Cutting Edge > Develop Program-Wide Abilities > Metacognition > Workshop 08 > Participants and their Contributions > Erin Peters
Author Profile

Using Self-Regulation to Develop Metacognition of the Scientific Enterprise


Erin E. Peters, Science Education and Educational Psychology, George Mason University

In studying science, many elementary and secondary students learn the subject as a collection of facts and gain little or no understanding of science as a discipline. The adoption of metacognitive strategies has the potential to help students make meaning out of science content and to become self-regulated learners. Self-regulated learners are desirable in the K-12 education setting because they are able to take control of their own learning, and are poised to become life-long learners. Learners that are self-regulated show higher levels of strategy use (Pressley, Goodchild, Fleet, Sajchowski, & Evans, 1987; Weinstein & Underwood, 1985), intrinsic motivation (Ryan, Connell & Deci, 1984), metacognitive engagement (Corno & Mandinach, 1983), and self-reflection (Ghatala, 1986; Paris, Cross & Lipson, 1984; Kitsantas & Zimmerman, 2006) than naïve self-regulated learners. Overall, students who are self-regulated are metacognitively, motivationally, and behaviorally active participants in their own learning process (Zimmerman, 1989).

Metacognition can be defined as the executive functions that control actions or the ability to recognize thinking patterns and evaluate them (Weinert, 1987) and is a portion of the continuum of self-regulation. Metacognition is the ability to think about and evaluate your own thinking processes (Brown, 1987) and is a part of being a self-regulated learner because self-regulatory strategies provide the mechanisms for students to regulate their cognition and learning (Zimmerman, 1989). Metacognitive control is the decisions, both conscious and non-conscious, that we make based on the output of our monitoring process (Schwartz & Perfect, 2002). Metacognitive monitoring and control can be a useful tool in helping students to identify scientific thinking and to check their own thinking for alignment with a scientific way of knowing. Since metacognition and self-regulation are related, it is possible that self-regulatory processes can be useful in developing metacognition in students.

Self-regulated learning can be used as a non-didactic instructional tool to relate the aspects of the nature of science to students on a metacognitive level as students engage in scientific inquiry. A student who self-regulates should be able to think about and evaluate their ideas according to a scientific way of knowing. One reason that teachers as well as students have difficulty understanding the nature of science is their lack of exposure to the same inherent ways of knowing as a scientist (Hogan, 2000). Self-regulated learning strategies could provide a framework that can scaffold naïve views of the nature of science to more developed views of the nature of science.

Self-regulated learning diagram
Figure 1. Self-Regulated Learning

Corno & Mandinach (1983) have found that metacognitive engagement can be learned by training in self-regulation of learning. As described from a social-cognitive perspective, self-regulated learners enter three phases that are cyclically related: forethought, performance, and self-reflection as illustrated in Figure 1 (Zimmerman, 2000). The forethought phase partially refers to analyzing tasks and setting process-oriented goals (e.g., asking students to organize the content they already knew about the inquiry problem). The performance phase includes implementation of the task and self-monitoring (e.g., asking students to conduct hands-on inquiries and to monitor their progress). The self-reflection phase refers to the use of standards to make self-judgments about the performance (e.g., students compare their activities in the inquiry against one aspect of the nature of science). Because students continue to cycle through the self-regulation feedback loops, when students enter successive iterations of the loop, they have more sophisticated forethought, performance, and self-reflection. There is evidence that attainment of high levels of academic achievement requires a self-regulatory dimension of competence in addition to basic talent and high-quality instruction (Zimmerman & Kitsantas, 2007).

