Formative assessment, growth mindset, and achievement: examining their relations in the East and the West

References

• Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/https://doi.org/10.1037/0033-2909.103.3.411
   Web of Science ®Google Scholar
• Beatty, B., & Brew, C. (2005). Measuring student sense of connectedness with school: The development of an instrument for use in secondary schools. Leading and Managing, 11(2), 103–118.
   Google Scholar
• Beecham, R. (2009). Teaching quality and student satisfaction: Nexus or simulacrum? London Review of Education, 7(2), 135–146. https://doi.org/https://doi.org/10.1080/14748460902990336
   Google Scholar
• Bennett, R. E. (2011). Formative assessment: A critical review. Assessment in Education: Principles, Policy & Practice, 18(1), 5–25. https://doi.org/https://doi.org/10.1080/0969594X.2010.513678
   Google Scholar
• Bernardo, A. B. I., Cai, Y., & King, R. B. (2021). Society-level social axiom moderates the association between growth mindset and achievement across cultures. British Journal of Educational Psychology, e12411. https://doi.org/https://doi.org/10.1111/bjep.12411
   Google Scholar
• Bibbens, T. (2018). Learning-driven data: Tracking improvement within a formative assessment cycle in English. English in Australia, 53(1), 33–41. https://eric.ed.gov/?id=EJ1183095
   Web of Science ®Google Scholar
• Biggs, J. (1999). Teaching for quality learning at university: What the student does. Open University Press.
   Google Scholar
• Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74. https://doi.org/https://doi.org/10.1080/0969595980050102
   Google Scholar
• 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. https://doi.org/https://doi.org/10.1111/j.1467-8624.2007.00995.x
   PubMed Web of Science ®Google Scholar
• Bosco, F. A., Aguinis, H., Singh, K., Field, J. G., & Pierce, C. A. (2015). Correlational effect size benchmarks. Journal of Applied Psychology, 100(2), 431–449. https://doi.org/https://doi.org/10.1037/a0038047
   PubMed Web of Science ®Google Scholar
• Briggs, D. C., Ruiz-Primo, M. A., Furtak, E., & Shepard, L. (2012). Meta-analytic methodology and inferences about the efficacy of formative assessment. Educational Measurement: Issues and Practice, 31(4), 13–17. https://doi.org/https://doi.org/10.1111/j.1745-3992.2012.00251.x
   Google Scholar
• Brown, G. T. L. (2021). Assessments cause and contribute to learning: If only we let them. In Z. Yan & L. Yang (Eds.), Assessment as learning: Maximising opportunities for student learning and achievement (pp. 38–52). Routledge.
   Google Scholar
• Burgoyne, A. P., Hambrick, D. Z., & Macnamara, B. N. (2020). How firm are the foundations of mind-set theory? The claims appear stronger than the evidence. Psychological Science, 31(3), 258–267. https://doi.org/https://doi.org/10.1177/0956797619897588
   PubMed Web of Science ®Google Scholar
• Burnette, J. L., O’Boyle, E., VanEpps, E., Pollack, J., & Finkel, E. (2012). Mind-sets matter: A meta-analytic review of implicit theories and self-regulation. Psychological Bulletin, 139(3), 655–701. https://doi.org/https://doi.org/10.1037/a0029531
   PubMed Web of Science ®Google Scholar
• Carless, D. (2011). From testing to productive student learning: Implementing formative assessment in Confucian-heritage settings. Routledge.
   Google Scholar
• Carless, D., & Boud, D. (2018). The development of student feedback literacy: Enabling uptake of feedback. Assessment & Evaluation in Higher Education, 43(8), 1315–1325. https://doi.org/https://doi.org/10.1080/02602938.2018.1463354
   Web of Science ®Google Scholar
• Caro, D. H., & Biecek, P. (2017). intsvy: An R package for analyzing international large-scale assessment data. JSS Journal of Statistical Software, 81(7), 1-44. https://doi.org/https://doi.org/10.18637/jss.v081.i07
   Web of Science ®Google Scholar
• Catley, K., Lehrer, R., & Reiser, B. (2005). Tracing a proposed learning progression for developing understanding of evolution. Paper Commissioned for the Committee on Test Design for K-12 Science Achievement Center for Education, National Research Council.
