Online learning has been recognized as a possible approach to increase students’ English language proficiency in developing countries where high-quality instructional resources are limited. Identifying factors that predict students’ performance in online courses can inform institutions and instructors of actionable interventions to improve learning processes and outcomes. Framed in Deci and Ryan’s self-determination theory (SDT) and using data from a pre-course student readiness survey, LMS log files, and a course Facebook page, this study identified key predictors of persistence and achievement among 716 Peruvian students enrolled in an online English language course. Factor analysis was used to identify latent factors from 7 behavioral variables and 18 pre-course student readiness variables. Nine factors emerged, which were classified into three categories of measures based on SDT: competence, autonomy, and relatedness. We found that factors in the categories of competence and autonomy significantly predicted persistence and achievement in online courses. Specifically, the midterm score and self-regulation skills significantly predicted students’ final test score. Counterintuitively, we also found that time spent on the course was a significantly negative predictor of the final test score and that the extent to which a student valued peer learning at the beginning of the course negatively predicted course achievement.
- Self-determination theory
- Student persistence
- Online language learning
- Developing countries
- Predictive analytics
- Factor analysis
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The CEFR is an international standard for describing language ability ranging from A1 (basic) up to C2 (proficient).
The MSLQ is in the public domain; therefore, permission to use this instrument is not needed.
Ai, J., & Laffey, J. (2007). Web mining as a tool for understanding online learning. MERLOT Journal of Online Learning and Teaching, 3(2), 160–169.
Allen, D. F., & Bir, B. (2012). Academic confidence and summer bridge learning communities: Path analytic linkages to student persistence. Journal of College Student Retention: Research, Theory & Practice, 13(4), 519–548.
Andrade, M. S., & Bunker, E. L. (2009). A model for self-regulated distance language learning. Distance Education, 30(1), 47–61.
Angelino, L. M., Williams, F. K., & Natvig, D. (2007). Strategies to engage online students and reduce attrition rates. Journal of Educators Online, 4(2), 1–14.
Appana, S. (2008). A review of benefits and limitations of online learning in the context of the student, the instructor, and the tenured faculty. International Journal on E-Learning, 7(1), 5–22.
Baard, P. P. (2002). Intrinsic need satisfaction in organizations: A motivational basis of success in for-profit and not-for-profit settings. Handbook of Self-Determination Research, 2, 255–275.
Bai, Y., Mo, D., Zhang, L., Boswell, M., & Rozelle, S. (2016). The impact of integrating ICT with teaching: Evidence from a randomized controlled trial in rural schools in China. Computers & Education, 96, 1–14.
Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In Learning analytics (pp. 61–75). New York, NY: Springer.
Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3–17.
Bakia, M., Shear, L., Toyama, Y., & Lasseter, A. (2012). Understanding the implications of online learning for educational productivity. Washington, DC: Office of Educational Technology, US Department of Education.
Bañados, E. (2006). A blended-learning pedagogical model for teaching and learning EFL successfully through an online interactive multimedia environment. Calico Journal, 23(3), 533–550.
Banditvilai, C. (2016). Enhancing student’s language skills through blended learning. The Electronic Journal of E-Learning, 14(3), 220–229. Available from www.ejel.org
Bandura, A., Barbaranelli, C., Caprara, G. V., & Pastorelli, C. (2001). Selfefficacy beliefs as shapers of children’s aspirations and career trajectories. Child Development, 72, 187–206.
Barani, G. (2011). The relationship between computer assisted language learning (CALL) and listening skill of Iranian EFL learners. Procedia-Social and Behavioral Sciences, 15, 4059–4063.
Black, A. E., & Deci, E. L. (2000). The effects of instructors’ autonomy support and students’ autonomous motivation on learning organic chemistry: A self-determination theory perspective. Science Education, 84, 740–756.
Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. Educause Review, 42(4), 42–57.
Cheng, J., Kulkarni, C., & Klemmer, S. (2013). Tools for predicting drop-off in large online classes. In Proceedings of the 2013 conference on computer supported cooperative work companion (pp. 121–124). New York, NY: ACM.
Clay, M., Rowland, S., & Packard, A. (2009). Improving undergraduate online retention through gated advisement and redundant communication. Journal of College Student Retention, 10(1), 93–102.
Cohen, A., & Nachmias, R. (2006). A quantitative cost effectiveness model for web-supported academic instruction. The Internet and Higher Education, 9(2), 81–90.
