Beyond performance metrics: Examining a decrease in students' physics self-efficacy through a social networks lens

Physical Review Physics Education Research

Volumn 12, Issue 2, 2016, Pages

  Dou, Remy ^a   Brewe, Eric ^a   Zwolak, Justyna P ^a   Potvin, Geoff ^a   Williams, Eric A ^a   Kramer, Laird H ^a  

^a FLORIDA INTERNATIONAL UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85011422142     PISSN: None     EISSN: 24699896     Source Type: Journal    
DOI: 10.1103/PhysRevPhysEducRes.12.020124     Document Type: Article

References (73)

1. S. Freeman, S. L. Eddy, M. McDonough, M. K. Smith, N. Okoroafor, H. Jordt, and M. P. Wenderoth, Investigating student communities with network analysis of interactions in a physics learning center, Proc. Natl. Acad. Sci. U.S.A. 111, 8410 (2014).
2. E. Etkina and A. Van Heuvelen, Investigative Science Learning Environment: Using the processes of science and cognitive strategies to learn physics, 2001 PERC Proceedings edited by K. Cummings, S. Franklin, and J. Marx (Rochester, NY, July 25-26, 2001).
3. J. Robert, M. Jeffery, J. Rhett, L. Duane, S. David, N. Carolina, D. L. Deardorff, and D. S. Abbott, Introduction to SCALE-UP: Student-Centered Activities for Large Enrollment University Physics (Fund for the Improvement of Postsecondary Education, Washington, DC, 2000).
4. P. W. Laws, Millikan lecture 1996: Promoting active learning based on physics education research in introductory physics courses, Am. J. Phys. 65, 14 (1997).
5. N. D. Finkelstein and S. J. Pollock, Replicating and understanding successful innovations: Implementing tutorials in introductory physics, Phys. Rev. ST Phys. Educ. Res. 1, 1 (2005).
6. E. Brewe, Modeling theory applied: Modeling Instruction in introductory physics, Am. J. Phys. 76, 1155 (2008).
7. National Research Council, Adapting to a Changing World - Challenges and Opportunities in Undergraduate Physics Education (National Academies Press, Washington, DC, 2013).
8. President's Council of Advisors on Science and Technology, Prepare and Inspire: K-12 Education in Science, Technology, Engineering, and Math (STEM) for America's Future: Executive Report, 2010, https://www.whitehouse.gov/sites/default/files/microsites/ostp/pcast-stemed-report.pdf.
9. National Research Council, Discipline-Based Education Research: Understanding and Improving Learning in Undergraduate Science and Engineering (National Academies Press, Washington, DC, 2012).
10. D. H. Schunk, Learning Theories: An Educational Perspective (Pearson Education, Boston, 2012).
11. Z. Hazari, G. Sonnert, P. M. Sadler, and M.-C. Shanahan, Connecting high school physics experiences, outcome expectations, physics identity, and physics career choice, J. Res. Sci. Teach. 47, 978 (2010).
12. R. W. Lent, S. D. Brown, and G. Hackett, Toward a unifying social cognitive theory of career and academic interest, choice, and performance, J. Vocat. Behav. 45, 79 (1994).
13. A. Bandura, C. Barbaranelli, G. V. Caprara, and C. Pastorelli, Self-efficacy beliefs as shapers of children's aspirations and career trajectories, Child Development 72, 187 (2001).
14. X. Wang, Why students choose STEM majors: Motivation, high school learning, and postsecondary context of support, Am. Educ. Res. J. 50, 1081 (2013).
15. M. Kjærnsli and S. Lie, Students' Preference for Science Careers: International comparisons based on PISA 2006, Int. J. Sci. Educ. 33, 121 (2011).
16. National Research Council, Expanding Underrepresented Minority Participation: America's Science and Technology Talent at the Crossroads (National Academies Press, Washington, DC, 2011).
17. National Science Board, Science and Engineering Indicators 2014 (National Science Foundation, Arlington, VA, 2014).
