A Study of Indonesian Pre-service English as a Foreign Language Teachers Values on Learning Statistics

  • Khairiani IdrisEmail author
  • Kai-Lin Yang
Part of the ICME-13 Monographs book series (ICME13Mo)


Pre-service English as a Foreign Language (EFL) teachers are service students of a statistics course who will apply statistics knowledge as a tool in their future profession. Their future learning of statistics might be related to the value they have for statistics at the end of the course. By using a phenomenographic approach, we investigated 38 Indonesian pre-service EFL teachers’ values on learning statistics. Three components of values on learning statistics were identified, which can be related to the components from task-value theory: intrinsic, attainment, and utility. The participants could be categorized as having either positive or negative values for each component. In addition, some conflicting characteristics were noticed, which could reflect the characteristics of Indonesian pre-service EFL teachers. Implications for college statistics teaching and future research are discussed.


Indonesian pre-service EFL teachers Introductory statistics Values on learning statistics 



The development of this paper was supported by a grant from the Ministry of Science and Technology (MOST 104-2511-S-003-006-MY3). We are grateful for the ICME-13 TSG-15 team and participants for their valuable comments and suggestions toward improving the paper.


  1. Aliaga, M., Cobb, G., Cuff, C., Garfield, J., Gould, R., Lock, R., Utts, J., & Witmer, J. (2005). Guidelines for assessment and instruction in statistics education: College report (Vol. 30). Alexandria, VA: American Statistical Association. Retrieved from
  2. Ames, C. (1992). Classrooms: Goals, structures, and student motivation. Journal of Educational Psychology, 84(3), 261–271.CrossRefGoogle Scholar
  3. Atkinson, J. W. (1964). An introduction to motivation. Oxford, England: Van Nostrand.Google Scholar
  4. Badan Standar Nasional Pendidikan. (2006). Standar isi untuk satuan pendidikan dasar dan menengah. [Content standard for primary and secondary education]. Jakarta: Badan Standar Nasional Pendidikan.Google Scholar
  5. Bandura, A. (1989). Social cognitive theory. In R. Vasta (Ed.), Annals of child development: Vol. 6. Six theories of child development: Revised formulation and current issues (pp. 1–60). Greenwich: JAI Press.Google Scholar
  6. Barron, K. E., & Hulleman, C. S. (2015). Expectancy-value-cost model of motivation. In J. D. Wright (Ed.), International Encyclopedia of the social & behavioral science (pp. 261–271). Oxford: Elsevier Ltd.Google Scholar
  7. Biggs, J. B. (1985). The role of metalearning in study processes. British Journal of Educational Psychology, 55(3), 185–212. Scholar
  8. Biggs, J., & Tang, C. (2011). Teaching for quality learning at university: What the student does. UK: McGraw-Hill Education.Google Scholar
  9. Bishop, A., FitzSimons, G., Seah, W. T., & Clarkson, P. (1999). Values in mathematics education: Making values teaching explicit in the mathematics classroom. Paper presented at 1999 Australian for Research in Education Annual Conference. Available online at
  10. Cheng, L., Li, M., Kirby, J. R., Qiang, H., & Wade-Woolley, L. (2010). English language immersion and students’ academic achievement in English, Chinese and mathematics. Evaluation & Research in Education, 23(3), 151–169.CrossRefGoogle Scholar
  11. Crandall, J. (1987). ESL through content-area instruction: Mathematics, science, social studies. Language in Education: Theory and Practice. NY: Prentice-Hall, Inc.Google Scholar
  12. Crawford, K., Gordon, S., Nicholas, J., & Prosser, M. (1998). University mathematics students’ conceptions of mathematics. Studies in Higher Education, 23(1), 87–94.CrossRefGoogle Scholar
  13. Cross, R. (2011). Troubling literacy: Monolingual assumptions, multilingual contexts, and language teacher expertise. Teachers and Teaching, 17(4), 467–478. Scholar
  14. Dauphinee, T. L., Schau, C., & Stevens, J. J. (1997). Survey of attitudes toward statistics: Factor structure and factorial invariance for women and men. Structural Equation Modeling: A Multidisciplinary Journal, 4(2), 129–141.CrossRefGoogle Scholar
  15. Davis, N. T., & Blanchard, M. R. (2004). Collaborative teams in a university statistics course: A case study of how differing value structures inhibit change. School Science and Mathematics, 104(6), 279–287.CrossRefGoogle Scholar
