SpringerLink
Log in

Research on the data analysis knowledge assessment of pre-service teachers from China based on cognitive diagnostic assessment

  • Published:
Current Psychology Aims and scope Submit manuscript

Abstract

Data analysis knowledge is an indispensable aspect of teachers’ data literacy, which not only has a profound impact on the cultivation of students’ data analysis ability, but also is related to reasonable decision-makings in education. Based on the analyses of teachers’ data literacy and data analysis ability, this study constructed a cognitive model of teachers’ data analysis knowledge and developed an instrument for measuring teachers’ data analysis knowledge. Meanwhile, from a data-driven approach, G-DINA package in software R was used to select a well-fitted GDM Model from a series of cognitive diagnostic models such as DINA, DINO, RRUM, ACDM, LLM, G-DINA and Mixed Model for parameter assessment of cognitive diagnosis, and the reliability and model fit of the instrument were analyzed afterwards. By analyzing the pre-service dataset of 531 Chinese teachers, it made the conclusion that teachers with the same attributes have differences in attribute mastery and knowledge status, and teachers show a main learning path of A8 → A4 → A1 → A3 → A7 → A5 → A2 → A6 in terms of data analysis knowledge. It provided a systematic case study for the assessment of pre-service teachers’ knowledge of data analysis, and also provides a new perspective in assessing other knowledges and abilities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Canada)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  • Akaike, H. (1973). Information theory as an extension of the maximum likelihood principle–In: Second International Symposium on Information Theory (Eds) BN Petrov, F. Csaki. BNPBF Csaki Budapest: .

  • Akaike, H. (1998). Information theory and an extension of the maximum likelihood principle. In. Selected papers of hirotugu akaike (pp. 199-213): Springer. https://doi.org/10.1007/978-1-4612-1694-0_15.

  • Arican, M., & Kuzu, O. (2020). Diagnosing preservice teachers’ understanding of statistics and probability: Develo** a test for cognitive assessment. International Journal of Science and Mathematics Education, 18(4), 771–790. https://doi.org/10.1007/s10763-019-09985-0.

    Article  Google Scholar 

  • Ball, D. L., Lubienski, S. T., & Mewborn, D. S. (2001). Research on teaching mathematics: The unsolved problem of teachers’ mathematical knowledge. Handbook of research on teaching, 4, 433–456.

    Google Scholar 

  • Batanero, C., & Díaz, C. (2010). Training teachers to teach statistics: What can we learn from research? Statistique et enseignement, 1(1), 5–20.

    Google Scholar 

  • Batanero, C., & Díaz, C. (2012). Training school teachers to teach probability: Reflections and challenges. Chilean Journal of Statistics, 3(1), 3–13.

    Google Scholar 

  • Batanero, C., Godino, J. D., & Roa, R. (2004). Training teachers to teach probability. Journal of Statistics Education, 12(1), 1–15. https://doi.org/10.1080/10691898.2004.11910715.

    Article  Google Scholar 

  • Baumert, J., Kunter, M., Blum, W., Brunner, M., Voss, T., Jordan, A., Klusmann, U., Krauss, S., Neubrand, M., & Tsai, Y. M. (2010). Teachers’ mathematical knowledge, cognitive activation in the classroom, and student progress. American Educational Research Journal, 47(1), 133–180. https://doi.org/10.3102/0002831209345157.

    Article  Google Scholar 

  • Borges-Rey, E. L. (2017). Data literacy and citizenship: Understanding ‘big Data'to boost teaching and learning in science and mathematics. In Handbook of research on driving STEM learning with educational technologies (pp. 65-79): IGI global.

  • Bradshaw, L., Izsák, A., Templin, J., & Jacobson, E. (2014). Diagnosing teachers’ understandings of rational numbers: Building a multidimensional test within the diagnostic classification framework. Educational Measurement: Issues and Practice, 33(1), 2–14. https://doi.org/10.1111/emip.12020.

    Article  Google Scholar 

  • Chinese Ministry of Education.(2019). Opinions on the Implementation of the National Information Technology Application Ability Improvement Project 2.0 for Elementary and Secondary School Teachers. Retrieved from http://www.moe.gov.cn/jyb_xwfb/s5147/201904/t20190403_376571.html

  • Council Australian Education. (1994). Mathematics: A curriculum profile for Australian schools. Curriculum Corporation Press.

  • Cui, Y., Gierl, M. J., & Chang, H. H. (2012). Estimating classification consistency and accuracy for cognitive diagnostic assessment. Journal of Educational Measurement, 49(1), 19–38. https://doi.org/10.1111/j.1745-3984.2011.00158.x.

    Article  Google Scholar 

  • De La Torre, J. (2009). DINA model and parameter estimation: a didactic. Journal of Educational and Behavioral Statistics, 34, 115–130. https://doi.org/10.3102/1076998607309474.