   Google Scholar
• Chen, C.-M., Chen, L.-C., & Horng, W.-J. (2019). A collaborative reading annotation system with formative assessment and feedback mechanisms to promote digital reading performance. Interactive Learning Environments, 29(5), 848-865. https://doi.org/https://doi.org/10.1080/10494820.2019.1636091
   Google Scholar
• Chen, -W.-W., & Wong, Y.-L. (2015). Chinese mindset: Theories of intelligence, goal orientation and academic achievement in Hong Kong students. Educational Psychology, 35(6), 714–725. https://doi.org/https://doi.org/10.1080/01443410.2014.893559
   Web of Science ®Google Scholar
• Chiu, M. M., Chow, B. W. Y., & Joh, S. W. (2017). Streaming, tracking and reading achievement: A multilevel analysis of students in 40 countries. Journal of Educational Psychology, 109(7), 915–934. https://doi.org/https://doi.org/10.1037/edu0000188
   Web of Science ®Google Scholar
• Chiu, M. M., Wing-Yin Chow, B., Mcbride, C., & Mol, S. T. (2016). Students’ sense of belonging at school in 41 countries: Cross-cultural variability. Journal of Cross-Cultural Psychology, 47(2), 175–196. https://doi.org/https://doi.org/10.1177/0022022115617031
   Web of Science ®Google Scholar
• Claro, S., Paunesku, D., Dweck, C. S., Thompson, R. A., & Wilson, T. D. (2016). Growth mindset tempers the effects of poverty on academic achievement. Proceedings of the National Academy of Sciences, 113(31), 8664–8668. https://doi.org/https://doi.org/10.1073/pnas.1608207113
   Google Scholar
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). L. Erlbaum Associates.
   Google Scholar
• Costa, A., & Faria, L. (2018). Implicit theories of intelligence and academic achievement: A meta-analytic review. Frontiers in Psychology, 9, 829. https://doi.org/https://doi.org/10.3389/fpsyg.2018.00829
   PubMed Web of Science ®Google Scholar
• Davison, C. (2004). The contradictory culture of teacher-based assessment: ESL teacher assessment practices in Australian and Hong Kong secondary schools. Language Testing, 21(3), 305–334. https://doi.org/https://doi.org/10.1191/0265532204lt286oa
   Google Scholar
• DeBacker, T. K., Heddy, B. C., Kershen, J. L., Crowson, H. M., Looney, K., & Goldman, J. A. (2018). Effects of a one-shot growth mindset intervention on beliefs about intelligence and achievement goals. Educational Psychology, 38(6), 711–733. https://doi.org/https://doi.org/10.1080/01443410.2018.1426833
   Web of Science ®Google Scholar
• Degol, J. L., Wang, M. Te, Zhang, Y., & Allerton, J. (2018). Do growth mindsets in math benefit females? Identifying pathways between gender, mindset, and motivation. Journal of Youth and Adolescence, 47(5), 976–990. https://doi.org/https://doi.org/10.1007/s10964-017-0739–8
   Google Scholar
• Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., & Kaiser, S. (2012). Guidelines for choosing between multi-item and single-item scales for construct measurement: A predictive validity perspective. Journal of the Academy of Marketing Science, 40(3), 434–449. https://doi.org/https://doi.org/10.1007/s11747-011-0300-3
   Web of Science ®Google Scholar
• Dunn, K. E., & Mulvenon, S. W. (2009). A critical review of research on formative assessments: The limited scientific evidence of the impact of formative assessments in education. Practical Assessment & Research and Evaluation, 14(7), 1–11. https://doi.org/https://doi.org/10.7275/jg4h-rb87
   Google Scholar
• Duschl, R., Maeng, S., & Sezen, A. (2011). Learning progressions and teaching sequences: A review and analysis. Studies in Science Education, 47(2), 123–182. https://doi.org/https://doi.org/10.1080/03057267.2011.604476
   Web of Science ®Google Scholar
• Dweck, C. S. (1996). Implicit theories as organizers of goals and behavior. In P. Gollwitzer & J. A. Bargh (Eds.), The psychology of action: The relation of cognition and motivation to behavior (pp. 69–90). New York: Guilford.
   Google Scholar
• Dweck, C. S. (2015, September 22). Revisits the ‘Digital Nomad Life Lessons & Mindset’.
   Google Scholar
• Dweck, C. S. (2017). Mindset: Changing the way you think to fulfil your potential. Robinson.
