Majors’ Gender-Based Affective States Toward Learning Physical Chemistry

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Abstract

This study examines the affective states of students who are chemistry majors at the junior and senior levels, in the context of a Physical Chemistry II (PChem II) course. The study relies on students’ self-reflections while they respond to an online survey system. The online survey includes three sections: demographics, Reformed Teaching Observation Protocol (RTOP), and Modified Fennema-Sherman Mathematics Attitudes Scales (mFSMAS). The RTOP instrument is used by the students to describe the teaching in the PChem II class. The mFSMAS was chosen to measure attitudes from the gender differences point of view. Internal consistency analyses indicate that the instruments are reliable. The findings reveal that females do not perceive themselves as being disadvantaged when it comes to learning PChem II topics. The same conclusion is valid for their male counterparts. In addition, RTOP, as rated by students, describes the nature of the PChem II as traditional, lecture-based instruction. A significant correlation coefficient between the composite scores of RTOP and mFSMAS indicates that the use of inquiry-based teaching strategies correlates to positive student affective states toward learning physical chemistry. Accordingly, in the case of the specific PChem II course examined in this study, the dominance of lecturing led to low to moderate positive attitudes toward the course.

Keywords

Physical chemistry education Affective states Fennema-Sherman scales Reformed teaching observation protocol Gender differences 
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Notes

Acknowledgement

The research reported here was supported by the Canakkale Onsekiz Mart University Scientific Research Projects, through grant # 2011/132. In addition, the author needs to express his best appreciation for the support of the Chemistry Department and of professors Dr. İsmet Kaya, Dr. Eyüp Özdemir, Dr. Ayhan Oral, and Dr. Sema Ekici for their openness to collaborate through their PChem courses in the years of 2012, 2013, and 2014.

