Identifying students who are off-track academically at the start of secondary school: The role of social-emotional learning trajectories.

Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 0370636 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2044-8279 (Electronic) Linking ISSN: 00070998 NLM ISO Abbreviation: Br J Educ Psychol Subsets: MEDLINE

Publication: <2012-> : Chichester : Wiley-Blackwell
Original Publication: Edinburgh : Scottish Academic Press

Schools* ; Social Learning*; Emotions ; Humans ; Social Skills ; Students/psychology

Background: Research shows that successfully transitioning from intermediate school to secondary school is pivotal for students to remain on track to graduate. Studies also indicate that a successful transition is a function not only of how prepared the students are academically but also whether they have the social-emotional learning (SEL) skills to succeed in a more independent secondary school environment.
Aim: Yet, little is known about whether students' SEL skills are stable over time, and if they are not, whether a student's initial level of SEL skills at the start of intermediate school or change in SEL skills over time is a better indicator of whether the student will be off track academically in 9th grade. This study begins to investigate this issue.
Sample: We use four years of longitudinal SEL data from students in a large urban district with a sample size of ~3,000 students per timepoint.
Methods: We use several years of longitudinal SEL data to fit growth models for three constructs shown to be related to successfully transitioning to secondary school. In so doing, we examine whether a student's mean SEL score in 6th grade (status) or growth between 6th and 8th grade is more predictive of being off track academically in 9th grade.
Result: Results indicate that, while status is more frequently significant, growth for self-management is also predictive above and beyond status on that construct.
Conclusion: Findings suggest that understanding how a student develops social-emotionally can improve identification of students not on track to succeed in high school.
(© 2021 The British Psychological Society.)

