Comparing Psychology Undergraduates Performance in Probabilistic Reasoning Under Verbal-Numerical and Graphical-Pictorial Problem Presentation Format: What is the Role of Individual and Contextual Dimensions?
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1 Department of Pedagogy, Psychology, Philosophy, Faculty of Humanistic Studies, University of Cagliari, Italy2 Department of Methodology of the Behavioral Sciences, Faculty of Psychology, University of Barcelona, Spain* Corresponding Author
Abstract
This study aims to investigate about the existence of a graphical facilitation effect on probabilistic reasoning. Measures of undergraduates’ performances on problems presented in both verbal-numerical and graphical-pictorial formats have been related to visuo-spatial and numerical prerequisites, to statistical anxiety, to attitudes towards statistics and to the confidence in response correctness. Psychology undergraduates in Italy and Spain with no statistical expertise (N= 676) completed a protocol under conditions of presence versus absence of time pressure. Hierarchical linear regressions and ANCOVAs with mixed design have been carried out separately for each sample. The best predictor of performance in both formats has been the confidence in solution correctness under the condition of time pressure administration, which seemed to promote the commitment to the task. The findings suggest that the eventual occurrence of a graphical facilitation could be the result of a multifactorial interaction among contextual and individual dimensions, rather than being strictly related to the problem presentation format.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article Type: Research Article
EURASIA J Math Sci Tech Ed, 2015, Volume 11, Issue 4, 735-750
https://doi.org/10.12973/eurasia.2015.1382a
Publication date: 13 Jul 2015
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Open Access References How to cite this articleReferences
- Ackerman, R., & Goldsmith, M. (2011). Metacognitive regulation of text learning: on screen versus on paper. Journal of Experimental Psychology: Applied, 17(1), 18-32. doi: http://dx.doi.org/10.1037/a0022086
- Ackerman, R., & Lauterman, T. (2012). Taking reading comprehension exams on screen or on paper? A metacognitive analysis of learning texts under time pressure. Computers in Human Behavior, 28(5), 1816–1828. doi:10.1016/j.chb.2012.04.023
- Agus, M., Peró-Cebollero, M., Guàrdia-Olmos, J., & Penna, M. P. (2014). Graphical statistics as an option for the improvement of learning in Psychology. In & R. G. K. Makar, B. de Sousa (Ed.), Sustainability in statistics education. Proceedings of the Ninth International Conference on Teaching Statistics (ICOTS9, July, 2014).
- Flagstaff - AZ - USA: Voorburg, The Netherlands: International Statistical Institute. Retrieved from http://iaseweb.org/icots/9/proceedings/pdfs/ICOTS9_C256_A GUS.pdf
- Agus, M., Peró-Cebollero, M., Penna, M., & Guàrdia-Olmos, J. (2015). Towards the development of problems comparing verbal-numerical and graphical formats in statistical reasoning. Quality & Quantity, 49(2), 691–709. http://doi.org/10.1007/s11135-014-0018-7
- Agasisti, T., & Cordero-Ferrera, J. M. (2013). Educational disparities across regions: A multilevel analysis for Italy and Spain. Journal of Policy Modeling, 35(6), 1079–1102. doi:10.1016/j.jpolmod.2013.07.002
- Beilock, S. L., & DeCaro, M. S. (2007). From poor performance to success under stress: Working memory, strategy selection, and mathematical problem solving under pressure. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33(6), 983-998.doi: http://dx.doi.org/10.1037/0278-7393.33.6.983
- Beilock, S. L., Kulp, C. A., Holt, L. E., & Carr, T. H. (2004). More on the fragility of performance: choking under pressure in mathematical problem solving. Journal of Experimental Psychology: General, 133(4), 584–600. doi:http://dx.doi.org/10.1037/0096-3445.133.4.584
- Ben-Zvi, D., & Garfield, J. B. (2004). The challenge of developing statistical literacy, reasoning and thinking. Kluwer Academic Pub.
