Top

Gepubliceerd in:

01-04-2025 | Research

An integrated approach to understanding negative math experiences

Auteurs: Anita A. Grabowska, Richard J. Daker, Katie Ho, Ian M. Lyons

Gepubliceerd in: Psychological Research | Uitgave 2/2025

Abstract

Despite numerous studies devoted to mathematics aptitude and achievement, research on how individuals experience math has remained relatively fragmented. Here, using a combined theoretical and data-driven approach, we sought to characterize self-reported math experiences, with a particular focus on negative math experiences. An examination of existing literature led to the identification of eight potential facets of math experiences: emotional, cognitive, physiological, behavioral, testing, classroom/social performance, self-efficacy, and attitudinal. We generated survey items intended to probe experiences within each of these facets and constructed a preliminary questionnaire of 107 candidate items, comprising positively and negatively framed statements about one’s math experiences, with data from a final analytic sample of N = 803 adult participants. Focusing on negative items, four key factors emerged from the data: negative attitudes and avoidance, physiological experiences, testing and educational experiences, and cognitive and emotional experiences. These results point to opportunities for contact between literatures (e.g., between negative attitudes and avoidance behaviors), and toward relatively unexplored topics, such as the importance of negative physiological experiences when facing math. On a practical level, we also provide short subscales with sound internal metrics for each of the four factors identified above. Taken together, this work may prove useful on both a theoretical and a methodological level for those looking to develop a unifying framework of negative math experiences.

vorige artikel Correction: Implications of neural integration of math and spatial experiences for math ability and math anxiety

volgende artikel Money counts: effects of monetary vs. purely numerical values on the mental representation of quantities

Alleen toegankelijk voor geautoriseerde gebruikers

[(I_n – I_n+1) / I_n ≥ .15], where I_n is the eigenvalue for the factor in question and I_n+1 is the eigenvalue for the factor with the next lowest eigenvalue.

Items were recoded according to their rank order in Fig. 2 for the relevant subcomponent: AA01-AA22, PH01-PH21, TE01-TE15, CE01-CE19.

Recall that individual items were scored as 0–4.

Here we adopt the general guidelines that acceptable skew is between -1 and 1, and acceptable kurtosis is between -2 and 2 (Hair et al., 2021).

Aiken, L. R. (1974). Two scales of attitude toward mathematics. Journal for Research in Mathematics Education, 5(2), 67–71. https://doi.org/10.2307/748616CrossRef

Alexander, L., & Martray, C. (1989). The development of an abbreviated version of the Mathematics Anxiety Rating Scale. Measurement and Evaluation in Counseling and Development, 22(3), 143–150.CrossRef

Alpert, R., & Haber, R. N. (1960). Anxiety in academic achievement situations. The Journal of Abnormal and Social Psychology, 61(2), 207. https://doi.org/10.1037/h0045464CrossRefPubMed

Ames, C. (1992). Classrooms: Goals, structures, and student motivation. Journal of Educational Psychology, 84(3), 261–271. https://doi.org/10.1037/0022-0663.84.3.261CrossRef

Ashcraft, M. H. (2002). Math anxiety: Personal, educational, and cognitive consequences. Current Directions in Psychological Science, 11(5), 181–185. https://doi.org/10.1111/1467-8721.00196CrossRef

Ashcraft, M. H., & Krause, J. A. (2007). Working memory, math performance, and math anxiety. Psychonomic Bulletin & Review, 14(2), 243–248. https://doi.org/10.3758/BF03194059CrossRef

Ashcraft, M. H., & Moore, A. M. (2009). Mathematics anxiety and the affective drop in performance. Journal of Psychoeducational Assessment, 27(3), 197–205. https://doi.org/10.1177/0734282908330580CrossRef

Aupperle, R. L., & Martin, M. P. (2010). Neural systems underlying approach and avoidance in anxiety disorders. Dialogues in Clinical Neuroscience, 12(4), 517–531. https://doi.org/10.31887/DCNS.2010.12.4/raupperleCrossRefPubMed