References

  1. Adelman, H. (1978). The concept of intrinsic motivation: implications for practice and research with the learning disabled. Learning Disability Quarterly, 1(2), 43–54.CrossRefGoogle Scholar
  2. Application of the ARCS model of motivational design. (1987). Application of the ARCS model of motivational design. Hillsdale, NJ: Lawrence Earlbaum Associates.Google Scholar
  3. Bandura, A. (1986). Social foundations of thought and action: a social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  4. Bandura, A., & Wood, R. E. (1989). Effect of perceived controllability and performance standards on self-regulation of complex decision making. Journal of Personality and Social Psychology, 56, 805–814.CrossRefGoogle Scholar
  5. Baxter Magolda, M. B. (1999). The evolution of epistemology: refining contextual knowledge at twentysomething. Journal of College Student Development, 40(4), 333–344.Google Scholar
  6. Belenky, M. F., Clinchy, B. M., Goldberger, N. R., & Tarule, J. M. (1986). Women’s ways of knowing: the development of self, voice, and mind. New York, NY: BasicBooks, Inc.Google Scholar
  7. Bendixen, L. D., Schraw, G., & Dunkle, M. E. (1998). Epistemic beliefs and moral reasoning. The Journal of Psychology, 132(2), 187–200.CrossRefGoogle Scholar
  8. Broadbooks, W., Elmore, P., & Pedersen, K. (1981). A construct validation study of the Fennema-Sherman Mathematics Attitudes Scales. Educational and Psychological Measurement, 41(2), 551–557. doi: 10.1177/001316448104100238.CrossRefGoogle Scholar
  9. Brown, T., & Onsman, A. (2013). Exploratory factor analysis: A five-step guide for novices. Australasian Journal of Paramedicine, 8(3), 1–14. Retrieved from http://ro.ecu.edu.au/jephc/vol8/iss3/1.Google Scholar
  10. Clinchy, B. M. (1995). A connected approach to the teaching of developmental psychology. Teaching Psychology, 22(2), 100–104.CrossRefGoogle Scholar
  11. Fennema, E., & Sherman, J. A. (1976). Fennema-Sherman Mathematics Attitude Scales. Instruments designed to measure the attitudes toward the learning of mathematics by females and males. JSAS: Catalog of Selected Documents in Psychology, 6(Ms. No. 1225), 31.Google Scholar
  12. Fraenkel, J. R., & Wallen, N. E. (2006). How to design and evaluate research in education (6th ed.). Boston: Mc Graw Hill.Google Scholar
  13. Hofer, B. (2001). Personal epistemology research: implications for learning and teaching. Educational Psychology Review, 13(4), 353–383.CrossRefGoogle Scholar
  14. Hofer, B. (2008). Personal epistemology and culture. In M. S. Khine (Ed.), Knowing, knowledge and beliefs: epistemological studies across diverse cultures (pp. 3–22). Dordrecht, The Netherlands: Springer.CrossRefGoogle Scholar
  15. Hofer, B. (2010). Epistemology, metacognition, and self-regulation: musings on an emerging field. Metacognition and Learning, 5, 113–120.CrossRefGoogle Scholar
  16. Jehng, J.-C. J., Johnson, S. D., & Anderson, R. C. (1993). Schooling and students’ epistemological beliefs about learning. Contemporary Educational Psychology, 18(1), 23–35.CrossRefGoogle Scholar
  17. Kahveci, M. (2009). Quantifying high school students’ self-perceptions in learning chemistry. Presented at the National Association for Research in Science Teaching International Conference (NARST). CA, USA: Garden Grove.Google Scholar
  18. Kahveci, M. (2010). Students’ perceptions to use technology for learning: Measurement integrity of the modified Fennema-Sherman Attitudes Scales. The Turkish Online Journal of Educational Technology, 9(1), 185–201. Retrieved from http://www.eric.ed.gov/ERICWebPortal/recordDetail?accno=EJ875782.Google Scholar
  19. Kahveci, M. (2011). Depicting chemistry majors’ self-perceptions in learning chemistry. Presented at the National Association for Research in Science Teaching International Conference (NARST), Orlando, FL, USA.Google Scholar
  20. Kahveci, M., Öztekin, B., Algedik, E. (2006). Matematiği öğrenmede kendini-kavrama. Presented at the Ulusal Fen Bilimleri ve Matematik Eğitimi Kongresi Bildiriler, Ankara, Turkey. p. 238.Google Scholar
  21. Kardash, C., & Howell, K. (2000). Effects of epistemological beliefs and topic-specific beliefs on undergraduates’ cognitive and strategic processing of dual-positional text. Journal of Educational Psychology, 92(3), 524–535.CrossRefGoogle Scholar
  22. Keller, J. M. (2006). Keller’s ARCS model of motivational design. Arcsmodel.com. Retrieved 2011, from http://www.arcsmodel.com/
  23. King, P. M., & Kitchener, K. S. (1994). Developing reflective judgment: Understanding and promoting intellectual growth and critical thinking in adolescents and adults. San Francisco, CA: Jossey-Bass.Google Scholar
  24. Kitchener, K. S., Lynch, C. L., Fischer, K. W., & Wood, P. K. (1993). Developmental range of reflective judgment: the effect of contextual support and practice on developmental stage. Developmental Psychology, 29(5), 893–906.CrossRefGoogle Scholar
  25. Lee, O., & Brophy, J. (1996). Motivational patterns observed in sixth-grade science classrooms. Journal of Research in Science Teaching, 33(3), 303–318.CrossRefGoogle Scholar
  26. Lirrg, C. D. (1993). Effects of same sex versus co-ed physical education on the self-perceptions of middle and high school students. Research Quarterly for Exercise and Sport, 64(3), 234–324.Google Scholar
  27. Melancon, J., Thompson, B., & Becnel, S. (1994). Measurement integrity of scores from the Fennema-Sherman Mathematics Attitudes Scales: The attitudes of public school teachers. Educational and Psychological Measurement, 54(1), 187–192.CrossRefGoogle Scholar
  28. Mulhern, F., & Rae, G. (1998). Development of a shortened form of the Fennema-Sherman Mathematics Attitudes Scales. Educational and Psychological Measurement, 58(2), 295.CrossRefGoogle Scholar
  29. Piburn, M., Sawada, D. (2000, September). Reformed Teaching Observation Protocol (RTOP): Reference manual. Retrieved July 3, 2014, from https://mathed.asu.edu/instruments/rtop/RTOP_Reference_Manual.pdf.Google Scholar
  30. Rocard, M., Csermely, P., Jorde, D., Lenzen, D., Walberg-Henriksson, H., & Hemmo, V. (2007). Science education NOW: a renewed pedagogy for the future of Europe (European Commission.). Luxembourg: European Commission.Google Scholar
  31. Schommer Aikins, M., Duell, O. K., & Hutter, R. (2005). Epistemological beliefs, mathematical problem-solving beliefs, and academic performance of middle school students. The Elementary School Journal, 105(3), 289–304.CrossRefGoogle Scholar
  32. Shirbagi, N. (2008). A confirmatory factor analysis of the Persian translation of the Fennema-Sherman mathematics attitudes scales. Pedagogy Studies (Pedagogika), 92, 46–55.Google Scholar
  33. Stricker, L. J., Rock, D. A., & Burton, N. W. (1993). Sex differences in predictions of college grades from scholastic aptitude test scores. Journal of Educational Psychology, 85(4), 710–718.CrossRefGoogle Scholar
  34. Survey Research Methods in Education. (1988). Survey research methods in education (pp. 303–330). Washington, DC: American Educational Research Association.Google Scholar
  35. Tsaparlis, G., & Finlayson, O. E. (2014). Physical chemistry education: Its multiple facets and aspects. Chemistry Education Research and Practice, 15(3), 257–265. doi: 10.1039/C4RP90006E.CrossRefGoogle Scholar
  36. Wiebe, E., Williams, L., Yang, K., Miller, C. (2003). Computer science attitude survey (No. NCSU CSC TR-2003-1). Raleigh, NC.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Chemistry Education DivisionCanakkale Onsekiz Mart UniversityCanakkaleTurkey

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