Anderson, T., & Shattuck, J. (2012). Design-based research: A decade of progress in education research? Educational Researcher, 41(1), 16–25.

Article  Google Scholar 

Bakshi, A., Webber, A. T., Patrick, L. E., Wischusen, W., & Thrash, C. (2019). The CURE for Cultivating Fastidious Microbes. Journal of Microbiology & Biology Education, 20(1). https://doi.org/10.1128/jmbe.v20i1.1635

Baumer, B. (2015). A data science course for undergraduates: Thinking with data. The American Statistician, 69(4), 334–342.

Article  Google Scholar 

Börner, K., Bueckle, A., & Ginda, M. (2019). Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments. Proceedings of the National Academy of Sciences, 116(6), 1857–1864.

Article  Google Scholar 

Chalmers, R. P., & Adkins, M. C. (2020). Writing effective and reliable Monte Carlo simulations with the SimDesign package. The Quantitative Methods for Psychology, 16(4), 248–280.

Article  Google Scholar 

Corbin, J. M., & Strauss, A. (1990). Grounded theory research: Procedures, canons, and evaluative criteria. Qualitative Sociology, 13(1), 3–21.

Article  Google Scholar 

Cruz, A., Selby, S., & Durham, W. (2018). Place-based education for environmental behavior: A ‘funds of knowledge’ and social capital approach. Environmental Education Research, 24(5), 627–647.

Article  Google Scholar 

Desimone, L., & Le Floch, K. (2004). Are we asking the right questions? Using cognitive interviews to improve surveys in education research. Educational Evaluation and Policy Analysis, 26(1), 1–22.

Article  Google Scholar 

Dole, J. A., & Sinatra, G. M. (1998). Reconceptualizing change in the cognitive construction of knowledge. Educational Psychologist, 33(2–3), 109–128.

Article  Google Scholar 

Eccles, J., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L., & Midgley, C. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motives: Psychological and sociological approaches (pp. 75–146). W. H. Freeman.

Google Scholar 

Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160.

Article  Google Scholar 

Fredricks, J. A., Filsecker, M., & Lawson, M. A. (2016). Student engagement, context, and adjustment: Addressing definitional, measurement, and methodological issues. Learning and Instruction, 43, 1–4.

Article  Google Scholar 

Fry, R., Kennedy, B., & Funk, C. (2021). STEM jobs see uneven progress in increasing gender, racial and ethnic diversity. Pew Research Center Science & Society.

Gonzalez, N., Moll, L. C., Tenery, M. F., Rivera, A., Rendon, P., Gonzales, R., & Amanti, C. (1995). Funds of knowledge for teaching in Latino households. Urban Education, 29(4), 443–470.

Article  Google Scholar 

Gottfried, A. E., Marcoulides, G. A., Gottfried, A. W., & Oliver, P. H. (2013). Longitudinal pathways from math intrinsic motivation and achievement to math course accomplishments and educational attainment. Journal of Research on Educational Effectiveness, 6(1), 68–92. https://doi.org/10.1080/19345747.2012.698376

Article  Google Scholar 

Greene, B. A. (2015). Measuring cognitive engagement with self-report scales: Reflections from over 20 years of research. Educational Psychologist, 50(1), 14–30.

Article  Google Scholar 

Harackiewicz, J. M., Canning, E. A., Tibbetts, Y., Priniski, S. J., & Hyde, J. S. (2016). Closing achievement gaps with a utility-value intervention: Disentangling race and social class. Journal of Personality and Social Psychology, 111(5), 745–765.

Article  Google Scholar 

Hoadley, C., & Campos, F. C. (2022). Design-based research: What it is and why it matters to studying online learning. Educational Psychologist, 57(3), 207–220.

Article  Google Scholar 

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.

Article  Google Scholar 

Hulleman, C. S., Godes, O., Hendricks, B. L., & Harackiewicz, J. M. (2010). Enhancing interest and performance with a utility value intervention. Journal of Educational Psychology, 102(4), 880–895.

Article  Google Scholar 

Hulleman, C. S., & Harackiewicz, J. M. (2021). The utility-value intervention. In G. M. Walton & A. J. Crum (Eds.), Handbook of wise interventions: How social psychology can help people change (pp. 100–125). The Guilford Press.

Google Scholar 

Hulleman, C. S., Kosovich, J. J., Barron, K. E., & Daniel, D. B. (2017). Making connections: Replicating and extending the utility value intervention in the classroom. Journal of Educational Psychology, 109(3), 387–404.

