Mastering Science with Metacognitive and Self-Regulatory Strategies: A Teacher-Researcher Dialogue of Practical Applications for Adolescent Students

Name: Mastering Science with Metacognitive and Self-Regulatory Strategies: A Teacher-Researcher Dialogue of Practical Applications for Adolescent Students
SKU: 4109
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Suzanne E. Hiller, PhD (Editor)

George Mason University, VA, USA

Series: Education in a Competitive and Globalizing World
BISAC: EDU029030

In pedagogical fields, there has been increased attention in helping students flourish in science-related occupations. This book centers on metacognitive and self-regulatory practices as predictors of academic achievement. The purpose of Mastering Science with Metacognitive and Self-Regulatory Strategies: A Teacher-Researcher Dialogue of Practical Applications for Adolescents is to provide information on both theoretical and practical understandings of the connection between metacognition, self-regulation, and academic performance.

Self-regulation centers on an individual’s behavior to accomplish a specific task through planning, monitoring, and self-reflecting in academic tasks. Based on social cognitive theory, there are three main aspects of self-regulation: a) the triadic social cognitive model, b) the multilevel training model, and c) the cyclical self-regulatory feedback loop model. In addition, metacognitive awareness is essential in these processes with the end goal of heightening academic performance.

This book outlines how to integrate metacognitive and self-regulatory strategies within a scientific context. In particular, the work emphasizes transitioning students from novice skill levels to more advanced cognitive development through metacognitive and self-regulatory practices. In addition, relevant context specific strategies and examples are described to promote high levels of science performance in both formal and informal learning contexts, including citizen science activities. Practical examples appear throughout the work in conjunction with theoretical explanations including guidelines related to lesson plan designs, scaffolding, and math integration. Furthermore, these strategies are extended in discussions of advancing at-risk students and promoting STEM career motivation.

The overarching aim of Mastering Science with Metacognitive and Self-Regulatory Strategies: A Teacher-Researcher Dialogue of Practical Applications for Adolescents is to highlight the interdependence between motivation, self-regulation, and achievement within a scientific context from a teacher-researcher perspective. This work may be of interest to researchers and college students interested in metacognitive and self-regulatory functions, as well as administrators, practitioners, and parents focused on encouraging student science achievement, and ultimately, STEM career motivation.

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ISBN: N/A Categories: Assessment, Evaluation and Research Methods, Education in a Competitive and Globalizing World Tags: 9781536118162, 9781536139013, assessment evaluation and research methods

Table of Contents
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Table of Contents

Preface

Chapter 1. Introduction to Self-Regulation, Metacognition and Science Achievement

