Assessing Instructional Modalities: Individualized Treatment Effects for Personalized Learning

References

• Alpaydin, E. (2009), Introduction to Machine Learning (2nd ed.), Cambridge, MA: MIT Press.
   Google Scholar
• California State University (2013), 2014–2015 Support Budget, November 2013. Available at: http://www.calstate.edu/budget/fybudget/2014-2015/executive-summary/documents/2014-15-Support-Budget.pdf
   Google Scholar
• Cruise, J. R., Cash, R. W., and Bolton, L. D. (1985), “Development and Validation of an Instrument to Measure Statistical Anxiety,” in American Statistical Association Proceedings of the Section on Statistical Education, pp. 92–98.
   Google Scholar
• delMas, R., Garfield, J., Ooms, A., and Chance, B. (2007), “Assessing Students Conceptual Understanding After a First Course in Statistics,” Statistics Education Research Journal, 6, 28–58.
   Google Scholar
• de Jong, N., Verstegen, D. M. L., Tan, F. E. S., and O’Connor, S. J. (2013), “A Comparison of Classroom and Online Asynchronous Problem-Based Learning for Students Undertaking Statistics Training as Part of a Public Health Masters Degree,” Advanced in Health Sciences Education, 18, 245–264.
   PubMed Web of Science ®Google Scholar
• DeVaney, T. A. (2010), “Anxiety and Attitude of Graduate Students in on-Campus vs. Online Statistics Courses,” Journal of Statistics Education, 18. Available at: https://doi.org/10.1080/10691898.2010.11889472.
   PubMedGoogle Scholar
• Dorresteijn, J. A. N., Visseren, F. L. J., Ridker, P. M., Wassink, A. M. J., Paynter, N. P., Steyerberg, W. W., van der Graaf, Y., and Cook, N. R. (2011), “Estimating Treatment Effects for Individual Patients Based on the Results of Randomised Clinical Trials,” BMJ, 343, d588. Available at: https://doi.org/10.1136/bmj.d588.
   PubMedGoogle Scholar
• Gundlach, E., Richards, K. A. R., Nelson, D., and Levesque-Bristol, C. (2015), “A Comparison of Student Attitudes, Statistical Reasoning, Performance, and Perceptions for Web-Augmented Traditional, Fully Online, and Flipped Sections of a Statistical Literacy Class,” Journal of Statistics Education, 23. Available at: https://doi.org/10.1080/10691898.2015.11889723.
   Google Scholar
• Gibbs, A. L. (2014), “Experiences Teaching an Introductory Statistics MOOC,” in Proceedings of the 9th International Conference on Teaching Statistics ( ICOTS 9), Flagstaff, AZ, USA, July 2014.
   Google Scholar
• Hapfelmeier, A., and Ulm, K. (2013), “A New Variable Selection Approach Using Random Forests,” Computational Statistics and Data Analysis, 60, 50–69.
   Web of Science ®Google Scholar
• James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013), An Introduction to Statistical Learning, New York: Springer.
   Google Scholar
• Long, P., and Siemens, G. (2011), “Penetrating the Fog Analytics in Learning and Education,” EDUCAUSE Review, 46, 31–40.
   Google Scholar
• Lu, F., and Lemonde, M. (2013), “A Comparison of Online Versus Face-to-Face Teaching Deliver in Statistics Instruction for Undergraduate Health Science Students,” Advances in Health Sciences Education, 18, 963–973.
   PubMed Web of Science ®Google Scholar
• Means, B., Toyama, Y., Murphy, R., Bakia, M., and Jones, K. (2010), Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies, Washington, DC: U.S. Department of Education, Office of Planning, Evaluation, and Policy Development.
   Google Scholar
• Mills, J. D., and Raju, D. (2011), “Teaching Statistics Online: A Decade’s Review of the Literature About What Works,” Journal of Statistics Education, 19. Available at: https://doi.org/10.1080/10691898.2011.11889613.
   Google Scholar
• Ott, R. L., and Longnecker, M. T. (2008), An Introduction to Statistical Methods and Data Analysis (6th ed.), New York: Cengage Learning.
   Google Scholar
• Polikar, R. (2006), “Ensemble Based Systems in Decision Making,” IEEE Circuits and Systems Magazine, 6, 21–45.
   Google Scholar
• R Core Team (2017), R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing, Available at: http://www.R-project.org/.
   Google Scholar
• Schau, C. (2000), “Survey of Attitudes Toward Statistics,” in Commissioned Reviews on 250 Psychological Tests, eds. J. Maltby, C. A. Lewis, and A. Hill, Lampeter, Wales: Edwin Mellen Press, pp. 898–901.
   Google Scholar
• Scherrer, C. R. (2011), “Comparison of an Introductory Level Undergraduate Statistics Course Taught With Traditional, Hybrid, and Online Delivery Methods,” INFORMS Transactions on Education, 11, 106–110.
   Google Scholar
• Simmons, G. R. (2014), “Business Statistics: A Comparison of Student Performance in Three Learning Modes,” Journal of Education for Business, 89, 186–195.
   Google Scholar
• Tibshirani, R. J., Taylor, J., Lockhart, R., and Tibshirani, R. (2016), “Exact Post-Selection Inference for Sequential Regression Procedures,” Journal of the American Statistical Association, 111, 600–620.
   Web of Science ®Google Scholar
• Tishkovskaya, S., and Lancaster, G. A. (2012), “Statistical Education in the 21st Century: A Review of Challenges, Teaching Innovations and Strategies for Reform,” Journal of Statistics Education, 20. Available at: https://doi.org/10.1080/10691898.2012.11889641.
   Google Scholar