  16. Deci, E. L., Vallerand, R. J., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and education: The self-determination perspective. Educational Psychologist, 26(3–4), 325–346.CrossRefGoogle Scholar
  17. Eccles, J., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L., et al. (1985). Self-perceptions, task perceptions, socializing influences, and the decision to enroll in mathematics. In S. F. Chipman, L. R. Brush, & D. M. Wilson (Eds.), Women and mathematics: Balancing the equation (pp. 95–121). NY: Psychology Press.Google Scholar
  18. Eccles, J. S., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., & Meece, J. L. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motives: Psychological and sociological approaches (pp. 75–146). San Francisco, CA: W.H. Freeman.Google Scholar
  19. Eccles, J. S., & Wigfield, A. (1995). In the mind of the actor: The structure of adolescents’ achievement task values and expectancy-related beliefs. Personality and Social Psychology Bulletin, 21(3), 215–225. Scholar
  20. Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109–132. Scholar
  21. Flake, J. K., Barron, K. E., Hulleman, C., McCoach, B. D., & Welsh, M. E. (2015). Measuring cost: The forgotten component of expectancy-value theory. Contemporary Educational Psychology, 41, 232–244. Scholar
  22. Franklin, C. A., & Garfield, J. B. (2006). The GAISE project: Developing statistics education guidelines for grades Pre-K-12 and college courses. In G. F. Burril & P. C. Elliott (Eds.), Thinking and reasoning with data and chance: 2006 NCTM yearbook (pp. 345–376). Reston, VA: NCTM.Google Scholar
  23. Gal, I., Ginsburg, L., & Schau, C. (1997). Monitoring attitudes and beliefs in statistics education. In I. Gal & J. B. Garfield (Eds.), The assessment challenge in statistics education (pp. 37–51). Amsterdam, Netherlands: International Statistical Institute/IOS Press.Google Scholar
  24. Giesbrecht, N. (1996). Strategies for developing and delivering effective introductory-level statistics and methodology courses. ERIC Document Reproduction Service, No. 393–668, Alberta, BC. Retrieved from
  25. Gordon, S. (1995). What counts for students studying statistics? Higher Education Research and Development, 14(2), 167–184. Scholar
  26. Gordon, S. (2004). Understanding students’ experiences of statistics in a service course. Statistics Education Research Journal, 3(1), 40–59.Google Scholar
  27. Hall, L. A. (2005). Teachers and content area reading: Attitudes, beliefs and change. Teaching and Teacher Education, 21(4), 403–414.CrossRefGoogle Scholar
  28. Heaton, R. M., & Mickelson, W. T. (2002). The learning and teaching of statistical investigation in teaching and teacher education. Journal of Mathematics Teacher Education, 5(1), 35–59.CrossRefGoogle Scholar
  29. Hiedemann, B., & Jones, S. M. (2010). Learning statistics at the farmers market? A comparison of academic service learning and case studies in an introductory statistics course. Journal of Statistics Education, 18(3). Available online at
  30. Hofstede, G. H. (2001). Culture’s consequences: Comparing values, behaviors, institutions and organizations across nations. Thousand Oaks, CA: Sage Publications Ltd.Google Scholar
  31. Idris, K., & Yang, K. L. (2017). Development and validation of an instrument to measure Indonesian pre-service teachers’ conceptions of statistics. The Asia-Pacific Education Researcher, 26(5), 281–290. Scholar
  32. Kolawole, E. B. (2008). Effects of competitive and cooperative learning strategies on academic performance of Nigerian students in mathematics. Educational Research and Reviews, 3(1), 33–37.Google Scholar
  33. Lazaraton, A. (2000). Current trends in research methodology and statistics in applied linguistics. TESOL Quarterly, 34(1), 175–181.CrossRefGoogle Scholar
  34. Liem, A. D., Lau, S., & Nie, Y. (2008). The role of self-efficacy, task value, and achievement goals in predicting learning strategies, task disengagement, peer relationship, and achievement outcome. Contemporary Educational Psychology, 33(4), 486–512.CrossRefGoogle Scholar
  35. Liem, G. A. D., Martin, A. J., Nair, E., Bernardo, A. B. I., & Prasetya, P. H. (2009). Cultural factors relevant to secondary school students in Australia, Singapore, the Philippines and Indonesia: Relative differences and congruencies. Australian Journal of Guidance and Counselling, 19(2), 161–178. Scholar
  36. Lucas, U. (2001). Deep and surface approaches to learning within introductory accounting: A phenomenographic study. Accounting Education, 10(2), 161–184.CrossRefGoogle Scholar