Chapter 2. Preparing for Success: The Forethought Phase

Chapter 3. Metacognitive Monitoring and the Performance Phase

Chapter 4. Self-Reflection and the Autonomous Learner

Chapter 5. Lesson Design: A Constructivist Approach

Chapter 6. Metacognitive and Self-Regulatory Strategies for At-Risk Students

Chapter 7. STEM Career Motivation

Chapter 8. Guideposts in Adolescent Science Achievement

Index

References

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Chapter 2:
[1] Zimmerman, Barry J. 2000. “Attaining Self-regulation: A Social Cognitive Perspective.” Handbook of Self-regulation, edited by Monique Boekaerts, Monique, Paul R. Pintrich, and Moshe Zeidner, 13-39. New York: Academic Press.
[2] Bandura, Albert 2005. “Adolescent Development from an Agent Perspective.” Self-efficacy Beliefs of Adolescents, edited by Frank Pajares and Tim Urdan, 2-43. Greenwich: Information Age Publishing.
[3] Kitsantas, Anastasia, Barry J. Zimmerman, and Timothy J. Cleary. 2000. “The Role of Observation and Emulation in the Development of Athletic Self-Regulation.” Journal of Educational Psychology 92(4): 811-817. doi:10.1037//0022-0663.
92.4.811.
[4] Kitsantas, Anastasia, and Nada Dabbagh. 2010. Learning to Learn with Integrative Learning Technologies. Charlotte: Information Age Publishing, Inc.
[5] Zepeda, Cristina, D., J. Elizabeth Richey, Paul Ronevich, and Timothy J. Nokes-Malach. 2015. “Direct Instruction of Metacognition Benefits Adolescent Science Learning, Transfer, and Motivation: An In Vivo Study.” Journal of Educational Psychology 107(4): 954-970. doi: 10.1037/edu0000022.
[6] Britner, Shari, L., and Frank Pajares. 2006. “Sources of Science Self-Efficacy Beliefs of Middle School Students.” Journal of Research in Science Teaching 43(5): 485-499. doi: 10.1002/tea.20131.
[7] Hiller, Suzanne, E., and Anastasia Kitsantas. 2014. “The Effect of a Horseshoe Crab Citizen Science Program on Student Science Performance and STEM Career Motivation.” School Science and Mathematics Journal 114(6): 302-311. doi: 10.1111/ssm.12081.
[8] Elliott, Andrew J. 2008. “Approach and Avoidance Motivation.” In Handbook of Approach and Avoidance Motivation, edited by Andrew J. Elliott, 3-16. New York: Psychology Press.
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[19] Bandura, Albert. 1997. The Exercise of Control. New York: Freeman and Company.
[20] Lee, Cristina S., Kathryn N. Hayes, Jeffery Seitz, Rachel DiStefano, and Dawn O’Connor. 2016. “Understanding Motivational Structures that Differentially Predict Engagement and Achievement in Middle School Science.” International Journal of Science Education 38(2): 192-215. doi: 10.1080/09500693.2015.1136452.
[21] Bol, Linda, Douglas J. Hacker, Camilla C. Walck, and John A. Nunnery. 2012. “The Effects of Individual or Group Guidelines on the Calibration Accuracy and Achievement of High School Biology Students.” Contemporary Educational Psychology 37: 280-287. doi: /10.1016/j.cedpsych.2012.02.004.
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[23] DiBenedetto, Maria K., and Barry J. Zimmerman. 2013. “Construct and Predictive Validity of Microanalytic Measures of Students’ Self-regulation of Science Learning.” Learning and Individual Differences 26: 30–41. doi: 10.1016/
j.lindif.2013.04.004.
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Chapter 3:
[1] Zimmerman, Barry J., and Anastasia Kitsantas. 2005. “The Hidden Dimension of Personal Competence: Self-Regulated Learning and Practice.” In Handbook of Competence and Motivation, edited by Andrew J. Elliott and Carol S. Dweck, 509-526. New York: The Guilford Press.
[2] Zimmerman, Barry J. 2000. “Attaining Self-regulation: A Social Cognitive Perspective.” Handbook of Self-regulation, edited by Monique Boekaerts, Monique, Paul R. Pintrich, and Moshe Zeidner, 13-39. New York: Academic Press.
[3] Veenman, Marcel V. J. 2013. “Training Metacognitive Skills in Students with Availability and Production Deficiencies.” In Applications of Self-Regulated Learning across Diverse Disciplines. A Tribute to Barry J. Zimmerman, edited by Héfer Bembenutty, Timothy J. Cleary, and Anastasia Kitsantas, 299-317. Charlotte: Information Age Publishing, Inc.
[4] Zimmerman, Barry J. 2013. “From Cognitive Modeling to Self-regulation: A Social Cognitive Career Path.” Educational Psychology 48(3): 135-147. doi: 10.1080/00461520.2013.794676.
[5] Zimmerman, Barry J. 2009. “Goal Setting: A Key Proactive Source of Academic Self-regulation. In Motivation and Self-Regulated Learning: Theory Research and Applications, edited by Dale H. Schunk and Barry J. Zimmerman, 267-295. New York: Routledge.
[6] Zimmerman, Barry J. 1989. “A Social Cognitive View of Self-Regulated Academic Learning.” Journal of Educational Psychology 81(3): 329-339. doi: 10.1037/
0022—0663.81.3.329.
[7] DiBenedetto, Maria K., and Barry J. Zimmerman. 2013. “Construct and Predictive Validity of Microanalytic Measures of Students’ Self-regulation of Science Learning.” Learning and Individual Differences 26: 30–41. doi: 10.1016/
