Ahmadi, N. (2015). Which learning style do you prefer to improve EFL learning? Journal of Research in Applied Linguistics, 5(Special Issue), 26-34. https://doi.org/10.22055/rals.2015.11266
Alario-Hoyos, C., Pérez-Sanagustín, M., Delgado-Kloos, C., Parada, H. A., & MuñozOrganero, M. (2014). Delving into participants’ profiles and use of social tools in MOOC. IEEE Transactions on Learning Technologies, 7(3), 260-266. https://doi.org /10.1109/TLT.2014.2311807
Alexander, B., Ashford-Rowe, K., Barajas-Murphy, N., Dobbin, G., Knott, J., McCormack, M., Pomerantz, J., Seilhamer, R., & Weber, N. (2019). EDUCAUSE horizon report: 2019 higher education edition. EDUCAUSE.
Anderson, A., Huttenlocher, D., Kleinberg, J., & Leskovec, J. (2014). Engaging with massive online courses. In Proceedings of the 23rd International Conference on World Wide Web (pp. 687-698). ACM. https://doi.org/10.1145/2566486.2568042.
Arora, S., Goel, M., Sabitha, A. S., & Mehrotra, D. (2017). Learner groups in massive open online courses. American Journal of Distance Education, 31(2), 80-97. https://doi.org/10.1080/08923647.2017.1300461
Barcena, E., & Martín-Monje, E. (2014). Language MOOCs: Providing learning, transcending boundaries. In E. Martín-Monje & E. Barcena (Eds.), Language MOOCs: Providing learning, transcending boundaries (pp. 175-189). Walter de Gruyter GmbH.
Bialystok, E. (1978). A theoretical model of second language learning 1. Language learning, 28(1), 69-83.
Brown, M., McCormack, M., Reeves, J., Brooks, D. C., Grajek, S., Alexander, B., Bali, M., Bulger, S., Dark, S., Engelbert, N., Gannon, K., Gauthier, A., Gibson, D., Gibson, R., Lundin, B., Veletsianos, G., & Weber, N. (2020). EDUCAUSE horizon report, teaching and learning edition. EDUCAUSE.
Castrillo, M. D., & Sedano, B. (2021). Joining Forces toward social inclusion: Language MOOC design for refugees and migrants through the lens of maker culture. CALICO Journal, 38(1), 79-102. https://doi.org/10.1558/cj.40900
Chaker, R., & Bachelet, R. (2020). Internationalizing professional development: Using educational data mining to analyze learners’ performance and dropouts in a French MOOC. The International Review of Research in Open and Distributed Learning, 21(4), 199-221. https://doi.org/10.19173/irrodl.v21i4.4787
Chatti, M. A., Dyckhoff, A.L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5 & 6), 318-331. https://doi.org/10.1504/IJTEL.2012.051815
Chong, S. W., Khan, M. A., & Reinders, H. (2022). A critical review of design features of LMOOCs. Computer Assisted Language Learning, 1-21. https://doi.org/10.1080/09588221.2022.2038632
Coleman, C. A., Seaton, D. T., & Chuang, I. (2015). Probabilistic use cases: Discovering behavioral patterns for predicting certification. In 2nd ACM Conference on Learning@ Scale (pp 141–148). ACM.
Díez-Arcón, P., & Martín-Monje, E. (2023). The coming of age of LMOOC research. A systematic review (2019-21). Innovation in Language Learning and Teaching, 17(3), 535-551.
Dhorne, L., Deflandre, J., Bernaert, O., Bianchi, S., & Thirouard, M. (2017). Mentoring learners in MOOCs: A new way to improve completion rates? In Digital Education: Out to the World and Back to the Campus (Vol. 10254, Lecture Notes in Computer Science, pp. 29-37). Cham: Springer International Publishing.
Ferguson, R., & Clow, D. (2015). Examining engagement: Analyzing learner subpopulations in massive open online courses (MOOCs). Proceedings of the Fifth International Conference on Learning Analytics and Knowledge (pp. 51-58). Vancouver, Canada: ACM.
Garreta, M., Aguado, G., Mor, Y., Fernandez, C., & Riviou, K. (2015). A peer-mentoring approach for the continuous professional development of educators in a MOOC setting. In Proceedings of the European Distance and E-Learning Network 2015 Annual Conference (pp. 149-158). Barcelona, Spain.
Gillespie, J. (2020). CALL research: Where are we now? ReCALL, 32(2), 127-144. https://doi.org/10.1017/S0958344020000051
Halawa, S., Greene, D., & Mitchell, J. (2014). Dropout prediction in MOOCs using learner activity features. In U. Cress & C. Delgado Kloos (Eds.), Proceedings of the European MOOC stakeholder summit (pp. 58-65). P.A.U. Education.
Hill, P. (2013). Emerging student patterns in MOOCs: A (revised) graphical view. Retrieved from http://mfeldstein.com/emerging-student-patterns-in-moocs-a-revised-graphical-view
Ho, A. D., Chuang, I., Reich, J., Coleman, C. A., Whitehill, J., Northcutt, C. G., et al. (2015). Harvardx and mitx: Two years of open online courses fall 2012-summer 2014. https://doi.org/10.2139/ssrn.2586847
Hsu, L. (2023). What makes good LMOOCs for EFL learners? Learners’ personal characteristics and information system success model. Computer Assisted Language Learning, 36(1-2), 1-25.
