PEN Academic Publishing   |  ISSN: 1554-5210

Original article | International Journal of Progressive Education 2020, Vol. 16(5) 472-493

Analysing Science Questions in terms of Visual Content in Higher Education Entrance Exams in Turkey

Tufan Inaltekin & Volkan Goksu

pp. 472 - 493   |  DOI: https://doi.org/10.29329/ijpe.2020.277.29   |  Manu. Number: MANU-2005-27-0001.R1

Published online: October 09, 2020  |   Number of Views: 8  |  Number of Download: 24


Abstract

The aim of this study is to analyse the science questions in terms of visual content in the higher education entrance exams in Turkey. In this context, 1714 questions in total prepared by the Center for Measurment, Selection and Placement (CMSP) between 1999 and 2019 in the fields of Physics (n=631), Chemistry (n=553) and Biology (n=530) constitute the data source of the study. This study includes case study which is one of the qualitative research patterns. The data of the study are analyzed by descriptive analysis based on the visual content of questions according to the fields of science, their years and their roles in solving questions (partial role and full role). According to the results, the science questions: i) are concentrated on greatly physics in terms of visual content compared to biology and chemistry on the basis of fields; ii) although visual content varies slightly over the years in terms of its type, the formatted drawing image is used quite a lot compared to other types; iii) formatted drawing and measurement diagrams in the field of physics in many years, formatted drawing and graphics in the field of chemistry , and flowchart and graphics in the field of biology have been largely included and iv) the role of visuals in solving the question has been partial in physics in many years, and in chemistry and biology it has been found to have a partial role in some years and in some cases it has a full role. As a result of the study, it is understood that the science questions applied to students at the entrance to university in Turkey do not show a balanced distribution in terms of visual content type on the basis of fields.

Keywords: University Entrance Exams, Science Questions, Visual Representations


How to Cite this Article?

APA 6th edition
Inaltekin, T. & Goksu, V. (2020). Analysing Science Questions in terms of Visual Content in Higher Education Entrance Exams in Turkey . International Journal of Progressive Education, 16(5), 472-493. doi: 10.29329/ijpe.2020.277.29

Harvard
Inaltekin, T. and Goksu, V. (2020). Analysing Science Questions in terms of Visual Content in Higher Education Entrance Exams in Turkey . International Journal of Progressive Education, 16(5), pp. 472-493.

Chicago 16th edition
Inaltekin, Tufan and Volkan Goksu (2020). "Analysing Science Questions in terms of Visual Content in Higher Education Entrance Exams in Turkey ". International Journal of Progressive Education 16 (5):472-493. doi:10.29329/ijpe.2020.277.29.

