Apt and actionable possible identities matter: The case of academic outcomes.
academic expectations
academic outcomes
identity-based motivation
machine learning
natural language processing
possible selves
Journal
Journal of adolescence
ISSN: 1095-9254
Titre abrégé: J Adolesc
Pays: England
ID NLM: 7808986
Informations de publication
Date de publication:
02 2023
02 2023
Historique:
revised:
21
10
2022
received:
27
08
2022
accepted:
22
10
2022
pubmed:
9
12
2022
medline:
10
2
2023
entrez:
8
12
2022
Statut:
ppublish
Résumé
We review the longitudinal evidence documenting that middle and high school students with school-focused possible future identities subsequently attain better school outcomes. Consistent results across operationalizations of possible identities and academic outcomes imply that results are robust. However, variability in study designs means that the existing literature cannot explain the process from possible identity to academic outcomes. We draw on identity-based motivation theory to address this gap. We predict that imagining a possible school-focused future drives school engagement to the extent that students repeatedly experience their school-focused future identities as apt (relevant) and actionable (linked to strategies they can use now). We operationalize aptness as having pairs of positive and negative school-focused possible identities (balance) and actionability as having a roadmap of concrete, linked strategies for school-focused possible selves (plausibility). We use machine learning to capture features of possible identities that predict academic outcomes and network analyses to examine these features (training sample USA 47% female, M We report regression analyses showing that balance, plausibility, and our machine algorithm predict better end-of-school-year grades (grade point average). We use network analysis to show that our machine algorithm is associated with structural features of possible identities and balance and plausibility scores. Our results support the inference that student academic outcomes are improved when students experience their school-focused possible identities as apt and actionable.
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
354-371Informations de copyright
© 2022 The Authors. Journal of Adolescence published by Wiley Periodicals LLC on behalf of Foundation for Professionals in Services to Adolescents.
Références
Amodio, D. M. (2019). Social cognition 2.0: An interactive memory systems account. Trends in Cognitive Sciences, 23(1), 21-33.
Anderman, E. M., Anderman, L. H., & Griesinger, T. (1999). The relation of present and possible academic selves during early adolescence to grade point average and achievement goals. The Elementary School Journal, 100(1), 3-17.
Azzalini, A., & Valle, A. (1996). The multivariate skew-normal distribution. Biometrika, 83(4), 715-726.
Bamakan, S. M. H., Nurgaliev, I., & Qu, Q. (2019). Opinion leader detection: A methodological review. Expert Systems with Applications, 115, 200-222.
Bargh, J., & Chartrand, T. (2014). The mind in the middle: A practical guide to priming and automaticity research. In H. Reis & C. Judd (Eds.), Handbook of research methods in social and personality psychology (pp. 311-344). Cambridge University Press.
Beal, S. J., & Crockett, L. J. (2010). Adolescents' occupational and educational aspirations and expectations: Links to high school activities and adult educational attainment. Developmental Psychology, 46(1), 258-265.
Benedek, M., Kenett, Y. N., Umdasch, K., Anaki, D., Faust, M., & Neubauer, A. C. (2017). How semantic memory structure and intelligence contribute to creative thought: A network science approach. Thinking & Reasoning, 23(2), 158-183.
Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(2), 281-305.
Beyer, S. (1999). Gender differences in the accuracy of grade expectancies and evaluations. Sex Roles, 41(3), 279-296.
Bi, C., & Oyserman, D. (2015). Left behind or moving forward? Effects of possible selves and strategies to attain them among rural Chinese children. Journal of Adolescence, 44, 245-258.
Bodenhausen, G., Macrae, C., & Hugenberg, K. (2003). Social cognition. In I. Weiner (Ed.), Handbook of psychology (5, pp. 257-282). Wiley.
Bürkner, P. C. (2017). Brms: An R package for Bayesian multilevel models using stan. Journal of Statistical Software, 80(1), 1-28.
Cadely, H. S. E., Pittman, J. F., Kerpelman, J. L., & Adler-Baeder, F. (2011). The role of identity styles and academic possible selves on academic outcomes for high school students. Identity, 11(4), 267-288.
Chowdhury, F. (2018). Grade inflation: Causes, consequences and cure. Journal of Education and Learning, 7(6), 86-92.
Collins, A. M., & Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychological Review, 82(6), 407-428.
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1-9.