j.lindif.2013.04.004.
[8] Bol, Linda, Douglas J. Hacker, Camilla C. Walck, and John A. Nunnery. 2012. “The Effects of Individual or Group Guidelines on the Calibration Accuracy and Achievement of High School Biology Students.” Contemporary Educational Psychology 37: 280-287. doi: /10.1016/j.cedpsych.2012.02.004.
[9] Bandura, Albert. 1997. The Exercise of Control. New York: Freeman and Company.
[10] Harris, Karen R.1990. “Developing Self-Regulated Learners: The Role of Private Speech and Self-Instruction.” Educational Psychologist 25(1): 35-49. doi: 10.1207/s15326985ep2501_4.
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[13] Hidi, Suzanne, and K. Ann Renninger. 2006. “The Four-Phase Model of Interest Development.” Educational Psychologist 41(2): 111-127. doi: 10.1207/
s15326985ep4102_4.
[14] Kitsantas, Anastasia, and Nada Dabbagh. 2010. Learning to Learn with Integrative Learning Technologies. Charlotte: Information Age Publishing, Inc.
[15] Labuhn, Andju Sara, Barry J. Zimmerman, and Marcus Haselhorn. 2010. “Enhancing Students’ Self-regulation and Mathematics Performance: The Influence of Feedback and Self-Evaluative Standards.” Metacognition Learning 5: 173-194. doi: 10.1007/s11409-010-9056-2.
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Chapter 4:
[1] Zimmerman, Barry J. 2000. “Attaining Self-regulation: A Social Cognitive Perspective.” Handbook of Self-regulation, edited by Monique Boekaerts, Monique, Paul R. Pintrich, and Moshe Zeidner, 13-39. New York: Academic Press.
[2] Kitsantas, Anastasia, and Barry J. Zimmerman. 2006. “Enhancing Self-Regulation of Practice: The Influence of Graphing and Self-Evaluative Standards.” Metacognition Learning 1: 201-212. doi: 10.1007/s11409-006-9000-7.
[3] Zimmerman, Barry J., and Anastasia Kitsantas. 1999. “Acquiring Writing Revision Skill. Shifting from Process to Outcome Self-Regulatory Skills.” Journal of Educational Psychology 91(2): 241-250. doi: 10.1037//0022-0663.91.2.241.
[4] DiBenedetto, Maria K., and Barry J. Zimmerman. 2013. “Construct and Predictive Validity of Microanalytic Measures of Students’ Self-regulation of Science Learning.” Learning and Individual Differences 26: 30–41. doi: 10.1016/j.
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[6] Zimmerman, Barry J. 2013. “From Cognitive Modeling to Self-regulation: A Social Cognitive Career Path.” Educational Psychology 48(3): 135-147. doi: 10.1080/
00461520.2013.794676.
[7] Zimmerman, Barry J. 1989. “A Social Cognitive View of Self-Regulated Academic Learning.” Journal of Educational Psychology 81(3): 329-339. doi: 10.1037/0022—0663.81.3.329.
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Chapter 5:
[1] Zimmerman, Barry J. 1989. “Models of Self-Regulated Learning and Academic Achievement.” In Self-Regulation: Theory, Research, and Applications, edited by Barry J. Zimmerman and Dale H. Schunk, 13-39: Orlando: Academic Press.
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Chapter 6:
[1] Zimmerman, Barry J. 1989. “A Social Cognitive View of Self-Regulated Academic Learning.” Journal of Educational Psychology 81(3): 329-339. doi: 10.1037/0022—0663.81.3.329.
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[26] Morgan, Paul L., George Farkas, Marianne M. Hillemeier, and Steve Maczuga. 2016. “Science Achievement Gaps Begin Very Early, Persist, and are Largely Explained by Modifiable Factors.” Educational Researcher, 45(1), 18-35. doi: 10.3102/0013189×16633182.
[27] Bol, Linda, Douglas J. Hacker, Camilla C. Walck, and John A. Nunnery. 2012. “The Effects of Individual or Group Guidelines on the Calibration Accuracy and Achievement of High School Biology Students.” Contemporary Educational Psychology 37: 280-287. doi: /10.1016/j.cedpsych.2012.02.004.
[28] Sheldrake, Richard. 2016. “Students’ Intentions Towards Studying Science at Upper-Secondary School: The Differential Effects of Under-Confidence and Over-Confidence.” International Journal of Science Education, 38(8): 1256-1277. doi: 10.1080/09500693. 2016.1186854.
[29] Chen, Peggy P., and Paul D. Rossi, 2013. “Utilizing Calibration Accuracy Information with Adolescents to Improve Academic Learning and Performance.” In Applications of Self-Regulated Learning across Diverse Disciplines. A Tribute to Barry J. Zimmerman, edited by HéferBembenutty, Timothy J. Cleary, and Anastasia Kitsantas, 263-297. Charlotte: Information Age Publishing, Inc.
[30] Chen, Sufen, Chih-Chi Huang, and Te-Lien Chou. 2016. “The Effect of Metacognitive Scaffolding on Low Achievers’ Laboratory Learning.” International Journal of Science and Mathematics Education 14: 281-296. doi: 10.1007/s10763-015-9691-9.
[31] Wandersee, James H. 1987. “Drawing Concept Circles: A New Way to
Teach and Test Students.” Science Activities 24: 9-24. doi: 0.1080/
