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dc.contributor.advisorChiappe Laverde, Andrés
dc.contributor.authorGonzalez Mosquera, Nubia Andrea del Pilar
dc.date.accessioned2020-05-19T12:39:40Z
dc.date.available2020-05-19T12:39:40Z
dc.date.issued2020-03-04
dc.identifier.urihttp://hdl.handle.net/10818/41096
dc.description37 páginases_CO
dc.description.abstractEducation in the 21st century is increasingly mediated by digital technologies in a context in which enormous amounts of information are daily generated. Regarding this and considering the imminent application of emerging trends such as "Internet of Things” (IoT), the study of its educational effects becomes a matter of great relevance for both educational researchers and practitioners. In this context, "Learning Analytics" takes on special importance as a perspective to approach the aforementioned issue, especially from a very relevant topic: the personalization of learning. In this sense, a systematic review of literature about learning analytics published in the last decade was carried out in order to identify its potential as a factor to strengthen the personalization of learning. The results show a set of key factors that include aspects related to assessment, the use of dashboards, social learning networks and intelligent tutoring and the importance of monitoring, feedback and supportes_CO
dc.formatapplication/pdfes_CO
dc.language.isoenges_CO
dc.publisherUniversidad de La Sabanaes_CO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceinstname:Universidad de La Sabanaes_CO
dc.sourcereponame:Intellectum Repositorio Universidad de La Sabanaes_CO
dc.subjectEducaciónes_CO
dc.subjectInnovaciones educativases_CO
dc.subjectTecnología educativaes_CO
dc.subjectBig Dataes_CO
dc.titleLearning analytics and personalization of learning: a reviewes_CO
dc.typemasterThesises_CO
dc.publisher.programMaestría en Informática Educativaes_CO
dc.publisher.departmentCentro de Tecnologías para la Academiaes_CO
dc.identifier.local276816
dc.identifier.localTE10647
dc.type.hasVersionpublishedVersiones_CO
dc.rights.accessRightsrestrictedAccesses_CO
dc.creator.degreeMagister en Informática Educativaes_CO
dcterms.referencesAdmiraal, W., Huisman, B., & Pilli, O. (2015). Assessment in massive open online courses. Electronic Journal of e-Learning, 13(4), 207–216eng
dcterms.referencesAlbelbisi, N., Yusop, F. D., & Salleh, U. K. M. (2018). Mapping the Factors Influencing Success of Massive Open Online Courses (MOOC) in Higher Education. Eurasia Journal of Mathematics, Science and Technology Education, 14(7), 2995–3012. doi:10.29333/ejmste/91486eng
dcterms.referencesAlbishi, S., Soh, B., Ullah, A., & Algarni, F. (2017). Challenges and Solutions for Applications and Technologies in the Internet of Things. Procedia Computer Science, 124, 608–614. doi:10.1016/j.procs.2017.12.196eng
dcterms.referencesAldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13–49. doi:10.1016/j.tele.2019.01.007eng
dcterms.referencesAment, V., & Edwards, R. (2018). Better teaching and more learning in mobile learning courses: Towards a model of personable learning. In Proceedings of the 14th International Conference on Mobile Learning 2018, ML 2018 (pp. 214–218). Presented at the 14th International Conference on Mobile Learning 2018, ML 2018, Lisbon, Portugal: IADIS. https://www.scopus.com/inward/record.uri?eid=2-s2.0- 85052241220&partnerID=40&md5=6ca1258e009575fc0f41ba59f5ac8718eng
dcterms.referencesArnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: using learning analytics to increase student success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK ’12 (p. 267). Presented at the the 2nd International Conference, Vancouver, British Columbia, Canada: ACM Press. doi:10.1145/2330601.2330666eng
