한국과학교육학회지 (Journal of The Korean Association For Science Education)

  • 제36권6호
  • /
  • Pages.835-849
  • /
  • 2016
  • /
  • 1226-5187(pISSN)
  • /
  • 2288-8489(eISSN)
한국과학교육학회 (The Korean Association for Science Education)
권호 표지
DOI QR코드

DOI QR Code

과학중점고등학교 학생들의 이공계 진로동기에 대한 종단분석

A Longitudinal Study of Science Core School Students' STEM Career Motivation

  • 투고 : 2016.09.26
  • 심사 : 2016.11.15
  • 발행 : 2016.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

이 연구의 목적은 과학중점고등학교 학생들의 이공계 진로동기 변화의 양상을 확인하고, 학생들의 이공계 진로동기의 변화양상과 그들의 계열(인문계열, 자연계열, 과학중점계열)간의 관계를 확인하는 것이다. 이를 위해 과학중점고등학교에 다니는 고등학생 256명으로부터 7개의 구인으로 구성된 이공계 진로동기를 5학기 동안 주기적으로 측정하여 종단자료를 얻었으며, 이 자료들을 바탕으로 집단중심추세모형(Group-based trajectory modeling)분석을 수행하였다. 또한 각 구인들의 추세 집단과 계열 사이의 관계를 이해하고자 카이스퀘어 검증을 실시하였다. 분석 결과 우선 이공계 진로 교육 경험과 이공계 진로에 대한 가치 인식 구인은 학생 모두가 유사한 추세 양상을 보이는 것으로 나타났다. 부모의 사회적 지지, 이공계 진로 관련 교과에 대한 자아효능감, 이공계 진로동기 구인의 추세는 '상위집단'과 '하위집단'의 두 추세집단으로 구별됨을 확인하였다. 또한 이공계 진로 자아효능감과 이공계 진로 흥미 구인의 추세는 '다소 감소 집단', '대폭 감소 집단', '증가집단'의 세 추세집단으로 구별되어 나타났다. 각 추세집단의 추세양상을 확인한 결과, 대부분의 이공계 진로동기 구인의 수준이 고등학교 2학년 1학기 말 증가했다가 학년이 올라갈수록 감소하는 양상을 나타내었다. 또한 각 구인들의 추세 집단은 계열과 모두 유의미한 관계가 있음을 확인하였다. 반면 인문계열 학생들의 경우 이공계 진로 동기 변화 양상이 서로 다르게 나타나는 집단으로 구별되는 것으로 나타났다. 이러한 연구 결과를 바탕으로 이 연구에서는 이공계 진로교육 환경이 학생들의 다양한 진로변화 양상을 고려하여 이루어져야 함을 제언하였다.

The purpose of the present study is to analyze the trajectory of science core school students' STEM career motivation and to examine the relationship between the trajectory patterns and students' tracks. Longitudinal STEM career motivation data with seven constructs were collected from 256 students for five semesters and the data were analyzed by using group-based trajectory modelling analysis. In order to examine the relationship between trajectory pattern groups and the tracks, chi-square tests were conducted. Based on our findings, we found that students are likely to have similar trajectory patterns in STEM career education experience and in their perception towards STEM career value. In terms of parents' support, academic self-efficacy and STEM career motivation aspects are divided into two distinctive trajectory groups ('high' and 'low' group), while two other variables, STEM career self-efficacy and STEM career interest, are divided into three trajectory groups ('moderate declining', 'high declining', 'increasing' group). Most of the trajectory groups are shown the pattern that the level of each constructs increase until their second academic year, then after that, the patterns started going down. Moreover, there are significant relationship between track and each trajectory groups. Science track and science-core track students have similar trajectory patterns. In contrast, humanities track students have different trajectory groups in some constructs. Based on these findings, we suggest that STEM career education environment should consider various patterns of students' STEM career development.