Cohen, A., & Nachmias, R. (2012). The implementation of a cost effectiveness analyzer for web-supported academic instruction: An example from life science. International Journal on E-Learning, 11(2), 5–22.
Cohen, A. (2017). Analysis of student activity in web-supported courses as a tool for predicting dropout. Educational Technology Research and Development, 65(5), 1285–1304.
Cronquist, K., & Fiszbein, A. (2017). English Language Learning in Latin America. Inter-American Dialogue. Available from: www.dropbox.com/s/zabf293t0a12ten/English-Language-Learning-in-Latin-America-Final.pdf?dl=0
Dawson, S., Gašević, D., Siemens, G., & Joksimovic, S. (2014). Current state and future trends: A citation network analysis of the learning analytics field. In Proceedings of the fourth international conference on learning analytics and knowledge (pp. 231–240). New York, NY: ACM. https://doi.org/10.1145/2567574.2567585
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York, NY: Plenum.
Dickinson, L. (1995). Autonomy and motivation a literature review. System, 23(2), 165–174.
Dietz-Uhler, B., & Hurn, J. E. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of Interactive Online Learning, 12(1), 17–26.
Dominguez, M., Bernacki, M. L., & Uesbeck, P. M. (2016). Predicting STEM achievement with learning management system data: Prediction modeling and a test of an early warning system. In EDM (pp. 589–590).
Driscoll, A., Jicha, K., Hunt, A. N., Tichavsky, L., & Thompson, G. (2012). Can online courses deliver in-class results? A comparison of student performance and satisfaction in an online versus a face-to-face introductory sociology course. Teaching Sociology, 40(4), 312–331. https://doi.org/10.1177/0092055X12446624
Duffy, T. M., & Kirkley, J. R. (2003). Learner-centered theory and practice in distance education: Cases from higher education. New York, NY: Routledge.
Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109–132.
EF English Proficiency Index. (2016). EF education first. Available from https://www.theewf.org/uploads/pdf/ef-epi-2016-english.pdf
Erdem, M., & Kibar, P. N. (2014). Students’ opinions on facebook supported blended learning environment. TOJET: The Turkish Online. Journal of Educational Technology, 13(1), 199–206.
Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71.
Gašević, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68–84.
Giesbers, B., Rienties, B., Tempelaar, D., & Gijselaers, W. (2013). Investigating the relations between motivation, tool use, participation, and performance in an e-learning course using web-videoconferencing. Computers in Human Behavior, 29(1), 285–292.
Glick, D., Xu, D., Warschauer, M., Rodriguez, F., Li, Q., & Cung, B. (2016). Maximizing learning outcomes through blended learning: What research shows. Paper presented at the 2016 Association of Binational Centers of Latin America Conference, Houston, TX.
Goldstein, P. J., & Katz, R. N. (2005). Academic analytics: The uses of management information and technology in higher education. Washington, DC: EDUCAUSE Center for Applied Research.
Hartnett, M., George, A. S., & Dron, J. (2011). Examining motivation in online distance learning environments: Complex, multifaceted and situation-dependent. The International Review of Research in Open and Distributed Learning, 12(6), 20–38.
Ho, J., & Crookall, D. (1995). Breaking with Chinese cultural traditions: Learner autonomy in English language teaching. System, 23(2), 235–243.
Holec, H. (1981). Autonomy and foreign language learning. Oxford, England/New York, NY: Pergamon Press (First Published 1979, Council of Europe).
Jaggers, S. S., & Xu, D. (2016). How do online course design features influence student performance? Computers & Education, 95, 270–284.
Järvelä, S., Volet, S., & Järvenoja, H. (2010). Research on motivation in collaborative learning: Moving beyond the cognitive–situative divide and combining. Educational Psychologist, 45, 15–27.
Johnson, L., Becker, S., Estrada, V., & Freeman, A. (2015). The NMC Horizon Report: 2015 Higher Education Edition. Austin, TX: New Media Consortium.
Komarraju, M., & Nadler, D. (2013). Self-efficacy and academic achievement: Why do implicit beliefs, goals, and effort regulation matter? Learning and Individual Differences, 25, 67–72.
Lee, L. (2016). Autonomous learning through task-based instruction in fully online language courses. Language Learning & Technology, 20(2), 81–97.