dcterms.referencesBoscardin, C., Fergus, K. B., Hellevig, B., & Hauer, K. E. (2018). Twelve tips to promote successful development of a learner performance dashboard within a medical education program. Medical Teacher, 40(8), 855–861. doi:10.1080/0142159X.2017.1396306eng
dcterms.referencesBozkurt, A., Ozdamar Keskin, N., & De Waard, I. (2016). Research Trends in Massive Open Online Course (MOOC) Theses and Dissertations: Surfing the Tsunami Wave. Open Praxis, 8(3), 203–221. doi:10.5944/openpraxis.8.3.287eng
dcterms.referencesBridgeman, A., & Rutledge, P. (2010). Getting Personal: feedBack for the Masses. Synergy, (30), 61–68.eng
dcterms.referencesBuitrago, M., & Chiappe, A. (2019). Representation of knowledge in digital educational environments: A systematic review of literature. Australasian Journal of Educational Technology, 35(4), 46–62. doi:10.14742/ajet.4041eng
dcterms.referencesBurrows, J., & Kumar, V. (2018). The Objective Ear: Assessing the Progress of a Music Task. In M. Chang, E. Popescu, Kinshuk, N.-S. Chen, M. Jemni, R. Huang, & J. M. Spector (Eds.), Challenges and Solutions in Smart Learning (pp. 107–112). Singapore: Springer Singapore. doi:10.1007/978-981-10-8743-1_15eng
dcterms.referencesChai, M., Lin, Y., & Li, Y. (2018). Machine Learning and Modern Education. In S. Liu, M. Glowatz, M. Zappatore, H. Gao, B. Jia, & A. Bucciero (Eds.), e-Learning, eEducation, and Online Training (Vol. 243, pp. 41–46). Shanghai, China: Springer International Publishing. doi:10.1007/978-3-319-93719-9_6eng
dcterms.referencesChatti, M. A., & Muslim, A. (2019). The PERLA framework: Blending personalization and learning analytics. International Review of Research in Open and Distance Learning, 20(1), 244–261.eng
dcterms.referencesChauhan, J., & Goel, A. (2016). An analysis of quiz in MOOC. In 2016 Ninth International Conference on Contemporary Computing (IC3) (pp. 1–6). Presented at the 2016 Ninth International Conference on Contemporary Computing (IC3), Noida, India: IEEE. doi:10.1109/IC3.2016.7880245eng
dcterms.referencesClow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683–695. doi:10.1080/13562517.2013.827653eng
dcterms.referencesConde, M. A., Colomo-Palacios, R., García-Peñalvo, F. J., & Larrucea, X. (2018). Teamwork assessment in the educational web of data: A learning analytics approach towards ISO 10018. Telematics and Informatics, 35(3), 551–563. doi:10.1016/j.tele.2017.02.001eng
dcterms.referencesDoko, E., & Bexheti, L. A. (2018). A systematic mapping study of educational technologies based on educational data mining and learning analytics. In 2018 7th Mediterranean Conference on Embedded Computing (MECO) (pp. 1–4). Presented at the 2018 7th Mediterranean Conference on Embedded Computing (MECO), Budva, Montenegro: IEEE. doi:10.1109/MECO.2018.8406052eng
dcterms.referencesDyckhoff, 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), 58–76eng
dcterms.referencesEckerson, W. W. (2006). Performance dashboards: measuring, monitoring, and managing your business. Hoboken, NJ, USA: John Wiley & Sons. http://paper.shiftit.ir/sites/default/files/book/04SPerformance%20Dashboards%2C%20measuring%2C%20monitoring%20and%20man aging%20your%20business-.pdfeng
dcterms.referencesEl Alfy, S., Marx Gómez, J., & Dani, A. (2019). Exploring the benefits and challenges of learning analytics in higher education institutions: a systematic literature review. Information Discovery and Delivery, 47(1), 25–34. doi:10.1108/IDD-06-2018-0018eng
dcterms.referencesEllaway, R. H., Pusic, M. V., Galbraith, R. M., & Cameron, T. (2014). Developing the role of big data and analytics in health professional education. Medical Teacher, 36(3), 216– 222. doi:10.3109/0142159X.2014.874553eng
dcterms.referencesEngeness, I., & Mørch, A. (2016). Developing Writing Skills in English Using ContentSpecific Computer-Generated Feedback with EssayCritic. Nordic Journal of Digital Literacy, 10(02), 118–135. doi:10.18261/issn.1891-943x-2016-02-03eng
