v.1.8, Ed.).
Carroll, J. B. (1989). The Carroll Model: A 25-Year Retrospective and Prospective View. Educational Researcher, 18(1), 26. https://doi.org/10.2307/1176007
Carrasco Díaz, S. (2006). Metodología de la investigación científica: pautas metodológicas para diseñar y elaborar el proyecto de investigación. San Marcos. https://drive.google.com/file/d/1GTWMTyAZDmzE0hJbUKSxsR-QJWsYugBV/view
Caselli Gismondi, H. E. (2021). Modelo predictivo basado en machine learning como soporte para el seguimiento académico del estudiante universitario [Universidad Nacional del Santa]. http://repositorio.uns.edu.pe/bitstream/handle/UNS/3804/52337.pdf?sequence=5&isAllowed=y
Caselli Gismondi, H. E. y Urrelo Huiman, L. V. (2021). Características para un modelo de predicción de la deserción académica universitaria. Caso Universidad Nacional de Santa. Llamkasun, 2(4), 02–22. https://doi.org/10.47797/llamkasun.v2i4.61
Castañon, E. R. (2023). ¿Qué es un ambiente de aprendizaje según autores? https://www.centrobanamex.com.mx/que-es-un-ambiente-de-aprendizaje-segun-autores/
Castrillón, O. D. y Sarache, W. y Ruiz-Herrera, S. (2020). Predicción del rendimiento académico por medio de técnicas de inteligencia artificial. Formación Universitaria, 13(1), 93–102. https://doi.org/10.4067/S0718-50062020000100093
Cecilia, O. N. y Bernedette, C.-U. y Emmanuel, A. E. y Hope, A. N. (2020). Enhancing students academic performance in Chemistry by using kitchen resources in Ikom, Calabar. Educational Research and Reviews, 15(1), 19–26. https://doi.org/10.5897/ERR2019.3810
Charbuty, B. y Abdulazeez, A. (2021). Classification Based on Decision Tree Algorithm for Machine Learning. Journal of Applied Science and Technology Trends, 2(01), 20–28. https://doi.org/10.38094/jastt20165
Contreras, L. E. y Fuentes, H. J. y Rodríguez, J. I. (2020). Predicción del rendimiento académico como indicador de éxito/fracaso de los estudiantes de ingeniería, mediante aprendizaje automático. Formación Universitaria, 13(5), 233–246. https://doi.org/10.4067/S0718-50062020000500233
Correia, M. I. T. D. (2023). Ethics in research. Clinical Nutrition Open Science, 47, 121–130. https://doi.org/10.1016/j.nutos.2022.12.010
de Oliveira Chagas, E. T. (2019). Deep Learning and its applications today. Revista Científica Multidisciplinar Núcleo Do Conhecimento, 04(05), 05–26. https://doi.org/10.32749/nucleodoconhecimento.com.br/business-administration/deep-learning-2
Díaz-Landa, B. y Meleán-Romero, R. y Marín-Rodriguez, W. (2021). Rendimiento académico de estudiantes en Educación Superior: predicciones de factores influyentes a partir de árboles de decisión. Telos Revista de Estudios Interdisciplinarios En Ciencias Sociales, 23(3), 616–639. https://doi.org/10.36390/telos233.08
Dong, Y. y Hou, J. y Zhang, N. y Zhang, M. (2020). Research on How Human Intelligence, Consciousness, and Cognitive Computing Affect the Development of Artificial Intelligence. Complexity, 2020, 1–10. https://doi.org/10.1155/2020/1680845
Edgard, T. W. y Manz, D. O. (2017). Research Methods for Cyber Security (1ra ed.). Elsevier Inc.