- Blais, A.-R., Thompson, M. M., & Baranski, J. V. (2005). Individual differences in decision processing and confidence judgments in comparative judgment tasks: The role of cognitive styles. Personality and Individual Differences, 38(7), 1701–1713. doi:10.1016/j.paid.2004.11.004
- Brase, G. L. (2000). “Bugs” built into the system: Learning and Individual Differences, 12(4), 391–409. doi:10.1016/S1041- 6080(02)00048-1
- Brase, G. L. (2009). Pictorial representations in statistical reasoning. Applied Cognitive Psychology, 23(3), 369–381. doi:10.1002/acp.1460
- Brase, G. L., & Hill, W. T. (2015). Good fences make for good neighbors but bad science: a review of what improves Bayesian reasoning and why. Frontiers in Psychology, 6, 340. doi:10.3389/fpsyg.2015.00340
- Britt, D. W., & Chen, Y.-C. (2013). Increasing the capacity of conceptual diagrams to embrace contextual complexity. Quality & Quantity, 47(1), 567–576. doi:10.1007/s11135- 011-9479-0
- Carmona-Màrquez, J. C. (2004). Una revisión de las evidencias de fiabilidad y validez de los cuestionarios de actitudes y ansiedad hacia la estadística. Statistics Education Research Journal, 3(1), 5–28. Retrieved from https://www.stat.auckland.ac.nz/~iase/serj/SERJ3(1).p df#page=8
- Chiesi, F., & Primi, C. (2009a). Un modello sul rendimento nelle materie quantitative degli studenti di psicologia. Giornale Italiano Di Psicologia, 36(1), 161–184.
- Chiesi, F., & Primi, C. (2009b). Assessing statistics attitudes among college students: Psychometric properties of the Italian version of the Survey of Attitudes toward Statistics (SATS). Learning and Individual Differences, 19(2), 309–313. doi:10.1016/j.lindif.2008.10.008
- Chiesi, F., & Primi, C. (2010). Cognitive and non-cognitive factors related to students’ statistics achievement. Statistics Education Research Journal, 9(1), 6–26. Retrieved from http://iase-web.org/documents/SERJ/ SERJ9(1)_Chiesi_Primi.pdf
- Chiesi, F., Primi, C., & Carmona, J. (2011). Measuring Statistics Anxiety. Cross-Country Validity of the Statistical Anxiety Scale (SAS). Journal of Psychoeducational Assessment, 29(6), 559–569. doi:10.1177/0734282911404985
- Colet, A. V, Seva, U. L., & Condon, L. (2008). Development and validation of the Statistical Anxiety Scale. Psicothema, 20(1), 174–180.
- Colom, R., Contreras, M. J., Botella, J., & Santacreu, J. (2002). Vehicles of spatial ability. Personality and Individual Differences, 32(5), 903–912. doi:10.1016/S0191- 8869(01)00095-2
- Cosmides, L., & Tooby, J. (1996). Are humans good intuitive statisticians after all? Rethinking some conclusions from the literature on judgment under uncertainty. Cognition, 58(1), 1–73. doi:10.1016/0010-0277(95)00664-8
- Cumming, G. (2007). Inference by eye: Pictures of confidence intervals and thinking about levels of confidence. Teaching Statistics, 29(3), 89–93. doi:10.1111/j.1467- 9639.2007.00267.x
- delMas, R., Garfield, J., & Ooms, A. (2005). Using assessment items to study students’ difficulty reading and interpreting graphical representations of distributions. In K. Makar (Ed.), Proceedings of the Fourth International Research Forum on Statistical Reasoning, Thinking and Literacy. Auckland, New Zealand: University of Auckland. Retrieved from https://www.causeweb.org/artist/ articles/SRTL4_ARTIST.pdf
- Dewolf, T., Van Dooren, W., Ev Cimen, E., & Verschaffel, L. (2014). The impact of illustrations and warnings on solving mathematical word problems realistically. The Journal of Experimental Education, 82(1), 103–120. doi:10.1080/00220973.2012.745468
- Evans, J. S. B. T., & Stanovich, K. E. (2013). Dual-Process Theories of Higher Cognition: Advancing the Debate. Perspectives on Psychological Science, 8(3), 223–241. http://doi.org/10.1177/1745691612460685
