Abu Zohair, L. M. (2019). Prediction of Student’s performance by modelling small dataset size. International Journal of Educational Technology in Higher Education, 16, 27springer. https://doi.org/10.1186/s41239-019-0160-3.
Article
Google Scholar
Alizadeh, M., Mehran, P., Koguchi, I., & Takemura, H. (2019). Evaluating a blended course for Japanese learners of English: Why quality matters. International Journal of Educational Technology in Higher Education, 16, 6springer. https://doi.org/10.1186/s41239-019-0137-2.
Article
Google Scholar
Altrabsheh, N. (2016). Sentiment analysis on students’ real-time feedback PhD Thesis, University of Portsmouth, United Kingdom.
Google Scholar
Badri, M., Abdulla, M., Kamali, M., & Dodeen, H. (2006). Identifying potential biasing variables in student evaluation of teaching in a newly accredited business program in the UAE. International Journal of Educational Management, 20(1), 43–59. https://doi.org/10.1108/09513540610639585.
Article
Google Scholar
Bianchini, S., Lissoni, F., & Pezzoni, M. (2013). Instructor characteristics and students’ evaluation of teaching effectiveness: Evidence from an Italian engineering school. European Journal of Engineering Education, 38(1), 38–57. https://doi.org/10.1080/03043797.2012.742868.
Article
Google Scholar
Binali, H. H., Wu, C., & Potdar, V. (2009). A new significant area: Emotion detection in e-learning using opinion mining techniques. In 3rd IEEE international conference on digital ecosystems and technologies, DEST'09, (pp. 259–264). https://doi.org/10.1109/DEST.2009.5276726.
Chapter
Google Scholar
Boex, L. (2000). Attributes of effective economics instructors: An analysis of student evaluations. The Journal of Economic Education, 211–227. https://doi.org/10.1080/00220480009596780.
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8. https://doi.org/10.1016/j.jocs.2010.12.007.
Article
Google Scholar
Boring, A. (2017). Gender biases in student evaluations of teaching. Journal of Public Economics, 145, 27–41. https://doi.org/10.1016/j.jpubeco.2016.11.006.
Article
Google Scholar
Boring, A., Ottoboni, K., & Stark, P. B. (2016). Student evaluations of teaching (mostly) do not measure teaching effectiveness. ScienceOpen Research. https://doi.org/10.14293/S2199-1006.1.SOR-EDU.AETBZC.v1.
Brinton, C. G., Chiang, M., Jain, S., Lam, H., Liu, Z., & Wong, F. M. F. (2014). Learning about social learning in moocs: From statistical analysis to generative model. IEEE Transactions on Learning Technologies, 7(4), 346–359. https://doi.org/10.1109/TLT.2014.2337900.
Article
Google Scholar
Clark, P. (2015). The Green Paper needs big data. Times Higher Education, retrieved from https://www.timeshighereducation.com/blog/green-paper-needs-big-data
Crues, R. W., Henricks, G. M., Perry, M., Bhat, S., Anderson, C. J., Shaik, N., & Angrave, L. (2018). How do gender, learning goals, and forum participation predict persistence in a computer science MOOC? ACM Transactions on Computing Education, 18(4) article 18, 14. https://doi.org/10.1145/3152892.
Article
Google Scholar
Daniel, B. K. (2015). 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.
Article
Google Scholar
Dommett, E. J., Gardner, B., & van Tilburg, W. (2019). Staff and student views of lecture capture:A qualitative study. International Journal of Educational Technology in Higher Education, 16, 23springer. https://doi.org/10.1186/s41239-019-0153-2.
Article
Google Scholar
Drowling, W. (2000) Why we should abolish teaching evaluations. The Daily Targum, Dec 2010.
El-Halees, A. (2011). Mining opinions in user-generated contents to improve course evaluation. Software Engineering and Computer SystemsSpringer, 107–115. https://doi.org/10.1007/978-3-642-22191-0_9.
Engen, B. K. (2019). Understanding social and cultural aspects of teachers’ digital competencies. Comprendiendo los aspectos culturales y sociales de las competencias digitales docentes. Comunicar, 61, XXVII. https://doi.org/10.3916/C61-2019-01.
Article
Google Scholar
Exter, M., Caskurlu, S., & Fernandez, T. (2018). Comparing computing professionals’ perceptions of importance of skills and knowledge on the job and coverage in undergraduate experiences. ACM Transactions on Computing Education, 18(4) article 21, 29. https://doi.org/10.1145/3218430.