De Paola, M., Gioia, F., & Scoppa, V. (2014). Overconfidence, omens and gender heterogeneity: Results from a field experiment. Journal of Economic Psychology, 45, 237-252.
Destin, M., & Oyserman, D. (2010). Incentivizing education: Seeing schoolwork as an investment, not a chore. Journal of Experimental Social Psychology, 46(5), 846-849.
Dimitriadou, E., Hornik, K., Leisch, F., Meyer, D., & Weingessel, A. (2008). Misc functions of the Department of Statistics (e1071), TU Wien. R package, 1, 5-24.
Eichas, K., Kurtines, W. M., Rinaldi, R. L., & Farr, A. C. (2018). Promoting positive youth development: A psychosocial intervention evaluation. Psychosocial Intervention, 27(1), 22-34.
Feliciano, C. (2012). The female educational advantage among adolescent children of immigrants. Youth & Society, 44(3), 431-449.
Frazier, L. D., Schwartz, B. L., & Metcalfe, J. (2021). The MAPS model of self-regulation: Integrating metacognition, agency, and possible selves. Metacognition and Learning, 16, 297-318.
Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1, 215-239.
Gelman, A., Jakulin, A., Pittau, M., & Su, Y. (2008). A default prior distribution for logistic and other regression models. Annals of Applied Statistics, 2(4), 1360-1383.
Gelman, A., Vehtari, A., Simpson, D., Margossian, C., Carpenter, B., Yao, Y., Kennedy, L., Gabry, J., Bürkner, P-C., & Modrák, M. (2020). Bayesian workflow. arXiv preprint arXiv: 2011.01808.
Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360-1380.
Hamedani, M. Y. G., & Markus, H. R. (2019). Understanding culture clashes and catalyzing change: A culture cycle approach. Frontiers in Psychology, 10, 700.
Higgins, E. T. (2005). Value from regulatory fit. Current Directions in Psychological Science, 14(4), 209-213.
Horowitz, E., Oyserman, D., Dehghani, M., & Sorensen, N. (2020). Do you need a roadmap or can someone give you directions: When school-focused possible identities change so do academic trajectories. Journal of Adolescence, 79, 26-38.
Hoyle, R. H., & Sherrill, M. R. (2006). Future orientation in the self-system: Possible selves, self-regulation, and behavior. Journal of Personality, 74(6), 1673-1696.
Hung, C. L., & Marjoribanks, K. (2005). Parents, teachers and children's school outcomes: A Taiwanese study. Educational Studies, 31(1), 3-13.
Kang, C., Molinaro, C., Kraus, S., Shavitt, Y., & Subrahmanian, V. (2012). Diffusion centrality in social networks. In 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 558-564). IEEE.
Kaplan, A., & Garner, J. K. (2020). Steps for applying the complex dynamical systems approach in educational research: A guide for the perplexed scholar. The Journal of Experimental Education, 88(3), 486-502.
Kihlstrom, J., & Klein, S. (1994). The self as a knowledge structure. In R. Wyer Jr & T. Srull (Eds.), Handbook of social cognition: Basic processes (pp. 153-208). Lawrence Erlbaum.
Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H. E., & Makse, H. A. (2010). Identification of influential spreaders in complex networks. Nature Physics, 6(11), 888-893.
Kruschke, J. K., & Liddell, T. M. (2018). The Bayesian new statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychonomic Bulletin & Review, 25(1), 178-206.
Landau, M. J., Oyserman, D., Keefer, L. A., & Smith, G. C. (2014). The college journey and academic engagement: How metaphor use enhances identity-based motivation. Journal of Personality and Social Psychology, 106(5), 679-698.
Landrum, R. (1999). Student expectations of grade inflation. Journal of Research and Development in Education, 32(2), 124-28.
Lee, J., Husman, J., Scott, K. A., & Eggum-Wilkens, N. D. (2015). Compugirls: Stepping stone to future computer-based technology pathways. Journal of Educational Computing Research, 52(2), 199-223.
Loersch, C., & Keith Payne, B. (2016). Demystifying priming. Current Opinion in Psychology, 12, 32-36.
Mackay, D. J. (2019). An ideal second language self intervention: Development of possible selves in an English as a foreign language classroom context. System, 81, 50-62.
Manger, T., & Teigen, K. (1988). Time horizon in students' predictions of grades. Scandinavian Journal of Educational Research, 32(2), 77-91.
Marjoribanks, K. (2003). Learning environments, family contexts, educational aspirations and attainment: A moderation-mediation model extended. Learning Environments Research, 6(3), 247-265.