Escobar-Pérez, J. y Cuervo-Martínez, Á. (2008). Validez de contenido y juicio de expertos: una aproximación a su utilización. Avances En Medición, 27–36. https://www.humanas.unal.edu.co/lab_psicometria/application/files/9416/0463/3548/Vol_6._Articulo3_Juicio_de_expertos_27-36.pdf
Esteso, A. y Peidro, D. y Mula, J. y Díaz-Madroñero, M. (2022). Reinforcement learning applied to production planning and control. International Journal of Production Research, 1–18. https://doi.org/10.1080/00207543.2022.2104180
Fernandes, E. y Holanda, M. y Victorino, M. y Borges, V. y Carvalho, R. y Erven, G. van. (2019). Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research, 94, 335–343. https://doi.org/10.1016/j.jbusres.2018.02.012
Fernández-Mellizo, M. y Constante-Amores, A. (2020). Determinantes del rendimiento académico de los estudiantes de nuevo acceso a la Universidad Complutense de Madrid. Revista de Educación, 213–240.
Figueroa-Abarzúa, C. y Meza-Vásquez, S. y Estrada-Lagos, R. (2022). Desarrollo de capacidad argumentativa en estudiantes universitarios, mediante uso del debate como estrategia didáctica. South Florida Journal of Development, 3(6), 6328–6346. https://doi.org/10.46932/sfjdv3n6-001
Fu, M. y Zhang, C. y Hu, C. y Wu, T. y Dong, J. y Zhu, L. (2023). Achieving Verifiable Decision Tree Prediction on Hybrid Blockchains. Entropy, 25(7), 1058. https://doi.org/10.3390/e25071058
Géron, A. (2019). Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow Concepts, Tools, and Techniques to Build Intelligent System (Ni. Tache, Ed.; 2nd ed.).
Ghasemi, A. y Zahediasl, S. (2012). Normality Tests for Statistical Analysis: A Guide for Non-Statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486–489. https://doi.org/10.5812/ijem.3505
Gogus, A. (2012). Bloom’s Taxonomy of Learning Objectives. In Encyclopedia of the Sciences of Learning (pp. 469–473). Springer US. https://doi.org/10.1007/978-1-4419-1428-6_141
Gopani, A. (2022). La historia de los algoritmos de aprendizaje automático. https://analyticsindiamag.com/the-history-of-machine-learning-algorithms/
Grasso Imig, P. (2020). Rendimiento académico: un recorrido conceptual que aproxima a una definición unificada para el ámbito superior. https://fh.mdp.edu.ar/revistas/index.php/r_educ/article/view/4165
Guill, K. y Ömeroğulları, M. y Köller, O. (2021). Intensity and content of private tutoring lessons during German secondary schooling: effects on students’ grades and test achievement. European Journal of Psychology of Education. https://doi.org/10.1007/s10212-021-00581-x
Hajjej, F. y Alohali, M. A. y Badr, M. y Rahman, M. A. (2022). A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data. BioMed Research International, 2022, 1–9. https://doi.org/10.1155/2022/9449497
Hathcoat, J. D. y Meixner, C. y Nicholas, M. C. (2019). Ontology and Epistemology. In Handbook of Research Methods in Health Social Sciences (pp. 99–116). Springer Singapore. https://doi.org/10.1007/978-981-10-5251-4_56
Hawkins, C. y Bailey, L. E. (2020). A New Data Landscape: IR’s Role in Academic Analytics. New Directions for Institutional Research, 2020(185–186), 87–103. https://doi.org/10.1002/ir.20331
Hernández Sampieri, R. y Fernàndez Collado, C. y Baptista Lucio, M. del P. (2014). Metodología de la investigación. Mc Graw Hill.
Hernandez-Manxilla J.M. (2011). El racionalismo cartesiano y su particular conquista de la subjetividad en el mundo moderno. FRENIA.
Hernández-Sampieri R. (2018). Metodología de la investigación (1ra Ed.).
Herrera Acosta, C. E. y Sánchez Pinedo, L. D. (2019). METODOLOGÍA DE APRENDIZAJE Y SISTEMA DE EVALUACIÓN PARA ALCANZAR RESULTADOS EN EL PROCESO EDUCATIVO . Evaluación de La Calidad Educativa. https://www.igobernanza.org/index.php/IGOB/Contacto
Hiller, J. (2016). Epistemological foundations of objectivist and interpretivist research (pp. 99–127).