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[32] Ward, Robin E., and James H. Wandersee, J. H. 2002. “Struggling to Understand Abstract Science Topics: A Roundhouse Diagram-Based Study.” International Journal of Science Education, 24(6), 575-591. doi: 10.1080/09500690110074017.
[33] Zheng, Binbin, Mark Warschauer, JinKyoung Hwang, and Penelope Collins. 2014. “Laptop Use, Interactive Science Software, and Science Learning Among At-Risk Students.” Journal of Science Education Technology 23: 591-603. doi: 10.1007/s10956-014-9489-5.
[34] Bembenutty, Héfer. 2013. “The Triumph of Homework Completion Through a Learning Academy of Self-Regulation.” In Applications of Self-Regulated Learning across Diverse Disciplines. A Tribute to Barry J. Zimmerman, edited by Héfer Bembenutty, Timothy J. Cleary, and Anastasia Kitsantas, 153-196. Charlotte: Information Age Publishing, Inc.
[35] Xu, Jianzhoung. 2009. “School Location, Student Achievement, and Homework Management Reported by Middle School Students.” The School Community Journal 19(2): 27-43.
[36] Fernández-Alonso, Rubén, Javier Suárez-Álvarez, and José Muñiz. 2015. “Adolescents’ Homework Performance in Mathematics and Science: Personal Factors and Teaching Practices.” Journal of Educational Psychology 107(4): 1075-1085. doi:10.1037/ edu0000032.
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[38] Zimmerman, Barry J., and Ted L. Rosenthal. 1974. “Observational Learning of Rule Governed Behavior by Children.” Psychological Bulletin 81: 29-42. doi: 10.1037/h0035553.
[39] Britner, Shari, L., and Frank Pajares. 2006. “Sources of Science Self-Efficacy Beliefs of Middle School Students.” Journalof Research in Science Teaching 43(5): 485-499. doi: 10.1002/tea.20131.
[40] Velayutham, Sunitadevi, and Jill M. Aldridge. 2013. “Influence of Psychosocial Classroom Environment on Students’ Motivation and Self-regulation in Science Learning: A Structural Equation Modeling Approach.” Research in Science Education 43: 507-527. doi: 10.1007/s11165-011-9273-y.
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Chapter 7:
[1] Hiller, Suzanne, E., and Anastasia Kitsantas. 2014. “The Effect of a Horseshoe Crab Citizen Science Program on Student Science Performance and STEM Career Motivation.” School Science and Mathematics Journal 114(6): 302-311. doi: 10.1111/ssm.12081.
[2] Inda, Mercedes, Carmen Rodriguez, and José Vicent Peña. 2013. “Gender Differences in Applying Social Cognitive Career Theory in Engineering Students.” Journal of Vocational Behavior 83: 346-355. doi: 10.1016/j.jvb.2013.06.010.
[3] Rogers, Mary E., and Peter A. Creed. 2011. “A Longitudinal Examination
of Adolescent Career Planning and Exploration Using a Social Cognitive
Career Theory Framework.” Journal of Adolescence 34: 163-172. doi: 10.1016/
j.adolescence.2009.12.010.
[4] Bandura, Albert 2005. “Adolescent development from an agent perspective.” Self-efficacy Beliefs of Adolescents, edited by Frank Pajares and Tim Urdan, 2-43. Greenwich: Information Age Publishing.
[5] Dabney, Katherine, Robert Tai, John Almarode, Jaimie Miller-Friedman, Gerhard Sonnert, Philip Sadler, and Zahra Hazari. 2011. “Out-of-School Time Science Activities and Their Association with Career Interest in STEM.” International Journal of Science Education 2(1): 63-79. doi: 10.1080/21548455.2011.629455.
[6] Dika, Sandra L., Jaquelina Alvarez, Jeannette Santos, and Oscar Marcelo Suárez. 2016. “A Social Cognitive Approach to Understanding Engineering Career Interest and Expectations among Underrepresented Students in School-Based Clubs.” Journal of STEM Education 17(1): 31-36.
[7] Flores, Lisa Y., Chris Robitsschek, Elif Celebi, Christie Anderson, and Ugen Hoang. 2010. “Social Cognitive Influences on Mexican Americans’ Career Choices across Holland’s Themes.” Journal of Vocational Behavior 76: 198-210. doi:10.1016/
j.jvb.2009.11.002.
[8] Holland, John L. 1997. Making Vocational Choices: A Theory of Vocational Personalities and Work Environments. Odessa: Psychological Assessment Resources.
[9] Fouad, Nancy A., Philip L. Smith, and Kathryn E. Zao. 2002. “Across Academic Domains: Extensions of the Social-Cognitive Career Model.” Journal of Counseling Psychology 2: 164-171. doi: 10.1037//0022-0167.49.2.164.
[10] Patrick, Lyn, Ester Care, and Mary Ainley. 2011. “The Relationship between Vocational Interests, Self-efficacy, and Achievement in the Prediction of Educational Pathways.” Journal of Career Assessment 19(1): 61-74. doi: 10.1177/1069072710382615.
[11] Lent, Robert W., Steven D. Brown, and Gail Hackett.1994. “Toward a Unifying Social Cognitive Theory of Career and Academic Interest, Choice, and Performance.” Journal of Vocational Behavior 45: 79-122. doi:10.1006/jvbe.
1994.1027.
[12] Lent, Robert W., Maria Paula Paixão, José da Tomás Silva and Lígia Mexia Leitão. 2010. “Predicting Occupational Interests and Choice Aspirations in Portuguese High School Students: A test of Social Cognitive Career Theory.” Journal of Vocational Behavior 76: 244-251. doi: 10.1016/j.jvb.2009.10.001.
[13] Peña-Calvo, José-Vicente, Mercedes