- Evans, J. S. B. T., Handley, S. J., Neilens, H., & Over, D. (2010). The influence of cognitive ability and instructional set on causal conditional inference. The Quarterly Journal of Experimental Psychology, 63(5), 892–909. http://doi.org/10.1080/17470210903111821
- Evans, J. S. B. T., Thompson, V., & Over, D. E. (2015). Uncertain deduction and conditional reasoning. Frontiers in Psychology, 6. http://doi.org/10.3389/fpsyg.2015.00398
- Fastame, M. C., & Penna, M. P. (2012). Does social desirability confound the assessment of self-reported measures of well-being and metacognitive efficiency in young and older adults? Clinical Gerontologist, 35(3), 239– 256. doi:10.1080/07317115.2012.660411
- Fernández-Aguirre, K., Garín-Martín, M. A., & ModroñoHerrán, J. I. (2014). Visual displays: analytical study and applications to graphs and real data. Quality & Quantity, 48(4), 2209–2224. doi:10.1007/s11135-013-9887-4
- Fischer, S. C., Hickey, D. T., Pellegrino, J. W., & Law, D. J. (1994). Strategic processing in dynamic spatial reasoning tasks. Learning and Individual Differences, 6(1), 65–105. doi:10.1016/1041-6080(94)90015-9
- Galli, S., Chiesi, F., & Primi, C. (2011). Measuring mathematical ability needed for “non-mathematical” majors: The construction of a scale applying IRT and differential item functioning across educational contexts. Learning and Individual Differences, 21(4), 392–402. doi:10.1016/j.lindif.2011.04.005
- Garcia-Retamero, R., Galesic, M., & Gigerenzer, G. (2011). Cómo favorecer la comprensión y la comunicación de los riesgos sobre la salud. Psicothema, 23(4), 599–605.
- Garfield, J., & Gal, I. (1999). Assessment and statistics education: Current challenges and directions. International Statistical Review, 67(1), 1–12. doi:10.1111/j.1751- 5823.1999.tb00377.x
- Gigerenzer, G., & Hoffrage, U. (1995). How to improve Bayesian reasoning without instruction: Frequency formats. Psychological Review, 102(4), 684–704. http://doi.org/http://dx.doi.org/10.1037/0033- 295X.102.4.684
- Gigerenzer, G., Hoffrage, U., & Kleinbölting, H. (1991). Probabilistic mental models: A Brunswikian theory of confidence. Psychological Review, 98(4), 506–528. doi: http://dx.doi.org/10.1037/0033-295X.98.4.506
- Girotto, V., & Gonzalez, M. (2001). Solving probabilistic and statistical problems: A matter of information structure and question form. Cognition, 78(3), 247–276. http://doi.org/10.1016/S0010-0277(00)00133-5
- Guàrdia-Olmos, J., Freixa-Blanxart, M., Peró-Cebollero, M., Turbany, J., Cosculluela, A., Barrios, M., & Rifà, X. (2006). Factors related to the academic performance of students in the statistics course in psychology. Quality & Quantity, 40(4), 661–674. doi:10.1007/s11135-005-2072- 7
- Harvey, N. (1997). Confidence in judgment. Trends in Cognitive Sciences, 1(2), 78–82. doi:10.1016/S1364-6613(97)01014- 0
- Hegarty, M., & Kozhevnikov, M. (1999). Types of visual– spatial representations and mathematical problem solving. Journal of Educational Psychology, 91(4), 684–689. doi:10.1037/0022-0663.91.4.684
- Henderson, M. D., Gollwitzer, P. M., & Oettingen, G. (2007). Implementation intentions and disengagement from a failing course of action. Journal of Behavioral Decision Making, 20(1), 81–102. doi:10.1002/bdm.553
- Hoffman, B. (2010). “I think I can, but I’m afraid to try”: The role of self-efficacy beliefs and mathematics anxiety in mathematics problem-solving efficiency. Learning and Individual Differences, 20(3), 276–283. http://doi.org/10.1016/j.lindif.2010.02.001
- Horton, W., & Aykin, N. (2005). Graphics: The not quite universal language. In N. Aykin (Ed.), Usability and internationalization of information technology (pp. 157–188). CRC Press.