IBM Cloud Education. (2020). Machine Learning This introduction to machine learning provides an overview of its history, important definitions, applications, and concerns within businesses today. https://www.ibm.com/cloud/learn/machine-learning
Ilić, M. y Srdjević, Z. y Srdjević, B. (2022). Water quality prediction based on Naïve Bayes algorithm. Water Science and Technology, 85(4), 1027–1039. https://doi.org/10.2166/wst.2022.006
Janiesch, C. y Zschech, P. y Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685–695. https://doi.org/10.1007/s12525-021-00475-2
Jiang, T. y Gradus, J. L. y Rosellini, A. J. (2020). Supervised Machine Learning: A Brief Primer. Behavior Therapy, 51(5), 675–687. https://doi.org/10.1016/j.beth.2020.05.002
Jin, S. y Fang, G. y Cheung, K. C. y Sit, P. S. (2022). Factors associated with academic resilience in disadvantaged students: An analysis based on the PISA 2015 B-S-J-G (China) sample. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.846466
Jovel, J. y Greiner, R. (2021). An Introduction to Machine Learning Approaches for Biomedical Research. Frontiers in Medicine, 8. https://doi.org/10.3389/fmed.2021.771607
Kaun, C. y Jhanjhi, N. Z. y Goh, W. W. y Sukumaran, S. (2021). Implementation of Decision Tree Algorithm to Classify Knowledge Quality in a Knowledge Intensive System. MATEC Web of Conferences, 335, 04002. https://doi.org/10.1051/matecconf/202133504002
Kaur Arora, S. (2022). Top Steps To Learn Naive Bayes Algorithm. https://hackr.io/blog/top-steps-to-learn-naive-bayes-algorithm
KOÇOĞLU, F. Ö. (2022). Research on the success of unsupervised learning algorithms in indoor location prediction. International Advanced Researches and Engineering Journal, 6(2 (under construction)), 148–153. https://doi.org/10.35860/iarej.1096573
Krathwohl, D. R. (2002). A Revision of Bloom’s Taxonomy: An Overview. In Theory Into Practice (Vol. 41, Issue 4). https://doi.org/10.1207/s15430421tip4104_2
Lampropoulos, A. S. y Tsihrintzis, G. A. (2015). Machine Learning Paradigms (Vol. 92). Springer International Publishing. https://doi.org/10.1007/978-3-319-19135-5
Laplante, P. A. (2001). Dictionary of computer science, engineering, and technology. www.crcpress.com
Lee, J. y Warner, E. y Shaikhouni, S. y Bitzer, M. y Kretzler, M. y Gipson, D. y Pennathur, S. y Bellovich, K. y Bhat, Z. y Gadegbeku, C. y Massengill, S. y Perumal, K. y Saha, J. y Yang, Y. y Luo, J. y Zhang, X. y Mariani, L. y Hodgin, J. B. y Rao, A. (2022). Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease. Scientific Reports, 12(1), 4832. https://doi.org/10.1038/s41598-022-08974-8
Mahesh, B. (2018). Machine Learning Algorithms - A Review. International Journal of Science and Research, 381–386.
Maina, J. J. y Zakari, A. T. y Alkali, I. A. y Salisu, R. A. (2021). Academic success predictors for architecture students at Kano University of Science and Technology, Wudil, Kano State, Nigeria. Bayero Journal of Pure and Applied Sciences, 13(2), 125–133. https://doi.org/10.4314/bajopas.v13i2.17
Mandal, L. y Jana, N. D. (2019). A Comparative Study of Naive Bayes and k-NN Algorithm for Multi-class Drug Molecule Classification. 2019 IEEE 16th India Council International Conference (INDICON), 1–4. https://doi.org/10.1109/INDICON47234.2019.9029095
Martínez Rebollar, A. y Campos Francisco, W. (2015). The Correlation Among Social Interaction Activities Registered Through New Technologies and Elderly’s Social Isolation Level. Revista Mexicana de Ingeniería Biomédica, 36(3), 177–188. https://doi.org/10.17488/RMIB.36.3.4
Mazana, M. Y. y Montero, C. S. y Casmir, R. O. (2019). Investigating Students’ Attitude towards Learning Mathematics. International Electronic Journal of Mathematics Education, 14(1). https://doi.org/10.29333/iejme/3997
McDaniel, M. y Storey, V. C. (2020). Evaluating Domain Ontologies. ACM Computing Surveys, 52(4), 1–44. https://doi.org/10.1145/3329124
McGinnis, D. (2020). ¿Qué es la Cuarta Revolución Industrial? | Fuerza de ventas. https://www.salesforce.com/blog/what-is-the-fourth-industrial-revolution-4ir/
Medina N. y Fereira J. y Marzol R. (2018). Factores personales que inciden en el bajo rendimiento académico de los estudiantes de geometría. Revista de Estudios Interdisciplinarios En Ciencias Sociales.