    Article  Google Scholar 

  • De La Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76(2), 179–199. https://doi.org/10.1007/s11336-011-9207-7.

    Article  Google Scholar 

  • De La Torre, J., & Minchen, N. (2014). Cognitively diagnostic assessments and the cognitive diagnosis model framework. Psicología Educativa, 20(2), 89–97. https://doi.org/10.1016/j.pse.2014.11.001.

    Article  Google Scholar 

  • Duschl, R., Maeng, S., & Sezen, A. (2011). Learning progressions and teaching sequences: A review and analysis. Studies in Science Education, 47(2), 123–182. https://doi.org/10.1080/03057267.2011.604476.

    Article  Google Scholar 

  • Ebbeler, J., Poortman, C. L., Schildkamp, K., & Pieters, J. M. (2016). Effects of a data use intervention on educators’ use of knowledge and skills. Studies in Educational Evaluation, 48, 19–31. https://doi.org/10.1016/j.stueduc.2015.11.002.

    Article  Google Scholar 

  • Ferrini-Mundy, J. (2000). Principles and standards for school mathematics: A guide for mathematicians. Notices of the American Mathematical Society, 47(8), 868–876.

    Google Scholar 

  • François, K., & Monteiro, C. (2018). Big data literacy. In Paper presented at the looking back, looking forward. Proceedings of the 10th International Conference on the Teaching of Statistics (p. 6).

    Google Scholar 

  • Franklin, C., & Mewborn, D. (2006). The statistical education of PreK-12 teachers: A shared responsibility. NCTM, 335–344.

  • Franklin, C., Kader, G., Mewborn, D., Moreno, J., Peck, R., Perry, M., & Scheaffer, R. (2007). Guidelines for assessment and instruction in statistics education (GAISE) report. https://www.amstat.org/asa/files/pdfs/gaise/gaiseprek-12_full.pdf

  • Garfield, J., & Ben-Zvi, D. (2008). Develo** students’ statistical reasoning: Connecting research and teaching practice: Springer Science & Business Media.

  • Gierl, M. J. (2007). Making diagnostic inferences about cognitive attributes using the rule-space model and attribute hierarchy method. Journal of Educational Measurement, 44(4), 325–340. https://doi.org/10.1111/j.1745-3984.2007.00042.x.

    Article  Google Scholar 

  • Gould, R. (2017). Data literacy is statistical literacy. Statistics Education Research Journal, 16(1), 22–25.

    Article  Google Scholar 

  • Haertel, E. H. (1989). Using restricted latent class models to map the skill structure of. Achievement items. Journal of Educational Measurement, 26, 301–321. https://doi.org/10.1111/j.1745-3984.tb00336.x.

    Article  Google Scholar 

  • Hagenaars, J. A. (1990). Categorical longitudinal data: Loglinear panel, trend, and cohort. Sage.

  • Hagenaars, J. A. (1993). Loglinear models with latent variables. Sage.

  • Hartz, S. M. (2002). A Bayesian framework for the unified model for assessing cognitive. Abilities: Blending theory with practicality. Doctoral dissertation, ProQuest Information & Learning, Ann Arbor, MI.

  • Henson, R. A., Templin, J. L., & Willse, J. T. (2009). Defining a family of cognitive diagnosis models using log-linear models with latent variables. Psychometrika, 74, 191–210. https://doi.org/10.1007/s11336-008-9089-5.

    Article  Google Scholar 

  • Hill, H. C., Schilling, S. G., & Ball, D. L. (2004). Develo** measures of teachers’ mathematics knowledge for teaching. The Elementary School Journal, 105(1), 11–30. https://doi.org/10.1086/428763.

    Article  Google Scholar 

  • Jones, G. A., Thornton, C. A., Langrall, C. W., Mooney, E. S., Perry, B., & Putt, I. J. (2000). A framework for characterizing children's statistical thinking. Mathematical Thinking and Learning, 2(4), 269–307. https://doi.org/10.1207/S15327833MTL0204_3.

    Article  Google Scholar 

  • Junker, B. W., & Sijtsma, K. (2001). Cognitive assessment models with few assumptions, and. Connections. With.Nonparametric item response theory. Applied Psychological Measurement, 25, 258–272. https://doi.org/10.1177/01466210122032064.

    Article  Google Scholar 

  • Kippers, W. B., Poortman, C. L., Schildkamp, K., & Visscher, A. J. (2018). Data literacy: What do educators learn and struggle with during a data use intervention? Studies in Educational Evaluation, 56, 21–31. https://doi.org/10.1016/j.stueduc.2017.11.001.