Aupperle, R. L., Melrose, A. J., Francisco, A., Paulus, M. P., & Stein, M. B. (2015). Neural substrates of approach-avoidance conflict decision-making. Human Brain Mapping, 36(2), 449–462. https://doi.org/10.1002/hbm.22639CrossRefPubMed

Avancini, C., & Szűcs, D. (2019). Psychophysiological correlates of mathematics anxiety. Mathematics Anxiety. https://doi.org/10.4324/9780429199981-3CrossRef

Bandura, A. (1989). Regulation of cognitive processes through perceived self-efficacy. Developmental Psychology, 25(5), 729. https://doi.org/10.1037/0012-1649.25.5.729CrossRef

Barroso, C., Ganley, C. M., McGraw, A. L., Geer, E. A., Hart, S. A., & Daucourt, M. C. (2021). A meta-analysis of the relation between math anxiety and math achievement. Psychological Bulletin, 147(2), 134. https://doi.org/10.1037/bul0000307CrossRefPubMed

Baumeister, R. F., Vohs, K. D., Nathan DeWall, C., & Zhang, L. (2007). How emotion shapes behavior: Feedback, anticipation, and reflection, rather than direct causation. Personality and Social Psychology Review, 11(2), 167–203. https://doi.org/10.1177/1088868307301033CrossRefPubMed

Beck, A. T., Epstein, N., Brown, G., & Steer, R. A. (1988). An inventory for measuring clinical anxiety: Psychometric properties. Journal of Consulting and Clinical Psychology, 56(6), 893.CrossRefPubMed

Beilock, S. L. (2008). Math performance in stressful situations. Current Directions in Psychological Science, 17(5), 339–343. https://doi.org/10.1111/j.1467-8721.2008.006CrossRef

Beilock, S. L., & Carr, T. H. (2005). When high-powered people fail: Working memory and “choking under pressure” in math. Psychological Science, 16(2), 101–105. https://doi.org/10.1111/j.0956-7976.2005.00789.xCrossRefPubMed

Benjamin, A. S., & Pashler, H. (2015). The value of standardized testing: A perspective from cognitive psychology. Policy Insights from the Behavioral and Brain Sciences, 2(1), 13–23. https://doi.org/10.1177/2372732215601116CrossRef

Bessant, K. C. (1997). The development and validation of scores on the mathematics information processing scale (MIPS). Educational and Psychological Measurement, 57(5), 841–857.CrossRef

Betz, N. E. (1978). Prevalence, distribution, and correlates of math anxiety in college students. Journal of Counseling Psychology, 25(5), 441–448. https://doi.org/10.1037/0022-0167.25.5.441CrossRef

Betz, N. E., & Hackett, G. (1983). The relationship of mathematics self-efficacy expectations to the selection of science-based college majors. Journal of Vocational Behavior, 23(3), 329–345. https://doi.org/10.1016/0001-8791(83)90046-5CrossRef

Boaler, J. (2014). Research suggests that timed tests cause math anxiety. Teaching Children Mathematics, 20(8), 469–474.CrossRef

Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s Mechanical Turk: A new source of inexpensive, yet high-quality data? Perspectives on Psychological Science, 6(1), 3–5. https://doi.org/10.1177/1745691610393980CrossRefPubMed

Bull, R., & Lee, K. (2014). Executive functioning and mathematics achievement. Child Development Perspectives, 8(1), 36–41. https://doi.org/10.1111/cdep.12059CrossRef

Cameron, J., & Nesse, R. (1986). The nature of emotion. Psychological Review, 93(1), 503–525.