   Google Scholar
• Ferguson, C. J. (2009). An effect size primer: A guide for clinicians and researchers. Professional Psychology: Research and Practice, 40(5), 532–538. https://doi.org/https://doi.org/10.1037/a0015808
   Web of Science ®Google Scholar
• Förster, N., & Souvignier, E. (2014). Learning progress assessment and goal setting: Effects on reading achievement, reading motivation and reading self-concept. Learning and Instruction, 32, 91–100. https://doi.org/https://doi.org/10.1016/j.learninstruc.2014.02.002
   Web of Science ®Google Scholar
• Gignac, G. E., & Szodorai, E. T. (2016). Effect size guidelines for individual differences researchers. Personality and Individual Differences, 102, 74–78. https://doi.org/https://doi.org/10.1016/j.paid.2016.06.069
   Web of Science ®Google Scholar
• Grigorenko, E. L., Geissler, P. W., Prince, R., Okatcha, F., Nokes, C., Kenny, D. A., Bundy, D. A., & Sternberg, R. J. (2001). The organisation of Luo conceptions of intelligence: A study of implicit theories in a Kenyan village. International Journal of Behavioral Development, 25(4), 367–378. https://doi.org/https://doi.org/10.1080/01650250042000348
   Web of Science ®Google Scholar
• Grund, S., Lüdtke, O., & Robitzsch, A. (2021). On the treatment of missing data in background questionnaires in educational large-scale assessments: An evaluation of different procedures. Journal of Educational and Behavioral Statistics, 46(4), 430-465. https://doi.org/https://doi.org/10.3102/1076998620959058
   Web of Science ®Google Scholar
• Guo, W. Y., & Yan, Z. (2019). Formative and summative assessment in Hong Kong primary schools: Students’ attitudes matter. Assessment in Education: Principles, Policy & Practice, 26(6), 675-699. https://doi.org/https://doi.org/10.1080/0969594X.2019.1571993
   Google Scholar
• Haimovitz, K., & Dweck, C. S. (2017). The origins of children’s growth and fixed mindsets: New research and a new proposal. Child Development, 88(6), 1849–1859. https://doi.org/https://doi.org/10.1111/cdev.12955
   PubMed Web of Science ®Google Scholar
• Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/https://doi.org/10.3102/003465430298487
   Web of Science ®Google Scholar
• Hau, K.-T., & Salili, F. (1991). Structure and semantic differential placement of specific causes: Academic causal attributions by Chinese students in Hong Kong. International Journal of Psychology, 26(2), 175–193. https://doi.org/https://doi.org/10.1080/00207599108247885
   Web of Science ®Google Scholar
• Ho, I. T. (2004). A comparison of Australian and Chinese teachers’ attributions for student problem behaviors. Educational Psychology, 24(3), 375–391. https://doi.org/https://doi.org/10.1080/0144341042000211706
   Google Scholar
• Hopfenbeck, T. N. (2016). The power of PISA – Limitations and possibilities for educational research. Assessment in Education: Principles, Policy & Practice, 23(4), 423–426. https://doi.org/https://doi.org/10.1080/0969594X.2016.1247518
   Web of Science ®Google Scholar
• Hopfenbeck, T. N., & Maul, A. (2011). Examining evidence for the validity of PISA learning strategy scales based on student response processes. International Journal of Testing, 11(2), 95–121. https://doi.org/https://doi.org/10.1080/15305058.2010.529977
   Google Scholar
• Hu, L.-T., & Bentler, P. M. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 76–99). Sage Publications, Inc.
   Google Scholar
• Kaplan, D., & Su, D. (2018). On imputation for planned missing data in context questionnaires using plausible values: A comparison of three designs. Large-Scale Assessments in Education, 6(1), 6. https://doi.org/https://doi.org/10.1186/s40536-018-0059-9
   Google Scholar
• Kember, D., & Wang, D. (2016). Why do Chinese students out-perform those from the West? Do approaches to learning contribute to the explanation? Cogent Education, 3(1), 1–15. https://doi.org/https://doi.org/10.1080/2331186X.2016.1248187
   Google Scholar
• Kennedy, P. (2008). Guide to econometrics (6th ed.). Wiley–Blackwell.
