What we know about students’ attitudes towards mathematics and mathematic achievement in Germany and Denmark: A classification based on PISA
Author(s):
Conference:
ECER 2017
Format:
Paper

Session Information

09 SES 03 B, Investigating Affective Outcomes in the STEM-Field at Primary and Secondary School Level

Paper Session

Time:
2017-08-22
17:15-18:45
Room:
W5.18
Chair:
Rolf Vegar Olsen

Contribution

The participation on work life and society, the opportunity of personal fulfillment and the solving of everyday tasks depend – among others – on peoples’ proficiencies in mathematics (European Commission, 2011; OECD, 2014a). These proficiencies assist people in making well-founded judgements and decisions and to behave appropriately in different situations (OECD, 2016). PISA (Programme for International Student Assessment), as well as other international studies, measures these proficiencies by focusing on the students’ achievement. Furthermore, according to the determinations of school achievement, theoretical frameworks and researches consider the development of students’ achievement as an interplay between factors on different levels (e.g. Guthrie, Wigfield & You, 2012; Hooper, Mullis & Martin, 2014; OECD, 2014a). Pointing out one of these theoretical frameworks, Schrader and Helmke (2015) developed a model to explain this interplay between individual and other determinants of school achievement. Within this model two different types of determinants are focused: individual motivational determinants – focusing also on emotional aspects like interest – and individual cognitive determinants which comprise self-concept and self-efficacy. These determinants are assumed to have an impact on the learning activities of students which influence the students’ school achievement at the output level. However, motivational and cognitive determinants are mainly influenced by the students’ background, family and school related factors. On the individual level findings reveal a significant effect of students’ attitudes towards mathematic achievement (Chen, Yeh & Hwang, 2013; Henderlong Corpus & Wormington, 2014; OECD, 2013). Analyses of students’ attitudes influencing the mathematic achievement offer more detailed insights into the determination of mathematic achievement and constitute the starting point for the presented research based on PISA 2012, using the data from two European countries (Germany and Denmark). In the international comparison of PISA 2012, which focus was on mathematic achievement, Germany (514 points) and Denmark (500 points) belong to the top fifteen performing countries worldwide and perform better than the OECD-average (494).

International researches have been conducted to portray the effect which different attitude variables have on mathematic achievement (Eccles, Adler & Meece, 1984; Marsh & Martin, 2011; OECD, 2013; Vecchione, Aessandri & Marsicano, 2014). In conclusion, most of these research findings reveal a considerable effect of emotional and motivational variables such as self-concept and self-efficacy towards mathematic achievement for Germany, Denmark and other countries. Apart from students’ attitudes as a relevant factor concerning mathematic achievement, findings from PISA 2012 outline differences in the mathematic achievement by controlling gender as well as the social and immigrant background: Both in Germany and in Denmark boys outperform girls and students with lower socio-economic status or with an immigration background achieve lower mathematic competences (OECD, 2013). Considering these findings, this paper focuses on a common consideration of these attitude variables by using a classification method. Against this backdrop, we focus the following research questions:

  1. Can different types of students in Germany and Denmark be identified according to their attitudes towards mathematics?
  2. How do different attitudes towards mathematics relate to mathematic achievement?
  3. To what extent do the identified types of students relate to the mathematic achievement controlling for students’ background variables?

Method

Data is gathered from a representative German and Danish subsample of 5001 resp. 7481 tested fifteen-year-old students as part of the large scale assessment PISA 2012. In order to identify different student types according to their answer patterns referring to attitude variables (RQ1), a latent class analysis (LCA) is deployed by using the statistic software Mplus 7. In this LCA, scaled indexes for different attitudes are used which have been outlined as important influencing factors in previous research. These indexes are computed by using variables according to emotional and motivational variables, self-concept as well as self-efficacy and lead to a total number of five considered indexes. Due to the use of scaled indexes, the mean is committed to zero with a standard deviation of one. Therefore, values above or below zero can be described as above-average or below-average peculiarity of the respective attitude variable (OECD, 2014b). In accordance with the scientific standard, the BIC-value is used to determine the number of classes who concur best with the data (Hagenaars & McCutcheon, 2002; Magidson & Vermut, 2004). To analyze the relationship between the identified types of students towards mathematic achievement (RQ2) linear regressions are applied by using mathematic achievement as the dependent variable and by contrasting the identified student types by dummy coding. In order to answer the third research question, the identified types and background variables gender, property of books at home and students’ country of birth are analyzed in different regression models for each type and country (RQ3).