Ifenthaler, D., Mah, D., & Yau, Y. J. (Eds.). (2019). Utilizing learning analytics to support study success. Springer.
Jitpaisarnwattana, N., Reinders, H., & Darasawang, P. (2021) Understanding the roles of personalization and social learning in a language MOOC through learning analytics. Online Learning, 25(4), 324-343.
Jordan, K. (2015). Massive open online course completion rates revisited: Assessment, length, and attrition. The International Review of Research in Open and Distributed Learning, 16(3), 30-42. http://dx.doi.org/10.19173/irrodl.v16i3.2112
Khalil, M., & Ebner, M. (2015). Learning analytics: Principles and constraints. In S. Carliner, C. Fulford, & N. Ostashewski (Eds.), Proceedings of EdMedia 2015—World Conference on Educational Media and Technology (pp. 1789-1799). Montreal, Quebec, Canada: Association for the Advancement of Computing in Education (AACE).
Khalil, M., & Ebner, M. (2016). What is learning analytics about? A survey of different methods used in 2013-2015. In Proceedings of Smart Learning Conference (pp. 294-304). Dubai: HBMSU Publishing House.
Kim, P., & Chung, C. (2015). Creating a temporary spontaneous mini-ecosystem through a MOOC. In C. J. Bonk, M. M. Lee, T. C. Reeves, & T. H. Reynolds (Eds.), MOOCs and open education around the world (pp. 157-168). New York, NY: Routledge.
Kizilcec, R. F., Piech, C., & Schneider, E. (2013). Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses. In 3rd International Conference on Learning Analytics and Knowledge (pp. 170-179). New York: ACM.
Koller, D., Ng, A., Do, C., & Chen, Z. (2013). Retention and intention in massive open online courses: In depth. Educause Review, 48(3), 62-63.
Krashen, S., & Scarcella, R. (1978). On routines and patterns in language acquisition and performance Language Learning, 28(2), 283-300.
Lang, C., Siemens, G., Wise, A., & Gaševic, D. (Eds.). (2017). Handbook of learning analytics. SOLAR.
Leys, C., Christophe Ley, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764-766. http://dx.doi.org/10.1016/j.jesp.2013.03.013
Li, Q, & Baker, R. (2018). The different relationships between engagement and outcomes across participant subgroups in Massive Open Online Courses. Computers & Education, 127, 41-65. https://doi.org/10.1016/j.compedu.2018.08.005
Li, S., Wang, S., Du, J., Pei, Y., & Shen, X. (2022). MOOC learners’ time—investment patterns and temporal—learning characteristics. Journal of Computer Assisted Learning, 38(1), 152-166. https://doi.org/10.1111/jcal.12597
Liu, B. (2006). Web data mining. Springer.
León Urrutia, M., Vázquez Cano, E., & López-Meneses, E. (2017). MOOC learning analytics using real-time dynamic metrics. @tic. Revista D'innovació Educativa, 18, 38-47. https://doi.org/10.7203/attic.18.10022
Lester, J., Klein, C., Johri, A., & Rangwala, H. (Eds.) (2018). Learning analytics in higher education: Current innovations, future potential, and practical applications. Routledge.
Liyanagunawardena, T. R., Adams, A. A., & Williams, S. A. (2013). MOOCs: A systematic study of the published literature 2008-2012. International Review of Research in Open and Distributed Learning, 14(3), 202-227. https://doi.org/10.19173/irrodl.v14i3.1455
Martín-Monje, E. (in press). Language MOOCs as an emerging field of research: From theory to practice. In M. M. Suárez & W. El-Henawy (Eds.), Optimizing ONLINE English language learning and teaching (pp. 25-43). London: Springer.
Martín-Monje, E., Castrillo, M. D., & Mañana-Rodríguez, J. (2018). Understanding online interaction in language MOOCs through learning analytics. Computer Assisted Language Learning, 31(3), 251-272. https://doi.org/10.1080/09588221.2017.1378237
Maseleno, A., Sabani, N., Huda, M., Ahmad, R., Jasmi, K. A., & Basiron, B. (2018). Demystifying learning analytics in personalised learning. International Journal of Engineering & Technology, 7(3), 1124-1129. https://doi.org/10.14419/ijet.v7i3.9789
Martín-Monje, E., & Barcena, E. (Eds.). (2014). Language MOOCs: Providing learning, transcending boundaries. De Gruyter Open.