- Jackson, S. A., & Kleitman, S. (2013). Individual differences in decision-making and confidence: capturing decision tendencies in a fictitious medical test. Metacognition and Learning, 9(1), 25–49. doi:10.1007/s11409-013-9110-y
- Johnson-Laird, P. N. (1983). Mental Models: Toward a Cognitive Science of Language, Inference and Consciousness. Cambridge: Harvard University Press.
- Johnson-Laird, P. N., Khemlani, S. S., & Goodwin, G. P. (2015). Logic, probability, and human reasoning. Trends in Cognitive Sciences, 19(4), 201–214. http://doi.org/10.1016/j.tics.2015.02.006
- Johnson, E. D., & Tubau, E. (2013). Words, numbers, & numeracy: Diminishing individual differences in Bayesian reasoning. Learning and Individual Differences, 28, 34–40. doi:10.1016/j.lindif.2013.09.004
- Juanchich, M., & Sirota, M. (2015). How to improve people’s interpretation of probabilities of precipitation. Journal of Risk Research, 1–17. http://doi.org/10.1080/13669877.2014.983945
- Kellen, V., Chan, S., & Fang, X. (2006). Individual Differences in Spatial Abilities and the Visualization of Conditional Probabilities. Retrieved from http://www.kellen.net/visualization_wp.htm
- Kellen, V., Chan, S., & Fang, X. (2007). Facilitating Conditional Probability Problems with Visuals. In J. Jacko (Ed.), Human-Computer Interaction. Interaction Platforms and Techniques (Vol. 4551, pp. 63–71). Berlin / Heidelberg: Springer. doi:10.1007/978-3-540-73107-8_8
- Kellen, V., Chan, S., & Fang, X. (2013). Improving user performance in conditional probability problems with computer-generated diagrams. In Human-Computer Interaction. Users and Contexts of Use (pp. 183–192). Berlin / Heidelberg: Springer.
- Kintsch, W., & Greeno, J. G. (1985). Understanding and solving word arithmetic problems. Psychological Review, 92(1), 109. http://doi.org/http://dx.doi.org/10.1037/0033- 295X.92.1.109
- Kirby, J. R., Moore, P. J., & Schofield, N. J. (1988). Verbal and visual learning styles. Contemporary Educational Psychology, 13(2), 169–184. doi:10.1016/0361- 476X(88)90017-3
- Klaczynski, P. A. (2014). Heuristics and biases: interactions among numeracy, ability, and reflectiveness predict normative responding. Frontiers in Psychology, 5, 665. http://doi.org/10.3389/fpsyg.2014.00665
- Knauff, M., & Johnson-Laird, P. N. (2002). Visual imagery can impede reasoning. Memory & Cognition, 30(3), 363– 371. doi:10.3758/BF03194937
- Lalonde, R. N., & Gardner, R. C. (1993). Statistics as a second language? A model for predicting performance in psychology students. Canadian Journal of Behavioural Science / Revue Canadienne Des Sciences Du Comportement, 25(1), 108–125. doi:http://dx.doi.org/10.1037/h0078792
- Lorenzo, M. (2005). The development, implementation, and evaluation of a problem solving heuristic. International Journal of Science and Mathematics Education, 3(1), 33–58. doi:10.1007/s10763-004-8359-7
- Mandel, D. R. (2014a). The psychology of Bayesian reasoning. Frontiers in Psychology, 5, 1144. doi:10.3389/fpsyg.2014.01144
- Mandel, D. R. (2014b). Visual representation of rational belief revision: another look at the Sleeping Beauty problem. Frontiers in Psychology, 5, 1232. doi:10.3389/fpsyg.2014.01232
- Mandel, D. R. (2015). Instruction in Information Structuring Improves Bayesian Judgment in Intelligence Analysts. Frontiers in Psychology, 6, 387 http://doi.org/10.3389/fpsyg.2015.00387
- Maule, A. J., Hockey, G. R. J., & Bdzola, L. (2000). Effects of time-pressure on decision-making under uncertainty: changes in affective state and information processing strategy. Acta Psychologica, 104(3), 283–301. doi:10.1016/S0001-6918(00)00033-0
- Moore, D. A., & Healy, P. J. (2008). The trouble with overconfidence. Psychological Review, 115(2), 502–517. doi: http://dx.doi.org/10.1037/0033-295X.115.2.502
- Moro, R., & Bodanza, G. A. (2010). El debate acerca del efecto facilitador en problemas de probabilidad condicional: ¿Un caso de experimentación crucial? Interdisciplinaria, 27(1), 163–174.