   Google Scholar
• King, R. B. (2012). How you think about your intelligence influences how adjusted you are: Implicit theories and adjustment outcomes. Personality and Individual Differences, 53(5), 705–709. https://doi.org/https://doi.org/10.1016/j.paid.2012.05.031
   Google Scholar
• King, R. B. (2017). A fixed Mindset leads to negative affect: The relations between implicit theories of intelligence and subjective well-being. Zeitschrift Fur Psychologie, 225(2), 137–145. https://doi.org/https://doi.org/10.1027/2151-2604/a000290
   Web of Science ®Google Scholar
• King, R. B., & McInerney, D. M. (2014). Culture’s consequences on student motivation: Capturing cross-cultural universality and variability through personal investment theory. Educational Psychologist, 49(3), 175–198. https://doi.org/https://doi.org/10.1080/00461520.2014.926813
   Web of Science ®Google Scholar
• King, R. B., & McInerney, D. M. (2019). Family-support goals drive engagement and achievement in a collectivist context: Integrating etic and emic approaches in goal research. Contemporary Educational Psychology, 58, 338–353. https://doi.org/https://doi.org/10.1016/j.cedpsych.2019.04.003
   Google Scholar
• King, R. B., McInerney, D. M., & Pitliya, R. J. (2018). Envisioning a culturally imaginative educational psychology. Educational Psychology Review, 30(3), 1031–1065. https://doi.org/https://doi.org/10.1007/s10648-018-9440-z
   Web of Science ®Google Scholar
• King, R. B., & Trinidad, J. E. (2020). Growth mindset predicts achievement only among rich students: examining the interplay between mindset and socioeconomic status. Social Psychology of Education, 24(3), 635–652. https://doi.org/https://doi.org/10.1007/s11218-021-09616-z
   Web of Science ®Google Scholar
• Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). Guilford Press.
   Google Scholar
• Laukaityte, I., & Wiberg, M. (2017). Using plausible values in secondary analysis in large-scale assessments. Communications in Statistics - Theory and Methods, 46(22), 11341–11357. https://doi.org/https://doi.org/10.1080/03610926.2016.1267764
   Web of Science ®Google Scholar
• Lee, J. (2009). Universals and specifics of math self-concept, math self-efficacy, and math anxiety across 41 PISA 2003 participating countries. Learning and Individual Differences, 19(3), 355–365. https://doi.org/https://doi.org/10.1016/j.lindif.2008.10.009
   Web of Science ®Google Scholar
• Lee, J., & Chen, M. (2019). Cross-country predictive validities of non-cognitive variables for mathematics achievement: evidence based on TIMSS 2015. EURASIA Journal of Mathematics, Science and Technology Education, 15(8), em1725. https://doi.org/https://doi.org/10.29333/ejmste/106230
   Google Scholar
• Li, H. (2016). How is formative assessment related to students’ reading achievement? Findings from PISA 2009. Assessment in Education: Principles, Policy & Practice, 23(4), 473–494. https://doi.org/https://doi.org/10.1080/0969594x.2016.1139543
   Web of Science ®Google Scholar
• Li, J. (2002). A cultural model of learning: Chinese “Heart and mind for wanting to learn.” Journal of Cross-Cultural Psychology, 33(3), 248–269. https://doi.org/https://doi.org/10.1177/0022022102033003003
   Google Scholar
• Li, J. (2003). U.S and Chinese cultural beliefs about learning. Journal of Educational Psychology, 95(2), 258–267. https://doi.org/https://doi.org/10.1037/0022-0663.95.2.258
   Web of Science ®Google Scholar
• Li, J. (2005). Mind or Virtue. Current Directions in Psychological Science, 14(4), 190–194. https://doi.org/https://doi.org/10.1111/j.0963-7214.2005.00362.x
   Web of Science ®Google Scholar
• Li, J., & Yamamoto, Y. (2020). Western and East Asian sociocultural learning models: Evidence from cross‐cultural and immigrant research. Asian Journal of Social Psychology, 23(2), 174–186. https://doi.org/https://doi.org/10.1111/ajsp.12384
   Web of Science ®Google Scholar
• Li, X., Han, M., Cohen, G. L., & Markus, H. R. (2021). Passion matters but not equally everywhere: Predicting achievement from interest, enjoyment, and efficacy in 59 societies. Proceedings of the National Academy of Sciences of the United States of America, 118(11). https://doi.org/https://doi.org/10.1073/PNAS.2016964118
   Web of Science ®Google Scholar
• Liou, P. Y. (2017). Profiles of adolescents’ motivational beliefs in science learning and science achievement in 26 countries: Results from TIMSS 2011 data. International Journal of Educational Research, 81, 83–96. https://doi.org/https://doi.org/10.1016/j.ijer.2016.11.006
   Web of Science ®Google Scholar
• Little, R. J. A., & Rubin, D. B. (2019). Statistical analysis with missing data (Vol. 793). John Wiley & Sons.