Inda-Caro, Carmen Rodríguez-Menéndez, and Carmen-María Fernández-García. 2016. “Perceived Supports and Barriers for Career Development for Second-Year STEM Students.” Journal of Engineering Education 105(2): 341-365. doi: 10.1002/jee.20115.
[14] Rice, Lindsay, Joan M. Barth, Rosanna E. Guadagno, Gabrielle P. A. Smith, and Debra M. McCallum. 2013. “The Role of Social Support in Students’ Perceived Abilities and Attitudes Toward Math and Science.” Journal of Youth Adolescence 42: 1028-1040. doi: 10.1007/s10964-012-9801-8.
[15] Nugent, Gwen, Bradley Barker, Greg Welch, Neal Grandgenett, ChaoRong Wu, and Carl Nelson. 2014. “A Model of Contributing STEM Learning and Career Orientation.” International Journal of Science Education 37(7): 1067-1088. doi: 10.1080/09500693. 2015.1017863.
[16] Bandura, Albert. 1997. The Exercise of Control. New York: Freeman and Company.
[17] Fouad, Nancy A., Gail Hackett, Philip L. Smith, Neeta Kantamneni, Mary Fitzpatrick, Susan Haag, and Dee Spencer. 2010. “Barriers and Supports for Continuing in Mathematics and Science: Gender and Educational Level Differences.” Journal of Vocational Behavior 77: 361-373. doi: 10.1016/j.jvb.
2010.06.004.
[18] Kitsantas, Anastasia, Barry J. Zimmerman, and Timothy J. Cleary. 2000. “The Role of Observation and Emulation in the Development of Athletic Self-regulation.” Journal of Educational Psychology 92(4): 811-817. doi:10.1037/0022-0663.92.
4.811.
[19] Zimmerman, Barry J. 1989. “A Social Cognitive View of Self-Regulated Academic Learning.” Journal of Educational Psychology 81(3): 329-339. doi: 10.1037/0022—0663.81.3.329.
[20] Britner, Shari, L., and Frank Pajares. 2006. “Sources of Science Self-Efficacy Beliefs of Middle School Students.” Journal of Research in Science Teaching 43(5): 485-499. doi: 10.1002/tea.20131.
[21] Zimmerman, Barry J. 2013. “From Cognitive Modeling to Self-regulation: A Social Cognitive Career Path.” Educational Psychology 48(3): 135-147. doi: 10.1080/
00461520.2013.794676.
[22] Restubog, Simon Lloyd, Afryll R. Florentino, Patrick Raymund, and James M. Garcia. 2010. “The Mediating Role of Career Self-Efficacy and Career Decidedness in Relationship between Contextual Support and Persistence.” Journal of Vocational Behavior 77: 186-195. doi: 10.1016/j.jvb.2010.06.005.
[23] Bonitz, Verena S., Lisa M. Larson, and Patrick Ian Armstrong. 2010. “Interests, Self-Efficacy, and Choice Goals: An Experimental Manipulation.” Journal of Vocational Behavior 76:223-233. doi: 10.1016/j.jvb.2009.09.003.
[24] Patrick, Lyn, Ester Care, and Mary Ainley. 2011. “The Relationship between Vocational Interests, Self-efficacy, and Achievement in the Prediction of Educational Pathways.” Journal of Career Assessment 19(1): 61-74. doi: 10.1177/
1069072710382615.
[25] Nauta, Margaret M., Jeffrey H. Kahn, James W. Angell, and Erika A. Cantarelli. 2002. “Identifying the Antecedent in the Relation between Career Interests and Self-Efficacy: Is It One, the Other, or Both?” Journal of Counseling Psychology, 49(3): 290-301. doi: 10.1037//0022-1067.49.3.290.
[26] Lee, Cristina S., Kathryn N. Hayes, Jeffery Seitz, Rachel DiStefano, R., and Dawn O’Connor. 2016. “Understanding Motivational Structures that Differentially Predict Engagement and Achievement in Middle School Science.” International Journal of Science Education 38(2): 192-215. doi: 10.1080/09500693. 2015.1136452.
[27] Sheu, Hung-Bin, Robert W. Lent, Steven D. Brown, Matthew J. Miller, Kelly D. Hennessy, and Ryan D. Duffy. 2010. “Testing the Choice Model of Social Cognitive Career Theory across Holland Themes: A Meta-Analytic Path Analysis. “Journal of Vocational Behavior 76: 252-264. doi: 10.1016/j.jvb.2009.10.015.
[28] Lent, Robert W., Antonio M. Lopez Jr., Frederick G. Lopez, and Hung-Bin Sheu. 2008. “Social Cognitive Career Theory and the Prediction of Interests and Choice Goals in the Computing Disciplines.” Journal of Vocational Behavior, 73: 52-62. doi: 10.1016/j.jvb.2008.01.002.
