Published October 11, 2019 | Version v1
Conference paper Open

ESCALATING INTERVENTIONS TO IMPROVE BEHAVIOUR AND PERFORMANCE IN AN UNDERGRADUATE STATISTICS MODULE

  • 1. University College Dublin
  • 2. Maynooth University

Description

In this study, we present the implementation of an early warning system in a large introductory statistics module which is escalated over four semester offerings. An early warning system identifies students who are at risk of failing or dropping out of a module, and provides them with supporting interventions. While familiar undergraduate mathematics supports include formative assessment and peer-assisted learning, our interventions tried to encourage student engagement through personalised emails which detailed supports and how students were progressing in the module. In later escalations, at-risk students received weekly emails encouraging them to use the Maths Support Centre. We believe our module-based interventions had limited impact upon the at-risk students. In hindsight, we believe these students needed programme-level interventions. Overall, this study provides insights for others into implementing learning analytics interventions.

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References

  • Atif, A., Richards, D., & Bilgin, A. (2015). Student preferences and attitudes to the use of early alerts. In T. Bandyopadhyay, D. Beyene, & S. Negash (Eds.), Americas Conference on Information Systems, AMCIS 2015 (pp. 1-14). Puerto Rico: AMCIS.
  • Cai, Q., Lewis, C. L., & Higdon, J. (2015). Developing an early-alert system to promote student visits to tutor centre. Learning Assistance Review, 20(1), 61-72.
  • Campbell, J. P., & Oblinger, D. G. (2007). Academic analytics. Educause. Retrieved from https://www.educause:edu/ir/library/pdf/PUB6101.pdf.
  • Chipman, H. A., George, E. I., & McCulloch, R. E. (2010). BART: Bayesian additive regression trees. The Annals of Applied Statistics, 4(1), 266-298.
  • Choi, S. P. M., Lam, S. S., Li, K. C., & Wong, B. T. M. (2018). Learning analytics at low cost: At-risk student prediction with clicker data and systematic proactive interventions. Educational Technology & Society, 21(2), 273-290.
  • Corrigan, O., Smeaton, A. F., Glynn, M., & Smyth, S. (2015). Using educational analytics to improve test performance. In G. Conole, T. Klobučar, C. Rensing, J. Konert, & E. Lavoué (Eds.), Proceedings of Design for Teaching and Learning in a Networked World, 10th European Conference on Technology Enhanced Learning (pp. 42-55). Toledo: Springer.
  • Dawson, S., Jovanovic, J., Gašević, D., & Pardo, A. (2017). From prediction to impact: Evaluation of a learning analytics retention program. In Proceedings of the 7th International Learning Analytics and Knowledge Conference (pp. 474-478). New York, USA: ACM.
  • Howard, E., Meehan, M., & Parnell, A. (2018). Contrasting prediction methods for early warning systems at undergraduate level. The Internet and Higher Education, 37(1), 66-75.
  • Na, K. S., & Tasir, Z. (2017). A systematic review of learning analytics intervention contributing to student success in online learning. In 2017 International Conference on Learning and Teaching in Computing and Engineering (LaTICE) (pp. 62-68). Retrieved from https://ieeexplore.ieee.org/document/8064433.