   Google Scholar
• Lou, N. M., & Noels, K. A. (2016). Changing language mindsets: Implications for goal orientations and responses to failure in and outside the second language classroom. Contemporary Educational Psychology, 46, 22–33. https://doi.org/https://doi.org/10.1016/j.cedpsych.2016.03.004
   Web of Science ®Google Scholar
• Lou, N. M., & Noels, K. A. (2017). Measuring language mindsets and modeling their relations with goal orientations and emotional and behavioral responses in failure situations. Modern Language Journal, 101(1), 214–243. https://doi.org/https://doi.org/10.1111/modl.12380
   Web of Science ®Google Scholar
• Lumley, T. (2004). Analysis of complex survey samples. Journal of Statistical Software, 9(8), 1–19. https://doi.org/https://doi.org/10.18637/jss.v009.i08
   Google Scholar
• Mangels, J. A., Butterfiels, B., Lamb, J., Good, C., & Dweck, C. S. (2006). Why do beliefs about intelligence influence learning success? A social cognitive neuroscience model. Social Cognitive and Affective Neuroscience, 1(2), 75–86. https://doi.org/https://doi.org/10.1093/scan/nsl013
   PubMed Web of Science ®Google Scholar
• McCutchen, K. L., Jones, M. H., Carbonneau, K. J., & Mueller, C. E. (2016). Mindset and standardized testing over time. Learning and Individual Differences, 45, 208–213. https://doi.org/https://doi.org/10.1016/j.lindif.2015.11.027
   Web of Science ®Google Scholar
• McMillan, J. H., Venable, J. C., & Varier, D. (2013). Studies of the effect of formative assessment on student achievement: So much more is needed. Practical Assessment, Research & Evaluation, 18, Article 2. https://doi.org/https://doi.org/10.7275/tmwm-7792
   Google Scholar
• Nicol, D. (2020). The power of internal feedback: Exploiting natural comparison processes. Assessment & Evaluation in Higher Education, 46(5), 756-778. https://doi.org/https://doi.org/10.1080/02602938.2020.1823314
   Google Scholar
• Oberski, D. (2014). Lavaan.survey: An R package for complex survey analysis of structural equation models. Journal of Statistical Software, 57(1), 1–27. https://doi.org/https://doi.org/10.18637/jss.v057.i01
   PubMed Web of Science ®Google Scholar
• OECD. (2009a) . Education at a glance 2009: OECD indicators. OECD.
   Google Scholar
• OECD. (2009b). PISA data analysis manual SPSS (2nd ed.). https://doi.org/https://doi.org/10.1787/9789264056275-en
   Google Scholar
• OECD. (2010). PISA 2009 results: Learning to learn student engagement, strategies and practices (Vol. III).
   Google Scholar
• OECD. (2016). How teachers teach and students learn: Successful strategies for school (OECD Education Working Paper No. 130). https://doi.org/https://doi.org/10.1787/19939019
   Google Scholar
• OECD. (2019a). PISA 2018 Assessment and analytical framework. Retrieved July 15, 2020, from https://doi.org/https://doi.org/10.1787/b25efab8-en
   Google Scholar
• OECD. (2019b). PISA 2018 Insights and interpretations. https://www.oecd.org/pisa/PISA%202018%20Insights%20and%20Interpretations%20FINAL%20PDF.pdf
   Google Scholar
• Petscher, Y., Al Otaiba, S., Wanzek, J., Rivas, B., & Jones, F. (2017). The relation between global and specific mindset with reading outcomes for elementary school students. Scientific Studies of Reading, 21(5), 376–391. https://doi.org/https://doi.org/10.1080/10888438.2017.1313846
   Web of Science ®Google Scholar
• R Core Team. (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.r-project.org/
   Google Scholar
• Reif, M., & Peterbauer, J. (2015). svyPVPack.
   Google Scholar
• Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling and more. Version 0.5–12 (BETA). Journal of Statistical Software, 48(2), 1–36. https://doi.org/https://doi.org/10.18637/jss.v048.i02
   Web of Science ®Google Scholar
• Rubin, D. B. (1996). Multiple imputation after 18+ years. Journal of the American Statistical Association, 91(434), 473–489. https://doi.org/https://doi.org/10.1080/01621459.1996.10476908
   Web of Science ®Google Scholar
• Rubin, D. B. (2004). Multiple imputation for nonresponse in surveys (Vol. 81). John Wiley & Sons.