Caviola, S., Carey, E., Mammarella, I. C., & Szucs, D. (2017). Stress, time pressure, strategy selection and math anxiety in mathematics: A review of the literature. Frontiers in Psychology, 8, 1488. https://doi.org/10.3389/fpsyg.2017.01488CrossRefPubMedPubMedCentral

Centerbar, D. B., & Clore, G. L. (2006). Do approach-avoidance actions create attitudes? Psychological Science, 17(1), 22–29. https://doi.org/10.1111/j.1467-9280.2005.01660.xCrossRefPubMed

Chang, H., & Beilock, S. L. (2016). The math anxiety-math performance link and its relation to individual and environmental factors: A review of current behavioral and psychophysiological research. Current Opinion in Behavioral Sciences, 10, 33–38. https://doi.org/10.1016/j.cobeha.2016.04.011CrossRef

Cragg, L., & Gilmore, C. (2014). Skills underlying mathematics: The role of executive function in the development of mathematics proficiency. Trends in Neuroscience and Education, 3(2), 63–68. https://doi.org/10.1016/j.tine.2013.12.001CrossRef

D’Ailly, H., & Bergering, A. J. (1992). Mathematics anxiety and mathematics avoidance behavior: A validation study of two MARS factor-derived scales. Educational and Psychological Measurement, 52(2), 369–377. https://doi.org/10.1177/0013164492052002012CrossRef

Daker, R. J., Gattas, S. U., Sokolowski, H. M., Green, A. E., & Lyons, I. M. (2021). First-year students’ math anxiety predicts STEM avoidance and underperformance throughout university, independently of math ability. npj Science of Learning, 6(1), 1–13. https://doi.org/10.1038/s41539-021-00095-7CrossRef

Daker, R. J., Gattas, S. U., Necka, E. A., Green, A. E., & Lyons, I. M. (2023a). Does anxiety explain why math-anxious people underperform in math? Npj Science of Learning, 8(1), 6. https://doi.org/10.1038/s41539-023-00156-zCrossRefPubMedPubMedCentral

Daker, R. J., Slipenkyj, M. S., Green, A. E., & Lyons, I. M. (2023b). Evidence for avoidance tendencies linked to anxiety about specific types of thinking. Scientific Reports, 13(1), 3294. https://doi.org/10.1038/s41598-023-29834-zCrossRefPubMedPubMedCentral

DeCaro, D. A., Rittle-Johnson, B., & Beilock, S. L. (2010). The impact of performance pressure on mathematical problem solving in children. Developmental Psychology, 46(5), 1097–1108.

Deci, E. L., & Ryan, R. M. (1985). The general causality orientations scale: Self-determination in personality. Journal of Research in Personality, 19(2), 109–134. https://doi.org/10.1016/0092-6566(85)90023-6CrossRef

Dew, K. H., Galassi, J. P., & Galassi, M. D. (1984). Math anxiety: Relation with situational test anxiety, performance, physiological arousal, and math avoidance behavior. Journal of counseling Psychology, 31(4), 580. https://doi.org/10.1037/0022-0167.31.4.580CrossRef

Dowker, A., Sarkar, A., & Looi, C. Y. (2016). Mathematics anxiety: What have we learned in 60 years? Frontiers in Psychology, 7, 508. https://doi.org/10.3389/fpsyg.2016.00508CrossRefPubMedPubMedCentral

Dreger, R. M., & Aiken, L. R., Jr. (1957). The identification of number anxiety in a college population. Journal of educational psychology, 48(6), 344. https://doi.org/10.1037/h0045894CrossRef

Eysenck, M. W., & Calvo, M. G. (1992). Anxiety and performance: The processing efficiency theory. Cognition & emotion, 6(6), 409–434. https://doi.org/10.1080/02699939208409696CrossRef

Eysenck, M. W., & Derakshan, N. (2011). New perspectives in attentional control theory. Personality and Individual Differences, 50(7), 955–960. https://doi.org/10.1016/j.paid.2010.08.019CrossRef

Felson, R. B. (1984). The effect of self-appraisals of ability on academic performance. Journal of personality and social psychology, 47(5), 944. https://doi.org/10.1037/0022-3514.47.5.944CrossRef

Fennema, E., & Sherman, J. A. (1976). Fennema-Sherman mathematics attitudes scales: Instruments designed to measure attitudes toward the learning of mathematics by females and males. Journal for Research in Mathematics Education, 7(5), 324–326. https://doi.org/10.2307/748467CrossRef

Foley, A. E., Herts, J. B., Borgonovi, F., Guerriero, S., Levine, S. C., & Beilock, S. L. (2017). The math anxiety-performance link: A global phenomenon. Current Directions in Psychological Science, 26(1), 52–58. https://doi.org/10.1177/0963721416672463CrossRef

Freedman N. D. (2022). Math Anxiety Online Self-Test: http://www.mathpower.com/anxtest.htm.