dcterms.referencesFasihuddin, H., Skinner, G., & Athauda, R. (2015). A Framework to Personalise Open Learning Environments by Adapting to Learning Styles: In Proceedings of the 7th International Conference on Computer Supported Education (pp. 296–305). Presented at the 7th International Conference on Computer Supported Education, Lisbon, Portugal: SCITEPRESS - Science and and Technology Publications. doi:10.5220/0005443502960305eng
dcterms.referencesFelder, R. M., & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering education, 78(7), 674–681.eng
dcterms.referencesFraifer, M., Kharel, S., Hasenfuss, H., Elmangoush, A., Ryan, A., Elgenaidi, W., & Fernstrom, M. (2017). Look before you leap: Exploring the challenges of technology and user experience in the Internet of Things. In 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI) (pp. 1–6). Presented at the 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry - Innovation to Shape the Future for Society and Industry (RTSI), Modena, Italy: IEEE. doi:10.1109/RTSI.2017.8065920eng
dcterms.referencesGašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71. doi:10.1007/s11528-014-0822-xeng
dcterms.referencesGee, J. P. (2003). What video games have to teach us about learning and literacy. Computers in Entertainment (CIE), 1(1), 1–4.eng
dcterms.referencesGoyal, T., Rathi, R., Jain, V. K., Pilli, E. S., & Mazumdar, A. P. (2018). Big Data Handling Over Cloud for Internet of Things: International Journal of Information Technology and Web Engineering, 13(2), 37–47. doi:10.4018/IJITWE.2018040104eng
dcterms.referencesHaladyna, T. M., & Downing, S. M. (2004). Construct-irrelevant variance in high-stakes testing. Educational Measurement: Issues and Practice, 23(1), 17–27. doi:https://doi.org/10.1111/j.1745-3992.2004.tb00149.xeng
dcterms.referencesHart, C. (2018). Doing a literature review: Releasing the research imagination. London, UK: Sage. https://s3.amazonaws.com/academia.edu.documents/35996527/Doing_a_L_review.pdf ?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1554924577&Signatu re=41OmDV63Ah92goxikF52BkU26hg%3D&response-contentdisposition=inline%3B%20filename%3DDoing_a_Literature_Review_Releasing_the. pdfeng
dcterms.referencesHattie, J. (2012). Visible learning for teachers: Maximizing impact on learning. The Main Idea. http://www.sst7.org/media/54c943ce56838.pdfeng
dcterms.referencesHersh, M. A., & Leporini, B. (2012). Accessibility and usability of educational games for disabled students. In C. Gonzalez (Ed.), Student Usability in Educational Software and Games: Improving Experiences: Improving Experiences (pp. 1–40). Hershey PA: IGI Global. https://tinyurl.com/y5gxydrbeng
dcterms.referencesHiggins, J. P. T., & Green, S. (2011). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0. The Cochrane Collaboration. https://es.cochrane.org/sites/es.cochrane.org/files/public/uploads/manual_cochrane_51 0_web.pdfeng
dcterms.referencesHolmes, M., Latham, A., Crockett, K., & OShea, J. D. (2018). Near Real-Time Comprehension Classification with Artificial Neural Networks: Decoding e-Learner Non-Verbal Behavior. IEEE Transactions on Learning Technologies, 11(1), 5–12. doi:10.1109/TLT.2017.2754497eng
dcterms.referencesHoward, E., Meehan, M., & Parnell, A. (2018). Contrasting prediction methods for early warning systems at undergraduate level. The Internet and Higher Education, 37, 66– 75. doi:10.1016/j.iheduc.2018.02.001eng
dcterms.referencesHwang, G.-J. (2014). Definition, framework and research issues of smart learning environments - a context-aware ubiquitous learning perspective. Smart Learning Environments, 1(1), 4. doi:10.1186/s40561-014-0004-5eng