Article
Google Scholar
Exter, M.E., Gray, C.M. & Fernandez, T.M. (2019). Conceptions of design by transdisciplinary educators: disciplinary background and pedagogical engagement. International Journal of Technology and Design Education. Springer. https://doi.org/10.1007/s10798-019-09520-w.
Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning., 4(5/6), 304–317. https://doi.org/10.1504/IJTEL.2012.051816.
Gallego-Arrufat, M., Torres-Hernández, N., & Pessoa, T. (2019). Competence of future teachers in the digital security area. Competencia de futuros docentes en el área de seguridad digital. Comunicar, 61, XXVII. https://doi.org/10.3916/C61-2019-05.
Article
Google Scholar
Gedrimiene, E., Silvola, A., Pursiainen, J., Rusanen, J., & Muukkonen, H. (2019). Learning analytics in education: Literature review and case examples from vocational education. Scandinavian Journal of Educational Research, 1–15. https://doi.org/10.1080/00313831.2019.1649718.
Gordillo, A., López-Pernas, S., & Barra, E. (2019). Effectiveness of MOOCs for teachers in safe ICT use training. Efectividad de los MOOC Para docentes en el uso seguro de las TIC. Comunicar, 61, XXVII. https://doi.org/10.3916/C61-2019-09.
Article
Google Scholar
Heath, J. K., Weissman, G. E., Clancy, C. B., Shou, H., Farrar, J. T., & Dine, C. J. (2019). Assessment of gender-based linguistic differences in physician trainee evaluations of medical faculty using automated text mining. JAMA Network Open, 2(5), e193520. https://doi.org/10.1001/jamanetworkopen.2019.3520.
Article
Google Scholar
Islahi, F., & Nasreen, N. (2013). Who make effective teachers, men or women? An Indian perspective. Universal Journal of Educational Research, 1(4), 285–293. https://doi.org/10.13189/ujer.2013.010402.
Article
Google Scholar
Jones, K. M. L. (2019). Learning analytics and higher education: A proposed model for establishing informed consent mechanisms to promote student privacy and autonomy. International Journal of Educational Technology in Higher Education, 16, 24springer. https://doi.org/10.1186/s41239-019-0155-0.
Article
Google Scholar
Kafedžić, E., Maleč, D., & Nikšić, E. (2018). Differences between male and female secondary school students in assessing their physical and health education teachers ’ competences. Sport Science, 11(Suppl 1), 64–70.
Google Scholar
Kechaou, Z., Ammar, M. B., & Alimi, A. M. (2011). Improving e-learning with sentiment analysis of users’ opinions. Global Engineering Education Conference, 6, 1032–1038. https://doi.org/10.1109/EDUCON.2011.5773275.
Article
Google Scholar
Kim, J. H., Hong, A. J., & Song, H. (2019). The roles of academic engagement and digital readiness in students’ achievements in university e-learning environments. International Journal of Educational Technology in Higher Education, 16, 21springer. https://doi.org/10.1186/s41239-019-0152-3.
Article
Google Scholar
Kori, K., Pedaste, M., & Must, O. (2018). The academic, social, and professional integration profiles of information technology students. ACM Transactions on Computing Education, 18(4) article 20, 19. https://doi.org/10.1145/3183343.
Article
Google Scholar
Kort, B., Reilly, R., & Picard, R. W. (2001). An affective model of interplay between emotions and learning: Reengineering educational pedagogybuilding a learning companion. Int. Conference on Advanced Learning Technologies (ICALT), IEEE Computer Society, Aug 2001, Madison, WI, USA. p. 0043. https://doi.org/10.1109/ICALT.2001.943850.
Kumakawa, T. (2017). A text mining examination of university students’ learning program posters. Open Access Library Journal, 4, e3639. https://doi.org/10.4236/oalib.1103639.
Article
Google Scholar
Lau, K., Lee, K., & Ho, Y. (2005). Text Mining for the Hotel Industry. Cornell Hotel and Restaurant Administration Quarterly, 46(3), 344–362. https://doi.org/10.1177/2F0010880405275966.
Article
Google Scholar
Laube, H., Massoni, K., Sprague, J., & Ferber, A. (2007). The impact of gender on the evaluation of teaching: What we know and what we can do. NWSA Journal, 19(3), 87–104 Retrieved March 17, 2020, from www.jstor.org/stable/40071230.