Ganley, C. M., & McGraw, A. L. (2016). The development and validation of a revised version of the math anxiety scale for young children. Frontiers in Psychology, 7, 211252.CrossRef

Good, C., Rattan, A., & Dweck, C. S. (2012). Why do women opt out? Sense of belonging and women’s representation in mathematics. Journal of personality and social psychology, 102(4), 700–717. https://doi.org/10.1037/a0026659CrossRefPubMed

Gunderson, E. A., Park, D., Maloney, E. A., Beilock, S. L., & Levine, S. C. (2018). Reciprocal relations among motivational frameworks, math anxiety, and math achievement in early elementary school. Journal of Cognition and Development, 19(1), 21–46. https://doi.org/10.1080/15248372.2017.1421538CrossRef

Hair Jr, J., Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications

Hannula, M. S., Pantziara, M., & Di Martino, P. (2018). Affect and mathematical thinking: Exploring developments, trends, and future directions. In T. Dreyfus, M. Artigue, D. Potari, S. Prediger, & K. Ruthven (Eds.), Developing Research in Mathematics Education Twenty Years of Communication, Cooperation and Collaboration in Europe (pp. 128–141). UK: Routledge.CrossRef

Harari, R. R., Vukovic, R. K., & Bailey, S. P. (2013). Mathematics anxiety in young children: An exploratory study. The Journal of Experimental Education, 81(4), 538–555.CrossRef

Hart, R., Casserly, M., Uzzell, R., Palacios, M., Corcoran, A., & Spurgeon, L. (2015). Student Testing in America's Great City Schools: An Inventory and Preliminary Analysis. Council of the Great City Schools

Hembree, R. (1990). The nature, effects, and relief of mathematics anxiety. Journal for Research in Mathematics Education, 21(1), 33–46. https://doi.org/10.2307/749455CrossRef

Hendy, H. M., Schorschinsky, N., & Wade, B. (2014). Measurement of math beliefs and their associations with math behaviors in college students. Psychological Assessment, 26(4), 1225.CrossRefPubMed

Henschel, S., & Roick, T. (2018). The multidimensional structure of math anxiety revisited. European Journal of Psychological Assessment. https://doi.org/10.1027/1015-5759/a000477CrossRef

Hollenbeck, K. (2002). Determining when test alterations are valid accommodations or modifications for large-scale assessment. Large-scale assessment programs for all students: Validity, technical adequacy, and implementation, 395–425. Lawrence Erlbaum Associates Publishers

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. https://doi.org/10.1037/a0019506CrossRef

Ito, R., & Lee, A. C. (2016). The role of the hippocampus in approach-avoidance conflict decision-making: Evidence from rodent and human studies. Behavioural Brain Research, 313, 345–357. https://doi.org/10.1016/j.bbr.2016.07.039CrossRefPubMed

Jackson, C. D., & Leffingwell, R. J. (1999). The role of instructors in creating math anxiety in students from kindergarten through college. The Mathematics Teacher, 92(7), 583–586.CrossRef

Jansen, M., Lüdtke, O., & Schroeders, U. (2016). Evidence for a positive relation between interest and achievement: Examining between-person and within-person variation in five domains. Contemporary Educational Psychology, 46, 116–127. https://doi.org/10.1016/j.cedpsych.2016.05.004CrossRef

Leary, M. R. (1983). A brief version of the fear of negative evaluation scale. Personality and social psychology bulletin, 9(3), 371–375. https://doi.org/10.1177/0146167283093007CrossRef

Lee, J. (2009). Mathematics self-concept, self-efficacy and anxiety scale [Database record]. Retrieved from PsycTESTS. https://doi.org/10.1037/t31473-000