dcterms.referencesIfenthaler, D., & Schumacher, C. (2016). Student perceptions of privacy principles for learning analytics. Educational Technology Research and Development, 64(5), 923– 938. doi:10.1007/s11423-016-9477-yeng
dcterms.referencesJena, R. K. (2018). Predicting students’ learning style using learning analytics: a case study of business management students from India. Behaviour & Information Technology, 37(10–11), 978–992. doi:10.1080/0144929X.2018.1482369eng
dcterms.referencesKato, T., Kambayashi, Y., & Kodama, Y. (2018). Using a Programming Exercise Support System as a Smart Educational Technology. In V. L. Uskov, J. P. Bakken, R. J. Howlett, & L. C. Jain (Eds.), Smart Universities (Vol. 70, pp. 295–324). Cham: Springer International Publishing. doi:10.1007/978-3-319-59454-5_10eng
dcterms.referencesKucak, D., Juricic, V., & Dambic, G. (2018). Machine Learning in Education - a Survey of Current Research Trends. In B. Katalinic (Ed.), DAAAM Proceedings (1st ed., Vol. 1, pp. 406–410). Zadar, Croatia: DAAAM International Vienna. doi:10.2507/29th.daaam.proceedings.059eng
dcterms.referencesKurilovas, E. (2019). Advanced machine learning approaches to personalise learning: learning analytics and decision making. Behaviour & Information Technology, 38(4), 410–421. doi:10.1080/0144929X.2018.1539517eng
dcterms.referencesLajoie, S., & Azevedo, R. (2012). Teaching and learning in technology-rich environments. In P. A. Alexander & P. H. Winne (Eds.), Handbook of Educational Psychology (Second Edition., pp. 803–823). New York, NY: Routledge. https://tinyurl.com/yy57bdgkeng
dcterms.referencesLiu, D. Y.-T., Bartimote-Aufflick, K., Pardo, A., & Bridgeman, A. J. (2017). Data-Driven Personalization of Student Learning Support in Higher Education. In A. Peña-Ayala (Ed.), Learning Analytics: Fundaments, Applications, and Trends (Vol. 94, pp. 143– 169). Cham: Springer International Publishing. doi:10.1007/978-3-319-52977-6_5eng
dcterms.referencesLlopart, M., & Esteban-Guitart, M. (2018). Funds of knowledge in 21st century societies: inclusive educational practices for under-represented students. A literature review. Journal of Curriculum Studies, 50(2), 145–161. doi:10.1080/00220272.2016.1247913eng
dcterms.referencesMartinez-Maldonado, R., Echeverria, V., Santos, O. C., Santos, A. D. P. D., & Yacef, K. (2018). Physical learning analytics: a multimodal perspective. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge - LAK ’18 (pp. 375–379). Presented at the the 8th International Conference, Sydney, New South Wales, Australia: ACM Press. doi:10.1145/3170358.3170379eng
dcterms.referencesMcKenna, H. P., Arnone, M. P., & Chauncey, S. A. (2013). Ambient intelligence & information interactions: Theorizing autonomies & awareness for 21st century society a technology-people balance. In 2013 IEEE International Symposium on Technology and Society (ISTAS): Social Implications of Wearable Computing and Augmediated Reality in Everyday Life (pp. 227–236). Presented at the 2013 IEEE International Symposium on Technology and Society (ISTAS), Toronto, ON, Canada: IEEE. doi:10.1109/ISTAS.2013.6613124eng
dcterms.referencesMiranda, J. A., Canabal, M. F., García, M. P., & Lopez-Ongil, C. (2018). Embedded emotion recognition: Autonomous multimodal affective internet of things. In CEUR Workshop Proceedings (Vol. 2208, pp. 22–29). Presented at the 2018 Cyber-Physical Systems PhD and Postdoc Workshop, CPSWS 2018, Alghero, Italy: CEUR-WS. https://www.scopus.com/inward/record.uri?eid=2-s2.0- 85053772645&partnerID=40&md5=3371b41ce73cea24f7cbb92c2f40e95deng
dcterms.referencesMohd, C. K. N. C. K., & Shahbodin, F. (2015). Personalized Learning Environment (PLE) integration in the 21st century classroom. International Journal of Computer Information Systems and Industrial Management Applications, 7(1), 14–21.eng