Levi-Gamlieli, H., Cohen, A., & Nachmias, R. (2015). Detection of overly intensive learning by using weblog of course website. Technology, Instruction, Cognition and Learning (TICL), 10(2), 151–171.
Levy, Y. (2007). Comparing dropouts and persistence in e-learning courses. Computers & Education, 48, 185–204. https://doi.org/10.1016/j.compedu.2004.12.004
Lim, J. M. (2016). Predicting successful completion using student delay indicators in undergraduate self-paced online courses. Distance Education, 37(3), 317–332. https://doi.org/10.1080/01587919.2016.1233050
Lu, J., Yu, C. S., & Liu, C. (2003). Learning style, learning patterns, and learning performance in a WebCT-based MIS course. Information & Management, 40(6), 497–507. https://doi.org/10.1016/S0378-7206(02)00064-2
Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G., & Loumos, V. (2009). Dropout prediction in e-learning courses through the combination of machine learning techniques. Computers & Education, 53, 950–965. https://doi.org/10.1016/j.compedu.2009.05.010
Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop “early warning system” for educators: A proof of concept. Computers & Education, 54, 588–599. https://doi.org/10.1016/j.compedu.2009.09.008
Marzban, A. (2011). Improvement of reading comprehension through computer-assisted language learning in Iranian intermediate EFL students. Procedia Computer Science, 3, 3–10.
Maki, R., & Maki, W. (2003). Prediction of learning and satisfaction in Webbased and lecture courses. Journal of Educational Computing Research, 28(3), 197–219.
Mandernach, B. J. (2009). Effect of instructor-personalized multimedia in the online classroom. International Review of Research in Open and Distance Learning, 10(3), 19.
Massengale, L. R., & Vasquez, E. (2016). Assessing accessibility: Are online courses better than face-to-face instruction at providing access to course content for students with disabilities? Journal of the Scholarship of Teaching and Learning, 16(1), 69–79. https://doi.org/10.14434/josotl.v16i1.19101
Miltiadou, M., & Savenye, W. C. (2003). Applying social cognitive constructs of motivation to enhance student success in online distance education. AACE Journal, 11(1), 78–95.
Ministério de Educación Perú. (2016a). Currículo nacional de la educación básica. Retrieved from http://www.minedu.gob.pe/curriculo/pdf/curriculo-nacional-2016-2.pdf.
Ministério de Educación Perú. (2016b). Plan de implementación al 2021 de la política nacional de enseñanza, aprendizaje y uso de idioma Inglés – Política “Inglés, puertas al mundo. Retrieved from http://www.minedu.gob.pe/ingles-puertas-al-mundo.
Ministério de Educación Perú. (2017). Jornada escolar completa: Secundaria. Retrieved from http://www.minedu.gob.pe/jec/escuela-jec.php.
Moore, M. G., & Kearsley, G. (2011). Distance education: A systems view of online learning. Belmont, CA: Cengage Learning.
Rios, S. M., & Cabrera, A. F. (2008). La efectividad de un model de aprendizaje combinado para la enseñanza del inglés como lengua extranjera: Estudio empírico. RLA, Revista de Linguistica Teórica y Aplicada, 46(2), 95–118. Retrieved from http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-48832008000200006
Muilenburg, L. Y., & Berge, Z. L. (2005). Student barriers to online learning: A factor analytic study. Distance Education, 26(1), 29–48. https://doi.org/10.1080/01587910500081269
Nistor, N., & Neubauer, K. (2010). From participation to dropout: Quantitative participation patterns in online university courses. Computers & Education, 55, 663–672. https://doi.org/10.1016/j.compedu.2010.02.026
OECD. (2015). Education at a glance 2015: OECD indicators. Paris, France: OECD Publishing. https://doi.org/10.1787/eag-2015-en
Otter, R. R., Seipel, S., Graeff, T., Alexander, B., Boraiko, C., Gray, J., … Sadler, K. (2013). Comparing student and faculty perceptions of online and traditional courses. The Internet and Higher Education, 19, 27–35. https://doi.org/10.1016/j.iheduc.2013.08.001
Palmer, S., & Holt, D. (2010). Students’ perceptions of the value of the elements of an online learning environment: Looking back in moving forward. Interactive Learning Environments, 18(2), 135–151.
Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the motivated strategies questionnaire (MSLQ). Ann Arbor, MI: University of Michigan, National Center for Research to Improve Postsecondary Teaching and Learning.