키워드

참고문헌

  1. An, H. J., & Chung, M. K. (2015). Longitudinal analysis of the effects of self concept, social support on career maturity mediated by self efficacy using youth panel survey. The Korea Educational Review, 21(3), 279-303.
  2. Boone, W. J., Staver, J. R., & Yale, M. S. (2014). Rasch analysis in the human sciences. New York, NY: Springer.
  3. Cho, H. C. (2015). The relations of personality and vocational interests to career development of the scientifically gifted students. The Journal of the Korean Society for the Gifted and Talented, 14(4), 149-171.
  4. Cho, J. M., & Jung, H. (2013). Population inferences and variance estimation based on PISA sampling designs. Journal of Educational Evaluation, 26(4), 875-896.
  5. Choi, J. A., & Lee, H. (2012). A comparative analysis of the academic achievements, learning attitudes, and career compromising processes of the undergraduate students in the colleges of engineering according to their levels of major-career connection : Focusing on the engineering students in Seoul National University. Journal of Engineering Education Research, 15(2), 20-29. https://doi.org/10.18108/jeer.2012.15.2.20
  6. Erosheva, E. A., Matsueda, R. L., & Telesca, D. (2014). Breaking bad: Two decades of life-course data analysis in criminology, developmental psychology, and beyond. Annual Review of Statistics and Its Application, 1, 301-332. https://doi.org/10.1146/annurev-statistics-022513-115701
  7. Franklin, J. M., Shrank, W. H., Pakes, J., & Sanfe, G. (2013). Group-based trajectory models: A new approach to classifying and predicting long-term medication adherence. Medical Care, 51(9), 789-79 https://doi.org/10.1097/MLR.0b013e3182984c1f
  8. Glynn, S. M., Brickman, P., Armstrong, N., & Taasoobshirazi, G. (2011). Science Motivation Questionnaire II: Validation With Science Majors and Nonscience Majors. Journal of Research in Science Teaching, 48(10), 1159-1176. https://doi.org/10.1002/tea.20442
  9. Ha, M., Shin, S., & Lee, J. K. (2016). Exploring the motivation for science learning of 3rd year high school students who chose different college majors from their track. Journal of the Korean Association for Science Education, 36(2), 317-324. https://doi.org/10.14697/jkase.2016.36.2.0317
  10. Hackett, G., & Betz, N. E. (1981). A self-efficacy approach to the career development of women. Journal of Vocational Behavior, 18, 326-339. https://doi.org/10.1016/0001-8791(81)90019-1
  11. Han, K. H.(2004). Reinterpretation of the crisis in science and technology sector and self-reflection of engineers. Korean Journal of Sociology, 38(4), 73-99.
  12. Heo, M. (2010). High-achieving high school students' science activities, self-concept, and choice of a science major. Journal of Gifted/Talented Education, 20(3), 885-899.
  13. Hong, S. M. (2015). An analysis of the deviation from S&T workforce career-path in Korea. Journal of Engineering Education Research, 18(1), 11-19.
  14. Hwang, S. M., & Chung, H. H. (2012). The moderating effects of academic self-efficacy and self-esteem on the relation between parental achievement pressure and high school students' test anxiety. Korean Journal of Youth Studies, 19(5), 1-20.
  15. Hwang, Y. J. (2007). Determinants of career decision and perceived level of career information of general high school students. Korean Journal of Sociology of Education, 17(1), 131-158.
  16. Jang, S. M., & Seo, H. A. (2005). Economic diagnosis of natural science and engineering avoidance phenomenon. The Journal of Economics and Finance of Education, 14(2), 25-52.
  17. Jung, C. Y., Jeong, J., Lee, J., Jeong, D., Lim, H., Lee, S., & Lim, J. (2015). Career education in South Korea: Current status and future direction. The Journal of Career Education Research, 28(3), 155-171.
  18. Jung, Y. H., Shin, S., & Lee, J. K. (2015). The qualitative case study on science core school teachers' experiences of reflective practice. Korean Journal of Teacher Education, 31(2), 1-35. https://doi.org/10.14333/KJTE.2015.31.2.1
  19. Kang, S. H. (2010). Relationships between major satisfaction and career decision efficacy and career attitude maturity of engineering college students. Journal of Fisheries and Marine Sciences Education, 22(2), 151-164.
  20. Kim, E. (2010). Factors influencing the choice of major for high achievement students in high school. Journal of Engineering Education Research, 13(6), 80-86. https://doi.org/10.18108/jeer.2010.13.6.80
