فراتحلیل سواد, تفکر و استدلال آماری دانش آموختگان تحصیلات تکمیلی رشته های ترویج, آموزش و توسعه کشاورزی

نوع مقاله: مقاله پژوهشی

نویسنده

هیات علمی دانشگاه آزاد اسلامی

چکیده

توسعه و تغییرات معنی دار در زمینه های آموزش آمار در سراسر دنیا در دهه گذشته دیده شده، و تاکید زیادی بر آموزش تفکر آماری، یادگیری دانشجو محور، استفاده از داده های واقعی زندگی، استفاده از تکنولوژی، و مهارت های ارتباطی شده است. با توجه به رشد کاربردها و آموزش های آمار، به نظر می رسد، انجام تحقیق ترکیبی که عصاره تحقیقات موجود در یک موضوع خاص را به روشی منظم فراهم می کند، باعث بهبود روند آموزشی می شود. هدف این تحقیق، بررسی تاثیر سواد، تفکر و استدلال آماری دانش آموختگان تحصیلات تکمیلی رشته های ترویج، آموزش و توسعه کشاورزی دانشگاه های ایران به عنوان جامعه آماری در کاربست روش های آماری در دو مقطع زمانی متفاوت است. این تحقیق انجام گرفته است. حجم نمونه مرحله اول در سال 1389، 315 نفر از میان فارع التحصیلان کارشناسی ارشد و دکتری دانشگاه های سراسر کشور به صورت طبقه ای متناسب به عنوان حجم نمونه انتخاب شد، و مرحله دوم در سال 1396 تعداد 110 نفر از میان فارغ التحصیلان جدید از همان دانشگاه ها انتخاب شدند. روایی پرسشنامه به عنوان ابزار تحقیق با گویه های ترکیبی استاندارد و محقق یافته، توسط اساتید و پایایی آن توسط آزمون α کرونباخ (82%)، θ ترتیبی(95%) و cr (89%)تائید شد. پژوهش حاضر با هدف ترکیب کمی نتایج در حوزه کاربست روش های آماری با استفاده از روش فراتحلیل و نرم افزار جامع فراتحلیل(CAM2) در دو زمان مختلف جوامع آماری هدف را مورد بررسی قرار داده است. نتیجه فراتحلیل نشان داد که کاربست روش های آماری در دو زمان مورد بررسی، رابطه ای در حد متوسط دارد (با میانگین اندازه اثراز 0.37 به 0.41). همچنین از میان متغیر های مورد بررسی متغیر نگرش آماری با اندازه اثر 0.49 دارای بیشترنی اندازه اثر در ارتباط با متغیر کاربست روش آماری هستند و بیشترین تاثیرگذاری را دارند.

کلیدواژه‌ها


عنوان مقاله [English]

Meta-analysis of the Literacy, Thinking and Statistical Reasoning Graduates of Graduate Studies in Agricultural Extension, Education, and Development

نویسنده [English]

  • sahar dehyori
azad.un
چکیده [English]

The purpose of this study was to investigate the effect of literacy, thinking and statistical reasoning of Graduates of Graduates of Education, Promotion, Education and Agricultural Development Universities of Iran as a statistical society in applying statistical methods at two different time points.
The sample size of the first stage in 2010, 315 graduates of the Master's degree and Ph.D. Universities across the country were selected in a proportional stratified sampling form, and the second phase in 1396, 110 graduates from the same Universities were selected. The validity of the questionnaire as a research tool with standardized and well-established combinations was confirmed by the professors and its reliability was confirmed by Cronbach's α (82%), sequential θ (95%) and cr (89%).
The purpose of this study is to investigate the quantitative results of the application of statistical methods using meta-analysis and comprehensive meta-analysis software (CAM2) in two different time periods of statistical societies.
The result of the meta-analysis showed that the statistical methods applied in the two examined periods had a moderate relationship (with a mean effect size of 0.37 to 0.41). Also, among the variables studied, statistical attitudes with a magnitude of 0.49 have the greatest effect size in relation to the statistical variables and have the most impact.

کلیدواژه‌ها [English]

  • Statistical Literacy
  • Statistical Argument
  • Statistical Thinking
  • Graduate Studies
  • Meta-Analysis
 