   Google Scholar
• Rutkowski, L., Gonzalez, E., Joncas, M., & von Davier, M. (2010). International large-scale assessment data: Issues in secondary analysis and reporting. Educational Researcher, 39(2), 142–151. https://doi.org/https://doi.org/10.3102/0013189X10363170
   Web of Science ®Google Scholar
• Rutkowski, L., von Davier, M., & Rutkowski, D. (2013). Handbook of international large-scale assessment: Background, technical issues, and methods of data analysis. CRC Press.
   Google Scholar
• Sanchez, C. E., Atkinson, K. M., Koenka, A. C., Moshontz, H., & Cooper, H. (2017). Self-grading and peer-grading for formative and summative assessments in 3rd through 12th grade classrooms: A meta-analysis. Journal of Educational Psychology, 109(8), 1049–1066. https://doi.org/https://doi.org/10.1037/edu0000190
   Web of Science ®Google Scholar
• Schäfer, T., & Schwarz, M. A. (2019). The meaningfulness of effect sizes in psychological research: Differences between sub-disciplines and the impact of potential biases. Frontiers in Psychology, 10(APR), 813. https://doi.org/https://doi.org/10.3389/fpsyg.2019.00813
   PubMedGoogle Scholar
• Schleider, J. L., Abel, M. R., & Weisz, J. R. (2015). Implicit theories and youth mental health problems: A random-effects meta-analysis. Clinical Psychology Review, 35, 1–9. https://doi.org/https://doi.org/10.1016/j.cpr.2014.11.001.
   PubMed Web of Science ®Google Scholar
• Schroder, H. S., Moran, T. P., Donnellan, M. B., & Moser, J. S. (2014). Mindset induction effects on cognitive control: A neurobehavioral investigation. Biological Psychology, 103(1), 27–37. https://doi.org/https://doi.org/10.1016/j.biopsycho.2014.08.004
   PubMedGoogle Scholar
• Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153–189. https://doi.org/https://doi.org/10.3102/0034654307313795
   Web of Science ®Google Scholar
• Sisk, V. F., Burgoyne, A. P., Sun, J., Butler, J. L., & Macnamara, B. N. (2018). To what extent and under which circumstances are growth mind-sets important to academic achievement? Two meta-analyses. Psychological Science, 29(4), 549–571. https://doi.org/https://doi.org/10.1177/0956797617739704
   PubMed Web of Science ®Google Scholar
• Smiley, P. A., Buttitta, K. V., Chung, S. Y., Dubon, V. X., & Chang, L. K. (2016). Mediation models of implicit theories and achievement goals predict planning and withdrawal after failure. Motivation and Emotion, 40(6), 878–894. https://doi.org/https://doi.org/10.1007/s11031-016-9575-5
   Web of Science ®Google Scholar
• Sperling, C., & Shapcott, S. (2012). Fixing students’ fixed mindsets: Paving the way for meaningful assessment. Legal Writing: Journal of Legal Writing Inst., 18, 39–84. Retrieved from http://heinonlinebackup.com/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/jlwriins18§ion=6.
   Google Scholar
• Stapleton, L. M., McNeish, D. M., & Yang, J. S. (2016). Multilevel and single-level models for measured and latent variables when data are clustered. Educational Psychologist, 51(3–4), 317–330. https://doi.org/https://doi.org/10.1080/00461520.2016.1207178
   Web of Science ®Google Scholar
• Sternberg, R. J. (2004). Culture and Intelligence. American Psychologist, 59(5), 325–338. https://doi.org/https://doi.org/10.1037/0003-066X.59.5.325
   PubMed Web of Science ®Google Scholar
• Struyven, K., Dochy, F., & Janssens, S. (2005). Students’ perceptions about evaluation and assessment in Higher Education: A review. Assessment & Evaluation in Higher Education, 30(4), 325–341. https://doi.org/https://doi.org/10.1080/02602930500099102
   Google Scholar
• Sun, K. L. (2015). There’s no limit: Mathematics teaching for a growth mindset. Doctoral dissertation, Stanford University.