From this theoretical foundation, a metacognitive prompts intervention (MPI-S) has been developed and tested experimentally with 8th grade students for 3 years. This intervention is based on a 4-phase developmental training approach from Zimmerman's work (2000) that includes observation, emulation, self-control, and self-regulation. Observation entails vicarious induction from a proficient model. Emulation is a duplication of the general pattern of the model. The self control-phase occurs when the student independently uses the strategy in similar contexts, and the self-regulation phase occurs when the student can adapt the use of the strategy across changing conditions by being metacognitively aware of his/her own learning. The intervention, Metacognitive Prompting Intervention – Science (MPI-S), consisted of metacognitive prompts such as examples, checklists, and questions for each of the chosen aspects of the nature of science. MPI-S was composed of examples for the observation phase, a checklist for the emulation phase, a short checklist and a few questions for the self-control phase, and questions eliciting student rationale for decisions for the self-regulation phase.

Students exposed to the intervention showed statistically significant higher levels of content knowledge and knowledge about the nature of science than students not exposed to the intervention. Effect size was measured by Cohen's d for the content measure and the nature of science measure. The effect size for the content measure was d = .5 and for the nature of science measure was d = .8. In addition, qualitative findings revealed that the experimental group made choices based on evidence in the inquiry unit. However, the control group differed to authority, even when it conflicted with the evidence presented in the activity. When asked what it was to think scientifically, the experimental group responded that the modules taught them that scientists use their prior understandings to explain new phenomena and that their explanations require a large amount of detail.

References

Brown, A. (1987). Metacognition, executive control, self-regulation, and other more mysterious mechanisms. In F. E. Weinert & R. H. Kluwe (Eds.), Metacognition, motivation and understanding. Hillsdale, NJ: Laurence Erlbaum Associates, Publishers.

Corno, L. & Mandinach, E. (1983). The role of cognitive engagement in classroom learning and motivation. Educational Psychologist, 18, 88-108.

Ghatala, E. S. (1986). Strategy monitoring training enables young learners to select effective strategies. Educational Psychologist, 21, 434-454.

Hogan, K. (2000). Exploring a process view of students' knowledge about the nature of science. Science Education, 84, 51-70.

Kitsantas, A. & Zimmerman, B. J. (2006). Enhancing self-regulation of practice: the influence of graphing and self-evaluative standards. Metacognition and Learning, 1, 201-212.

Paris, S. G., Cross, D. R. & Lipson, M. Y. (1984). Informed strategies for learning: A program to improve children's reading awareness and comprehension. Journal of Educational Psychology, 76, 1239-1252.

Pressley, M., Goodchild, F., Fleet, J., Sajchowski, R., & Evans, E. D. (1987, April) What is good strategy use and why is it hard to teach?: An optimistic appraisal of the challenges associated with strategy instruction. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.

Ryan, R. M., Connell, J. P., & Deci, E. L. (1984). A motivational analysis of self-determination and self-regulation in education. In C. Ames & R. Ames (Eds.) Research on motivation in education (Vol. 2, pp. 13-52). New York: Academic Press.

Schwartz, B. L. & Perfect, T. J. (2002). Toward an applied metacognition. In T. J. Perfect & B. L. Schwartz (Eds.) Applied metacognition. Cambridge: University Press.

Weinert, F. E. (1987). Introduction and overview: Metacognition and motivation as determinants of effective learning and understanding. In F. E. Weinert & R. H. Kluwe (Eds.), Metacognition, motivation and understanding. Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers.

Weinstein, C. E. & Underwood, V. L. (1985). Learning strategies: The how of learning.

In J. W. Segal, S. F. Chipman & R. Glaser (Eds.), New directions in Piagetian theory and practice (pp. 39-49). Hillsdale, NJ: Erlbaum.

Zimmerman, B. J. (1989). Developing self-fulfilling cycles of academic regulation: An analysis of exemplary instructional models. In D. H. Schunk & B. J. Zimmerman (Eds.), Self-regulated learning: From teaching to self-reflective practice (pp. 1-19). New York: The Guildford Press.

Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. Handbook of Self-Regulation. New York: Academic Press.

Zimmerman, B. J., & Kitsantas, A. (2007). Reliability and validity of the Self-Efficacy for Learning Form (SELF) scores of college students. Journal of Psychology, 215(3), 157-163.


« Jason McGraw       David Gosselin »