  21. Kim, E., Ahn, Y., Jung, W. Y., Kye, Y. H., Kim, H. B., Noh, T., Yoo, J., Choe, S., & Kim, C. J. (2014). Comparison of four factors: Reasons for jobs, science and math preferences, interests in science, and science aspirations for children hoping for careers in science, engineering or medicine. Journal of the Korean Association for Science Education, 34(8), 779-786. https://doi.org/10.14697/jkase.2014.34.8.0779
  22. Kim, M. S., & Seo, Y. S. (2010). A longitudinal study on the influence of self-efficacy, status and contextual variables on adolescents' career indecision. Studies on Korean Youth, 21(2), 67-96.
  23. Kim, S. C., & Moon, H. Y. (2014). An exploratory study of the influence factors for career decision making by science and engineering college students. Korea Business Review, 29(3), 1-17.
  24. Kim, S. K. & Yoo, M. H. (2012). Comparison on the vocational values and the science career orientation between middle school scientifically gifted students and non-gifted students. Journal of the Korean Association for Science Education, 32(7), 1222-1240. https://doi.org/10.14697/jkase.2012.32.7.1222
  25. Kim, Y. H. (2010). A study on the policy guideline on the re-structurization of social position for scientific technologists through the analysis of the avoidance of engineering course. Journal of Human Resource Management Research, 17(2), 183-202.
  26. Krumboltz, J. D. (2009). The happenstance learning theory. Journal of Career Assesment, 17(2), 135-154. https://doi.org/10.1177/1069072708328861
  27. Lee, B. W., Choi, J. H., Son, J. W., Kim, J. H., Park, J. S., Seo, H. A., & Shim, K. C. (2012). A study on the development plan for a science core school through a satisfaction survey. New Physics, 62(8), 826-839. https://doi.org/10.3938/NPSM.62.826
  28. Lee, J. (2006). The relationship among parent's social support, career decision-making self-efficacy, and career identity in the third grade of high school students. The Korean Journal of Educational Psychology, 20(3), 571-586.
  29. Lee, J. A., Park, S., & Kim, Y. (2012). An analysis of educational factors on career choice of science-gifted students to science and technology bound universities. Journal of the Korean Association for Science Education, 32(1), 15-29. https://doi.org/10.14697/jkase.2012.32.1.015
  30. Lee, M. K. (2008). Analysis of college students' narratives about their educational experience - Focusing on the college students' signification of their experience on the college preparation and the college entrance examination - .The Journal of Research in Education, 31, 79-102.
  31. Lee, S. H., & Shin, S. (2012). Concept mapping analysis of happenstance in career choice. Journal of Human Understanding and Counseling, 33(1), 1-20.
  32. Lee, S. Y. (2011). Effects of elementary students' perception and experiences of science and mathematics on their science-related career aspiration. The Journal of Korea Elementary Education, 22(1), 99-117. https://doi.org/10.20972/kjee.22.1.201104.99
  33. Lee, U. J., Sbeglia, G. C., Ha, M., Finch, S. J., & Nehm, R. H. (2015). Clicker score trajectories and concept inventory scores as predictors for early warning systems for large STEM classes. Journal of Science Education and Technology, 24(6), 848-860. https://doi.org/10.1007/s10956-015-9568-2
  34. Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79-122. https://doi.org/10.1006/jvbe.1994.1027
  35. Lim, H. J. (2014). The relationship between elementary students' perception of science learning and their perception of science career. The Journal of Korea Elementary Education, 25(3), 227-238.
  36. Min, H. J. (2012). Mothersʼ employment experience surrounding a first childbirth in South Korea: An Application of group-based trajectory model. Korean Journal of Sociology, 46(2), 61-87.
  37. Min, Y. S., Baek, K. S., & Han, H. C. (2012). A study on designing the effective scheme for organizing and implementing ‘Pathway-focused course' in 2009 revised high school curriculum. Journal of Learner-Centered Curriculum and Instruction, 12(2), 143-164.
  38. Ministry of Education & Korea Foundation for the Advancement of Science and Creativity. (2015). Guide for management science-core school and STEAM. Retrieved from https://www.kofac.re.kr/wp-content/plugins/kboard/execute/download.php?uid=6056&file=file1
  39. Nagin, D. S. & Tremblay, R. E. (2001) Analyzing developmental trajectories of distinct but related behaviors: A group-based method. Psychological Methods, 6(1), 18-34. https://doi.org/10.1037/1082-989X.6.1.18
  40. Nagin, D. S. (2016). Group-based trajectory modeling and criminal career research. Journal of Research in Crime and Delinquency, 53(3), 356-371. https://doi.org/10.1177/0022427815611710
  41. Nagin, D. S., & Odgers, C. L. (2010). Group-based trajectory modeling in clinical research. Annual Review of Clinical Psychology, 6, 109-138. https://doi.org/10.1146/annurev.clinpsy.121208.131413