  37. Marton, F. (1981). Phenomenography—describing conceptions of the world around us. Instructional Science, 10(2), 177–200.CrossRefGoogle Scholar
  38. Marton, F. (1994). Phenomenography. In T. Husén & N. Postlethwaite (Eds.), International Encyclopedia of education. Oxford, England: Pergamon.Google Scholar
  39. Marton, F., & Pong, W. Y. (2005). On the unit of description in phenomenography. Higher Education Research & Development, 24(4), 335–348.CrossRefGoogle Scholar
  40. Marton, F., & Säljö, R. (1984). Approaches to learning. In F. Marton, D. J. Hounsell, & N. J. Entwistle (Eds.), The experience of learning (pp. 36–55). Edinburgh: Scottish Academic Press.Google Scholar
  41. OECD. (2014). PISA 2012 results in focus: What 15-year-olds know what they can do with what they know. OECD Publishing.Google Scholar
  42. Padilla, A. M., & Gonzalez, R. (2001). Academic performance of immigrant and US-born Mexican heritage students: Effects of schooling in Mexico and bilingual/English language instruction. American Educational Research Journal, 38(3), 727–742.CrossRefGoogle Scholar
  43. Parsons, J. E., Adler, T., & Meece, J. L. (1984). Sex differences in achievement: A test of alternate theories. Journal of Personality and Social Psychology, 46(1), 26–43.CrossRefGoogle Scholar
  44. Petocz, P., & Reid, A. (2005). Something strange and useless: Service students’ conceptions of statistics, learning statistics and using statistics in their future profession. International Journal of Mathematical Education in Science and Technology, 36(7), 789–800. Scholar
  45. Pintrich, P. R., & Schrauben, B. (1992). Students’ motivational beliefs and their cognitive engagement in classroom academic tasks. In D. H. Schunk & J. L. Meece (Eds.), Student Perceptions in the Classroom (pp. 149–183). NY: Lawrence Erlbaum Associates Inc.Google Scholar
  46. Reid, A., & Petocz, P. (2002). Students’ conceptions of statistics: A phenomenographic study. Journal of Statistics Education, 10(2), 1–12.CrossRefGoogle Scholar
  47. Rumsey, D. J. (2002). Statistical literacy as a goal for introductory statistics courses. Journal of Statistics Education, 10(3), 6–13.Google Scholar
  48. Sailah, I. (2014). Buku Panduan Kurikulum Pendidikan Tinggi [Curriculum guide book for higher education]. Jakarta: Direktorat Jenderal Pendidikan Tinggi.Google Scholar
  49. Schau, C., Stevens, J., Dauphinee, T. L., & Vecchio, A. D. (1995). The development and validation of the survey of antitudes toward statistics. Educational and Psychological Measurement, 55(5), 868–875.CrossRefGoogle Scholar
  50. Scheaffer, R. L., & Stasny, E. A. (2004). The state of undergraduate education in statistics: A report from the CBMS 2000. The American Statistician, 58(4), 265–271.CrossRefGoogle Scholar
  51. Smith, G. (1998). Learning statistics by doing statistics. Journal of Statistics Education, 6(3), 1–10.CrossRefGoogle Scholar
  52. Triandis, H. C. (1995). Individualism and collectivism. Boulder, CO: Westview Press.Google Scholar
  53. Tsai, C. (2004). Conceptions of learning science among high school students in Taiwan: A phenomenographic analysis. International Journal of Science Education, 26(14), 1733–1750. Scholar
  54. Utts, J. (2003). What educated citizens should know about statistics and probability. The American Statistician, 57(2), 74–79.CrossRefGoogle Scholar
  55. Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81. Scholar
  56. Wild, C. J., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review, 67(3), 223–248.CrossRefGoogle Scholar
  57. Wise, S. L. (1985). The development and validation of a scale measuring attitudes toward statistics. Educational and Psychological Measurement, 45(2), 401–405.CrossRefGoogle Scholar
  58. Yang, K.-L. (2014). An exploratory study of Taiwanese mathematics teachers’ conceptions of school mathematics, school statistics, and their differences. International Journal of Science and Mathematics Education, 12(6), 1497–1518. Scholar
  59. Yang, N.-D. (1999). The relationship between EFL learners’ beliefs and learning strategy use. System, 27(4), 515–535.CrossRefGoogle Scholar
  60. Yushau, B. (2009). Mathematics and language: Issues among bilingual Arabs in English medium universities. International Journal of Mathematical Education in Science and Technology, 40(7), 915–926.CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.State Institute for Islamic Studies of LhokseumaweAceh ProvinceIndonesia
  2. 2.National Taiwan Normal UniversityTaipeiTaiwan

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