Strategies for Supporting Students’ Self-Regulated Learning within the Course

Concurrent Session 4

Brief Abstract

This session will provide resources and strategies for instructors to directly assess and address students’ self-regulated learning, efficacy, and anxiety. Drawing from my experience teaching college statistics, these strategies can be adapted to other disciplines.

Presenters

Lead Presenter: Jason Bryer, City University of New York - School of Professional Studies

Dr. Jason Bryer is currently an Assistant Professor and Associate Director in the Data Science and Information Systems department at the City University of New York. He is currently the Principal Investigator of the FIPSE ($3 million #P116F150077) and IES funded ($3.8 million R305A210269) Diagnostic Assessment and Achievement of College Skills. Previously, Dr. Bryer was a consultant with Research Analyst with Cornell University where he developed a research and data collection platform for New York State’s Office of Special Education (https://data.osepartnership.org). Dr. Bryer’s research interests include quasi-experimental designs with an emphasis on propensity score analysis, data systems to support formative assessment, and the use of open source software for conducting reproducible research. He is the author of over a dozen R packages, including three related to conducting propensity score analyses. When not crunching numbers, Jason is a wedding photographer and proud dad to three boys.

Extended Abstract

For students to be successful in online learning environments they need to be self-motivated and goal-directed, making effective use of learning strategies for learning and for managing competing demands. These are the cornerstone qualities of self-regulated learners (Zimmerman et al., 2011; Zimmerman & Schunk). Students who possess these skills are much more likely to be successful (Efklides, 2011; Winne & Hadwin, 1998; Zimmerman, 2000). Given the demands on course content, learning objectives, and outcomes, instructors rarely explicitly address students’ self-regulatory strategy use (Dignath-van Ewijk & Van der Werf, 2012). Moreover, students’ self-efficacy is a significant contributor and predictor of success (Bandura, 1993). These effects are often more pronounced within mathematics, statistics, or other STEM courses (see e.g. Nunez-Pena, Suarez-Pellicioni, & Bono, 2013).

This Discovery Session will introduce how self-regulated learning, self-efficacy, affect, and anxiety are directly addressed through formative assessments and class discussions. Although these strategies have been used in graduate statistics courses, they are more widely applicable. Specifically, students are asked to complete the self-regulated learning component of the Diagnostic Assessment and Achievement of College Skills (DAACS; see https://daacs.net for more information; Bryer, et al, 2019) along with the Math Anxiety Scale Survey (Bai, Wang, Pan, & Frey, 2011) to supplement the DAACS assessment with domain specific information. In addition to providing the instructor with information about students’ self-regulatory skills, DAACS provides students with personalized feedback, suggested strategies, and links to additional open education resources. Within the first week of the course, student data is aggregated and presented to them. I use this as an opportunity to motivate discussions addressing, at minimum, the following questions:

  1. What surprised you about your results?
  2. What strategies do you currently use that you can rely on to be successful this semester?
  3. What strategies were recommended that you think you can work on this semester?

In addition to getting students to think about their own learning, this often provides an opportunity to reinforce that they are not in this alone. For example, given that I teach statistics many students indicate that they have low affect and high anxiety when it comes to statistics, and mathematics more broadly. My goal in discussing these outcomes is to: 1) Indicate that they are not alone; 2) Share strategies to help deal with anxiety and to build efficacy; and 3) Reinforce that they should not be ashamed of these feelings and that there are supports available, including engaging with me as the instructor.

At the completing of this Discover Session participants will have a list of resources, including the technological implementation, and strategies they can use with their students to directly address students self-regulated learning knowledge and strategy use.

References

Bai, H., Wang, L., Pan, W., & Frey, M. (2009). Measuring mathematics anxiety: Psychometric analysis of a bidimensional affective scale. Journal of Instructional Psychology, 36(3), 185- 193. 

Bandura, A. (1993). Perceived self-efficacy in cognitive development and functioning. Educ. Psychol. 28, 117–148. doi: 10.1207/s15326985ep2802_3

Bryer, J., Lui, A. M., Andrade, H. L., Franklin, D., & Cleary, T. (2019, April 5-9). Efficacy of the Diagnostic Assessment and Achievement of College Skills on multiple success indicators. Annual meeting of the American Educational Research Association (AERA), Toronto, Canada.

Bryer, J., Akhmedjanova, D., Andrade, H., & Lui, A. (2020). The use of predictive modeling for assessing college readiness. In H. Jiao & R. Lissitz (Eds.), Enhancing effective instruction and learning using assessment data: Theory and practice. Information Age Publishing.

Efklides, A. (2011). Interactions of metacognition with motivation and affect in self-regulated learning: The MASRL model. Educational Psychologist, 46(1), 6–25. https://doi.org/10.1080/00461520.2011.538645

Dignath-van Ewijk, C., & Van der Werf, G. (2012). What teachers think about self-regulated learning: Investigating teacher beliefs and teacher behavior of enhancing students’ self-regulation. Education Research International, 2012, 1-10.

Lui, A., Franklin, D., Akhmedjanova, D., Gorgun, G., Bryer, J., Andrade, H., & Cleary, T. (2018). Validity evidence of the internal structure of the DAACS self-regulated learning survey. Future Review: International Journal of Transition, College, and Career Success, 1(1), 1-18. 

Nunez-Pena, M.I., Suarez-Pellicioni, M., & Bono, R. (2013). Effects of math anxiety on student success in higher education. International Journal of Educational Research, 48, 36-43.

Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated engagement in learning. In D. Hacker, J. Dunlosky, & A. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277-304). Lawrence Erlbaum.

Zimmerman, B. J., & Schunk, D. H. (2011). Self-regulated learning and performance: An introduction and overview. In B. J Zimmerman & D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance: Educational psychology handbook. Routledge. 

Zimmerman, B. J., Moylan, A., Hudesman, J., White, N., & Flugman, B. (2011). Enhancing self-reflection and mathematics achievement of at-risk urban technical college students. Psychological Test and Assessment Modeling, 53(1), 141-160.  

Zimmerman, B.J. (2000). Attaining self-regulation: A social-cognitive perspective. In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Self-regulation: Theory, research, and applications (pp. 13-39). Academic Press.