[29] Navarro, Rachel, L., Lisa Y. Flores, and Roger L. Worthington. 2007. “Mexican American Middle School Students’ Goal Intentions in Mathematics and Science: A Test of Social Cognitive Career Theory.” Journal of Counseling Psychology 54(3): 320-335. doi: 10.1037/0022-0167.54.3.320.
[30] Wild, Andrew. 2015. “Relationships between High School Chemistry Students’ Perceptions of a Constructivist Learning Environment and STEM Career Expectations.” International Journal of Science Education 37(14): 2284-2305. doi: 10.1080/09500693.2015.1076951.
[31] Lent, Robert W., Frederick G. Lopez, and Kathleen J. Bieschke. 1991. “Mathematics Self-Efficacy: Sources and Relation to Science-Based Career Choices. Journal of Counseling Psychology 38(4): 424-430. doi: 10.1037/0022-0167.38.4.424.
[32] Byars-Winston, Angela, Yannine Estrada, Christina Howard, Dalelia Davis, D., and Juan Zalapa. 2010. “Influence of Social Cognitive and Ethnic Variables on Academic Goals of Underrepresented Students in Science and Engineering: A Multiple-Group Analysis.” Journal of Counseling Psychology 57(2): 205-218. doi: 10.1037/a0018608.
[33] AfterSchool Alliance. 2016. “The Impact of After School STEM: Examples
from the Field.” Accessed January15,2017.http://afterschoolalliance.org/documents/
AfterschoolSTEMImpacts2016.pdf.
[34] Mejia, Joel Alejandro, Dustin Drake, and Amy Wilson-Lopez. 2015. “Changes in Latino/a Adolescents’ Engineering Self-Efficacy and Perceptions of Engineering After Addressing Authentic Engineering Design Challenges.” Paper presented at the annual meeting of the American Society for Engineering Education. Seattle, Washington, June 14-17.
[35] Constan, Zachary, and Justina Judy Spider. 2015. “Maximizing Future Potential in Physics and STEM: Evaluating a Summer Program through a Partnership Between Science Outreach and Education Research.” Journal of Higher Education Outreach and Engagement 19(2): 117-135.
[36] Paige, Kathy, David Lloyd, Yvonne Zeegers, Philip Roetman, Chris Daniels, Brad Hoekman, Lisa Linnell, Ann-Louise George, and David Szilassy. 2012. “Connecting Teachers and Students to the Natural World through Operation Spider: An Aspirations Citizen Science Project.” Teaching Science 58(1): 13-20.
[37] Pocock, Michael J. O, and Darren M. Evans. 2014. “The Success of the Horse-Chestnut Leaf-Miner, Cameraria ohridella, in the UK Revealed with Hypothesis-Led Citizen” PLos ONE 9(1): 1-9.
[38] Zooinverse. 2017. Accessed February 2. https://www.zooniverse.org/projects?
status=live.
[39] Hiller, Suzanne E., and Anastasia Kitsantas. 2015. “Fostering Student Metacognition and Motivation in STEM through Citizen Science Programs.” In Metacognition: Fundaments, Applications, and Trends, edited by Alejandro Peña-Ayala, 193-221. Cham: Springer.
[40] Zimmerman, Barry J., and Anastasia Kitsantas. 1997. “Developmental Phases in Self-regulation: Shifting from Process to Outcome Goals.” Journal of Educational Psychology89(1): 29–36. doi:10.1037/0022-0663.89.1.29.
[41] Jeanpierre, Bobby, Karen Oberhauser, and Carol Freeman. 2005. “Characteristics of Professional Development that Effect Change in Secondary Science Teachers’ Classroom Practices.” Journal of Research in Science Teaching42(6): 668-690. doi: 10.1002/tea.20069.
[42] Trumbull, Deborah J., Rick Bonney, and Nancy Grudens-Schuck. 2005. “Developing Materials to Promote Inquiry: Lessons Learned.” Science Education 89: 879-900. doi: 10.1002/sce.20081.
[43] Hiller, Suzanne E., and Anastasia Kitsantas. 2016. “The Validation of the Citizen Science Self-Efficacy Scale (CSSES).” Internation Journal of Environmental and Science Education 11(5): 543-558. doi: 10.12973/ijese.2016.405a.