dcterms.referencesMothukuri, U. K., Reddy, B. V., Reddy, P. N., Gutti, S., Mandula, K., Parupalli, R., et al. (2017). Improvisation of learning experience using learning analytics in eLearning. In 2017 5th National Conference on E-Learning & E-Learning Technologies (ELELTECH) (pp. 1–6). Presented at the 2017 5th National Conference on E-Learning & E-Learning Technologies (ELELTECH), Hyderabad, India: IEEE. doi:10.1109/ELELTECH.2017.8074995eng
dcterms.referencesMuslim, A., Chatti, M. A., Mughal, M., & Schroeder, U. (2017). The goal-Question-Indicator approach for personalized learning analytics. In CSEDU 2017 - Proceedings of the 9th International Conference on Computer Supported Education (Vol. 1, pp. 371–378). Presented at the 9th International Conference on Computer Supported Education, CSEDU 2017, Porto, Portugal. https://www.scopus.com/inward/record.uri?eid=2-s2.0- 85023740180&partnerID=40&md5=c502f0b0510b6daacd769b919c086da0eng
dcterms.referencesNandigam, D., Tirumala, S. S., & Baghaei, N. (2014). Personalized learning: Current status and potential. In 2014 IEEE Conference on e-Learning, e-Management and e-Services (IC3e) (pp. 111–116). Presented at the 2014 IEEE Conference on e-Learning, eManagement and e-Services (IC3e), Hawthorn, Australia: IEEE. doi:10.1109/IC3e.2014.7081251eng
dcterms.referencesNguyen, Q., Rienties, B., Toetenel, L., Ferguson, R., & Whitelock, D. (2017). Examining the designs of computer-based assessment and its impact on student engagement, satisfaction, and pass rates. Computers in Human Behavior, 76, 703–714. doi:10.1016/j.chb.2017.03.028eng
dcterms.referencesPappas, I. O., Giannakos, M. N., & Sampson, D. G. (2019). Fuzzy set analysis as a means to understand users of 21st-century learning systems: The case of mobile learning and reflections on learning analytics research. Computers in Human Behavior, 92, 646– 659. doi:10.1016/j.chb.2017.10.010eng
dcterms.referencesParthiban, P., & Selvakumar, S. (2016). Big Data Analysis in the Internet of Things Platform. Indian Journal of Science and Technology, 9(41), 1–4. doi:10.17485/ijst/2016/v9i41/91747eng
dcterms.referencesRaposo-Rivas, M., Martínez-Figueira, E., & Sarmiento-Campos, J. A. (2015). A Study on the Pedagogical Components of Massive Online Courses. Comunicar, 22(44), 27–35. doi:10.3916/C44-2015-03eng
dcterms.referencesRienties, B., Cross, S., & Zdrahal, Z. (2017). Implementing a learning analytics intervention and evaluation framework: What works? In Big data and learning analytics in Higher Education (pp. 147–166). Switzerland: Springer.eng
dcterms.referencesRoll, I., & Wylie, R. (2016). Evolution and Revolution in Artificial Intelligence in Education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. doi:10.1007/s40593-016-0110-3eng
dcterms.referencesRomero, L., Saucedo, C., Caliusco, Ma. L., & Gutiérrez, M. (2019). Supporting self-regulated learning and personalization using ePortfolios: a semantic approach based on learning paths. International Journal of Educational Technology in Higher Education, 16(1), 16. doi:10.1186/s41239-019-0146-1eng
dcterms.referencesRowe, E., Asbell-Clarke, J., Baker, R. S., Eagle, M., Hicks, A. G., Barnes, T. M., et al. (2017). Assessing implicit science learning in digital games. Computers in Human Behavior, 76, 617–630. doi:10.1016/j.chb.2017.03.043eng
dcterms.referencesSakulkueakulsuk, B., Witoon, S., Ngarmkajornwiwat, P., Pataranutapom, P., Surareungchai, W., Pataranutaporn, P., & Subsoontorn, P. (2018). Kids making AI: Integrating Machine Learning, Gamification, and Social Context in STEM Education. In 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE) (pp. 1005–1010). Presented at the 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), Wollongong, NSW: IEEE. doi:10.1109/TALE.2018.8615249eng
