Editors' Preface


by Tova Michalsky & Chen Schechter - 2017

Preface to the yearbook issue on self-regulated learning.

In today’s society, individuals are expected to self-regulate their learning in various academic and everyday contexts (Organisation for Economic Co-operation and Development, 2014; Zimmerman, 2008). National and international institutions have integrated the instruction of self-regulated learning (SRL) into their educational programs’ lifelong learning curricula (e.g., U.S. Department of Education, 2011). Hence, embedding SRL into school curricula is currently widely recommended, to lay the roots for fostering students’ ability to learn how to learn (e.g., Azevedo & Aleven, 2013; Randi, Corno, & Johnson, 2011; Winne & Azevedo, 2014).


SRL refers to students’ self-generated thoughts, feelings, and actions targeted toward attaining their own goals (Azevedo et al., 2013; Schunk & Zimmerman, 2007; Veenman, 2011). Highly self-regulating students participate actively in their learning at the metacognitive, motivational, and behavioral levels—in school and after school (Zimmerman, 2008). A cumulative body of empirical findings suggests that higher SRL is strongly linked with better academic performance, achievements, and learning efficiency both in and out of school (e.g., Pintrich, 2000; Randi, 2004; Schraw, 2010).


Unfortunately, most learners, across a wide range of ages and contexts, do not spontaneously regulate their learning in effective ways (Dignath van Ewijk & Büttner, 2013; Michalsky, 2012). Moreover, although consensus exists among contemporary teachers regarding the importance of helping students become independent and academically effective learners, most teachers remain unsure of how to scaffold and stimulate students’ SRL (Michalsky, 2014; Michalsky & Schechter, 2013; Schechter & Michalsky, 2014; Veenman, 2011) and rarely exhibit such practices (Azevedo, Johnson, Chauncey, & Graesser, 2011; Kistner et al., 2010; Zimmerman, 2008). Accordingly, today’s students are not adequately supported to develop their own SRL.


Learning the skills of SRL is an essential task for students in the 21st century. Educators’ ultimate goal—one could argue—is to prepare their students as optimally as possible for the issues and challenges that the students will face when they leave the classrooms and enter their careers. Thus, educators must ultimately teach students to manage their learning by themselves—by knowing how to manage their own progress and how to effectively plan, monitor, and regulate their development and performance. Vitally, as future active 21st-century citizens, students need the SRL skills and knowledge to constantly learn anew and adapt to novel situations (Organization for Economic Co-operation and Development, 2014).


This TCR Yearbook intentionally emphasizes the links between conceptual SRL frameworks and empirically based models for developing SRL in teachers and students, thereby aiming to make SRL knowledge, training, and supportive strategies accessible to multiple audiences seeking to prepare lifelong future learners to successfully maneuver in our global society. Thus, the yearbook offers theoretical, empirical, and practical implications for a wide gamut of readers, including scholars and researchers; teacher educators in university schools of education; teachers at the preschool, elementary, and secondary levels; and local, national, and international policy makers. Underpinning this aim as its guiding theme, this issue furnishes clear examples of empirically supported strategies for promoting SRL in diverse educational settings.


ORGANIZATION OF THE ISSUE


PART I. CONCEPTUALIZATIONS OF SRL


Addressing the importance of SRL conceptualizations, Part I is grounded in the theoretical position that SRL is essential to and a key driver of individuals’ academic achievements and well-being (Efklides, 2011; Winne, 2010; Zimmerman & Schunk, 2011). In the first article, Winne (Simon Fraser University) discusses the trajectory of research on SRL and how it would shape educational practices. In second article, Schechter (Bar-Ilan University) suggests that self-regulation should be complemented by a more holistic, integrated, and collaborative framework—that of communal-regulated learning, which may serve as a better framework to develop effective learners in today’s fast-changing educational scene. In the third article, Efklides (Aristotle University of Thessaloniki) builds on her extensive work as a senior applied developmental psychologist, suggesting the role of emotions, anxiety, and pride as contributing to SRL attainments. In the fourth article, Azevedo (North Carolina State University), Mudrick (North Carolina State University), Taub (North Carolina State University), and Wortha (TU Dresden) describe how successful STEM learning depends on the conceptual, methodological, and analytical coupling of metacognition and emotions while learning 21st-century skills with advanced learning technologies.   