Marjoribanks, K. (2004). Immigrant adolescents’ individual and environmental influences on young adults’ educational attainment. Journal of Comparative Family Studies, 35(3), 485-499.
Marjoribanks, K. (2005). Family background, adolescents' educational aspirations, and Australian young adults' educational attainment. International Education Journal, 6(1), 104-112.
Mattern, K. D., & Shaw, E. J. (2010). A look beyond cognitive predictors of academic success: Understanding the relationship between academic self-beliefs and outcomes. Journal of College Student Development, 51(6), 665-678.
McElvany, N., Ferdinand, H. D., Gebauer, M. M., Bos, W., Huelmann, T., Köller, O., & Schöber, C. (2018). Attainment-aspiration gap in students with a migration background: The role of self-efficacy. Learning and Individual Differences, 65, 159-166.
Merolla, D. M. (2013). The net Black advantage in educational transitions: An education careers approach. American Educational Research Journal, 50(5), 895-924.
Messersmith, E. E., & Schulenberg, J. E. (2008). When can we expect the unexpected? Predicting educational attainment when it differs from previous expectations. Journal of Social Issues, 64(1), 195-212.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In C. Burges, L. Bottou, M. Welling, Z. Ghahramani, & K. Weinberger (Eds.), Advances in neural information processing systems (pp. 3111-3119). Curran Associates.
Molina, M., Raimundi, M., & Gimenez, M. (2017). Los posibles sí mismos de los adolescentes de Buenos Aires. Revista Latinoamericana de Ciencias Sociales. Niñez y Juventud, 15(1), 455-470.
Mousavi, H. (2018). The roles of possible selves in Iranian EFL learners' L2 learning motivation. Journal on English Language Teaching, 8(3), 18-28.
Muller, C. (2001). The role of caring in the teacher-student relationship for at-risk students. Sociological Inquiry, 71(2), 241-255.
Nurra, C., & Oyserman, D. (2018). From future self to current action: An identity-based motivation perspective. Self and Identity, 17(3), 343-364.
Oettingen, G., Mayer, D., Thorpe, J. S., Janetzke, H., & Lorenz, S. (2005). Turning fantasies about positive and negative futures into self-improvement goals. Motivation and Emotion, 29(4), 236-266.
Ou, S. R., & Reynolds, A. J. (2008). Predictors of educational attainment in the Chicago longitudinal study. School Psychology Quarterly, 23(2), 199-229.
Oyserman, D. (2007). Social identity and self-regulation. In A. Kruglanski & E. T. Higgins (Eds.), Handbook of social psychology: Basic principles (pp. 432-453). Guilford.
Oyserman, D. (2017). Culture three ways: Culture and subcultures within countries. Annual Review of Psychology, 68, 435-463.
Oyserman, D. (2019). The essentialized self: Implications for motivation and self-regulation. Journal of Consumer Psychology, 29(2), 336-343.
Oyserman, D., Bybee, D., & Terry, K. (2006). Possible selves and academic outcomes: How and when possible selves impel action. Journal of Personality and Social Psychology, 91(1), 188-204.
Oyserman, D., Bybee, D., Terry, K., & Hart-Johnson, T. (2004). Possible selves as roadmaps. Journal of Research in Personality, 38(2), 130-149.
Oyserman, D., Destin, M., & Novin, S. (2015). The context-sensitive future self: Possible selves motivate in context, not otherwise. Self and Identity, 14(2), 173-188.
Oyserman, D., Elmore, K., & Smith, G. (2012). Self, self-concept, and identity. In M. R. Leary & J. P. Tangney (Eds.), Handbook of self and identity (pp. 69-104). Guilford.
Oyserman, D., Gant, L., & Ager, J. (1995). A socially contextualized model of African American identity: Possible selves and school persistence. Journal of Personality and Social Psychology, 69(6), 1216-1232.
Oyserman, D., & Horowitz, E. (in press). When and how possible selves predict future action. Advances in Motivation Science.
Oyserman, D., & James, L. (2009). Possible selves: From content to process. In K. D. Markman, W. M. P. Klein, & J. A. Suhr (Eds.), Handbook of imagination and mental simulation (pp. 373-394). Psychology Press.
Oyserman, D., & James, L. (2011). Possible identities. In S. Schwartz, K. Luyckx, & V. Vignoles (Eds.), Handbook of identity theory and research (pp. 117-145). Springer.