dcterms.referencesSan Pedro, M. O. Z., Baker, R. S., & Heffernan, N. T. (2017). An Integrated Look at Middle School Engagement and Learning in Digital Environments as Precursors to College Attendance. Technology, Knowledge and Learning, 22(3), 243–270. doi:10.1007/s10758-017-9318-zeng
dcterms.referencesScholes, V. (2016). The ethics of using learning analytics to categorize students on risk. Educational Technology Research and Development, 64(5), 939–955. doi:10.1007/s11423-016-9458-1eng
dcterms.referencesShute, V. J., Masduki, I., Donmez, O., & Wang, C. Y. (2010). Assessing key competencies within game environments. In D. Ifenthaler, P. Pirnay-Dummer, & N. M. Seel (Eds.), Computer-based diagnostics and systematic analysis of knowledge (pp. 281–309). Boston, MA: Springer. https://link.springer.com/book/10.1007%2F978-1-4419-5662- 0#abouteng
dcterms.referencesSiemens, G. (2013). Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist, 57(10), 1380–1400. doi:10.1177/0002764213498851eng
dcterms.referencesSijing, L., & Lan, W. (2018). Artificial Intelligence Education Ethical Problems and Solutions. In 2018 13th International Conference on Computer Science & Education (ICCSE) (pp. 1–5). Presented at the 2018 13th International Conference on Computer Science & Education (ICCSE), Colombo: IEEE. doi:10.1109/ICCSE.2018.8468773eng
dcterms.referencesSingh, A. B., & Mørch, A. I. (2018). An Analysis of Participants’ Experiences from the First International MOOC Offered at the University of Oslo. Nordic Journal of Digital Literacy, 13(01), 40–64. doi:10.18261/issn.1891-943x-2018-01-04eng
dcterms.referencesSlade, S., & Prinsloo, P. (2013). Learning Analytics: Ethical Issues and Dilemmas. American Behavioral Scientist, 57(10), 1510–1529. doi:10.1177/0002764213479366eng
dcterms.referencesSunar, A. S., Abdullah, N. A., White, S., & Davis, H. (2016). Personalisation in MOOCs: A Critical Literature Review. In S. Zvacek, M. T. Restivo, J. Uhomoibhi, & M. Helfert (Eds.), Computer Supported Education (Vol. 583, pp. 152–168). Cham: Springer International Publishing. doi:10.1007/978-3-319-29585-5_9eng
dcterms.referencesTam, V., Lam, E. Y., & Huang, Y. (2014). Facilitating a personalized learning environment through learning analytics on mobile devices. In 2014 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) (pp. 429–432). Presented at the 2014 International Conference of Teaching, Assessment and Learning (TALE), Wellington, New Zealand: IEEE. doi:10.1109/TALE.2014.7062580eng
dcterms.referencesTempelaar, D., Rienties, B., Mittelmeier, J., & Nguyen, Q. (2018). Student profiling in a dispositional learning analytics application using formative assessment. Computers in Human Behavior, 78, 408–420. doi:10.1016/j.chb.2017.08.010eng
dcterms.referencesTerras, M. M., Boyle, E. A., Ramsay, J., & Jarrett, D. (2018). The opportunities and challenges of serious games for people with an intellectual disability: The opportunities and challenges of serious games. British Journal of Educational Technology, 49(4), 690–700. doi:10.1111/bjet.12638eng
dcterms.referencesThomas, D., & Brown, J. S. (2011). A new culture of learning: Cultivating the imagination for a world of constant change. American Journal of Play, 219(2), 121–123eng
dcterms.referencesThompson, G., & Cook, I. (2017). The logic of data-sense: thinking through Learning Personalisation. Discourse: Studies in the Cultural Politics of Education, 38(5), 740– 754. doi:10.1080/01596306.2016.1148833eng
dcterms.referencesVahdat, M., Ghio, A., Oneto, L., Anguita, D., Funk, M., & Rauterberg, M. (2015). Advances in Learning Analytics and Educational Data Mining. In 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Proceedings (pp. 297–306). Presented at the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015, Bruges, Belgium: i6doc.com publication. https://www.scopus.com/inward/record.uri?eid=2-s2.0- 84961807010&partnerID=40&md5=bea2c40d7940bd536a62e9f571b18f72eng