Allbright, T. N., Marsh, J. A., Kennedy, K. E., Hough, H. J., & McKibben, S. (2019). Social-emotional learning practices: Insights from outlier schools. Journal of Research in Innovative Teaching & Learning, 12(1), 35-52. https://doi.org/10.1108/JRIT-02-2019-0020.
Allensworth, E. (2013). The use of ninth-grade early warning indicators to improve Chicago schools. Journal of Education for Students Placed at Risk (JESPAR), 18(1), 68-83.
Allensworth, E. M., & Easton, J. Q. (2005). The on-track indicator as a predictor of high school graduation. Chicago, IL: Consortium on Chicago School Research, University of Chicago.
Almlund, M., Duckworth, A. L., Heckman, J. J., & Kautz, T. D. (2011). Personality psychology and economics. Washington, DC: National Bureau of Economic Research.
Archambault, I., Janosz, M., Fallu, J.-S., & Pagani, L. S. (2009). Student engagement and its relationship with early high school dropout. Journal of Adolescence, 32, 651-670.
Aronson, J., Fried, C. B., & Good, C. (2002). Reducing the effects of stereotype threat on African American college students by shaping theories of intelligence. Journal of Experimental Social Psychology, 38(2), 113-125.
Balfanz, R., Herzog, L., & Mac Iver, D. J. (2007). Preventing student disengagement and keeping students on the graduation path in urban middle-grades schools: Early identification and effective interventions. Educational Psychologist, 42(4), 223-235.
Balfanz, R., & Legters, N. (2004). Locating the dropout crisis. Which high schools produce the nation’s dropouts? Where are they located? Who attends them? Report 70. Center for Research on the Education of Students Placed at Risk CRESPAR.
Bandura, A. (1993). Perceived self-efficacy in cognitive development and functioning. Educational Psychologist, 28(2), 117-148.
Belfield, C., Bowden, A. B., Klapp, A., Levin, H., Shand, R., & Zander, S. (2015). The economic value of social and emotional learning. Journal of Benefit-Cost Analysis, 6, 508-544.
Blackwell, L. S., Trzesniewski, K. H., & Dweck, C. S. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child development, 78(1), 246-263.
Britner, S. L., & Pajares, F. (2006). Sources of science self-efficacy beliefs of middle school students. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 43(5), 485-499.
Caprara, G. V., Fida, R., Vecchione, M., Del Bove, G., Vecchio, G. M., Barbaranelli, C., & Bandura, A. (2008). Longitudinal analysis of the role of perceived self-efficacy for self-regulated learning in academic continuance and achievement. Journal of educational psychology, 100(3), 525.
Cai, L. (2017). flexMIRT version 3.51: Flexible multilevel multidimensional item analysis and test scoring [Computer software]. Chapel Hill, NC: Vector Psychometric Group.
Carl, B., Richardson, J. T., Cheng, E., Kim, H., & Meyer, R. H. (2013). Theory and application of early warning systems for high school and beyond. Journal of Education for Students Placed at Risk (JESPAR), 18(1), 29-49.
CASEL (2021). Fundamentals of SEL. Retrieved October 24, 2021, from https://casel.org/fundamentals-of-sel/.
Conley, D. T. (2007). The challenge of college readiness. Educational Leadership, 64(7), 23.
Curran, P. J., Howard, A. L., Bainter, S. A., Lane, S. T., & McGinley, J. S. (2014). The separation of between-person and within-person components of individual change over time: A latent curve model with structured residuals. Journal of Consulting and Clinical Psychology, 82, 879.
Duckworth, A. L., & Seligman, M. E. (2005). Self-discipline outdoes IQ in predicting academic performance of adolescents. Psychological Science, 16, 939-944.
Dweck, C. (2006). Mindset: The new psychology of success. New York, NY: Random House.
Dweck, C. S. (2010). Even geniuses work hard. Educational Leadership, 68(1), 16-20.
Dweck, C., Walton, G. M., & Cohen, G. L. (2011). Academic tenacity: Mindset and skills that promote long-term learning. Seattle, WA: Bill & Melinda Gates Foundation.
Farrington, C. A., Roderick, M., Allensworth, E., Nagaoka, J., Keyes, T. S., Johnson, D. W., & Beechum, N. O. (2012). Teaching adolescents to become learners: The role of noncognitive factors in shaping school performance-A critical literature review. Chicago, IL: Chicago Consortium on School Research at the University of Chicago.
Good, C., & Dweck, C. S. (2006). A motivational approach to reasoning, resilience, and responsibility. Optimizing student success with the other three Rs, Reasoning, resilience, and responsibility, 39-56.
Grimm, K. J., Ram, N., & Hamagami, F. (2011). Nonlinear growth curves in developmental research. Child development, 82(5), 1357-1371.
Heckman, J. J., Stixrud, J., & Urzua, S. (2006). The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior. Journal of Labor Economics, 24, 411-482.
Heckman, J., & Vytlacil, E. (2001). Identifying the role of cognitive ability in explaining the level of and change in the return to schooling. Review of Economics and Statistics, 83(1), 1-12.
Heppen, J. B., & Therriault, S. B. (2008). Developing early warning systems to identify potential high school dropouts. Washington, DC: American Institutes for Research, National High School Center.
Hertzog, C., von Oertzen, T., Ghisletta, P., & Lindenberger, U. (2008). Evaluating the power of latent growth curve models to detect individual differences in change. Structural Equation Modeling: A Multidisciplinary Journal, 15(4), 541-563.
Hough, H., Kalogrides, D., & Loeb, S. (2017). Using surveys of students’ social-emotional learning and school climate for accountability and continuous improvement. Stanford, CA: Policy Analysis for California Education, PACE.