Oyserman, D., Lewis, Jr., N. A., Yan, V. X., Fisher, O., O'Donnell, S. C., & Horowitz, E. (2017). An identity-based motivation framework for self-regulation. Psychological Inquiry, 28(2-3), 139-147.
Oyserman, D., & Markus, H. (1990). Possible selves in balance: Implications for delinquency. Journal of Social Issues, 46(2), 141-157.
Oyserman, D., O'Donnell, S. C., Sorensen, N., & Wingert, K. M. (2021). Process matters: Teachers benefit their classrooms and students when they deliver an identity-based motivation intervention with fidelity. Contemporary Educational Psychology, 66, 101993.
Oyserman, D., & Packer, M. (1996). Social cognition and self-concept: A socially contextualized model of identity. In J. Nye & A. Brower (Eds.), What's social about social cognition? Research on socially shared cognition in small groups (pp. 175-201). Sage. https://doi.org/10.4135/9781483327648.n8
Paluck, E., & Shepherd, H. (2012). The salience of social referents: A field experiment on collective norms and harassment behavior in a school social network. Journal of Personality and Social Psychology, 103(6), 899-915. https://doi.org/10.1037/a0030015
Papafilippou, V., & Bathmaker, A. (2018). Transitions from higher education to employment among recent graduates in England: Unequal chances of achieving desired possible selves. In H. Henderson, J. Stevenson, & A. M. Bathmaker (Eds.), Possible selves and higher education (pp. 123-138). Routledge.
Rudin, C., Wang, C., & Coker, B. (2018). The age of secrecy and unfairness in recidivism prediction. Science Review, 2(1). https://doi.org/10.1162/99608f92.6ed64b30
Ruvolo, A. P., & Markus, H. R. (1992). Possible selves and performance: The power of self-relevant imagery. Social Cognition, 10(1), 95-124.
Schlegel, R., Chu, S., Chen, K., Deuermeyer, E., Christy, A., & Quek, F. (2019). Making in the classroom: Longitudinal evidence of increases in self-efficacy and STEM possible selves over time. Computers & Education, 142, 103637.
Schoon, I., & Ng-Knight, T. (2017). Co-development of educational expectations and effort: Their antecedents and role as predictors of academic success. Research in Human Development, 14(2), 161-176.
Siew, C. S. Q., Wulff, D. U., Beckage, N. M., Kenett, Y. N., & Meštrović, A. (2019). Cognitive network science: A review of research on cognition through the lens of network representations, processes, and dynamics. Complexity, 2019, 2108423.
Sordo, M., & Zeng, Q. (2005). On sample size and classification accuracy: A performance comparison. In J. Oliveira, V. Maojo, F. Martín-Sánchez, & A. Pereira (Eds.), Biological and medical data analysis (pp. 193-201). Springer.
Svanum, S., & Bigatti, S. (2006). Grade expectations: Informed or uninformed optimism, or both? Teaching of Psychology, 33(1), 14-18.
Timmons, K. (2019). Kindergarten expectations and outcomes: understanding the influence of educator and child expectations on children's self-regulation, early reading, and vocabulary outcomes. Journal of Research in Childhood Education, 33(3), 471-489.
U.S. Department of Education. (2018). Table 216.60. Digest of education statistics.
Vehtari, A., Gelman, A., & Gabry, J. (2016). Practical Bayesian model evaluation using leave-one-out cross-validation and waic. Statistics and Computing, 27(5), 1413-1432.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge University Press.
Webb, R. M., Lubinski, D., & Benbow, C. P. (2002). Mathematically facile adolescents with math-science aspirations: New perspectives on their educational and vocational development. Journal of Educational Psychology, 94(4), 785-794. https://doi.org/10.1037/0022-0663.94.4.785
Wendorf, C. (2002). Grade point average and changes in (great) grade expectations. Teaching of Psychology, 29(2), 136-137.
Woolley, M. E., Rose, R. A., Orthner, D. K., Akos, P. T., & Jones-Sanpei, H. (2013). Advancing academic achievement through career relevance in the middle grades: A longitudinal evaluation of CareerStart. American Educational Research Journal, 50(6), 1309-1335.
Zhoc, K. C. H., King, R. B., Law, W., & McInerney, D. M. (2019). Intrinsic and extrinsic future goals: Their differential effects on students' self-control and distal learning outcomes. Psychology in the Schools, 56(10), 1596-1613.