Martín-Monje, E., & Borthwick, K. (2021). Researching massive open online courses for language teaching and learning. ReCALL, 33(2), 107-110. https://doi.org/10.1017/S0958344021000094
Martín-Monje, E., Castrillo, M. D., & Mañana-Rodríguez, J. (2018). Understanding online interaction in language MOOCs through learning analytics. Computer Assisted Language Learning, 31(3), 251-272. https://doi.org/10.1080/09588221.2017.1378237
Maya-Jariego, I., Holgado, D., González-Tinoco, E., Castaño-Muñoz, J., & Punie, Y. (2020). Typology of motivation and learning intentions of users in MOOCs: The MOOCKNOWLEDGE study. Educational Technology Research and Development, 68(1), 203-224. https://doi.org/10.1007/s11423-019-09682-3
Mehnert, U. (1998). The effects of different lengths of time for planning on second language performance. Studies in Second Language Acquisition, 20, 83-108. https://doi.org/10.1017/S0272263198001041
Mullaney, T. (2014). Making sense of MOOCs: A reconceptualization of HarvardX courses and their students. Retrieved from https://ssrn.com/abstract=2463736
Nurieva, G. R., & Garaeva, L, M. (2020). Zoom-based distance learning of English as a foreign language. Journal of Research in Applied Linguistics, 11, 439-448. https://doi.org/10.22055/rals.2020.16344
Panagiotidis, P. (2019). MOOCs for language learning: Reality and prospects. Society for Information Technology & Teacher Education International Conference.
del Peral Pérez, J. J. (2019). Patrones temporales de participación en MOOC. Estudio de un MOOC de lenguas. RIED. Revista Iberoamericana de Educación a Distancia, 22(2), 287-303. http://dx.doi.org/10.5944/ried.22.2.23109
del Peral Pérez, J. J. (2022). El perfil del estudiante en los MOOC: identificación de patrones temporales de uso a partir del análisis de dos MOOC de lenguas. [Unpublished doctoral dissertation, Universidad Nacional de Educación a Distancia (UNED)].
Poellhuber, B., Roy, N., & Bouchoucha, I. (2019). Understanding participant’s behavior in massively open online courses. International Review of Research in Open and Distributed Learning, 20(1), 28-41. https://doi.org/10.19173/irrodl.v20i1.3709
Poy, Y., & Gonzales-Aguilar, A. (2014). Factores de éxito de los MOOC: algunas consideraciones críticas. Revista Ibérica de Sistemas y Tecnologías de Información, SPE1, 105-118. https://doi.org/10.4304/risti.e1.105-118
Read, T., & Sedano, B. (2021). The role of scaffolding in LMOOCs for displaced people. Lengua y Migración/Language and Migration, 13(2), 115-133.
Reich, J. (2014). MOOC completion and retention in the context of student intent. EDUCAUSE Review. Retrieved from http://www.educause.edu/ero/article/mooc-completion-and-retentioncontext-student-intent
Reich, J., & Ruipérez-Valiente, J. A. (2019). The MOOC pivot. Science 363, 130-13. https://doi.org/10.1126/science.aav7958
Romero, C., Cerezo, R., Bogarín, A., & Sánchez-Santillán, M. (2016). Educational process mining: A tutorial and case study using Moodle data sets. In S. ElAtia, D. Ipperciel, & O. R. Zaïane (Eds.), Data mining and learning analytics (pp. 1-28). London: Wiley.
Romero, C., & Ventura, S. (2020) Educational data mining and learning analytics: An updated survey. WIREs Data Mining Knowl Discov, 10:e1355. Retrieved from https://onlinelibrary.wiley.com/doi/abs/10.1002/widm.1355
Sallam, M. H., Martín-Monje, E., & Li, Y. (2022). Research trends in language MOOC studies: A systematic review of the published literature (2012-2018). Computer Assisted Language Learning, 35(4), 764-791. https://doi.org/10.1080/09588221.2020.1744668
Sanz Gil, M. (2021). LMOOC para la integración de personas desplazadas. Anales de Filología Francesa, 29, 463-477. https://doi.org/10.6018/analesff.483181
Sclater, N. (2017). Learning analytics explained. Routledge.
Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524. https://doi.org/10.1016/j.asoc.2019.105524
Sunar, A., Abbasi, R., Davis, H., White, S., & Aljohani, N. (2020). Modelling MOOC learners' social behaviors. Computers in Human Behavior, 107, 50-62. https://doi.org/10.1016/j.chb.2018.12.013
Tatman, R. (2018). Data cleaning challenge: Scale and normalize data. Kaggle. Retrieved from https://www.kaggle.com/rtatman/data-cleaning-challenge-scale-and- normalize-data
Tavakoli, P., & Foster, P. (2011). Task design and second language performance: The effect of narrative type on learner output. Language Learning, 61, 37-72. https://doi.org/10.1111/j.1467-9922.2011.00642.x
Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423. https://doi.org/10.1111/1467-9868.00293
Xu, B., & Yang, D. (2016). Motivation classification and grade prediction for MOOCs learners. Computational Intelligence and Neuroscience, 4, 1-7. https://doi.org/10.1155/2016/2174613
Zeng, S., Zhang, J., Gao, M., Xu, K. M., & Zhang, J. (2020). Using learning analytics to understand collective attention in language MOOCs. Computer Assisted Language Learning, 35(7), 1594-1619. https://doi.org/10.1080/09588221.2020.1825094
Zhang, Y., & Sun, R. (2023). LMOOC research 2014 to 2021: What have we done and where are we going next? ReCALL, 1-16. https://doi.org/10.1017/S0958344022000246