Article  Google Scholar 

Hulleman, C. S., Wormington, S. V., Tibbetts, C. Y., & Philipoom, M. (2018). A meta-analytic synthesis of utility-value interventions in education. Paper presented at the bi-annual meeting of the International Conference on Motivation. Aarhus, Denmark.

Hurley, A., Chevrette, M. G., Acharya, D. D., Lozano, G. L., Garavito, M., Heinritz, J., & Handelsman, J. (2021). Tiny earth: A big idea for STEM education and antibiotic discovery. MBio, 12(1), 10–1128.

Article  Google Scholar 

Kosovich, J. J., Hulleman, C. S., Barron, K. E., & Getty, S. (2015). A practical measure of student motivation: Establishing validity evidence for the expectancy-value-cost scale in middle school. The Journal of Early Adolescence, 35(5–6), 790–816.

Article  Google Scholar 

Lombardi, D., Nussbaum, E. M., & Sinatra, G. M. (2016). Plausibility judgments in conceptual change and epistemic cognition. Educational Psychologist, 51(1), 35–56.

Article  Google Scholar 

Long, J. S., & Ervin, L. H. (2000). Using heteroscedasticity consistent standard errors in the linear regression model. The American Statistician, 54(3), 217–224.

Article  Google Scholar 

National Science Foundation. (2015). Science and engineering degrees, by race/ethnicity of recipients: 2002–12. VA: Arlington.

Google Scholar 

Pekrun, R., Vogl, E., Muis, K. R., & Sinatra, G. M. (2017). Measuring emotions during epistemic activities: The Epistemically-Related Emotion Scales. Cognition and Emotion, 31(6), 1268–1276.

Article  Google Scholar 

Rosenzweig, E. Q., Wigfield, A., & Hulleman, C. S. (2020). More useful or not so bad? Examining the effects of utility value and cost reduction interventions in college physics. Journal of Educational Psychology, 112(1), 166–182.

Article  Google Scholar 

Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1–36.

Article  Google Scholar 

Schiefele, U. (2009). Situational and individual interest. In K. R. Wenzel & A. Wigfield (Eds.), Handbook of motivation at school (pp. 197–222). Routledge/Taylor & Francis Group.

Google Scholar 

Schwartz, D. L., & Heiser, J. (2006). Spatial representations and imagery in learning. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 283–298). Cambridge University Press.

Seyranian, V., Thacker, I., Madva, A., Abramzon, N., & Beardsley, P. (2023). A Utility Value Intervention to support undergraduate student interest, engagement, and achievement in calculus and calculus-based physics. In T. Lamberg & D. Moss (Eds.), Proceedings of the forty-fifth annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (Vol. 2, pp. 464–473). University of Nevada, Reno.

Tanner, K., & Allen, D. (2005). Approaches to biology teaching and learning: Understanding the wrong answers—teaching toward conceptual change. Cell Biology Education, 4(2), 112–117.

Article  Google Scholar 

Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2013). Using multivariate statistics (Vol. 6, pp. 497–516). Pearson.

Thacker. (2023). Climate change by the numbers: Leveraging mathematical skills for science learning online. Learning & Instruction, 86, 101782. https://doi.org/10.1016/j.learninstruc.2023.101782

Thacker, I. (2024). Supporting secondary students’ climate change learning and motivation using novel data and data visualizations. Contemporary Educational Psychology, 78, 102285. https://doi.org/10.1016/j.cedpsych.2024.102285

Thacker & Sinatra. (2022). Supporting climate change understanding with novel data, estimation instruction, and epistemic prompts. Journal of Educational Psychology, 114(5), 910–927. https://doi.org/10.1037/edu0000729

Article  Google Scholar 

Vu, T., et al. (2022). Motivation-Achievement Cycles in Learning: A Literature Review and Research Agenda. Educational Psychology Review, 34(1), 39–71. https://doi.org/10.1007/s10648-021-09616-7

Article  Google Scholar 

Wigfield, A., Rosenzweig, E. Q., & Eccles, J. S. (2017). Achievement values. In A. J. Elliot, C. S. Dweck, & D. S. Yeager (Eds.), Handbook of competence and motivation (2nd ed., pp. 116–134). Guilford Press.

Google Scholar 

Wilson-Lopez, A., Mejia, J. A., Hasbún, I. M., & Kasun, G. S. (2016). Latina/o adolescents’ funds of knowledge related to engineering. Journal of Engineering Education, 105(2), 278–311.

Article  Google Scholar 

Zeileis, A., Köll, S., & Graham, N. (2020). Various versatile variances: An object-oriented implementation of clustered covariances in R. Journal of Statistical Software, 95(1), 1–36. https://doi.org/10.18637/jss.v095.i01

Article  Google Scholar