Menacho Chiok, C. H. (2017). Predicción del rendimiento académico aplicando técnicas de minería de datos. Anales Científicos, 78(1), 26. https://doi.org/10.21704/ac.v78i1.811
Mienye, I. D. y Sun, Y. y Wang, Z. (2019). Prediction performance of improved decision tree-based algorithms: a review. Procedia Manufacturing, 35, 698–703. https://doi.org/10.1016/j.promfg.2019.06.011
Mishra, P. y Pandey, C. y Singh, U. y Gupta, A. y Sahu, C. y Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of Cardiac Anaesthesia, 22(1), 67. https://doi.org/10.4103/aca.ACA_157_18
Misra, S. y Li, H. (2020). Noninvasive fracture characterization based on the classification of sonic wave travel times. In Machine Learning for Subsurface Characterization (pp. 243–287). Elsevier. https://doi.org/10.1016/B978-0-12-817736-5.00009-0
Mohammed, M. y Khan, M. B. y Mohammed Bashier, E. B. (2017). Machine Learning Algorithms and Applications.
Morales Hernández, M. Á. y González Camacho, J. M. y Robles Vásquez, H. y Del Valle Paniagua, D. H. y Durán Moreno, J. R. (2022). Algoritmos de aprendizaje automático para la predicción del logro académico. RIDE Revista Iberoamericana Para La Investigación y El Desarrollo Educativo, 12(24). https://doi.org/10.23913/ride.v12i24.1180
Münch, M. y Raab, C. y Biehl, M. y Schleif, F.-M. (2020). Data-Driven Supervised Learning for Life Science Data. Frontiers in Applied Mathematics and Statistics, 6. https://doi.org/10.3389/fams.2020.553000
Nedeva, V. y Pehlivanova, T. (2021). Students’ Performance Analyses Using Machine Learning Algorithms in WEKA. IOP Conference Series: Materials Science and Engineering, 1031(1), 012061. https://doi.org/10.1088/1757-899X/1031/1/012061
Ong, A. K. S. y Chuenyindee, T. y Prasetyo, Y. T. y Nadlifatin, R. y Persada, S. F. y Gumasing, Ma. J. J. y German, J. D. y Robas, K. P. E. y Young, M. N. y Sittiwatethanasiri, T. (2022). Utilization of Random Forest and Deep Learning Neural Network for Predicting Factors Affecting Perceived Usability of a COVID-19 Contact Tracing Mobile Application in Thailand “ThaiChana.” International Journal of Environmental Research and Public Health, 19(10), 6111. https://doi.org/10.3390/ijerph19106111
Orlikowsld W. y Baroudi J. (1991). Studying lnformation Technology in Organimtions: Research Approaches and Assumptions. Information Systems Research.
Osisanwo, F. Y. y Akinsola, J. E. T. y Awodele, O. y Hinmikaiye, J. O. y Olakanmi, O. y Akinjobi, J. (2017). Supervised Machine Learning Algorithms: Classification and Comparison. International Journal of Computer Trends and Technology, 48(3), 128–138. https://doi.org/10.14445/22312803/IJCTT-V48P126
Oxford University. (2016). A Dictionary of Computer Science.
