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Utilizing Learning Analytics to Support Study Success pp 249–273Cite as

Predicting Success, Preventing Failure

Using Learning Analytics to Examine the Strongest Predictors of Persistence and Performance in an Online English Language Course

Abstract

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.

Keywords

  • Self-determination theory
  • Student persistence
  • Online language learning
  • Developing countries
  • Predictive analytics
  • Factor analysis

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Notes

  1. 1.

    The CEFR is an international standard for describing language ability ranging from A1 (basic) up to C2 (proficient).

  2. 2.

    The MSLQ is in the public domain; therefore, permission to use this instrument is not needed.

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Acknowledgments

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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Authors and Affiliations

  1. Edusoft, a Subsidiary of ETS, and UC Irvine’s Digital Learning Lab, University of California, Irvine, CA, USA

    Danny Glick

  2. Tel-Aviv University, Tel Aviv, Israel

    Anat Cohen & Eitan Festinger

  3. University of California, Irvine, CA, USA

    Di Xu, Qiujie Li & Mark Warschauer

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  1. Danny Glick
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Correspondence to Danny Glick .

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  1. University of Mannheim, Mannheim, BW, Germany

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  2. Curtin University, Perth, WA, Australia

    Dirk Ifenthaler

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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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