Monday, April 12, 2010

Self Regulation of Learning within Computer based Learning Environments: A Critical Analysis

Winters, F. Greene, J. Costich, C (2008) Self-Regulation of Learning within Computer-based Learning Environments: A Critical Analysis. Education Psychology Review, 20, 429-444

This article is an analysis of the current studies that have been done in regards to self regulation of learning (SRL) in a computer-based learning environment (CBLE). Through their search of hundreds of articles the authors pinpointed 33 articles that have foci that relate to the topics of both SRL and CBLE. The authors had three research questions that were seeking to address:

  1. How do learner and task characteristics relate to students' SRL with CBLE?

  2. Can various learning supports or conditions enhance the quality of students' SRL as they learn with CBLEs?

  3. What conceptual, theoretical, and methodological issues exist in this burgeoning field of research?

The discussion of the studies were presented in an unbiased manner. Within each question they used the current work of others to determine what is working and what needed to be improved upon. In the final section that authors give their opinion as to further studies that need to be done to build off of the work that has been published.

This article is of great value to my study because it is a wealth of resources for me to further examine. In reading this article my ideas about the direction for my study have expanded and there are immediate resources that I can access to further my research. I feel that the comments in regards to the third research question can be an area that I focus on for my study. No use doing what has already been done.

Some quotes

For instance,Winters and Azevedo found that high prior knowledge high school students working collaboratively with low prior-knowledge students did not make significant learning gains during the task, although the low prior-knowledge students did make significant gains from pretest to posttest. Low prior-knowledge students relied on their partners for cognitive and metacognitive support, and in response, the high prior-knowledge students spent time providing this support to their peers, at the possible detriment to their own learning. In another collaborative-learning study, investigated low-achieving high school students’ SRL as they worked in dyads. They found that while students did make statistically significant gains from pretest to posttest, the

gains were small. Analysis of student discourse revealed that students spent much of their time on a few low-level strategies, such as following procedural tasks and searching the environment, rather than planning, monitoring, or engaging in higher-level strategies.” (435)

The results of these studies indicate that students adapted their SRL processes to web-based learning, and that learner and task characteristics influenced these processes. In particular, high-prior knowledge students tended to engage in greater instances

of planning and monitoring than low-prior knowledge students, who in turn tended to use more of just a few select strategies. Students who were more academically successful, or who showed higher learning gains during a task tended to use more active learning strategies as compared to students who did not demonstrate as much success learning. Students working collaboratively supported each other in a regulatory manner, but the success of the collaboration depended in part on the ability and prior knowledge levels of the collaborating students.” (435)

The justification for this line of research has been to determine what supports can help students learn more effectively with CBLEs. As such, the studies employed specific supports, often tailored to a particular CBLE or task, with little similarity between them.

Dabbagh and Kitsantas asked students in an undergraduate distributed learning course to rate how well particular web-based pedagogical tools supported SRL processes that are contained in Zimmerman’s model of SRL. Consistent with previous research, the authors found that students thought content delivery and creation tools supported goal setting, help-seeking, self-evaluation, and task strategies; collaborative and communication tools supported goal-setting, time planning and management, and help-seeking; administrative tools supported self-monitoring, self-evaluation, time planning and management, and help-seeking; and assessment tools supported task strategies, self monitoring, and self-evaluation. However, no observational data were included to provide evidence for students’ actual use of these tools in relation to their SRL.” (436)

The studies in this section demonstrated that while students may have viewed support tools as aiding their SRL, they did not always use tools and supports available to them. Students’ poor calibration between what they think they do and what they actually do when learning with a CBLE may provide one explanation for this contradiction.” (438)


Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). San Diego, CA: Academic.

Zimmerman, B. J. (2001). Theories of self-regulated learning and academic achievement: an overview and analysis. In B. J. Zimmerman, & D. E. Schunk (Eds.), Self-regulated learning and academic achievement: Theoretical perspectives (pp. 1–37). Mahwah, NJ: Erlbaum.

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

Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). San Diego, CA: Academic.

No comments:

Post a Comment