  1. Anderson.G,Arsenault.N.(2002). Fundamentals of educational research. Routledge falmer. Taylor & francis group. 2th edition. great Britain ( کتابخانه ملی)
  2. Aquilonius, B. Ch.(2005). How Do College Students Reason About Hypothesis Testing in Introductory Statistics Courses?. Unpublished dissertation. University of California. Santa Barbara.
  3. Bakker,A.& van Eerde, D.(2012).An Introduction to Design-Based Research with an Example From Statistics Education. Approaches to Qualitative Research in Mathematics Education. Part of the series Advances in Mathematics Education pp 429-466
  4. Barbieri, G.A., & Giacché, P. (2006). The worth of data: The tale of an experience for promoting and improving statistical literacy. In A. Rossman & B. Chance (Eds.), Proceedings, 7th International Conference on Teaching Statistics (Salvador, Brazil). [www.stat.auckland.ac.nz/~iase/publications/17/1A1_BARB.pdf]
  5. Begg, A. M., Pfannkuch. M., Camden, M., Hughes, P., Noble, A. and Wild, C. (2004), The School Statistics Curriculum: Statistics and Probability Education Literature Review. Auckland Uniservices Ltd, University of Auckland.
  6. Cohen ,J.(1988), Statistical Power Analysis for the Behavioral Sciences (2nd ed.), New Jersey: Lawrence Erlbaum Associates, ISBN 0-8058-0283-5, retrieved 10 July 2010
  7. Dehyouri.S,MalekMohammadi.I,Hosseini.S.M,Mirdamadi.S.M.(2011).Path Analysis of Direct and Indirect Effect of Statistical literacy on Applying Proper Statistical Test (Case Study of agricultural extension and education graduated students). Journal of American Science;7(1):144-153
  8. DelMas, R , C.(2002).Statistical Literacy, Reasoning, and Learning: A Commentary. Journal of Statistics Education Volume 10, Number 3 (2002)Copyright © 2002 by Robert C. delMas, all rights reserved.
  9. Evans, D. E., & Rothbart, M. K. (2007). Sensory sensitivity and sensory discomfort as orthogonal components of temperament. Submitted for pubished
  10. Fawcett,L.& Newman,K.( 2017).The storm of the century! Promoting student enthusiasm for applied statistics. journal of Minds and MachinesVolume 39, Issue 1. Pages 1–38 Version of Record online: 5 SEP 2016 | DOI: 10.1111/test.12115
  11. Garfield,Joan & Ben-Zvi,Dani.(2009). Helping Students Develop Statistical Reasoning: Implementing a Statistical Reasoning Learning Environment. Journal of teaching statistical. Volume 31, Issue 3
    Autumn2009,Pages 72–77
  12. Gal, I. (2002). Adult’s statistical literacy: meanings, components, responsibilities. International Statistical Review, 70, 1-51
  13. Garson. D. (2008). 'Structural Equation Modeling' from Statnotes: Topics in Multivariate Analysis. North Carolina State University, Retrieved May 25, 2009.http://faculty.chass.ncsu.edu/garson/pa765/statnote.htm.
  14. Garfield, J. (2003), “Assessing Statistical Reasoning,” Statistics Education Research Journal [Online], 2(1), 22-38. http://www.stat.auckland.ac.nz/~iase/serj/SERJ2(1).pdf
  15. Groth, R.E. (2002a). Construction of Thought-Eliciting Statistical Tasks for High School Students. Unpublished manuscript, Illinois State University.
  16. Grüne-Yanoff, T. & Hertwig, R. Minds & Machines (2016). Nudge Versus Boost: How Coherent are Policy and Theory?. journal of Minds and Machines. March 2016, Volume 26, Issue 1, pp 149–183. 26: 149. doi:10.1007/s11023-015-9367-9
  17. Haase. R., D. Waechter, and G. Solomom (1982) "How significant is a significant difference? Average effect size of research in counseling." Journal of Counseling Psychology 29: 58-65.
  18. M.hedges,lhiggins,j,&Rothstein,H.(2009).CAM2. Translated to Persian Delavar and Ganji 2011. Allameh Tabatabaee university press.
  19. Malek Mohammadi,A.(2009). Sequential Statistical Analysis Approach (SSAA) towards Contingency Framework Purification in Bevavioral Research and Practice. Presenred at First International Conference on Educational Research and Practice. University Putra Malaysia
  20.  Makar, K., & Confrey, J. (2004). Modeling fairness in student achievement in mathematics using statistical software by preservice secondary teachers. Paper presented at the ICMI Study 14: Applications and modeling in mathematics education, Dortmund, Germany.
  21. MacGillivray,H and Hewson,P.(2017)Countering default mode in Teaching Statistics. journal of Minds and Machines Volume 39, Issue 1.  Pages 36–38
  22. Meletiou, M. (2000). Student Understanding of Variation. An Untapped Well in Statistical Reasoning. Published dissertation. The University of Texas at Austin
  23. Middleton.J, King.K.(2006). Data protection definition. The university of Edinburgh. records management section. Available at:  http://www.recordsmanagement.ed.ac.uk/InfoStaff/DPstaff/DPDefinitions.htm. 24 November 2006
  24. Odhiambo, J., & Onyango, S. (2008). Statistics education in Kenya: Developments and Challenges. Retrieved 04 28, 2014, from Strathmore University: http://www.strathmore.edu/research/images/docs/pubs/statistics-in-education.pdf
  25. Pfannkuch, M. (2005b). Informal inferential reasoning: A case study. In K. Makar (Ed.), Reasoning About Distribution: A Collection of Research Studies. The Fourth International Forum on Statistical Reasoning, Thinking, and Literacy, 2-7 July, 2005, Auckland, New Zealand [CD-ROM].
  26. Rumsey,D,J.(2002). From the Literature on Teaching and Learning Statistics. Journal of Statistics Education Volume 10, Number 2 (2002),ww2.amstat.org/publications/jse/v10n2/rumsey.html
www.stat.auckland.ac.nz/~iase/publications/dissertations/dissertations.php

  1. Susan T. Fiske,Amy J. C. Cuddy, Glick. P. Jun Xu.(2002).A Model of (Often Mixed) Stereotype Content: Competence and Warmth Respectively Follow From Perceived Status and Competition. Journal of Personality and Social Psychology 2002, Vol. 82, No. 6, 878–902
  2. Sorto, M. A. (2004). Prospective Middle School Teachers’ Knowledge about Data Analysis and its Application to Teaching. Doctoral dissertation, Michigan State University, East Lansing.
  3. Watson, J. M., & Moritz, J. B. (2000). Developing concepts of sampling. Journal for Research in Mathematics Education, 31, 44-70.
  4. Wallman,K.K.(1993). Enhancing Statistical Literacy: Enriching Our Society. Journal of the American Statistical Association . Volume 88, 1993 - Issue 421
  5. Watt, J. H., & van den Berg, S. (1995). Research methods for communication science. Boston: Allyn & Bacon.
  6. Wild C, Pfannkuch M (2002). Statistical Thinking Models. The Sixth International Conference of Teaching Statistics. Cape Town, South Africa 7 – 12 July.
  7. World Bank .(2002). World Bank Development in Practice Enriching Lives.London,UK.