The Influence of Students' Gender on the Use of Virtual Campuses. A Case Study

Referencias bibliográficas

• Abbott, M. L. (2011). Understanding educational statistics using microsoft excel and SPSS (1st ed.). John Wiley & Sons Inc. URL: https://bit.ly/31FpWbQ
• Ahmadi, F., & Ahmad, S. (2013). Data mining in teacher evaluation system using WEKA. International Journal of Computer Applications, 63(10), 14-18. https://doi.org/10.5120/10501-5268
• Álvarez Vázquez, M. P., Álvarez-Méndez, A., Bravo-Llatas, C., Barrios, J. & Angulo Carrere, T. (2020). Tipologías de estudiantes de fisioterapia según el uso que hacen del campus virtual. RIDU 12, 74-81. https://doi.org/10.1344/RIDU2020.12.8
• Álvarez Vázquez, M. P. (2019). Estudiantes y Campus Virtual. Utilidad del Learning Analytics para identificar luces y sombras y caminar hacia metodologías adaptativas. Proyecto de Innovación y Mejora de la Calidad Docente, convocatoria 2019/20. Universidad Complutense, Madrid. https://bit.ly/2NMEBK7
• Arias, F., & Defiore, C. (2019). Avances y actualizaciones en torno al Campus Virtual de la Facultad de Ciencias Sociales de la UBA. XXI Congreso de la Red de Carreras de Comunicación Social y Periodismo. https://bit.ly/3ina0kg
• Badilla Quintana, M. G., Vera Sagredo, A., & Lytras, M. D. (2017). Pre-service teachers’ skills and perceptions about the use of virtual learning environments to improve teaching and learning. Behaviour & Information Technology, 36(6), 575-588. https://doi.org/10.1080/0144929X.2016.1266388
• Ballesteros Regaña, C., Cabero Almenara, J., Llorente Cejudo, M., & Morales Lozano, J. A.. (2010). Usos del e-learning en las universidades andaluzas: estado de la situación y análisis de buenas prácticas. Pixel-Bit. Revista de Medios y Educación, 37, 7-18.
• Caballé, S., & Xhafa, F. (2013). Distributed-based massive processing of activity logs for efficient user modeling in a Virtual Campus. Cluster computing, 16(4), 829-844. https://doi.org/10.1007/s10586-013-0256-9
• Cabanillas, J. L., Luengo, R., & Carvalho, J. L. (2019). Análisis de los objetos de aprendizaje y de la percepción docente del campus virtual de la Universidad de Extremadura. International Journal of Information Systems and Software Engineering for Big Companies (IJISEBC), 6(2), 41-61.
• Cantabella, M., Martínez-España, R., Ayuso, B., Yáñez, J. A. & Muñoz, A. (2019). Analysis of student behavior in learning management systems through a Big Data framework. Future Generation Computer Systems, 90, 262-272. https://doi.org/10.1016/j.future.2018.08.003
• Capel, H. (2009). La enseñanza digital, los campus virtuales y la Geografía. Ar@cne, nº 125. https://bit.ly/2NO9EIn.
• Castro, M., Menacho, A., & Perez-Molina, C. (2018, April). Mining LMS students' data on online task-based master degree studies. 2018 IEEE Global Engineering Education Conference (EDUCN) (pp. 661-668). IEEE. https://doi.org/10.1109/EDUCON.2018.8363294.
• Cerezo, R., Esteban, M., Sánchez-Santillán, M., & Núñez, J. C. (2017). Procrastinating behavior in computer-based learning environments to predict performance: A case study in moodle. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.01403.
• Charanya, R. & Kesavan, M. (2019). Analysis of Factors Influencing the Virtual Learning Environment in a Sri Lankan Higher Studies Institution. 2019 International Research Conference on Smart Computing and Systems Engineering (SCSE), 240-244. Colombo: Sri Lanka. https://doi.org/10.23919/SCSE.2019.8842719
• Cohen, R., Rahimi, I. D., & Zilka, G. C. (2019). Self-efficacy, challenge, threat and motivation in virtual and blended courses on multicultural campuses. Issues in Informing Science and Information Technology, 16, 71-95. https://doi.org/10.28945/4295
• Correa, J. M., & Paredes, J. (2009). Cambio tecnológico, usos de plataformas de e-learning y transformación de la enseñanza en las universidades españolas: la perspectiva de los profesores. Revista de Psicodidáctica, 14(2), 261-277.