References
  1. Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16(3), 183-198. [Google Scholar]
  2. Ainsworth, S. (2008). The educational value of multiple-representations when learning complex scientific concepts. In J. K. Gilbert, M. Reiner, & M. Nakhleh (Eds.), Visualization: Theory and practice in science education (pp. 191–208). London: Springer. [Google Scholar]
  3. Anagnostopoulou, K., Hatzinikita, V. & Christidou, V. (2012a). PISA and biology school textbooks: The role of visual material. Procedia–Social and Behavioral Sciences, 46, 1839-1845. [Google Scholar]
  4. Anagnostopoulou, K., Hatzinikita, V. & Christidou, V. (2012b). Exploring visual material in PISA and school-based examination tests. SKHOLE, 17, 47-56. [Google Scholar]
  5. Anderson, K. J. B. (2012). Science education and test-based accountability: Reviewing theirrelationship and exploring implications for future policy. Science Education, 96(1), 104–129.  [Google Scholar]
  6. Ardasheva, Y., Wang, Z., Roo, A. K., Adesope, O. O., & Morrison, J. A. (2018). Representation visuals’ impacts on science interest and reading comprehension of adolescent English learners. Journal of Educational Research, 111(5), 631–643. doi: 10.1080/00220671.2017.1389681 [Google Scholar] [Crossref] 
  7. Altun, E., Sendur, G., & Alpat, S. (2016). Comparison of the main features and the chemistry questions of university entrance examinations in China and Turkey. Kastamonu Education Journal, 24(2), 857-874. [Google Scholar]
  8. Atalmis, E. H., Avgin, S. S., Demir, P., & Yildirim, B. (2016). Examination of science achievement in the 8th grade level in Turkey in terms of national and international exams depending upon various variables. Journal of Education and Practice, 7(10), 152–162. [Google Scholar]
  9. Borji, V. & Sánchez, A. (2019).  An exploratory analysis of the representations of functions in the university entrance exam in Spain and Iran. Eurasia - Journal of Mathematics, Science and Technology Education, 15(8), 1-12. doi:10.29333/ejmste/106258 [Google Scholar] [Crossref] 
  10. Bretschneider, P., Cirilli, S., Jones, T., Lynch, S., & Wilson, S. A.  (2017). Document review as a qualitative research data collection method for teacher research. In P. Pringle (Ed). SAGE Research Methods Cases. Thousand Oaks, CA: Sage Publications. [Google Scholar]
  11. Bulunuz, N., Bulunuz, M., Karagoz, F., & Tavsanli, Ö. F. (2016). Achievement levels of middle school students in the standardized science and technology exam and formative assessment probes: A Comparative study. Journal of Education in Science, Environment and Health, 2(1), 33-50. [Google Scholar]
  12. Carney, R. N. & Levin, J. R. (2002). Pictorial illustrations still improve students’learning from text. Educational Psychology Review, 14, 5-26. [Google Scholar]
  13. Chang, N. (2012). The role of drawing in young children’s construction of science concepts. Early Childhood Education Journal, 40, 187–193.  [Google Scholar]
  14. Cheng, M. M. W., & Gilbert, J. K. (2014). Students’ visualization of metallic bonding and the malleability of metals. International Journal of Science Education, 36(8), 1373-1407.  [Google Scholar]
  15. Coleman, J. M., McTigue, E. M., Smolkin, L. B. (2011). Elementary teachers’ use of graphical representation in science teaching. Journal of Science Teacher Education, 22(7), 613-643. [Google Scholar]
  16. Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches. Thousand Oaks, CA: Sage. [Google Scholar]
  17. Dupuis, J., & Abrams, E. (2017). Student science achievement and the integration of Indigenous knowledge on standardized tests. Cultural Studies of Science Education, 12, 581-604. doi:10.1007/s11422-016-9728-6  [Google Scholar] [Crossref] 
  18. Feniger, Y. & Lefstein, A. (2014). How not to reason with PISA data: An ironic investigation. Journal of Education Policy, 29, 845–855.  [Google Scholar]
  19. Grey, S. & Morris, P. (2018). PISA: Multiple ‘truths’ and mediatised global governance. Comparative Education, 54(2), 109-131. doi:10.1080/03050068.2018.1425243 [Google Scholar] [Crossref] 
  20. Gross, M. M., Wright, M. C., & Anderson, O. S. (2017). Effects of image-based and textbased active learning exercises on student examination performance in a musculoskeletal anatomy course. Anatomical Sciences Education, 10(5), 444-455. doi:10.1002/ase.1684 [Google Scholar] [Crossref] 