18. A. L. Zeldin, S. L. Britner, and F. Pajares, A comparative study of the self-efficacy beliefs of successful men and women in mathematics, science, and technology careers, J. Res. Sci. Teach. 45, 1036 (2008).
19. K. C. Larkin, R. W. Lent, and S. D. Brown, Self-efficacy in the prediction of academic performance and perceived career options, J. Counsel. Psychol. 33, 265 (1986).
20. A. L. Zeldin and F. Pajares, Against the odds: Self-efficacy beliefs of women in mathematical, scientific, and technological careers, Am. Educ. Res. J. 37, 215 (2000).
21. C. Hill, C. Corbett, and A. St. Rose, Why so Few? Women in Science, Technology, Engineering and Mathematics (AAUW, Washington, DC, 2010).
22. R. Dou and K. D. Gibbs, Engaging all students in the pursuit of STEM careers, Sch. Sci. Rev. 95, 106 (2013).
23. S. D. Brown, R. W. Lent, and K. C. Larkin, Self-efficacy as a moderator of scholastic aptitude-academic performance relationships, J. Vocat. Behav. 35, 64 (1989).
24. R.W. Lent, F. G. Lopez, and K. J. Bieschke, Predicting mathematics-related choice and success behaviors: Test of an expanded social cognitive model, J. Vocat. Behav. 42, 223 (1993).
25. R.W. Lent, S. D. Brown, and K. C. Larkin, Comparison of three theoretically derived variables in predicting career and academic behavior: Self-efficacy, interest congruence, and consequence thinking, J. Counsel. Psychol. 34, 293 (1987).
26. F. Pajares and T. C. Urdan, Adolescence and Education (Information Age, Charlotte, NC, 2006).
27. A. Bandura, Self-Efficacy: The Exercise of Control (W. H. Freeman and Co., New York, 1997).
28. F. Pajares, Adv. Motiv. Achievement 10, 1 (1997).
29. A. Bandura, Perceived self-efficacy in cognitive development and functioning, Educ. Psychol. 28, 117 (1993).
30. A. Bandura, Self-efficacy: Toward a unifying theory of behavioral change, Psychol. Rev. 84, 191 (1977).
31. D. H. Schunk, Ability versus effort attributional feedback: Differential effects on self-efficacy and achievement, J. Educ. Psychol. 75, 848 (1983).
32. C. S. Dweck, The role of expectations and attributions in the alleviation of learned helplessness, J. Pers. Soc. Psychol. 31, 674 (1975).
33. D. Hestenes, Toward a modeling theory of physics instruction, Am. J. Phys. 55, 440 (1987).
34. D. Desbien, Ph.D. dissertation, Arizona State University, 2002.
35. J. Bruun and E. Brewe, Talking and learning physics: Predicting future grades from network measures and Force Concept Inventory pretest scores, Phys. Rev. ST Phys. Educ. Res. 9, 020109 (2013).
36. E. Brewe, V. Sawtelle, L. H. Kramer, G. E. O'Brien, I. Rodriguez, and P. Pamelá, Toward equity through participation in Modeling Instruction in introductory university physics, Phys. Rev. ST Phys. Educ. Res. 6, 010106 (2010).
37. E. Brewe, A. Traxler, J. De La Garza, and L. H. Kramer, Extending positive CLASS results across multiple instructors and multiple classes of Modeling Instruction, Phys. Rev. ST Phys. Educ. Res. 9, 020116 (2013).
38. J. Lynch, Douglas, Coll. Stud. J. 44, 920 (2010).
39. H. Fencl and K. Scheel, Engaging students: An examination of the effects of teaching strategies on self-efficacy and course climate in a nonmajors physics course, J. Coll. Sci. Teach. 35, 20 (2005).
40. J. M. Smist, in Proceedings of the National Meeting of the American Chemical Society (ACS,Chicago, 1993), pp. 1-27.
41. S. Larose, C. F. Ratelle, F. Guay, C. Senécal, and M. Harvey, Educ. Res. Eval. 12, 373 (2006).
42. P. Askar and D. Davenport, Turk. Online J. Educ. Technol. 8 (2009).
43. C. Lindstrøm and M. D. Sharma, Int. J. Innov. Sci. Math. Educ. 19, 1 (2011).
44. V. Sawtelle, E. Brewe, and L. H. Kramer, Exploring the relationship between self-efficacy and retention in introductory physics, J. Res. Sci. Teach. 49, 1096 (2012).