- Moro, R., Bodanza, G. A., & Freidin, E. (2011). Sets or frequencies? How to help people solve conditional probability problems. Journal of Cognitive Psychology, 23(7), 843–857. doi:10.1080/20445911.2011.579072
- Morony, S., Kleitman, S., Lee, Y. P., & Stankov, L. (2013). Predicting achievement: Confidence vs self-efficacy, anxiety, and self-concept in Confucian and European countries. International Journal of Educational Research, 58, 79–96. doi:10.1016/j.ijer.2012.11.002
- Oaksford, M. (2015). Imaging deductive reasoning and the new paradigm. Frontiers in Human Neuroscience, 9(FEB). http://doi.org/10.3389/fnhum.2015.00101
- Paivio, A. (1971). Imagery and verbal processes. New York: Holt, Rinehart & Winston.
- Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95(4), 667–686. doi: http://dx.doi.org/10.1037/0022-0663.95.4.667 Polaki, M. V. (2005). Dealing with compound events. In G. A. Jones (Ed.), Exploring Probability in School (pp. 191– 214). New York: Springer. doi:10.1007/0-387-24530- 8_9
- Rieskamp, J., & Hoffrage, U. (2008). Inferences under time pressure: How opportunity costs affect strategy selection. Acta Psychologica, 127(2), 258–276. doi:10.1016/j.actpsy.2007.05.004
- Rundgren, C.-J., & Tibell, L. A. E. (2010). Critical features of visualizations of transport through the cell membrane – An empirical study of upper secondary and tertiary students’ meaning – Making of a still image and an animation. International Journal of Science and Mathematics Education, 8(2), 223–246. doi:10.1007/s10763-009-9171- 1
- Schau, C., Stevens, J., Dauphinee, T. L., & Vecchio, A. D. (1995). The Development and Validation of the Survey of Attitudes toward Statistics. Educational and Psychological Measurement, 55(5), 868–875. doi:10.1177/0013164495055005022
- Sirota, M., & Juanchich, M. (2011). Role of numeracy and cognitive reflection in Bayesian reasoning with natural frequencies. Studia Psychologica, 53(2), 151–161. Retrieved from http://yadda.icm.edu.pl/cejsh/element/bwmeta1.eleme nt.defbace5-44e3-3d1b-8464-09dde16918e3
- Sirota, M., Juanchich, M., & Hagmayer, Y. (2014). Ecological rationality or nested sets? Individual differences in cognitive processing predict Bayesian reasoning. Psychonomic Bulletin & Review, 21(1), 198–204. http://doi.org/10.3758/s13423-013-0464-6
- Sirota, M., Kostovičová, L., & Juanchich, M. (2014). The effect of iconicity of visual displays on statistical reasoning: evidence in favor of the null hypothesis. Psychonomic Bulletin & Review, 21(4), 961–968. http://doi.org/10.3758/s13423-013-0555-4
- Sirota, M., Kostovičová, L., & Vallée-Tourangeau, F. (2015). Now you Bayes, now you don’t: effects of set-problem and frequency-format mental representations on statistical reasoning. Psychonomic Bulletin & Review, 1–9. http://doi.org/10.3758/s13423-015-0810-y