  21. Guo, D., Wright, K. L., & McTigue, E. M. (2018). A content analysis of visuals in elementary school textbooks. The Elementary School Journal, 119(2), 244–269. doi: 10.1086/700266 [Google Scholar] [Crossref] 
  22. Guo, D., Zhang, S., McTigue, E., & Wright, L. K. (2017, April). Do you get the picture?: A meta-analysis of the effect of graphics on reading comprehension. Paper presented at the American Educational Research Association conference, San Antonio.  [Google Scholar]
  23. Hamilton, L.S. , & Berends, M. ( 2006, April 8-12). Instructional practices related to standards and assessments (Rand Working Paper No. WR-374-EDU). Paper presented at the 2006 annual meeting of the American Educational Research Association, San Francisco, CA. [Google Scholar]
  24. He, J., Barrera-Pedemonte, F., & Buchholz, J. (2019). Cross-cultural comparability of noncognitive constructs in TIMSS and PISA. Assessment in Education: Principles, Policy & Practice, 26(4), 369-385. doi:10.1080/0969594X.2018.1469467 [Google Scholar] [Crossref] 
  25. Hursh, D. (2001). Neoliberalism and the control of teachers, students, and learning: The rise of standards, standardization, and accountability. Cultural Logic, 4(1), 3–15. [Google Scholar]
  26. Incikabi, L., Pektas, M., & Sule, C. (2016). An analysis of SSIPE mathematics and science items in terms of PISA problem solving framework. Journal of Kirsehir Education Faculty, 17(2), 649-662. [Google Scholar]
  27. Konecny, T., Basl, J., Myslivecek, J., & Simonova, N. (2012). Alternative models of entrance exams and access to higher education: The case of the Czech Republic. Higher Education, 63(2), 219-235. [Google Scholar]
  28. Kuramoto, N., & Koizumi, R. (2018). Current issues in large-scale educational assessment in Japan: Focus on national assessment of academic ability and university entrance examinations. Assessment in education: Principles, policy, and practice, 25(4), 415-433. doi: 10.1080/0969594X.2016.1225667  [Google Scholar] [Crossref] 
  29. Kusayanagi, C. (2013). Constructing and understanding an incident as a social problem: A case study of university entrance exam cheating in Japan. Human Studies, 36(1), 133-148. [Google Scholar]
  30. LaDue, N. D., Libarkin, J. C., & Thomas, S. R. (2015). Visual representations on high school biology, chemistry, earth science, and physics assessments. Journal of Science Education and Technology, 24(6), 818–834. doi: 10.1007/s10956-015-9566-4  [Google Scholar] [Crossref] 
  31. Lee, J., & Stankov, L. (2018). Non-cognitive predictors of academic achievement: Evidence from TIMSS and PISA. Learning and Individual Differences, 65, 50-64. doi: 10.1016/j.lindif.2018.05.009 [Google Scholar] [Crossref] 
  32. Lidar, M., Lundqvist, E., Ryder, J., & Ostman, L. (2020). The transformation of teaching habits in relation to the introduction of grading and national testing in science education in Sweden. Research in Science Education, 50, 151–173. doi: 10.1007/s11165-017-9684-5 [Google Scholar] [Crossref] 
  33. Lindner, M. A., Eitel, A., Strobel, B., & Koller, O. (2017). Identifying processes underlying the multimedia effect in testing: An eye-movement analysis. Learning and Instruction, 47, 91-102. [Google Scholar]
  34. Liu, D. (2017).  An exploration of experiences of low socioeconomic chinese students who achieved high scores on the national college entrance exam (Doctoral Dissertation). University of Northern Colorado, Greeley, CO. [Google Scholar]
  35. Lofgren, R., & Lofgren, H. (2017). Swedish students’ experiences of national testing in science: A narrative approach. Curriculum Inquiry, 47, 390–410. doi: 10.1080/03626784.2017.1368350 [Google Scholar] [Crossref] 
  36. Lohse, G. L., Biolsi, K., Walker, N., & Rueler, H. (1994). A classification of visual representations. Communications of the A.C.M., 37(12), 36-49. [Google Scholar]
  37. MacDonald, K., & Tipton, C. (1996). Using Documents. N.Gilbert (ed.), Researching Social Life. London: Sage. [Google Scholar]
  38. Martin, M. O., Mullis, I. V. S., Foy, P., & Stanco, G. M. (2012). TIMSS 2011 international results in science. Chestnut Hill, MA: TIMSS & PIRLS International Study. Center, Boston College. [Google Scholar]
  39. Mayer, R. E. (2013). Fostering learning with visual displays. In G. Schraw, M. T. McCrudden, & D. Robinson (Eds.), Learning through visual displays (pp. 47–74). Charlotte, NC: Information Age Publishing. [Google Scholar]