45. V. Sawtelle, E. Brewe, R. M. Goertzen, L. H. Kramer, N. S. Rebello, P. V. Engelhardt, and C. Singh, Creating opportunities to influence self-efficacy through modeling instruction, AIP Conf. Proc. 1413, 339 (2012).
46. V. Sawtelle, E. Brewe, L. H. Kramer, C. Singh, M. Sabella, and S. Rebello, AIP Conf. Proc. 1289, 289 (2010).
47. H. S. Fencl and K. R. Scheel, AIP Conf. Proc. 720, 173 (2004).
48. J. Scott, Trend report: Social network analysis, Sociology 22, 109 (1988).
49. E. Brewe, L. Kramer, and V. Sawtelle, Investigating student communities with network analysis of interactions in a physics learning center, Phys. Rev. ST Phys. Educ. Res. 8, 010101 (2012).
50. J. Forsman, R. Moll, and C. Linder, Extending the theoretical framing for physics education research: An illustrative application of complexity science, Phys. Rev. ST Phys. Educ. Res. 10, 020122 (2014).
51. D. Z. Grunspan, B. L. Wiggins, and S. M. Goodreau, Understanding classrooms through social network analysis: A primer for social network analysis in education research, Cell Biol. Educ. 13, 167 (2014).
52. S. L. Thomas, Ties that bind: A social network approach to understanding student integration and persistence, J. Higher Educ. 71, 591 (2000).
53. J. Scott and P. J. Carrington, The SAGE Handbook of Social Network Analysis, (SAGE, London, 2011).
54. S. Brin and L. Page, Seventh Int. World Wide Web Conf. (Brisbane, Australia, 1998), pp. 107-117.
55. S. Fortunato and A. Flammini, Random walks on directed networks: The case of PageRank, Int. J. Bifurcation Chaos Appl. Sci. Eng. 17, 2343 (2007).
56. State University System of Florida, Annual Accountability Report (Miami, Florida, 2014), http://opir.fiu.edu/BOG/FIU-2014-Annual-Report.pdf.
57. Office of Governmental Relations, STEM Success by Numbers (Florida International University, Miami, Florida, 2014), http://government.fiu.edu/federal/stem/stem?success?numbers/index.html.
58. E. L. Usher and F. Pajares, Rev. Educ. Res. 78, 751 (2008).
59. V. Sawtelle, E. Brewe, and L. H. Kramer, in Proceedings of the Tenth Annual College of Education & GSN Research Conference, edited by M. S. Plakhotnik, S. M. Nielsen, and D. M. Pane (Florida International University, Miami, 2011), pp. 216-225.
60. D. B. Rubin, Multiple imputation after 18+ years, J. Am. Stat. Assoc. 91, 473 (1996).
61. J. L. Schafer, Stat. Methods Med. Res. 8, 3 (1999).
62. J. Honaker, G. King, and M. Blackwell, Amelia II: Aprogram for missing data, J. Stat. Softw. 45, 1 (2011).
63. R Core, Team, R:A Language Environment For Statistical Computing (R Foundation for Statistical Computing, Vienna, 2015); http://www.R?project.org.
64. J. L. Schafer and J.W. Graham, Missing data: Our view of the state of the art, Psychol. Methods 7, 147 (2002).
65. D. B. Rubin, Multiple Imputation for Nonresponse Surveys (J. Wiley & Sons, New York, 1987).
66. M. Bastian, S. Heymann, and M. Jacomy, in Proceedings of the Third International ICWSM Conference (San Jose, CA, 2009), pp. 361-362.
67. G. Csárdi and T. Nepusz, The igraph software package for complex network research, InterJournal Complex Syst. (1695), pp. 1-9.
68. J. Fox, Bootstrapping regression models, in Applied Regression Analysis and Generalized Linear Models, 3rd ed. (SAGE Publications, Inc., Thousand Oaks, CA, 2015), pp. 587-606.
69. D. Brownstone and R. Valletta, J. Econ. Perspect. 15, 129 (2001).
70. B. Efron, J. Am. Stat. Assoc. 82, 171 (1987).
71. M. D. Siciliano, Am. Educ. Res. J. 53, 227 (2016).
72. P. Bourdieu, The forms of capital, in Handbook of Research for the Sociology of Education (Greenwood, Westport, 1983), pp. 241-258.
73. M. A. Rittmayer and M. E. Beier, Overview: Self-efficacy in STEM, Appl. Res. Pract. Resour. 1 (2009).