PART II. MEASURING AND EVALUATING SRL


Debate is ongoing as to optimal methods for assessing SRL processes. Assessment tools are classically divided into two major approaches: offline and online measures (e.g., Azevedo & Aleven, 2013; Perry & Winne, 2013; Veenman, 2011; Zimmerman, 2008). In the fifth article, Veenman (Institute for Metacognition Research) discusses the assessment of SRL skills. Researchers advise against offline instruments such as questionnaires because of poor validity. Online instruments, such as documentation of thinking aloud during task performance, are time consuming and unsuitable for assessment in large groups. Recently, computerized tasks have been developed for tracing SRL skills during task performance. In the sixth article, Michalsky (Bar-Ilan University) discusses three assessment tools—a measure of SRL events, questionnaires, and a teacher scale—for measuring SRL during mathematical problem solving.


PART III. DEVELOPING TEACHERS’ SRL


Teachers are asked (both as learners and as teachers) to understand the role of SRL in their pedagogical content knowledge and to plan materials and strategies for infusing SRL into lessons (Kramarski & Friedman, 2014; Michalsky, 2012; Michalsky & Schechter, 2013; Schechter & Michalsky, 2014). If teachers are unable to self-regulate their own learning, it prevents them from developing these capacities in their students (Borko, Koellner, & Jacobs, 2014; Randi, 2004). In the seventh article, White (Nyack College) presents how an integrated framework of cyclical phases and developmental levels of SRL play a significant role in modeling and self-regulatory learning as key processes for learning. In the eighth article, Randi (University of New Haven) focuses on how teachers develop students’ capacity to adapt to the learning environment and how students’ own SRL, in turn, contributes to and enables adaptive teaching. In the ninth article, Eilam (University of Haifa) discusses the Lesson Planning-Monitoring Tool (LPMT) that she developed for improving teachers’ lesson planning and monitoring of their own scripts’ enactment, thereby enhancing their self-regulation of instruction.


PART IV. ENHANCING STUDENTS’ SRL IN DIVERSE EDUCATIONAL CONTEXTS


This part focuses on empirically based strategies to enhance students’ SRL in diverse educational contexts. In the 10th article, Ben-Eliyahu (University of Haifa) examines how individual differences (giftedness) interact with learning contexts (favorite versus least favorite courses) to influence learning processes and outcomes. In the 11th article, Spektor-Levy (Bar-Ilan University), Basilio (University of Cambridge), Zachariou (University of Roehampton), and Whitebread (University of Cambridge) discuss young children’s self-regulation processes and metacognitive knowledge through a model building from plan task. In the 12th article, Tzohar-Rozen (Levinsky College of Education) and Kramarski (Bar-Ilan University) explore how young students became actively engaged in learning mathematics through two self-regulation programs: meta-cognition and motivation–emotion. In the 13th article, Shamir (Bar-Ilan University) focuses on the benefits of embedded SRL guidance for enhancing e-book reading among kindergarteners at risk for learning disabilities. In the 14th article, Berglas-Shapiro, Eylon, and Scherz (Weizmann Institute of Science) discuss a technology-enhanced SRL intervention designed to foster SRL skills among elementary school science students.


References


Azevedo, R., & Aleven, V. (Eds.). (2013). International handbook of metacognition and learning technologies. Amsterdam, The Netherlands: Springer.


Azevedo, R., Harley, J., Trevors, G., Duffy, M., Feyzi-Behnagh, R., & Bouchet, F. (2013). Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agents systems. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 427–449). Amsterdam, The Netherlands: Springer.


Azevedo, R., Johnson, A., Chauncey, A., & Graesser, A. (2011). Use of hypermedia to convey and assess self-regulated learning. In B. Zimmerman & D. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 102–121). New York, NY: Routledge.