- Sloman, S. A., Over, D., Slovak, L., & Stibel, J. M. (2003). Frequency illusions and other fallacies. Organizational Behavior and Human Decision Processes, 91(2), 296–309. doi:10.1016/S0749-5978(03)00021-9
- Sorge, C., & Schau, C. (2002). Impact of engineering students’ attitudes on achievement in statistics: A structural model. In Annual meeting of the American Educational Research Association. (Vol. New Orleans). Retrieved from http://evaluationandstatistics.com/AERA2002.pdf
- Stankov, L. (2013). Noncognitive predictors of intelligence and academic achievement: An important role of confidence. Personality and Individual Differences, 55(7), 727–732. doi:10.1016/j.paid.2013.07.006
- Stankov, L., Lee, J., Luo, W., & Hogan, D. J. (2012). Confidence: A better predictor of academic achievement than self-efficacy, self-concept and anxiety? Learning and Individual Differences, 22(6), 747–758. doi:10.1016/j.lindif.2012.05.013
- Stanovich, K. E. (2009). Distinguishing the reflective, algorithmic, and autonomous minds: Is it time for a triprocess theory. In J. St. B. T. Evans & K. Frankish (Ed.), In two minds: Dual processes and beyond (pp. 55–88). Oxford, England: Oxford University Press.
- Stanovich, K. E. (2011). Rationality and the reflective mind. Oxford, England: Oxford University Press.
- Stanovich, K. E., & West, R. F. (2000). Individual differences in reasoning: implications for the rationality debate? The Behavioral and Brain Sciences, 23(5), 645–665; discussion 665–726.
- Sternberg, R. J., & Grigorenko, E. L. (1997). Are cognitive styles still in style? American Psychologist, 52(7), 700–712. doi:10.1037/0003-066X.52.7.700
- Tempelaar, D. T. (2009). The Role of Self-theories of Intelligence and Self-perceived Metacognitive Knowledge, Skills, and Attitudes, in Learning Statistics. In Fifth Global SELF International Biennial Conference. Enabling Human Potential: The Centrality of Self and Identity Constructs. (Vol. 13–15 Ja). Retrieved from http://www.self.ox.ac.uk/documents/Tempelaar.pdf
- Tempelaar, D. T., Van Der Loeff, S. S., & Gijselaers, W. H. (2007). A structural equation model analyzing the relationship of students’ attitudes toward statistics, prior reasoning abilities and course performance. Statistics Education Research Journal, 6(2), 78–102.
- Thompson, V. A., Prowse, J. A., & Pennycook, G. (2011). Intuition, reason, and metacognition. Cognitive Psychology, 63(3), 107–40. http://doi.org/10.1016/j.cogpsych.2011.06.001
- Thurstone, L. L., & Thurstone, T. G. (1981). PMA: abilità mentali primarie: manuale di istruzioni Livello intermedio (11- 17)(Batteria fattoriale delle abilità mentali primarie). Firenze: Organizzazioni Speciali.
- Thurstone, L. L., & Thurstone, T. G. (1987). TEA: tests de aptitudes escolares [niveles 1, 2 y 3]: manual. Madrid: Tea.