  40. McTigue, E.M., & Flowers, A.C. (2010). Illustration inquiry: Visual literacy in science. Science Scope, 33(9), 17–22. [Google Scholar]
  41. McTigue, E. M. & Flowers, A. C. (2011). Science visual literacy: Learners’ perceptions and knowledge of diagrams. The Reading Teacher, 64(8), 578–589. [Google Scholar]
  42. Merriam, S. B. (2002). Qualitative research in practice: examples for discussion and analysis. San Francisco, CA: Jossey-Bass. [Google Scholar]
  43. Ministry of National Education (MNE).  (2019). PISA 2018 Turkey Preliminary Report. Education Analysis and Evaluation Reports Series.  [Google Scholar]
  44. Mohammadi, R., Moradi, N., & Goldasteh, A. (2019). A comparative study of higher education entrance examinations in Iran with some selected countries to optimize entrance examination. Iranian Journal of Comparative Education, 2(4), 518-532. doi: 10.22034/IJCE.2020.105009 [Google Scholar] [Crossref] 
  45. Moline, S. (1995). I see what you mean. York, ME: Stenhouse Publishing. [Google Scholar]
  46. Moon, T. R., Brighton, C. M., Jarvis, J. R., Hall, C. J. (2007). State standardized testing programs: Their effects on teachers and students. Storrs: National Research Center on the Gifted and Talented, University of Connecticut. [Google Scholar]
  47. Newman, M. & Ogle, D. (2019).  Visual literacy: Reading, thinking, and communicating with visuals. London, SE: The Rowman&Littlefield Publishing. [Google Scholar]
  48. National Research Council (NRC). (2012). A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas (FK12). Washington, DC: National Academies Press. [Google Scholar]
  49. National Research Council (NRC). (2013). Developing assessments for the Next Generation Science Standards. Washington, DC: National Academy Press. [Google Scholar]
  50. NGSS Lead States. (2013). Next Generation Science Standards: For states, by states. Washington, DC: The National Academies Press. [Google Scholar]
  51. OECD (2015). PISA 2015 Science Test Questions. Retrieved from https://www.oecd.org/pisa/pisaproducts/PISA2015-Released-FT-Cognitive-Items.pdf  [Google Scholar]
  52. Olcme Secme ve Yerlestirme Merkezi. (OSYM). (2019). 2019-YKS Evaluation Report. Retrieved from https://www.osym.gov.tr/TR,16919/2019-yks-degerlendirme-raporu.html [Google Scholar]
  53. Olcme Secme ve Yerlestirme Merkezi.(OSYM). (2020). About and history. Retrieved from https://www.osym.gov.tr/TR,8789/hakkinda.html [Google Scholar]
  54. Petterson, R. (2002). Information design: An introduction. Philadelphia: John Benjamin. [Google Scholar]
  55. Preston, C. M. (2017). Effect of a diagram on primary students’ understanding about electric circuits. Research in Science Education, 1-24. doi:10.1007/s11165-017-9662-y. [Google Scholar] [Crossref] 
  56. Rau, M. A. (2018). Making connections among multiple visual representations: how do sense-making competencies and perceptual fluency relate to learning of chemistry knowledge? Instructional Science, 46(2), 209 –243. doi:10.1007/s11251-017-9431-3 [Google Scholar] [Crossref] 
  57. Rau, M. A., Michaelis, J. E., Fay, N. (2015). Connection making between multiple graphical representations: A multi-methods approach for domain-specific grounding of an intelligent tutoring system for chemistry. Computers& Education, 82, 460–485.  [Google Scholar]
  58. Roberts, K. L., Norman, R. R., Duke, N. K., Morsink, P., Martin, N. M., & Knight, J. A. (2013). [Google Scholar]
  59. Diagrams, timelines, and tables—Oh, my! Fostering graphical literacy. Reading Teacher, 67, [Google Scholar]
  60. 12–24. [Google Scholar]
  61. Rodrigo, A., Penas, A., Miyao, Y., & Kando, N. (2018). Do systems pass university entrance exams? Information Processing and Management, 54, 564-575. doi: 10.1016/j.ipm.2018.03.002 [Google Scholar] [Crossref] 
  62. Said, Z. (2016). Science education reform in Qatar: Progress and challenges. Eurasia Journal of Mathematics, Science & Technology Education, 12(8), 2253-2265.  [Google Scholar]
  63. Saß, S., Schütte, K., & Lindner, M. A. (2017). Test-takers’ eye movements: Effects of integration aids and types of graphical representations. Computers & Education, 109, 85-97. doi: 10.1016/j.compedu.2017.02.007 [Google Scholar] [Crossref] 
  64. Saß, S., Wittwer, J., Senkbeil, M., & Köller, O. (2012). Pictures in test items: Effects on response time and response correctness. Applied Cognitive Psychology, 26(1), 70–81. [Google Scholar]