dcterms.referencesVekariya, V., & Kulkarni, G. R. (2012). Notice of Violation of IEEE Publication Principles - Hybrid recommender systems: Survey and experiments. In 2012 Second International Conference on Digital Information and Communication Technology and it’s Applications (DICTAP) (pp. 469–473). Presented at the 2012 Second International Conference on Digital Information and Communication Technology and it’s Applications (DICTAP), Bangkok: IEEE. doi:10.1109/DICTAP.2012.6215409eng
dcterms.referencesVeletsianos, G., & Shepherdson, P. (2016). A Systematic Analysis and Synthesis of the Empirical MOOC Literature Published in 2013–2015. The International Review of Research in Open and Distributed Learning, 17(2). doi:10.19173/irrodl.v17i2.2448eng
dcterms.referencesVerbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning Analytics Dashboard Applications. American Behavioral Scientist, 57(10), 1500–1509. doi:10.1177/0002764213479363eng
dcterms.referencesVives-Varela, T., Durán-Cárdenas, C., Varela-Ruíz, M., & Fortoul van der Goes, T. (2014). La autorregulación en el aprendizaje, la luz de un faro en el mar. Investigación en educación médica, 3(9), 34–39.spa
dcterms.referencesWang, F., & Tao, X. (2018). Visual Analysis of the Application of Artificial Intelligence in Education. In 2018 International Joint Conference on Information, Media and Engineering (ICIME) (pp. 187–191). Presented at the 2018 International Joint Conference on Information, Media and Engineering (ICIME), Osaka: IEEE. doi:10.1109/ICIME.2018.00046eng
dcterms.referencesWenger, E. (2014). Artificial intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge. Los Altos, CA: Morgan Kaufmann Publishers, INC. https://tinyurl.com/yxf9m93feng
dcterms.referencesWilliams, J. J., Kim, J., & Keegan, B. (2015). Supporting Instructors in Collaborating with Researchers using MOOClets. In Proceedings of the Second (2015) ACM Conference on Learning @ Scale - L@S ’15 (pp. 413–416). Presented at the the Second (2015) ACM Conference, Vancouver, BC, Canada: ACM Press. doi:10.1145/2724660.2728705eng
dcterms.referencesWilliams, P. (2017). Assessing collaborative learning: big data, analytics and university futures. Assessment & Evaluation in Higher Education, 42(6), 978–989. doi:10.1080/02602938.2016.1216084eng
dcterms.referencesXing, W., & Du, D. (2019). Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention. Journal of Educational Computing Research, 57(3), 547– 570. doi:10.1177/0735633118757015eng
dcterms.referencesYi, B., Zhang, D., Wang, Y., Liu, H., Zhang, Z., Shu, J., & Lv, Y. (2017). Research on Personalized Learning Model under Informatization Environment. In 2017 International Symposium on Educational Technology (ISET) (pp. 48–52). Presented at the 2017 International Symposium on Educational Technology (ISET), Hong Kong: IEEE. doi:10.1109/ISET.2017.19eng
dcterms.referencesZhang, J.-H., Zhang, Y.-X., Zou, Q., & Huang, S. (2018). What learning analytics tells us: Group behavior analysis and individual learning diagnosis based on long-term and large-scale data. Educational Technology and Society, 21(2), 245–258.eng
dcterms.referencesZhang, W., & Qin, S. (2018). A brief analysis of the key technologies and applications of educational data mining on online learning platform. In 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA) (pp. 83–86). Presented at the 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA), Shanghai: IEEE. doi:10.1109/ICBDA.2018.8367655eng
dcterms.referencesZimmerman, B. J. (2002). Becoming a Self-Regulated Learner: An Overview. Theory Into Practice, 41(2), 64–70. doi:10.1207/s15430421tip4102_2eng


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