Google Scholar
Liao, S. N., Zingaro, D., Thai, K., Alvarado, C., Griswold, W. G., & Porter, L. (2019). A robust machine learning technique to predict low-performing students. ACM Transactions on Computing Education, 19(3) article 18, 19. https://doi.org/10.1145/3277569.
Article
Google Scholar
Litman, D. J., & Forbes-Riley, K. (2004). Predicting student emotions in computer human tutoring dialogues. In 42nd annual meeting on ass. For computational linguistics, (pp. 351–358). https://doi.org/10.3115/1218955.1219000.
Chapter
Google Scholar
Mackness, J., Mak, S., & Williams, R. (2010). The ideals and reality of participating in a moocNetworked Learning Conference, University of Lancaster, (pp. 266–275).
Google Scholar
Mayer-Schönberger, V., & Cukier, K. (2014). Learning with big data: The future of education. New York: Houghton Mifflin Harcourt.
Google Scholar
Mengel, F., Sauermann, J., & Zölitz, U. (2019). Gender bias in teaching evaluations. Journal of the European Economic Association, 17(2), 535–566. https://doi.org/10.1093/jeea/jvx057.
Article
Google Scholar
Moshinskie, J. (2001). How to keep e-learners from e-scaping. Performance Improvement, 40(6), 30–37. https://doi.org/10.1002/pfi.4140400607.
Article
Google Scholar
Munezero, M., Montero, C. S., Mozgovoy, M., & Sutinen, E. (2013). Exploiting sentiment analysis to track emotions in students’ learning diaries. In 13th ACM international conference on computing education research, (pp. 145–152). https://doi.org/10.1145/2526968.2526984.
Chapter
Google Scholar
Munro, M. (2018). The complicity of digital technologies in the marketisation of UK higher education: Exploring the implications of a critical discourse analysis of thirteen national digital teaching and learning strategies. International Journal of Educational Technology in Higher Education, 15, 11springer. https://doi.org/10.1186/s41239-018-0093-2.
Article
Google Scholar
Nosu, K., & Kurokawa, T. (2006). A multi-modal emotion-diagnosis system to support e-learning. Innovative Computing, Information and Control, 2, 274–278. https://doi.org/10.1109/ICICIC.2006.203.
Article
Google Scholar
Ortigosa, A., Martín, J. M., & Carro, R. M. (2014). Sentiment analysis in facebook and its application to e-learning. Computers in Human Behavior, 31, 527–541. https://doi.org/10.1016/j.chb.2013.05.024.
Article
Google Scholar
Pandey, S., & Pandey, S. K. (2019). Applying natural language processing capabilities in computerized textual analysis to measure organizational culture. Organizational Research Methods, 22(3), 765–797. https://doi.org/10.1177/1094428117745648.
Article
Google Scholar
Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systemic literature review of empirical evidence. Journal of Educational Technology & Society, 17(4), 49–64.
Google Scholar
Payne, A. (2006). Handbook of CRM. Achieving excellence in customer management. Butterworth-Heinemann, imprint Elsevier, Oxford, UK.
Pedró, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. Education Sector, UNESCO, ED-2019/WS/8, 46.
Google Scholar
Perrotta, C., & Williamson, B. (2018). The social life of learning analytics: Cluster analysis and the ‘performance’ of algorithmic education. Learning, Media and Technology, 43(1), 3–16. https://doi.org/10.1080/17439884.2016.1182927.
Article
Google Scholar
Piedade, M. B., & Santos, M. Y. (2010). Business intelligence in higher education: Enhancing the teaching-learning process with a SRM system. In 5th Iberian conference on information systems and technologies, (pp. 1–5).
Google Scholar
Prinsloo, P. (2017). Fleeing from Frankenstein’s monster and meeting Kafka on the way: Algorithmic decision-making in higher education. E-Learning and Digital Media, 14(3), 138–163. https://doi.org/10.1177/2042753017731355.
Article
Google Scholar
Rivera, L. A., & Tilcsik, A. (2019). Scaling down inequality: Rating scales, gender bias, and the architecture of evaluation. American Sociological Review, 84(2), 248–274. https://doi.org/10.1177/0003122419833601.
Article
Google Scholar
Romero, L., Saucedo, C., Caliusco, M. 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, 16, springer. https://doi.org/10.1186/s41239-019-0146-1.