Lee, J., Cho, S., Kim, B., & Bong, M. (2014). Math interest, anxiety, and self-efficacy: Examining reciprocal relations. Learning and Individual Differences, 35, 99–108. https://doi.org/10.1016/j.lindif.2021.102060CrossRef

Lee, J., & Stankov, L. (2018). Non-cognitive predictors of academic achievement: Evidence from TIMSS and PISA. Learning and individual Differences, 65, 50–64. https://doi.org/10.1016/j.lindif.2018.05.009CrossRef

Lerner, J. S., Li, Y., Valdesolo, P., & Kassam, K. S. (2015). Emotion and decision making. Annual review of psychology, 66, 799–823. https://doi.org/10.1146/annurev-psych-010213-115043CrossRefPubMed

Liebert, R. M., & Morris, L. W. (1967). Cognitive and emotional components of test anxiety: A distinction and some initial data. Psychological Reports, 20(3), 975–978.CrossRefPubMed

Lim, S. Y., & Chapman, E. (2015). Effects of using history as a tool to teach mathematics on students’ attitudes, anxiety, motivation and achievement in grade 11 classrooms. EDucational Studies in Mathematics, 90, 189–212.CrossRef

Lovett, B. J. (2010). Extended time testing accommodations for students with disabilities: Answers to five fundamental questions. Review of Educational Research, 80(4), 611–638. https://doi.org/10.3102/0034654310364063CrossRef

Luttrell, V. R., Callen, B. W., Allen, C. S., Wood, M. D., Deeds, D. G., & Richard, D. C. (2010). The mathematics value inventory for general education students: Development and initial validation. Educational and Psychological Measurement, 70(1), 142–160. https://doi.org/10.1177/0013164409344526CrossRef

Lyons, I. M., & Beilock, S. L. (2012). Mathematics anxiety: Separating the math from the anxiety. Cerebral Cortex, 22(9), 2102–2110. https://doi.org/10.1093/cercor/bhr289CrossRefPubMed

Malinsky, M., Ross, A., Pannells, T., & McJunkin, M. (2006). Math anxiety in pre-service elementary school teachers. Education, 127(2), 274–280.

Meece, J. L., Wigfield, A., & Eccles, J. S. (1990). Predictors of math anxiety and its influence on young adolescents' course enrollment intentions and performance in mathematics. Journal of Educational Psychology, 82(1), 60–70. https://doi.org/10.1037/0022-0663.82.1.60CrossRef

Melnikoff, D. E., Lambert, R., & Bargh, J. A. (2020). Attitudes as prepared reflexes. Journal of Experimental Social Psychology, 88, 103950. https://doi.org/10.1016/j.jesp.2019.103950CrossRef

Mulhern, F., & Rae, G. (1998). Development of a shortened form of the Fennema-Sherman Mathematics Attitudes Scales. Educational and Psychological Measurement, 58(2), 295–306. https://doi.org/10.1177/0013164498058002012CrossRef

National Assessment of Educational Progress [NAEP]. (2019). 2019 National Assessment of Educational Progress (NAEP) Mathematics Report Card. U.S. Department of Education, National Center for Education Statistics.

Necka, E. A., Cacioppo, J. T., Norman, G. J., & Cacioppo, S. (2016). Data quality and generalizability of Amazon Mechanical Turk (MTurk) samples. Behavior Research Methods, 48(4), 1403–1414.

Necka, E. A., Sokolowski, H. M., & Lyons, I. M. (2015). The role of self-math overlap in understanding math anxiety and the relation between math anxiety and performance. Frontiers in Psychology, 6, 1543. https://doi.org/10.3389/fpsyg.2015.01543CrossRefPubMedPubMedCentral

Ochsner, K. N., & Phelps, E. (2007). Emerging perspectives on emotion–cognition interactions. Trends in Cognitive Sciences, 11(8), 317–318. https://doi.org/10.1016/j.tics.2007.06.008CrossRefPubMed

Olivier, E., Archambault, I., De Clercq, M., & Galand, B. (2019). Student self-efficacy, classroom engagement, and academic achievement: Comparing three theoretical frameworks. Journal of Youth and Adolescence, 48, 326–340. https://doi.org/10.1007/s10964-018-0952-0CrossRefPubMed