Expected Outcomes

The LCA identifies three classes of student types whose profiles differ between the focused attitude variables concerning emotional and motivational aspects such as self-concept and self-efficacy (RQ1): The first type (T1) can be described as anxious students with low motivation and self-concept. It constitutes the smallest group of students both in Germany and in Denmark (25.3%; 15.2%). This group of students can be characterized by an over-average anxiety towards mathematics and under-average motivation, self-concept and self-efficacy. Students constituting the second type (T2) are average students and build the largest group of students in both countries (Germany: 48.5%; Denmark: 54.0%). The third type (T3) can be described as interested students with high motivation, self-concept and self-efficacy (Germany: 26.2%; Denmark: 30.8%). Regarding the relationship between students’ typology and students’ mathematic achievement (RQ2) students in T3 obtain significantly higher mathematic achievements in both countries. Referring to student background variables (RQ3) it becomes obvious that students in T1 perform significantly lower, while in Denmark the difference accounts for 30 points in contrast to only 21.3 points in Germany. These results are also given for T2. Here the differences account for 10.4 points in Denmark comparing to only 5.3 points in Germany. Moreover T3-students’ achievement is significantly higher for about 30 points in both countries. Over all classes a higher performance can be attested to students who are born in the tested country, own more than 100 books at home and are male. Results point to the significant role of students’ attitudes for mathematic achievement taking different types of students into account. While the achievement of the identified groups differs between countries, results might be a starting point for discussions about what factors on school system, school and classroom level might have to be taken into account to explain these results.

References

Chen, S.-K., Yeh, Y.-C., Hwang, F.-M. & Lin, S. S. (2013). The relationship between academic self-concept and achievement. A multicohort-multioccasion study. Learning and Individual Differences, 23(85), 172–178. Eccles, J.S., 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. European Commission (2011). Mathematic Education in Europe: Common Challenges and National Policies. Brussels: Education, Audiovisual and Culture Executive Agency. Guthrie, J.T., Wigfield, A. & You, W. (2012). Instructional contexts for engagement and achievement in reading. In S.L. Christenson, A.L. Reschly & C. Wylie (Ed.), Handbook of Student Engagement (pp. 601–634). Springer: New York. Hagenaars, J., & McCutcheon, A. (2002). Applied latent class analysis models. New York: Cambridge University Press. Henderlong Corpus, J. & Worminton, S. (2014). Profiles of Intrinsic and Extrinsic Motivations in Elementary School: A Longitudinal Analysis. The Journal of Experimental Education, 82(4), 480–501. Hooper, M., Mullis, V.S. & Martin. M.O. (2014). TIMSS advanced 2014 Context Questionaire Framework. In I.V.S. Mullis & M.O. Martin (Ed.), TIMSS Advanced 2015 Assessment Frameworks (pp. 27–42). Chestnut Hill, MA: Boston College. Magidson, J., & Vermunt, J. (2004). Latent class models. In D. Kaplan (Ed.), Handbook of quantitative methodology for the social sciences (pp. 175–198). Newbury Park, CA: Sage. Marsh, H. W. & Martin, A. J. (2011). Academic self-concept and academic achievement: Relations and causal ordering. British Journal of Educational Psychology, 81(1), 59–77. OECD (2013). PISA 2012 Results: Ready to Learn: Students’ Engagement, Drive and Self-Beliefs (Volume III). Paris: OECD-Publishing. OECD (2014a). PISA 2012 Results: What Students Know and Can Do (Volume I). Paris: OECD-Publishing. OECD (2014b). PISA 2012. Technical Report. Paris: OECD-Publishing. OECD (2016). PISA 2015 Results: Excellence and Equity in Education (Volume I). Paris: OECD-Publishing. Schrader, F.-W. & Helmke, A. (2015). School Achievement: Motivational Determinants and Processes. In J.D. Wright (Ed.), International Encyclopedia of the Social & Behavioral Science (pp. 48–54). Oxford: Elsevier. Vecchione, M., Alessandri, G. & Marsicano, G. (2014). Academic motivation predicts educational attainment: Does gender make a difference? Learning and Individual Differences, 32, 124–131.

Author Information

Sascha Jarsinski (presenting / submitting)
Paderborn University
Paderborn
Paderborn University, Germany
TU Dortmund University
Center for Research on Education and School Development
Dortmund

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