Borko, H., Koellner, K., & Jacobs, J. (2014). Examining novice teacher leaders’ facilitation of mathematics professional development. Journal of Mathematical Behavior, 33, 149–167.


Dignath van Ewijk, C., & Büttner, G. (2013). Assessing how teachers enhanced self- regulated learning: A multiperspective approach. Journal of Cognitive Education and Psychology, 12(3), 338–358.


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


Kistner, S., Rakoczy, K., Otto, B., Dignath van Ewijk, C., Büttner, G., & Klieme, E. (2010). Promoting self-regulated learning in classrooms: Investigating frequency, quality, and consequences for student performance. Metacognition and Learning, 5, 157–171.


Kramarski, B., & Friedman, S. (2014). Solicited versus unsolicited metacognitive prompts for fostering mathematical problem-solving using multimedia. Journal of Educational Computing Research, 50(3), 285–314.


Michalsky, T. (2012). Shaping self-regulation in science teachers’ professional growth: Inquiry skills. Science Education, 96(6(, 1106–1133.


Michalsky, T. (2014). Developing the SRL-PV assessment scheme: Preservice teachers’ professional vision for teaching self-regulated learning. Studies in Educational Evaluation. http://dx.doi.org/10.1016/j.stueduc.2014.05.003


Michalsky, T., & Schechter, C. (2013). Preservice teachers’ self-regulated learning: Integrating learning from problems and learning from successes. Teaching and Teacher Education, 30, 60–73.


Organisation for Economic Co-operation and Development. (2014). Skills beyond school: Synthesis Report, OECD Reviews of Vocational Education and Training. Paris, France: Author. http//dx.doi.org/10.1787/9789264214682-en


Perry, N. E., & Winne, P. H. (2013). Tracing students’ regulation of learning in complex collaborative tasks. In S. Volet & M. Vauras (Eds.), Interpersonal regulation of learning and motivation: Methodological advances (pp. 45–66). London, England: Routledge


Pintrich, P. R. (2000). Multiple goals, multiple pathways: The role of goal orientation in learning and achievement. Journal of Educational Psychology, 92, 544–555.


Randi, J. (2004). Teachers as self-regulated learners. Teachers College Record, 106(9), 1825–1853.


Randi, J., Corno, L., & Johnson, E. (2011). Transitioning from college classroom to teaching career: Self-regulation in prospective teachers. New Directions for Teaching and Learning, 12, 89–98.


Schechter, C., & Michalsky, T. (2014). Juggling our mindsets: Learning from success as a complementary instructional framework in teacher education. Teachers College Record, 116(2), 1–48.


Schraw, G. (2010). Measuring self-regulation in computer-based learning environments. Educational Psychologist, 45(4), 258–266.


Schunk, D. H., & Zimmerman, B. J. (2007). Influencing children’s self-efficacy and self-regulation of reading and writing through modeling. Reading & Writing Quarterly, 23(1), 7–25.


U.S. Department of Education. (2011). The new consensus on middle-grades reform. Remarks of U.S. Secretary of Education Arne Duncan to the Association for Middle Level Education (AMLE) Annual Conference. Retrieved from http:// www.ed.gov/news/speeches/new-consensus-middle-grades-reform


Veenman, M. V. J. (2011). Learning to self-monitor and self-regulate. In R. Mayer & P. Alexander (Eds.), Handbook of research on learning and instruction (pp. 197–218). New York, NY: Routledge.


Winne, P. H. (2010). Improving measurements of self-regulated learning. Educational Psychologist, 45(4), 267–276.


Winne, P., & Azevedo, R. (2014). Metacognition. In K. Sawyer (Ed.), Cambridge handbook of the learning sciences (2nd ed., pp. 63–87). Cambridge, England: Cambridge University Press.


Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166–183.


Zimmerman, B. J., & Schunk, D. H. (Eds.). (2011). Handbook of self-regulation of learning and performance. New York, NY: Routledge.




Cite This Article as: Teachers College Record Volume 119 Number 13, 2017, p. 1-7
https://www.tcrecord.org ID Number: 22192, Date Accessed: 10/28/2021 3:40:03 AM

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