Padilla-Cuevas, J. y Reyes-Ortiz, J. A. y Bravo, M. (2021). Ontology-Based Context Event Representation, Reasoning, and Enhancing in Academic Environments. Future Internet, 13(6), 151. https://doi.org/10.3390/fi13060151
Palanichamy, K. (2019). Integrative Omic Analysis of Neuroblastoma. In Computational Epigenetics and Diseases (pp. 311–326). Elsevier. https://doi.org/10.1016/B978-0-12-814513-5.00019-2
Parhizkar, A. y Tejeddin, G. y Khatibi, T. (2023). Student performance prediction using datamining classification algorithms: Evaluating generalizability of models from geographical aspect. Education and Information Technologies, 28(11), 14167–14185. https://doi.org/10.1007/s10639-022-11560-0
Park, Y. S. y Konge, L. y Artino, A. R. (2020). The Positivism Paradigm of Research. Academic Medicine, 95(5), 690–694. https://doi.org/10.1097/ACM.0000000000003093
Parth Shukla. (2023). Naive Bayes Algorithms: A Complete Guide for Beginners. https://www.analyticsvidhya.com/blog/2023/01/naive-bayes-algorithms-a-complete-guide-for-beginners/
Pastor, M. (1997). Deep Blue venció a Kasparov en un campo donde el hombre no tenía rival | | ComputerWorld. Computer World. https://www.computerworld.es/archive/deep-blue-vencio-a-kasparov-en-un-campo-donde-el-hombre-no-tenia-rival
Peconcillo Jr, L. B. y D. Peteros, E. y O. Mamites, I. y T. Sanchez, D. y L. Tenerife, J. J. y L. Suson, R. (2020). Structuring Determinants to Level Up Students Performance. International Journal of Education and Practice, 8(4), 638–651. https://doi.org/10.18488/journal.61.2020.84.638.651
Pellegrino, E. y Jacques, C. y Beaufils, N. y Nanni, I. y Carlioz, A. y Metellus, P. y Ouafik, L. (2021). Machine learning random forest for predicting oncosomatic variant NGS analysis. Scientific Reports, 11(1), 21820. https://doi.org/10.1038/s41598-021-01253-y
Pirneskoski, J. y Tamminen, J. y Kallonen, A. y Nurmi, J. y Kuisma, M. y Olkkola, K. T. y Hoppu, S. (2020). Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality: A retrospective study. Resuscitation Plus, 4, 100046. https://doi.org/10.1016/j.resplu.2020.100046
Prabhakaran, S. (2018). How Naive Bayes Algorithm Works? Machine Learning. https://www.machinelearningplus.com/predictive-modeling/how-naive-bayes-algorithm-works-with-example-and-full-code/
Pugliese, R. y Regondi, S. y Marini, R. (2021). Machine learning-based approach: global trends, research directions, and regulatory standpoints. Data Science and Management, 4, 19–29. https://doi.org/10.1016/j.dsm.2021.12.002
Quiñones, L. y Quiñones, Y. L. (2020). Rendimiento académico empleando minería de datos. Espacios, 41(44), 277–285. https://doi.org/10.48082/espacios-a20v41n44p17
Ray, S. (2019). A Quick Review of Machine Learning Algorithms. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 35–39. https://doi.org/10.1109/COMITCon.2019.8862451
Raynor, W. J. (1999). The International Dictionary of Artificial Intelligence.
Rigdon, J. C. (2016). Dictionary of Computer and Internet Terms (Microsoft Corporation, Ed.; 1st Edition). http://www.wordsrus.info
Roman, V. (2019). Algoritmos Naive Bayes: Fundamentos e Implementación. Ciencia de Datos. https://medium.com/datos-y-ciencia/algoritmos-naive-bayes-fudamentos-e-implementaci%C3%B3n-4bcb24b307f
Rosales Sánchez, E. M. y Rodríguez Ortega, P. G. y Romero Ariza, M. (2020). Conocimiento, demanda cognitiva y contextos en la evaluación de la alfabetización científica en PISA. Revista Eureka Sobre Enseñanza y Divulgación de Las Ciencias, 17(2), 1–22. https://doi.org/10.25267/Rev_Eureka_ensen_divulg_cienc.2020.v17.i2.2302
Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 19–27.