   Google Scholar
• Teasdale, A., & Leung, C. (2000). Teacher assessment and psychometric theory: A case of paradigm crossing? Language Testing, 17(2), 163–184. https://doi.org/https://doi.org/10.1177/026553220001700204
   Google Scholar
• Triandis, H. C. (2002). Subjective culture, Unit 2. Online Readings in Psychology and Culture. http://scholarworks.gvsu.edu/orpc/vol2/iss2/6
   Google Scholar
• Van Buuren, S., & Groothuis-Oudshorn, K. (2011). MICE: Multivariate imputation by chained equation. Journal of Statistical Software, 45(3), 1–67. https://doi.org/https://doi.org/10.18637/jss.v045.i03
   Web of Science ®Google Scholar
• Van der Kleij, F. M., Feskens, R. C. W., & Eggen, T. J. H. M. (2015). Effects of feedback in a computer-based learning environment on students’ learning outcomes. Review of Educational Research, 85(4), 475–511. https://doi.org/https://doi.org/10.3102/0034654314564881
   Web of Science ®Google Scholar
• Wicking, P. (2020). Formative assessment of students from a Confucian heritage culture: Insights from Japan. Assessment & Evaluation in Higher Education, 45(2), 180–192. https://doi.org/https://doi.org/10.1080/02602938.2019.1616672
   Web of Science ®Google Scholar
• Wiliam, D., & Leahy, S. (2007). A theoretical foundation for formative assessment. In J. H. McMillan (Ed.), Formative assessment: Theory into practice (pp. 29–42). Teachers College Press.
   Google Scholar
• Wiliam, D., & Thompson, M. (2008). Integrating assessment with instruction: What will it take to make it work? In C. A. Dwyer Ed.) The future of assessment: Shaping teaching and learning (pp. 53–82). Erlbaum.
   Google Scholar
• Wiliam, D. (2010). An integrative summary of the research literature and implications for a new theory of formative assessment. In H. A. Andrade & G. J. Cizek (Eds.), Handbook for formative assessment (pp. 18–40). Routledge.
   Google Scholar
• Yan, W., & Gaier, E. L. (1994). Causal attributions for college success and failure. Journal of Cross-Cultural Psychology, 25(1), 146–158. https://doi.org/https://doi.org/10.1177/0022022194251009
   Web of Science ®Google Scholar
• Yan, Z., & Brown, G. T. L. (2017). A cyclical self-assessment process: Towards a model of how students engage in self-assessment. Assessment & Evaluation in Higher Education, 42(8), 1247-1262. https://doi.org/https://doi.org/10.1080/02602938.2016.1260091
   Google Scholar
• Yan, Z., & Brown, G. T. L. (2021). Assessment for learning in the Hong Kong assessment reform: A case of policy borrowing. Studies in Educational Evaluation, 68, 100985. https://doi.org/https://doi.org/10.1016/j.stueduc.2021.100985
   Google Scholar
• Yan, Z., Chiu, M. M., & Ko, P. Y. (2020). Effects of self-assessment diaries on academic achievement, self-regulation, and motivation. Assessment in Education: Principles, Policy & Practice, 27(5), 562-583. https://doi.org/https://doi.org/10.1080/0969594X.2020.1827221
   Google Scholar
• Yeager, D. S., & Dweck, C. S. (2012). Mindsets that promote resilience: When students believe that personal characteristics can be developed. Educational Psychologist, 47(4), 302–314. https://doi.org/https://doi.org/10.1080/00461520.2012.722805
   Web of Science ®Google Scholar
• Yeager, D. S., & Dweck, C. S. (2020). What can be learned from growth mindset controversies? American Psychologist, 75(9), 1269–1284. https://doi.org/https://doi.org/10.1037/amp0000794
   PubMed Web of Science ®Google Scholar
• Yeager, D. S., Romero, C., Paunesku, D., Hulleman, C. S., Schneider, B., Hinojosa, C., Lee, H. Y., O’Brien, J., Flint, K., Roberts, A., Trott, J., Greene, D., Walton, G. M., & Dweck, C. S. (2016). Using design thinking to improve psychological interventions: The case of the growth mindset during the transition to high school. Journal of Educational Psychology, 108(3), 374–391. https://doi.org/https://doi.org/10.1037/edu0000098
   PubMed Web of Science ®Google Scholar
• Zhao, Y. (2020). PISA peculiarities: Why doesn’t growth mindset work for Chinese students? Retrieved from https://nepc.colorado.edu/blog/pisa-peculiarities
   Google Scholar