Article
Google Scholar
RStudio (2018). Open source and enterprise-ready professional software for R Available at: https://www.rstudio.com/ last accessed September 2019.
Google Scholar
Sabbe, E., & Aelterman, A. (2007). Gender in teaching: A literature review. Teachers and Teaching, 13(5), 521–538. https://doi.org/10.1080/13540600701561729.
Article
Google Scholar
Sánchez, A., Domínguez, C., Blanco, J. M., & Jaime, A. (2019). Incorporating computing professionals’ know-how: Differences between assessment by students, academics, and professional experts. ACM Transactions on Computing Education, 19(3) article 26, 18. https://doi.org/10.1145/3309157.
Article
Google Scholar
Shen, L., Wang, M., & Shen, R. (2009). Affective e-learning: Using "emotional" data to improve learning in pervasive learning environment. Educational Technology & Society, 12(2), 176–189.
Google Scholar
Silva, J., Usart, M., & Lázaro-Cantabrana, J. (2019). Teacher’s digital competence among final year pedagogy students in Chile and Uruguay. Competencia digital docente en estudiantes de último año de Pedagogía de Chile y Uruguay. Comunicar, 61, XXVII. https://doi.org/10.3916/C61-2019-03.
Article
Google Scholar
Slade, S., & Prinsloo, P. (2013). Learning analytics, ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529. https://doi.org/10.1177/0002764213479366.
Article
Google Scholar
Song, D., Lin, H., & Yang, Z. (2007). Opinion mining in e-learning system. In Network and parallel computing workshops, 2007, (pp. 788–792. IFIP Int. Conference, IEEE). https://doi.org/10.1109/NPC.2007.51.
Chapter
Google Scholar
TEC (2018). TEC21 Modelo Educativo Retrieved August 2019, from Tecnologico de Monterrey (ITESM): http://modelotec21.itesm.mx/files/folletomodelotec21.pdf.
Google Scholar
Tian, F., Zheng, Q., & Zheng, D. (2010). Mining patterns of e-learner emotion communication in turn level of chinese interactive texts: Experiments and findings. In Computer supported cooperative work in design, (pp. 664–670). IEEE. https://doi.org/10.1109/CSCWD.2010.5471892.
Troussas, C., Virvou, M., Espinosa, K. J., Llaguno, K., & Caro, J. (2013). Sentiment analysis of facebook statuses using naive bayes classifier for language learning. Information, Intelligence, Systems and Applications, 4, 1–6. https://doi.org/10.1109/IISA.2013.6623713.
Article
Google Scholar
Tur, G., Marín, V. I., & Carpenter, J. (2017). Using twitter in higher education in Spain and the USA. Comunicar, 25(51), 19–27. https://doi.org/10.3916/C51-2017-02.
Article
Google Scholar
UNESCO (2015). Competency based education. Tecnologico de Monterrey, educational innovation observatory. Mexico: Learning Portal - Planning education for improved learning outcome.
Google Scholar
Wang, K., & Zhu, C. (2019). MOOC-based flipped learning in higher education: Students’ participation, experience and learning performance. International Journal of Educational Technology in Higher Education, 16, 33springer. https://doi.org/10.1186/s41239-019-0163-0.
Article
Google Scholar
Wen, M., Yang, D., & Rosé, C. P. (2014). Sentiment analysis in MOOC discussion forums: What does it tell us? In 7th international conference on educational data mining EDM 2014.
Google Scholar
Weston, T. J., Dubow, W. M., & Kaminsky, A. (2019). Predicting Women's persistence in computer science- and technology-related majors from high school to college. ACM Transactions on Computing Education, 20(1) article 1, 16. https://doi.org/10.1145/3343195.
Article
Google Scholar
Whitney, B., Hayter, J., & Marshall, E. C. (2019). Gender bias and temporal effects in standard evaluations of teaching. AEA Papers and Proceedings, 109, 261–265. https://doi.org/10.1257/pandp.20191104.
Article
Google Scholar
Williamson, B. (2018). The hidden architecture of higher education: Building a big data infrastructure for the ‘smarter university’. International Journal of Educational Technology in Higher Education, 15(1), 12. https://doi.org/10.1186/s41239-018-0094-1.
Article
Google Scholar
Yadav, A., & Berges, M. (2019). Computer science pedagogical content knowledge: Characterizing teacher performance. ACM Transactions on Computing Education, 19(3) article 29, 24. https://doi.org/10.1145/3303770.
Article
Google Scholar