  42. Neumann, I., Neumann, K., & Nehm, R. (2011). Evaluating instrument quality in science education: Rasch‐based analyses of a nature of science test. International Journal of Science Education, 33(10), 1373-1405. https://doi.org/10.1080/09500693.2010.511297
  43. Nielsen, J. D., Rosenthal, J. S., Sun, Y., Day, D. M., Bevc, I., & Duchesne, T. (2012). Group-based criminal trajectory analysis using cross-validation criteria group-based criminal trajectory analysis using cross-validation criteria, Communications in Statistics - Theory and Methods, 43(20), 4337-4356.
  44. Oh, H. S., Bae, J. H., Lee, S. E., & Jang, H. J. (2010). The comparative study on career decision level of high school students. The Journal of Vocational Education Research, 29(2), 245-265.
  45. Oh, S. H., & Kang, J. T. (2011). An ethnographic study on school life of 3rd graders of an academic high school. The Korean Society of Phenomenological and Hermeneutic Educational Practice, 8(1), 29-55.
  46. Park, M. H. & Hong, B. E. (2014). A study on the type of school-to work transition and its determinants. Social Welfare Policy, 41(4), 21-49. https://doi.org/10.15855/swp.2014.41.4.21
  47. Rauch, D. P., & Hartig, J. (2010). Multiple-choice versus open-ended response formats of reading test items: A two-dimensional IRT analysis. Psychological Test and Assessment Modeling, 52, 354-379.
  48. Ryoo, J. S. C., Yun, J., & Lee, Y. O. (2014). A case study on curriculum management of science core high school. Journal of Learner-Centered Curriculum and Instruction, 14(10), 305-328.
  49. Seon, H. Y., Lee, J. K., & Kim, S. K. (2012). Career problems of college students: A concensual qualitative research study. Asian Journal of Education, 13(2), 1-24.
  50. Shim, K. C., Son, J., Cha, J. H., Park, J., Lee, B., Choi, J., Lee, K., Kim, J., & Seo, H. A. (2016). Study on cognition of graduates from Science core schools about the management of science core schools. New Physics: Sae Mulli, 66(7), 845-852. https://doi.org/10.3938/NPSM.66.845
  51. Shin, S., Ha, M., & Lee, J. K. (2015). The development and validation of instrument for measuring high school students' STEM career motivation. Journal of the Korean Association for Science Education, 36(1), 75-86.
  52. Shin, S., Lee, J. K, Ha, M., Lee, T. K., & Jung, Y. H. (2015). Analyzing the structure of science gifted and general middle school students' values of career: Social network approach. Journal of Gifted/Talented Education, 25(2), 199-220.
  53. Song, B. R., Lee, J. W., Choi, H. S., Heo, J. G., & Lee, K. H. (2014). Adult's lifelong career satisfaction: The meta-analysis study on the social cognitive career variables by life stage and life satisfaction. Korean Journal of Counseling, 15(5), 1791-1810. https://doi.org/10.15703/kjc.15.5.201410.1791
  54. Super, D. E. (1990). A life-span, life-space approach to career development. In D. Brown, L. Brooks, & Associates (Eds.), Career choice and development (pp. 197-261). San Francisco: Jossey-Bass.
  55. Super, D. F. (1963). Career development: Self-concept theory. In D. E. Super, R. Starishevshky, N. Matlin, & J. P. Jordan (Eds.), Self-concepts in vocational development (pp. 17-31). New York: College Entrance Examination Board.
  56. Wright, B.D. & Linacre, J.M. (1994). Reasonable mean-square-fit values. Rasch Measurement Transactions, 8(3), 370.
  57. Woo, J. I., & Lee, J. K. (2014). Development and application of pre-scientist experience program in biotechnology for career education of high school students. Biology Education, 42(3), 304-325. https://doi.org/10.15717/bioedu.2014.42.3.304
  58. Wu, M. (2005). The role of palusible values in large-scale surveys. Studies in Educational Evaluation, 31, 114-128. https://doi.org/10.1016/j.stueduc.2005.05.005
  59. Wu, M. L., Adams, R. J., Wilson, M. R., & Haldane, S. A. (2007). ACER ConQuest version 2.0: Generalized item response modeling software. Camberwell, Australia: ACER Press.
  60. Yoon, J. (2007). The analysis of causal relationship among students' science-related career choice and its factors. Journal of the Korean Association for Science Education, 27(7), 570-582.

피인용 문헌

  1. 언어네트워크분석을 통해 본 과학중점학교 과학수업의 특징 vol.38, pp.4, 2016, https://doi.org/10.14697/jkase.2018.38.4.565
  2. 과학, 기술, 공학, 수학(STEM) 직종에 요구되는 핵심 역량 분석 vol.38, pp.6, 2016, https://doi.org/10.14697/jkase.2018.38.6.781
  3. Content Analysis of TechnologyᆞHome Economics and General Engineering Curriculum for Subject Integrated Engineering Career Education in High School vol.32, pp.4, 2020, https://doi.org/10.13000/jfmse.2020.8.32.4.1007