Russell, S. y Norvig, P. (2008). Inteligencia Artificial Un Enfoque Moderno (D. F. Aragón, Ed.; 2nd ed.).
Salatino, A. A. y Thanapalasingam, T. y Mannocci, A. y Birukou, A. y Osborne, F. y Motta, E. (2020). The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas. Data Intelligence, 2(3), 379–416. https://doi.org/10.1162/dint_a_00055
Sarker, I. H. (2021a). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x
Sarker, I. H. (2021b). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x
Sauce, B. y Liebherr, M. y Judd, N. y Klingberg, T. (2022). The impact of digital media on children’s intelligence while controlling for genetic differences in cognition and socioeconomic background. Scientific Reports, 12(1), 7720. https://doi.org/10.1038/s41598-022-11341-2
Saunders M. y Lewis, P. y Thornhill, A. (2019). Research Methods for Business Students (Pearson Education, Ed.; Eighth).
Saunders, M. N. K. y Lewis, P. y Thornhill, A. (2029). Research Methods for Business Students (8va ed.).
Schonlau, M. y Zou, R. Y. (2020). The random forest algorithm for statistical learning. The Stata Journal: Promoting Communications on Statistics and Stata, 20(1), 3–29. https://doi.org/10.1177/1536867X20909688
Scriven, Mi. (1967). The methodology of evaluation.
Seel, N. M. (2012a). Bloom’s Model of School Learning. In Encyclopedia of the Sciences of Learning (pp. 466–469). Springer US. https://doi.org/10.1007/978-1-4419-1428-6_979
Seel, N. M. (2012b). Carroll’s Model of School Learning. In Encyclopedia of the Sciences of Learning (pp. 501–503). Springer US. https://doi.org/10.1007/978-1-4419-1428-6_980
Sepúlveda, A. y Minte, A. y Díaz-Levicoy, D. (2020). Caracterización de preguntas en libros de texto de Ciencias Naturales en Educación Primaria chilena. Educação e Pesquisa, 46. https://doi.org/10.1590/s1678-4634202046224118
Serumena D. y Utan F. y Poernomo M. (2021). The Effectiveness of Social Media as An Online Learning Pattern in Improving the 3 Domains of Student Intellectual Ability During the Pandemic (Covid-19). Advances in Engineering Research.
Sharma, N. y Sharma, R. y Jindal, N. (2021). Machine Learning and Deep Learning Applications-A Vision. Global Transitions Proceedings, 2(1), 24–28. https://doi.org/10.1016/j.gltp.2021.01.004
Simplilearn. (2022). The Complete Guide to Machine Learning Steps. https://www.simplilearn.com/tutorials/machine-learning-tutorial/machine-learning-steps
Singh, V. y Chen, S.-S. y Singhania, M. y Nanavati, B. y kar, A. kumar y Gupta, A. (2022). How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries–A review and research agenda. International Journal of Information Management Data Insights, 2(2), 100094. https://doi.org/10.1016/j.jjimei.2022.100094
Sivasubramaniam, S. y Dlabolová, D. H. y Kralikova, V. y Khan, Z. R. (2021). Assisting you to advance with ethics in research: an introduction to ethical governance and application procedures. International Journal for Educational Integrity, 17(1), 14. https://doi.org/10.1007/s40979-021-00078-6
Stern, M. y Arinze, C. y Perez, L. y Palmer, S. E. y Murugan, A. (2020). Supervised learning through physical changes in a mechanical system. Proceedings of the National Academy of Sciences, 117(26), 14843–14850. https://doi.org/10.1073/pnas.2000807117
Tai, M.-T. (2020). The impact of artificial intelligence on human society and bioethics. Tzu Chi Medical Journal, 32(4), 339. https://doi.org/10.4103/tcmj.tcmj_71_20
Tamayo y Tamayo, M. (2004). El proceso de la investigación científica. Limusa. https://www.academia.edu/29308889/Tamayo_Mario_El_Proceso_De_La_Investigacion_Cientifica_pdf
Tebani, A. y Bekri, S. (2020). High-throughput omics in the precision medicine ecosystem. In Precision Medicine for Investigators, Practitioners and Providers (pp. 19–31). Elsevier. https://doi.org/10.1016/B978-0-12-819178-1.00003-4
Torres-Malca, J. R. y Vera-Ponce, V. J. y Zuzunaga-Montoya, F. E. y Talavera, J. E. y De La Cruz-Vargas, J. A. (2022). Content validity by expert judgment of an instrument to measure knowledge, attitudes and practices about salt consumption in the peruvian population. Revista de La Facultad de Medicina Humana, 22(2), 273–279. https://doi.org/10.25176/RFMH.v22i2.4768
Triwiyanto T. y Suyanto y Prasojo L.D. y Wardana Y. (2020). Factors Affecting Educational Productivity at Private Elementary Schools in Indonesia. Advances in Social Science, Education and Humanities Research, 487, 318–323.