Paige, L., & Mansell, W. (2013). To attend or not attend? A critical review of the factors impacting on initial appointment attendance from an approach–avoidance perspective. Journal of Mental Health, 22(1), 72–82. https://doi.org/10.3109/09638237.2012.705924CrossRefPubMed

Pajares, F., & Graham, L. (1999). Self-efficacy, motivation constructs, and mathematics performance of entering middle school students. Contemporary Educational Psychology, 24(2), 124–139. https://doi.org/10.1006/ceps.1998.0991CrossRefPubMed

Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010). Running experiments on Amazon Mechanical Turk. Judgment and Decision Making, 5(5), 411–419. https://doi.org/10.1017/S1930297500002205CrossRef

Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18, 315–341. https://doi.org/10.1007/s10648-006-9029-9CrossRef

Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37(2), 91–105. https://doi.org/10.1207/S15326985EP3702_4CrossRef

Perry, A. B. (2004). Decreasing math anxiety in college students. College student journal, 38(2)

Ramirez, G., Shaw, S. T., & Maloney, E. A. (2018). Math anxiety: Past research, promising interventions, and a new interpretation framework. Educational Psychologist, 53(3), 145–164. https://doi.org/10.1080/00461520.2018.1447384CrossRef

Reyes, M. R., Brackett, M. A., Rivers, S. E., White, M., & Salovey, P. (2012). Classroom emotional climate, student engagement, and academic achievement. Journal of Educational Psychology, 104(3), 700–712. https://doi.org/10.1037/a0027268CrossRef

Richardson, F. C., & Suinn, R. M. (1972). The mathematics anxiety rating scale: Psychometric data. Journal of Counseling Psychology, 19(6), 551. https://doi.org/10.1037/h0033456CrossRef

Roos, A. L., Goetz, T., Voracek, M., Krannich, M., Bieg, M., Jarrell, A., & Pekrun, R. (2021). Test anxiety and physiological arousal: A systematic review and meta-analysis. Educational Psychology Review, 33, 579–618. https://doi.org/10.1007/s10648-020-09543-zCrossRef

Scherer, K. R. (2009). The dynamic architecture of emotion: Evidence for the component process model. Cognition and Emotion, 23(7), 1307–1351. https://doi.org/10.1080/02699930902928969CrossRef

Schiefele, U., & Csikszentmihalyi, M. (1995). Motivation and ability as factors in mathematics experience and achievement. Journal for Research in Mathematics Education, 26(2), 163–181.CrossRef

Schunk, D. H. (1991). Self-efficacy and academic motivation. Educational Psychologist, 26(3–4), 207–231.CrossRef

Spielberger, C. C., Gorsuch, R. L., & Lushene, R. (1970). State-Trait Anxiety Inventory. Consulting Psychology Press.

Suárez-Pellicioni, M., Demir-Lira, Ö. E., & Booth, J. R. (2021). Neurocognitive mechanisms explaining the role of math attitudes in predicting children’s improvement in multiplication skill. Cognitive, Affective, & Behavioral Neuroscience, 21, 917–935. https://doi.org/10.3758/s13415-021-00906-9CrossRef

Tapia, M. (1996). The Attitudes toward Mathematics Instrument

Tindal, G., & Fuchs, L. (1999). A summary of research on test changes: An empirical basis for defining accommodations. University of Kentucky, Mid-South Regional Resource Center.

Tobias, S., & Weissbrod, C. (1980). Anxiety and mathematics: An update. Harvard Educational Review, 50(1), 63–70. https://doi.org/10.17763/haer.50.1.xw483257j6035084CrossRef

Valiente, C., Swanson, J., & Eisenberg, N. (2012). Linking students’ emotions and academic achievement: When and why emotions matter. Child Development Perspectives, 6(2), 129–135. https://doi.org/10.1111/j.1750-8606.2011.00192.xCrossRefPubMedPubMedCentral