[44] Mu, X. 1998. “High School Experience and Career Maturity in Young Adulthood.” Paper presented at the 24th International Congress of Applied Psychology. San Francisco, California, August 9-14.
[45] Crall, Alycia W., Gregory J. Newman, Thomas J. Stohlgren, Kristin A. Holfelder, Jim Graham, and Donald M. Waller. 2011. “Assessing Citizen Science Data Quality: An Invasive Species Case Study.” Conservation Letters 4: 433-442. doi: 10.1111/j.1755-263X.2011.00196.
[46] Kosmola, Margaret, Andrea Wiggins, Alexandra Swanson, and Brooke Simmons. 2016. “Assessing Data Quality in Citizen Science.” Frontiers in Ecology and the Environment 14(10): 551-560. doi: 10.1002/fee.1436.
[47] Hiller, Suzanne E., and Earle L. Reybold. 2011. “Field Expert’s Perceptions of Scientific Observations in the Natural World.” Paper presented at the annual research symposium of the North American Association for Environmental Education, Raleigh, North Caroling. October 12-16.
[48] Houseal, Ana K., Fouad Abd-El-Khalick, and Lizanne Destefano. 2014. “Impact of a Student–Teacher–Scientist Partnership on Students’ and Teachers’ Content Knowledge, Attitudes Toward Science, and Pedagogical Practices.” Journal of Research in Science Teaching 51(1): 84-115. doi: 10.1002/tea.21126.
[49] Cleary, Timothy J., and Barry J. Zimmerman. 2004. “Self-regulation Empowerment Program: A School-based Program to Enhance Self-regulated and Self-motivated Cycles of Student Learning.” Psychology in the Schools 41(5): 537-550. doi: 10.1002/pits.10177.
[50] Hiller, Suzanne E. 2012. “The Impact of a Citizen Science Program on Student Achievement and Motivation: A Social Cognitive Career Perspective.” PhD diss., George Mason University. ProQuest (UMI Number 3547088).
[51] Gerlach, Jonathan. 2012. “STEM: Defying a Simple Definition.” National Science Teachers Association, April 11. Accessed January 12, 2017. http://www.nsta.org/
publications/news/story.aspx?id=59305.
[52] Shanahan, Marie-Claire, Lydia E. Carol-Ann Burke, and Krista Francis. 2016. “Using a Boundary Object Perspective to Reconsider the Meaning of STEM in a Canadian Context.” Canadian Journal of Science, Mathematics and Technology Education 16(2): 129-139. doi: 10.1080/14926156.2016.1166296.
[53] Campbell, Todd, Stephen B. Witzig, David J. Welty, and Margaret M. French. 2014. “STEM Education in the Science Classroom: A Critical Examination of Mathematics Manifest in Science Teaching and Learning.” In Exemplary STEM Programs: Designs for Success, edited by Robert E. Yager and Herbert Brunkhorst, 233-249. Arlington: National Science Teachers Association Press.
[54] Jolly, Anne. 2014. “Six Characteristics of a Great STEM Lesson.” Education
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[55] Bybee, Rodger W., Nancy M. Landes, Judith Opert Sandler, Karen Worth, Carolee Matsumoto, June Foster, Candace L. Julyan, Janice Mokros, William C. Kyle Jr., and Michael Kane. 1988. “The Biological Sciences Curriculum Study (BSCS).” Science and Children 25(8): 35-39.
[56] Zimmerman, Barry J. 1989. “Models of Self-Regulated Learning and Academic Achievement.” Self-Regulation: Theory, Research, and Applications, edited by Barry J. Zimmerman and Dale H. Schunk, 13-39. Orlando: Academic Press.
[57] National Science Foundation. 2013. “Cubelets: Small Robots Teach Big Science Lessons.” Accessed February 3, 2017. https://www.nsf.gov/news/special_
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[58] Kaplon, Megan. 2015. “The Cost of Conservation and Restoration.” Art Business News. December 7. Accessed January 10, 2017. http://artbusinessnews.com/2015/
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[59] Brown, Angelette M., Gillian H. Roehrig, and Tamara J. Moore.2014. “Middle School Engineering Education.” Exemplary STEM Programs: Designs for Success, edited by Robert E. Yager and Herbert Brunkhorst, 125-142. Arlington: National Science Teachers Association Press.
[60] van der Wal, Ren

