Current Research

Journal Articles:

  • Crues, R. W., Henricks, G. M, Perry, M., Bhat, S., Anderson, C. J., Shaik, N., Angrave, L. (in press). How does gender, learning goals, and forum participation predict persistence in a computer science MOOC? ACM Transactions on Computing Education. pdf

Conference Proceedings:

  • Crues, R. W., Bosch, N., Anderson, C. J., Perry, M., Bhat, S., & Shaik, N. (2018). Who they are and what they want: Understanding the reason for MOOC enrollment. In K. E. Boyer & M. Yudelson (Eds.), Proceedings of the 11th International Conference on Educational Data Mining (pp. 177-186). Buffalo, New York, United States: International Educational Data Mining Society. pdf
  • Bosch, N., Crues, R. W., Shaik, N. (2018). Diverse learners, diverse motivations. Exploring the sentiment of learning objectives. In K. E. Boyer & M. Yudelson (Eds.), Proceedings of the 11th International Conference on Educational Data Mining (pp. 553-556). Buffalo, New York, United States: International Educational Data Mining Society. pdf
  • Crues, R. W., Bosch, N., Perry, M., Angrave, L., Shaik, N., & Bhat, S. (2018). Refocusing the lens on engagement in MOOCs. In S. Klemmer & K. Koedinger (Eds.), Proceedings of the Fifth ACM Conference on Learning at Scale (L@S). New York, New York: ACM. pdf
  • Bosch, N., Crues, R. W., Henricks, G. M., Perry, M., Angrave, L., Shaik, N., Bhat, S., & Anderson, C. J. (2018). Modeling key differences in underrepresented students’ interactions with an online STEM course. In A.  Story (Ed.), Proceeding of the Technology, Mind, and Society Conference. New York, New York: ACM. pdf
  • Crues, R. W. (2017). Automated Extraction of Results from Full Text Journal Articles. In X. Hu, T. Barnes, A. Hershkovitz, & L. Paquette (Eds.), Proceedings of the Tenth International Conference on Educational Data Mining (pp. 436-438). Wuhan, Hubei, China. pdf
  • Crues, R. W. (2017). Untangling The Program Name Versus The Curriculum: An Investigation of Titles and Curriculum Content. In X. Hu, T. Barnes, A. Hershkovitz, & L. Paquette (Eds.), Proceedings of the Tenth International Conference on Educational Data Mining (pp. 366-367). Wuhan, Hubei, China. pdf

Conference Posters:

  • Henricks, G. M., Crues, R. W., Bhat, S., & Perry, M. (2018, April). Predicting learning by using students perceptions of and experiences with statistics online course videos. Poster to appear at the Annual Meeting of the American Educational Research Association 2018 in New York, New York.
  • Crues, R. W., & Anderson, C. J. (2016, July). Stratified Doubly Robust Estimator when Data are MAR or MCAR. Poster at International Meeting of the Psychometric Society 2016 in Asheville, North Carolina.

Works-in-Progress:

  • Crues, R. W. (in-prep). A text mining method to detect replications in educational research.
  • Crues, R. W. (in-prep). Maximizing the replicability and reproducibility of educational studies: A framework for evidence.
  • Bhat, S., Anderson, C. J., Crues, R. W., Angrave, L., Perry, M. (in-prep). Engagement in MOOCs: Who is served and predicting their actions.
  • Jay, V., Perry, M., Crues, R. W., Bosch, N., Angrave, L., et al. (in-prep). Help-seeking in an online STEM college course.
  • Bosch, N., Paquette, L., Shaik, N., Crues, R. W. (in-prep). Strict anonymization methods for improving privacy in student discussion forum analyses.

Affiliations:

  • iLearn. Supervisors: Carolyn Anderson, Michelle Perry, Suma Bhat, Lawrence Angrave, and Naj Shaik. Summer 2016-present.
  • Academy for Entrepreneur Leadership. Supervisors: Cindy Kehoe and Paul Magelli, Summer 2015-Spring 2016.