Universidad de Lehigh. (2013). Historia de las Pruebas Estandarizadas. https://ed.lehigh.edu/news-events/news/history-standardized-testing
Vadlamudi, P. S. y Gunasekaran, M. y Nagalakshmi, T. J. (2023). An Analysis of the Effectiveness of the Naive Bayes Algorithm and the Support Vector Machine for Detecting Fake News on Social Media. 2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), 726–731. https://doi.org/10.1109/IITCEE57236.2023.10090978
Vigo, R. y Zeigler, D. E. y Wimsatt, J. (2022). Uncharted Aspects of Human Intelligence in Knowledge-Based “Intelligent” Systems. Philosophies, 7(3), 46. https://doi.org/10.3390/philosophies7030046
Villalba, F. (2018). Naive Bayes- clasificación bayesiano ingenuo. In Aprendizaje supervisado en R. https://fervilber.github.io/Aprendizaje-supervisado-en-R/ingenuo.html
Vostroknutov, A. y Polonio, L. y Coricelli, G. (2018). The Role of Intelligence in Social Learning. Scientific Reports, 8(1), 6896. https://doi.org/10.1038/s41598-018-25289-9
Walberg, H. J. (1984). Improving the Productivity of America’s Schools.
Wang, P. (2019). On Defining Artificial Intelligence. Journal of Artificial General Intelligence, 10(2), 1–37. https://doi.org/10.2478/jagi-2019-0002
Xiang, X. y Foo, S. y Zang, H. (2021). Recent Advances in Deep Reinforcement Learning Applications for Solving Partially Observable Markov Decision Processes (POMDP) Problems Part 2—Applications in Transportation, Industries, Communications and Networking and More Topics. Machine Learning and Knowledge Extraction, 3(4), 863–878. https://doi.org/10.3390/make3040043
Yağcı, M. (2022). Educational data mining: prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9(1), 11. https://doi.org/10.1186/s40561-022-00192-z
Yeturu, K. (2020). Machine learning algorithms, applications, and practices in data science (pp. 81–206). https://doi.org/10.1016/bs.host.2020.01.002
Yildiz, M. y Börekci, C. (2020). Predicting Academic Achievement with Machine Learning Algorithms. Journal of Educational Technology and Online Learning. https://doi.org/10.31681/jetol.773206
Zambrano Aranda, G. (2022). Análisis de la satisfacción con respecto a una maestría de una universidad de Lima desde la perspectiva de sus egresados. Revista Educación y Sociedad, 3(5), 9–22. https://doi.org/10.53940/reys.v3i5.89
Zhao, W. (2022). Inspired, but not mimicking: a conversation between artificial intelligence and human intelligence. National Science Review, 9(6). https://doi.org/10.1093/nsr/nwac068
Zheng, Y. (2019). Identification of microRNAs From Small RNA Sequencing Profiles. In Computational Non-coding RNA Biology (pp. 35–82). Elsevier. https://doi.org/10.1016/B978-0-12-814365-0.00012-9
Žukauskas, P. y Vveinhardt, J. y Andriukaitienė, R. (2018). Philosophy and Paradigm of Scientific Research. In Management Culture and Corporate Social Responsibility. InTech. https://doi.org/10.5772/intechopen.70628