é, Helen Anderson, Annie Robinson, Nirwan Sharma, Chris Mellish, Stuart Roberts, Ben Darvill, and Advaith Siddharthan. 2016. “Mapping Species Distributions: A Comparison of Skilled Naturalist and Lay Citizen Science Recording.” Ambio 44(Suppl. 4): S584–S600. doi: 10.1007/s13280-015-0709-x.
[61] Laughlin, Stewart, Evan L. MacLean, David Ivy, Vanessa Woods, Eliot Cohen, Kerri Rodriguez, Matthew McIntryre, Sayan Mukherjee, Josep Call, Juliane Kaminski, J. Ádám Milósi, Richard W. Wrangham, and Brian Hare. 2015. “Citizen Science as a New Tool in Dog Cognition Research.”PLoS ONE 10(9): 1-16. doi: 10.1371/journal.pone.0135176.
[62] Black, Jeffrey M., Erin Wampole, and Jeanne E. Mayer, J. E. 2016. “Fifteen Years of River Otter Monitoring by Citizen Science Volunteers in Northern California: Litter size.” Northwestern Naturalist 97: 226-236.
[63] Le Féon, Violette, Mickaël Henry, Laurent Guilbaud, Clémentine Coiffait-Gombault, Eric Dufrêne, Emilie Kolodziejczyk, Michael Kuhlmann, Fabrice Requier, and Bernard E. Vaissière. 2016. “An Expert-Assisted Citizen Science Program Involving Agricultural High Schools Provides National Patterns on Bee Species Assemblages.” Journal of Insect Conservation 20: 905–918. doi: 10.1007/
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[64] Meyer, Nathan J., Siri Scott, Andrea Lorek Strauss, Pamela L. Nippolt, Karen S. Oberhauser, and Robert B. Blair. 2014. “Citizen Science as a REAL Environment for Authentic Scientific inquiry. Journal of Extension 52(4): 1-7. Accessed February 3, 2017. https.//www.joe.org/joe/2014august/iw3.php.
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Chapter 8:
[1] Shapiro, Jordan. 2016. “How to Train 68.8 Million Teachers. Because That’s How Many the World Needs.” Forbes. Accessed December 11. http://www.
forbes.com/sites/jordanshapiro/2016/10/09/how-to-train-68-8-million-teachers-because-thats-how-many-the-world-needs/#31f995a66da7.
[2] Yu, Angela Yan, Stella Wen Tian, Douglas Vogel, and Ron Chi-Wai Kwok.
2010. “Can Learning be Virtually Boosted? An Investigation of Online Social Networking Impacts.” Computers and Education 55: 1494-1503. doi: 10.1016/j.
compedu.2010.06.015.
[3] Becker, Ryan, and Penny Bishop. 2016. “Think Bigger About Science: Using Twitter for Learning in the Middle Grades.” Middle School Journal 47(3): 4-16. doi: 10.1080/00940771.2016.1135097.
[4] Cho, Kwangsu, and Moon-Heum Cho. 2013. “Training of Self-Regulated Learning Skills on a Social Network System.” Social Psychological Education 16: 617-634. doi:10.1007/s11218-013-9229-3.
[5] Kitsantas, Anastasia, and Nada Dabbagh. 2010. Learning to Learn with Integrative Learning Technologies. Charlotte: Information Age Publishing, Inc.
[6] Zooinverse. 2017. Accessed February 2. https://www.zooniverse.org/projects?
status=live.
[7] Dawson, Gowan, Chris Lintott, and Sally Shuttleworth. 2015. “Constructing Scientific Communities: Citizen Science in the Nineteenth and Twenty-
First Centuries.” Journal of Victorian Culture 20(2): 246-254. doi: 10.1080/
13555502.2015.1022053.
[8] Bybee, Rodger W. 2014. “The BSCS 5E Instructional Model: Personal Reflections and Contemporary Implications.” Science and Children, 51(8): 10-13. doi: 10.2505/4/sc14_051_08_10.
[9] Campbell, Todd, Stephen B. Witzig, David J. Welty, and Margaret M. French. 2014. “STEM Education in the Science Classroom: A Critical Examination of Mathematics Manifest in Science Teaching and Learning.” In Exemplary STEM Programs: Designs for Success , edited by Robert E. Yager and Herbert Brunkhorst, 233-249. Arlington: National Science Teachers Association Press.
[10] Bleicher, Robert E. 2006. “Nurturing Conﬁdence in Preservice Elementary Science Teachers.” Journal of Science Teacher Education 17: 165–187. doi: 10.1007/
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[11] Ladson-Billings, Gloria. 2016. “And Then There is This Thing Called the Curriculum: Organization, Imagination, and Mind.” Educational Researcher, 45(2): 100-104. doi: 10.3102/0013189X16639042.
[12] Shanahan, Marie-Claire, Lydia E. Carol-Ann Burke, and Krista Francis. 2016. “Using a Boundary Object Perspective to Reconsider the Meaning of STEM in a Canadian Context.” Canadian Journal of Science, Mathematics and Technology Education 16(2): 129-139. doi: 10.1080/14926156.2016.1166296

Reviews

“This is a must read book for any teacher interested in fostering student self-regulation in science education classrooms. Grounded in empirical research, the author provides guidance for teachers on how to create learning environments that support and promote student self-regulatory functioning. Practical step-by-step lesson plans and related instructional artifacts are thoroughly described and ready for use by novices and veteran teachers alike. An amazing book that will both guide and transform teaching in science education contexts. ” – Anastasia Kitsantas, Ph. D., Professor of Educational Psychology in the College of Education and Human Development, USA

This book would be relevant to college instructors who teach undergraduate and graduate level educational psychology courses as well as instructors working with science preservice teachers. In addition, this work would assist researchers interested in the interaction of metacognition, self-regulation, and science achievement.
Within formal and informal learning contexts, stakeholders interested in advancing the science potential of students such as educators, administrators, and parents would find this work applicable. Overall, individuals who would be interested in this work would be focused on improved adolescent science achievement by connecting theory with authentic applications, particularly in terms of developing metacognitive skills within a self-regulatory cyclical feedback loop model.
As a practical tool, this work is useful for classroom practitioners, administrators, and parents as there are explicit instructional artifacts and lesson design descriptions on how to infuse metacognitive and self-regulatory features. For university instructors, this work may serve as a course text used for discussion.

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