• De la Iglesia-Villasol, M.C. (2019). Huellas de los estudiantes en las plataformas virtuales. Aplicación para evaluar una metodología de aprendizaje activo. Revista Electrónica Interuniversitaria de Formación del Profesorado, 22(3), 173-191. http://dx.doi.org/10.6018/reifop.22.3.371341
• Demsar J, Curk T, Erjavec A, Gorup C, Hocevar T, Milutinovic M, Mozina M, Polajnar M, Toplak M, Staric A, Stajdohar M, Umek L, Zagar L, Zbontar J, Zitnik M, & Zupan B (2013) Orange: Data Mining Toolbox in Python. Journal of Machine Learning Research, 14(Aug), 2349−2353.
• Demšar, J., Zupan, B., Leban, G., & Curk, T. (2004, September). Orange: From experimental machine learning to interactive data mining. In European Conference on Principles of Data Mining and Knowledge Discovery (pp. 537-539). Springer.
• Desai, R., Chavan, A. & Tendulkar, H. (2020). Virtual Campus. Studies in Indian Place Names, 40(53), 268-270.
• Dobesova, Z. (2011). Visual programming language in geographic information systems. Proceedings of the 2nd international conference on Applied informatics and computing theory, pp. 276-280. World Scientific and Engineering Academy and Society (WSEAS). URL: https://bit.ly/2MoDWxY.
• Fakir, M., & Touya, K. (2014). Mining students' learning behavior in moodle system. Journal of Information Technology Research (JITR), 7(4), 12-26. https://doi.org/10.1109/ICCCT2.2014.7066695
• García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016). Big data preprocessing: methods and prospects. Big Data Analytics, 1(1), 9. https://doi.org/10.1186/s41044-016-0014-0
• Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71. https://doi.org/10.1007/s11528-014-0822-x.
• Gómez Collado, M. E., Contreras Orozco, L., & Gutiérrez Linares, D. (2016). El impacto de las tecnologías de la información y la comunicación en estudiantes de ciencias sociales: un estudio comparativo de dos universidades públicas. Innovación educativa, 16(71), 61-80.
• Hägerstrand, Torsten (1985). Time geography: focus on the corporeality of man, society and environment. Aida, Shūhei, ed. The science and praxis of complexity: contributions to the symposium held at Montpellier, France, 9–11 May, 1984. United Nations University Press. pp. 193-216.
• Hamutoglu, N., Gemikonakli, O., Duman, I., Kirsekiz, A. & Kiyici, M. (2020). Evaluating students experiences using a virtual learning environment: satisfaction and preferences. Educational Technology Research and Development, 68, 437–462. https://doi.org/10.1007/s11423-019-09705-z
• Kaur, P., Singh, M. & Josan, G. (2015). Classification and Prediction Based Data Mining Algorithms to Predict Slow Learners in Education Sector. Procedia Computer Science, 57, 500-508. https://doi.org/10.1016/j.procs.2015.07.372.
• Koedinger, K. R., D'Mello, S., McLaughlin, E. A., Pardos, Z. A., & Rosé, C. P. (2015). Data mining and education. Wiley Interdisciplinary Reviews: Cognitive Science, 6(4), 333-353. https://doi.org/10.1002/wcs.1350
• Licklider, J. C. R. (1960). Man-computer symbiosis. IRE Transactions on Human Factors in Electronics, HFE-1, 4-11. https://goo.gl/1AjMzS
• Lu, J., & Law, N. W. Y. (2012). Understanding collaborative learning behavior from moodle log data. Interactive Learning Environments, 20(5), 451-466. https://doi.org/10.1080/10494820.2010.529817
• Manne, S., Yelisetti, S., Kakarla, M., & Fatima, S. (2014). Mining VRSEC student learning behaviour in moodle system using datamining techniques. International Conference on Computing and Communication Technologies. India, Hyderabad, pp. 1-7. https://doi.org/10.1109/ICCCT2.2014.7066695
• Martínez Romera, D. D. (2017). Profesorado en Formación y Ambientes Educativos Virtuales. Campus Virtuales, 6(2), 69-78.
• Minguillón, J., Santanach, F., & Appel, M. C. (2016). Using learning analytics to support applied research and innovation in higher education. EUA 2016 Annual Conference, Galway, April 7th. URI: http://hdl.handle.net/10609/47501
• Morales Salas, R. E., Infante-Moro, J. C., & Gallardo-Pérez, J. (2019). La mediación e interacción en un AVA para la gestión eficaz en el aprendizaje virtual. Campus Virtuales, 8(1), 49-61.