Wang, Z., Lukowski, S. L., Hart, S. A., Lyons, I. M., Thompson, L. A., Kovas, Y., & Petrill, S. A. (2015). Is math anxiety always bad for math learning? The role of math motivation. Psychological science, 26(12), 1863–1876. https://doi.org/10.1177/0956797615602471CrossRefPubMed

Watt, H. M., Shapka, J. D., Morris, Z. A., Durik, A. M., Keating, D. P., & Eccles, J. S. Math Motivational Beliefs Scales. Developmental Psychology

Werner, L. (2001). Changing student attitudes toward math: Using dance to teach math. center for applied research and educational improvement. Retrieved from the University of Minnesota Digital Conservancy, https://hdl.handle.net/11299/143714

Wigfield, A., & Meece, J. L. (1988). Math anxiety in elementary and secondary school students. Journal of Educational Psychology, 80(2), 210. https://doi.org/10.1037/0022-0663.80CrossRef

Won, H. W., Lee, J. H., & Park, M. K. (2018). Academic procrastination in mathematics learning: A mediating role of mathematics anxiety. Educational Psychology in Practice, 34(4), 377–391.

Yang, B. W., Razo, J., & Persky, A. M. (2019). Using testing as a learning tool. American journal of pharmaceutical education. https://doi.org/10.5688/ajpe7324CrossRefPubMedPubMedCentral

Zan, R., Brown, L., Evans, J., & Hannula, M. S. (2006). Affect in mathematics education: An introduction. Educational studies in mathematics. https://doi.org/10.1007/s10649-006-9028-2CrossRef

Titel: An integrated approach to understanding negative math experiences
Auteurs: Anita A. Grabowska
Richard J. Daker
Katie Ho
Ian M. Lyons
Publicatiedatum: 01-04-2025
Uitgeverij: Springer Berlin Heidelberg
Gepubliceerd in: Psychological Research / Uitgave 2/2025
Print ISSN: 0340-0727
Elektronisch ISSN: 1430-2772
DOI: https://doi.org/10.1007/s00426-025-02096-2

Andere artikelen Uitgave 2/2025

Age-related changes in attentional selection: revealing processes underlying the degradation of task set quality

Research

Cognitive, Neurophysiological, and Behavioral Adaptations in Golf Putting Motor Learning: A Holistic Approach

Research

Practice makes perfect, but to what end? Computerised brain training has limited cognitive benefits in healthy ageing

Open Access
Research

Facial expression adaptation impairs perceived social signal across expressions

Open Access
Research

The effect of response inhibition on the aftereffects of completed prospective memory

Research

Repetition increases the perceived truth of inferred statements: evidence from transitive relations and non-transitive relations

Research

Bohn Stafleu van Loghum

Welkom bij Erasmus MC & Bohn Stafleu van Loghum

Registreer

Login

An integrated approach to understanding negative math experiences

Abstract

Andere artikelen Uitgave 2/2025

Age-related changes in attentional selection: revealing processes underlying the degradation of task set quality

Cognitive, Neurophysiological, and Behavioral Adaptations in Golf Putting Motor Learning: A Holistic Approach

Practice makes perfect, but to what end? Computerised brain training has limited cognitive benefits in healthy ageing

Facial expression adaptation impairs perceived social signal across expressions

The effect of response inhibition on the aftereffects of completed prospective memory

Repetition increases the perceived truth of inferred statements: evidence from transitive relations and non-transitive relations

Bohn Stafleu van Loghum

Welkom bij Erasmus MC & Bohn Stafleu van Loghum

Registreer

Login

Deel dit onderdeel of sectie (kopieer de link)

Abstract

Log in om toegang te krijgen

Age-related changes in attentional selection: revealing processes underlying the degradation of task set quality

Cognitive, Neurophysiological, and Behavioral Adaptations in Golf Putting Motor Learning: A Holistic Approach

Practice makes perfect, but to what end? Computerised brain training has limited cognitive benefits in healthy ageing

Facial expression adaptation impairs perceived social signal across expressions

The effect of response inhibition on the aftereffects of completed prospective memory

Repetition increases the perceived truth of inferred statements: evidence from transitive relations and non-transitive relations