Jones, S., Bailey, R., Brush, K., Nelson, B., & Barnes, S. (2016). What is the same and what is different?. Making sense of the “non-cognitive” domain: Helping educators translate research into practice. Cambridge, MA: Harvard University Graduate School of Education EASEL Lab. easel.gse.harvard.edu/files/gse-easel-lab/files/words_matter_paper.pdf.
Kemple, J. J., Segeritz, M. D., & Stephenson, N. (2013). Building on-track indicators for high school graduation and college readiness: Evidence from New York City. Journal of Education for Students Placed at Risk (JESPAR), 18(1), 7-28.
Kennelly, L., & Monrad, M. (2007). Approaches to dropout prevention: Heeding early warning signs with appropriate interventions. Washington, DC: American Institutes for Research.
Koufteros, X., Babbar, S., & Kaighobadi, M. (2009). A paradigm for examining second-order factor models employing structural equation modeling. International Journal of Production Economics, 120(2), 633-652.
Krumpal, I. (2013). Determinants of social desirability bias in sensitive surveys: A literature review. Quality & Quantity, 47, 2025-2047.
Kuhfeld, M., & Soland, J. (2020). Avoiding bias from sum scores in growth estimates: An examination of IRT-based approaches to scoring longitudinal survey responses. Psychological Methods.
Mac Iver, M. A. (2013). Early warning indicators of high school outcomes. Journal of Education for Students Placed at Risk (JESPAR), 18(1), 1-6.
Martínez, R. S., Aricak, O. T., Graves, M. N., Peters-Myszak, J., & Nellis, L. (2011). Changes in perceived social support and socioemotional adjustment across the elementary to junior high school transition. Journal of Youth and Adolescence, 40, 519-530.
Meyer, R. H., Wang, Y. C., & Rice, A. B. (2018). Measuring students’ social-emotional learning among California’s CORE districts: An IRT modeling approach. Stanford, CA: Policy Analysis for California Education. Retrieved from https://edpolicyinca.org/sites/default/files/Measuring_SEL_May-2018.pdf.
Muthén, B., & Muthén, L. (2017). Mplus (pp. 507-518). Chapman and Hall/CRC.
Neild, R. C. (2009). Falling off track during the transition to high school: What we know and what can be done. The Future of Children, 19(1), 53-76.
Neild, R. C., Stoner-Eby, S., & Furstenberg, F. (2008). Connecting entrance and departure: The transition to ninth grade and high school dropout. Education and Urban Society, 40, 543-569.
Pintrich, P. R. (1999). The role of motivation in promoting and sustaining self-regulated learning. International Journal of Educational Research, 31, 459-470.
Schunk, D. H. (1991). Self-efficacy and academic motivation. Educational Psychologist, 26(3-4), 207-231.
Schunk, D. H. (2005). Self-regulated learning: The educational legacy of Paul R. Pintrich. Educational Psychologist, 40(2), 85-94.
Schunk, D. H., & Zimmerman, B. J. (2012). Motivation and self-regulated learning: Theory, research, and applications. Abingdon-on-Thames, UK: Routledge.
Soland, J. (2013). Predicting high school graduation and college enrollment: Comparing early warning indicator data and teacher intuition. Journal of Education for Students Placed at Risk (JESPAR), 18(3-4), 233-262.
Soland, J. (2017). Combining academic, noncognitive, and college knowledge measures to identify students not on track for college: A data-driven approach. Research & Practice in Assessment, 12, 5-19.
Soland, J. (2019). Modeling academic achievement and self-efficacy as joint developmental processes: Evidence for education, counseling, and policy. Journal of Applied Developmental Psychology, 65, 101076.
Soland, J., Jensen, N., Keys, T. D., Bi, S. Z., & Wolk, E. (2019). Are test and academic disengagement related? Implications for measurement and practice. Educational Assessment, 24(2), 119-134. https://doi.org/10.1080/10627197.2019.1575723.
Soland, J., & Kuhfeld, M. (2020). Do response styles affect estimates of growth on social-emotional constructs? Evidence from four years of longitudinal survey scores. Multivariate Behavioral Research, 1-21.
Soland, J., & Sandilos, L. E. (2021). English language learners, self-efficacy, and the achievement gap: Understanding the relationship between academic and social-emotional growth. Journal of Education for Students Placed at Risk (JESPAR), 26(1), 20-44.
Sperling, R. A., Howard, B. C., Staley, R., & DuBois, N. (2004). Metacognition and self-regulated learning constructs. Educational Research and Evaluation, 10(2), 117-139.
Tangney, J. P., Baumeister, R. F., & Boone, A. L. (2004). High self-control predicts good adjustment, less pathology, better grades, and interpersonal success. Journal of Personality, 72(2), 271-324.
West, M. R. (2016). Should non-cognitive skills be included in school accountability systems? Preliminary evidence from California’s CORE districts. Evidence Speaks Reports, 1(13), 1-7.
West, M. R., Pier, L., Fricke, H., Hough, H., Loeb, S., Meyer, R. H., & Rice, A. B. (2018). Trends in student social-emotional learning: Evidence from the CORE districts. Stanford, CA: Policy Analysis for California Education (PACE).
Yeager, D. S., & Dweck, C. S. (2012). Mindsets that promote resilience: When students believe that personal characteristics can be developed. Educational Psychologist, 47, 302-314.
Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25(1), 82-91.

Keywords: dropout; growth/development; school engagement; social-emotional learning; structural equation modelling

Date Created: 20211029 Date Completed: 20220418 Latest Revision: 20220418

20240513

10.1111/bjep.12463

34713891