Park, J.-H., & Choi, H. J. (2009). Factors influencing adult learners’ decision to drop out or persist in online learning. Educational Technology & Society, 12(4), 207–217.
Rienties, B., Tempelaar, D., Van den Bossche, P., Gijselaers, W., & Segers, M. (2009). The role of academic motivation in computer-supported collaborative learning. Computers in Human Behavior, 25(6), 1195–1206.
Roby, T., Ashe, S., Singh, N., & Clark, C. (2013). Shaping the online experience: How administrators can influence student and instructor perceptions through policy and practice. The Internet and Higher Education, 17, 29–37.
Rodriguez, M. C., Rooms, A., & Montañez, M. (2008). Students’ perceptions of online-learning quality given comfort, motivation, satisfaction, and experience. Journal of Interactive Online Learning, 7(2), 105–125.
Romero, C., López, M. I., Luna, J. M., & Ventura, S. (2013). Predicting students’ final performance from participation in on-line discussion forums. Computers & Education, 68, 458–472. https://doi.org/10.1016/j.compedu.2013.06.009
Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135–146. https://doi.org/10.1016/j.eswa.2006.04.005
Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67.
Santana, M. A., Costa, E. B., Neto, B. F. D. S., Silva, I. C. L., & Rego, J. B. (2015). A predictive model for identifying students with dropout profiles in online courses. In Proceeding of the 8th international conference on educational data mining, EDM Workshops.
Schunk, D. H. (2012). Learning theories, an educational perspective (6th ed.). Boston, MA: Pearson Education Inc.
Siemens, G., & Gašević, D. (2012). Special issue on learning and knowledge analytics. Educational Technology & Society, 15(3), 1–163.
Sife, A., Lwoga, E., & Sanga, C. (2007). New technologies for teaching and learning: Challenges for higher learning institutions in developing countries. International Journal of Education and Development Using ICT, 3(2), 57–67.
Vahdat, S., & Eidipour, M. (2016). Adopting CALL to improve listening comprehension of iranian junior high school students. Theory and Practice in Language Studies, 6(8), 1609–1617.
Vansteenkiste, M., Lens, W., & Deci, E. L. (2006). Intrinsic versus extrinsic goal contents in self-determination theory: Another look at the quality of academic motivation. Educational Psychologist, 41(1), 19–31.
Willging, P. A., & Johnson, S. D. (2009). Factors that influence students’ decision to dropout of online courses. Journal of Asynchronous Learning Networks, 13(3), 115–127.
Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In Handbook of self-regulation (pp. 531–566).
Wladis, C., & Samuels, J. (2016). Do online readiness surveys do what they claim? Validity, reliability, and subsequent student enrollment decisions. Computers & Education, 98, 39–56. https://doi.org/10.1016/j.compedu.2016.03.001
Xie, K. U. I., Debacker, T. K., & Ferguson, C. (2006). Extending the traditional classroom through online discussion: The role of student motivation. Journal of Educational Computing Research, 34(1), 67–89.
Xu, D., & Jaggars, S. S. (2014). Performance gaps between online and face-to-face courses: Differences across types of students and academic subject areas. The Journal of Higher Education, 85(5), 633–659.
You, J. W. (2016). Identifying significant indicators using LMS data to predict course achievement in online learning. The Internet and Higher Education, 29, 23–30. https://doi.org/10.1016/j.iheduc.2015.11.003
Yuan, J., & Kim, C. (2014). Guidelines for facilitating the development of learning communities in online courses. Journal of Computer Assisted Learning, 30(3), 220–232.
Zakrzewska, D. (2009). Cluster analysis in personalized e-learning systems. In N. T. Nguyen & E. Szczerbicki (Eds.), Intelligent systems for knowledge management (pp. 229–250). Berlin, Germany: Springer.
We would like to thank Betty Luz Zegarra Angulo of the Universidad Señor de Sipán for helping make available the data for this study as well as providing detailed information on the study context.
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Glick, D., Cohen, A., Festinger, E., Xu, D., Li, Q., Warschauer, M. (2019). Predicting Success, Preventing Failure. In: Ifenthaler, D., Mah, DK., Yau, J.YK. (eds) Utilizing Learning Analytics to Support Study Success. Springer, Cham. https://doi.org/10.1007/978-3-319-64792-0_14
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