  65. Schraw, G., McCrudden, M. T., & Robinson, D. (2013). Visual displays and learning. Theoretical and practical considerations. In G. Schraw, M. T. McCrudden, & D. Robinson (Eds.), Learning through visual displays (pp. 3–19). Charlotte, NC: Information Age Publishing. [Google Scholar]
  66. Schraw, G., & Paik, E. (2013). Toward a typology of instructional visual displays. In G. Schraw, M. T. McCrudden, & D. Robinson (Eds.), Learning through visual displays (pp. 97–129). Charlotte, NC: Information Age Publishing. [Google Scholar]
  67. Setiawan, H., Garnier, K., & & Isnaeni, W. (2019). Rethinking standardized test of science education in Indonesian high school. Journal of Physics: Conference Series. [Google Scholar]
  68. She, H. C., Stacey, K., & Schmidt, W. H. (2018). Science and mathematics literacy: PISA for better school education. International Journal of Science and Mathematics Education, 16(1), 1-5. doi:10.1007/s10763-018-9911-1 [Google Scholar] [Crossref] 
  69. Shi, W. Z., He. X., Wang, Y., Fan, Z. G. & Guo, L. (2016). PISA and TIMSS science score, which clock is more accurate to indicate national science and technology competitiveness?. Eurasia Journal of Mathematics, Science & Technology Education, 12(4), 965-974. doi: 10.12973/eurasia.2016.1239a [Google Scholar] [Crossref] 
  70. Sievertsen H. H., Gino, F., & Piovesan, M. (2016). Cognitive fatigue influences students’ performance on standardized tests. Proceedings of the National Academy of Sciences. 113(10), 2621–2624.  [Google Scholar]
  71. Slough, S. W., McTigue, E. M., Kim, S., & Jennings, S. K. (2010). Science textbooks’ use of graphical representation: A d scriptive analysis of four sixth-grade science texts. Reading Psychology, 31(3), 301–325. [Google Scholar]
  72. Stieff, M., Werner, S., DeSutter, D., Franconeri, S., & Hegarty, M. (2020). Visual chunking as a strategy for spatial thinking in STEM. Cognitive Research: Principles and Implications, 5(18), 1-15.  doi.org/10.1186/s41235-020-00217-6.  [Google Scholar]
  73. Tippett, C. D. (2016). What recent research on diagrams suggests about learning with rather than learning from visual representations in science. International Journal of Science Education, 38(5), 725-746. doi: 10.1080/09500693.2016.1158435 [Google Scholar] [Crossref] 
  74. Turkoguz, S., Balim, A., & Bardakci, V. (2019). A comparison of 2016 Izmir and 2011 Turkey data by TIMSS 2011 science test. Journal of the Human and Social Science Researches, 8(1), 64-90. [Google Scholar]
  75. Vekeri, I. (2002). What is the value of graphical displays? Educational Psychology, 14(3), 261-312. [Google Scholar]
  76. Visone, J. (2010). Science or reading: What is being measured by standardized tests? American Secondary Education, 39(1), 95–112. [Google Scholar]
  77. Wiberg, M. & Rolfsman, E. (2019). The association between science achievement measures in schools and TIMSS science achievements in Sweden. International Journal of Science Education, 41(16), 2218-2232. doi:10.1080/09500693.2019.1666217  [Google Scholar] [Crossref] 
  78. Wilson, R. E., & Bradbury, L. U. (2016). The pedagogical potential of drawing and writing in a primary science multimodal unit. International Journal of Science Education, 38(17), 2621-2641. [Google Scholar]
  79. Wilson, R. & Bradbury, L. (2019). Methods and strategies: Multiple modes in science instruction. Science and Children, 57(1), 77-81. [Google Scholar]
  80. Yeh, Y., & McTigue, E. (2009). The frequency, variation and function of graphical representations within standardised state tests. School Science and Mathematics, 109(8), 435–449. [Google Scholar]
  81. Yemini, M., & Gordon, N. (2017). Media representations of national and international standardized testing in the Israeli education system. Discourse: Studies in the Cultural Politics of Education, 38(2), 262–276. doi: 10.1080/01596306.2015.1105786 [Google Scholar] [Crossref] 
  82. Yin R. K. (2009). Case study research: design and methods. Los Angeles, CA: Sage. [Google Scholar]
  83. Zhang, Y. (2016). National college entrance exam in China: Perspectives on education quality and equity. Singapore: Springer.  [Google Scholar]
  84. Zhang, Y., Chen, D. S., & Wang, W. (2014). The heterogeneous effects of ability grouping on national college entrance exam performance–evidence from a large city in China. International Journal of Educational Development, 39, 80-91. doi: 10.1016/j.ijedudev.2014.08.012 [Google Scholar] [Crossref]