• Orostica Verdugo, K. V. (2020). Entornos Virtuales de Aprendizaje: Campus UVM ONLINE. UTE 1, 6-21. https://doi.org/10.17345/ute.2019.1
• Pakanen, M., Alavesa, P., Arhippainen, L., & Ojala, T. (2020). Stepping out of the classroom: Anticipated user experiences of web-based mirror world like virtual campus. International Journal of Virtual and Personal Learning Environments (IJVPLE), 10(1), 1-23. https://doi.org/10.4018/IJVPLE.2020010101
• Papoušek, J., Pelánek, R., & Stanislav, V. (2016). Adaptive geography practice data set. Journal of Learning Analytics, 3(2), 317-321. https://doi.org/10.18608/jla.2016.32.17
• Parise, P. (2016). A preliminary look at online learner behavior: What can the moodle logs tell us? Bulletin of Kanagawa Prefectural Institute of Language and Culture Studies (6), 15-31.
• Quintas Mendes, A., Bastos, G., Amante, L., Lebres, L., & Cardoso, T. (2019). Virtual pedagogical model: development scenarios. Universidade Aberta.
• Ramírez‐Gallego, S., García, S., Mouriño‐Talín, H., Martínez‐Rego, D., Bolón‐Canedo, V., Alonso‐Betanzos, A., & Herrera, F. (2016). Data discretization: taxonomy and big data challenge. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 6(1), 5-21. https://doi.org/10.1002/widm.1173
• Ruipérez-Valiente, J. A., Halawa, S., Slama, R. & Reich, J. (2020). Using multi-platform learning analytics to compare regional and global MOOC learning in the Arab world. Computer & Education, 146,. https://doi.org/10.1016/j.compedu.2019.103776
• Sarduy Domínguez, Y., Vialart Vidal, N., Vidal Leo, M. & Paredes Esponda, E. (2020). Estrategias para el desarrollo de repositorios de recursos educativos abiertos del campus virtual de salud pública. XVIII Convención y Feria Internacional de Informática en Salud. Habana: Cuba.
• Sclater, N., Peasgood, A., & Mullan, J. (2016). Learning analytics in higher education. Jisc. https://bit.ly/2tnQyig
• Sebastian, S., & Puthiyidam, J. J. (2015). Evaluating students’ performance by artificial neural network using WEKA. International Journal of Computer Applications, 119(23), 36-39. https://doi.org/10.5120/21380-4370
• Siddiqa, A., Hashem, I. A. T., Yaqoob, I., Marjani, M., Shamshirband, S., Gani, A., & Nasaruddin, F. (2016). A survey of big data management: Taxonomy and state-of-the-art. Journal of Network and Computer Applications, 71, 151-166. https://doi.org/10.1016/j.jnca.2016.04.008
• Siemens, G. & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review, 46(5).
• Sin, K., & Muthu, L. (2015). Application of big data in education data mining and learning analytics - A literature review. ICTACT Journal on Soft Computing, 5(4), 1035-1049. https://doi.org/10.21917/ijsc.2015.0145
• Sola-Martínez, T., Cáceres-Reche, M. P., Romero-Rodríguez, J. M., & Navas-Parejo, M. R. (2020). Estudio Bibliométrico de los documentos indexados en Scopus sobre la Formación del Profesorado en TIC que se relacionan con la Calidad Educativa. Revista Electrónica Interuniversitaria de Formación del Profesorado, 23(2). https://doi.org/10.6018/reifop.418611
• Taleb, I., Dssouli, R., & Serhani, M. A. (2015). Big data pre-processing: A quality framework. In 2015 IEEE International Congress on Big Data (pp. 191-198). IEEE. https://doi.org/10.1109/BigDataCongress.2015.35
• Tape, T. G. (s.f.). Interpreting diagnostic tests. University of Nebraska Medical Center. URL: https://bit.ly/2NESlcs.
• Tartia, J. (2020). The Temporality and Rhythmicity of Lived Street Space. Tampere University.
• Tsai, C. W., Lai, C. F., Chao, H. C., & Vasilakos, A. V. (2015). Big data analytics: a survey. Journal of Big data, 2(1), 21. https://doi.org/10.1186/s40537-015-0030-3
• Tuan, Y. F. (1990). Topophilia: A study of environmental perceptions, attitudes, and values. Columbia University Press.
• Tuan, Y. F. (1977). Space and place: The perspective of experience. University of Minnesota Press.
• Thulin, E., Vilhelmson, B. & Schwanen, T. (2020). Absent Friends? Smartphones, Mediated Presence, and the Recoupling of Online Social Contact in Everyday Life. Annals of the American Association of Geographers, 110(1), 166-183. https://doi.org/10.1080/24694452.2019.1629868
• Vega Valverde, M. (2017). Aplicaciones didácticas de la Cronogeografía. A propósito de nuestra relación espacio - tiempo. Repositorio Abierto de la Universidad de Cantabria. http://hdl.handle.net/10902/