- Tremblay, P. F., Gardner, R. C., & Heipel, G. (2000). A model of the relationships among measures of affect, aptitude, and performance in introductory statistics. Canadian Journal of Behavioral Science, 32(1), 40–48. doi: http://dx.doi.org/10.1037/h0087099
- Tubau, E. (2008). Enhancing probabilistic reasoning: The role of causal graphs, statistical format and numerical skills. Learning and Individual Differences, 18(2), 187–196. doi:10.1016/j.lindif.2007.08.006
- Wilkinson, S. C., Reader, W., & Payne, S. J. (2012). Adaptive browsing: Sensitivity to time pressure and task difficulty. International Journal of Human-Computer Studies, 70(1), 14– 25. doi:10.1016/j.ijhcs.2011.08.003
- Yamagishi, K. (2003). Facilitating normative judgments of conditional probability: Frequency or nested sets? Experimental Psychology (formerly Zeitschrift Für Experimentelle Psychologie), 50(2), 97–106. doi:10.1026//1618- 3169.50.2.97
- Zhu, L., & Gigerenzer, G. (2006). Children can solve Bayesian problems: The role of representation in mental computation. Cognition, 98(3), 287–308. doi:10.1016/j.cognition.2004.12.003
- Pisa Reports 2012. (n.d.-a). Retrieved from http://www.oecd.org/pisa/pisaproducts/pisainfocus/pi sa-in-focus-n38-(eng)-final.pdf
- Pisa Reports 2012. (n.d.-b). Retrieved from http://www.oecd.org/pisa/keyfindings/pisa-2012- results.htm
How to cite this article
APA
Agus, M., Peró-Cebollero, M., Penna, M. P., & Guàrdia-Olmos, J. (2015). Comparing Psychology Undergraduates Performance in Probabilistic Reasoning Under Verbal-Numerical and Graphical-Pictorial Problem Presentation Format: What is the Role of Individual and Contextual Dimensions?. Eurasia Journal of Mathematics, Science and Technology Education, 11(4), 735-750. https://doi.org/10.12973/eurasia.2015.1382a
Vancouver
Agus M, Peró-Cebollero M, Penna MP, Guàrdia-Olmos J. Comparing Psychology Undergraduates Performance in Probabilistic Reasoning Under Verbal-Numerical and Graphical-Pictorial Problem Presentation Format: What is the Role of Individual and Contextual Dimensions?. EURASIA J Math Sci Tech Ed. 2015;11(4):735-50. https://doi.org/10.12973/eurasia.2015.1382a
AMA
Agus M, Peró-Cebollero M, Penna MP, Guàrdia-Olmos J. Comparing Psychology Undergraduates Performance in Probabilistic Reasoning Under Verbal-Numerical and Graphical-Pictorial Problem Presentation Format: What is the Role of Individual and Contextual Dimensions?. EURASIA J Math Sci Tech Ed. 2015;11(4), 735-750. https://doi.org/10.12973/eurasia.2015.1382a
Chicago
Agus, Mirian, Maribel Peró-Cebollero, Maria Pietronilla Penna, and Joan Guàrdia-Olmos. "Comparing Psychology Undergraduates Performance in Probabilistic Reasoning Under Verbal-Numerical and Graphical-Pictorial Problem Presentation Format: What is the Role of Individual and Contextual Dimensions?". Eurasia Journal of Mathematics, Science and Technology Education 2015 11 no. 4 (2015): 735-750. https://doi.org/10.12973/eurasia.2015.1382a
Harvard
Agus, M., Peró-Cebollero, M., Penna, M. P., and Guàrdia-Olmos, J. (2015). Comparing Psychology Undergraduates Performance in Probabilistic Reasoning Under Verbal-Numerical and Graphical-Pictorial Problem Presentation Format: What is the Role of Individual and Contextual Dimensions?. Eurasia Journal of Mathematics, Science and Technology Education, 11(4), pp. 735-750. https://doi.org/10.12973/eurasia.2015.1382a
MLA
Agus, Mirian et al. "Comparing Psychology Undergraduates Performance in Probabilistic Reasoning Under Verbal-Numerical and Graphical-Pictorial Problem Presentation Format: What is the Role of Individual and Contextual Dimensions?". Eurasia Journal of Mathematics, Science and Technology Education, vol. 11, no. 4, 2015, pp. 735-750. https://doi.org/10.12973/eurasia.2015.1382a