    Article  Google Scholar 

  • Koedinger, K. R., Stamper, J. C., McLaughlin, E. A., & Nixon, T. (2013). Using data-driven discovery of better student models to improve student learning. Paper presented at the International Conference on Artificial Intelligence in Education.

  • Koltay, T. (2015). Data literacy: In search of a name and identity. Journal of Documentation, 72(2), 401–415. https://doi.org/10.1108/JD-02-2014-0026.

    Article  Google Scholar 

  • Kunina-Habenicht, O., Rupp, A. A., & Wilhelm, O. (2012). The impact of model misspecification on parameter estimation and item-fit assessment in log-linear diagnostic classification models. Journal of Educational Measurement, 49(1), 59–81. https://doi.org/10.1111/j.1745-3984.2011.00160.x.

    Article  Google Scholar 

  • Leavy, A. M. (2010). The challenge of preparing preservice teachers to teach informal inferential reasoning. Statistics Education Research Journal, 9(1), 46–67.

    Article  Google Scholar 

  • Maloney, A. & Confrey, J. (2010). The construction, refinement, and early validation of the Equipartitioning learning trajectory. In Gomez, K., Lyons, L., & Radinsky, J. (Eds.), Learning in the disciplines: Proceedings of the 9th international conference of the learning sciences (ICLS 2010) - volume 1, full papers (pp. 968-975). International Society of the Learning Sciences. https://doi.org/10.22318/icls2010.1.968.

  • Mandinach, E. B. (2009). Data use: What we know about school-level use (1135681333). 45.55.127.102/handle/1/2780.

  • Mandinach, E. B. (2012). A perfect time for data use: Using data-driven decision making to inform practice. Educational Psychologist, 47(2), 71–85. https://doi.org/10.1080/00461520.2012.667064.

    Article  Google Scholar 

  • Mandinach, E. B., & Gummer, E. S. (2013a). Building educators’ data literacy: Differing perspectives. The Journal of Educational Research & Policy Studies, 13(2), 1–5.

    Google Scholar 

  • Mandinach, E. B., & Gummer, E. S. (2013b). A systemic view of implementing data literacy in educator preparation. Educational Researcher, 42(1), 30–37. https://doi.org/10.3102/0013189X12459803.

    Article  Google Scholar 

  • Mandinach, E. B., & Gummer, E. S. (2016). What does it mean for teachers to be data literate: Laying out the skills, knowledge, and dispositions. Teaching and Teacher Education, 60, 366–376. https://doi.org/10.1016/j.tate.2016.07.011.

    Article  Google Scholar 

  • Maris, E. (1999). Estimating multiple classification latent class models. Psychometrika, 64, 187–212. https://doi.org/10.1007/bf02294535.

    Article  Google Scholar 

  • Means, B., Chen, E., DeBarger, A., & Padilla, C. (2011). Teachers' ability to use data to inform instruction: Challenges and supports. Office of Planning, Evaluation and Policy Development, US Department of Education.

  • Mooney, E. S. (2002). A framework for characterizing middle school students' statistical thinking. Mathematical Thinking and Learning, 4(1), 23–63. https://doi.org/10.1207/S15327833MTL0401_2.

    Article  Google Scholar 

  • National Research Council of the United States. (2007). Taking science to school: Learning and teaching science in grades K-8: National Academies Press.

  • Nunnaley, D. (2013). Professional development to build data literacy: The view from a professional development provider. Journal of Educational Research & Policy Studies, 13(2), 39–49.

    Google Scholar 

  • Oliveri, M. E., & von Davier, M. (2011). Investigation of model fit and score scale comparability in international assessments. Psychological Test and Assessment Modeling, 53(3), 315.

    Google Scholar 

  • Prado, J. C., & Marzal, M. Á. (2013). Incorporating data literacy into information literacy programs: Core competencies and contents. Libri, 63(2), 123–134. https://doi.org/10.1515/libri-2013-0010.

    Article  Google Scholar 

  • Reading, C. (2002). Profile for statistical understanding. Paper presented at the proceedings of the sixth international conference on teaching statistics, Cape Town, .

  • Reeves, T. D. (2017). Pre-service teachers' data use opportunities during student teaching. Teaching and Teacher Education, 63(4), 263–273. https://doi.org/10.1016/j.tate.2017.01.003.

    Article  Google Scholar 

  • Satorra, A., & Bentler, P. M. (2010). Ensuring positiveness of the scaled difference chi-square test statistic. Psychometrika, 75(2), 243–248. https://doi.org/10.1007/s11336-009-9135-y.

    Article  PubMed  PubMed Central  Google Scholar 

  • Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464 https://doi.org/org/stable/2958889.

    Article  Google Scholar 

  • Seels, B. B., & Richey, R. C. (2012). Instructional technology: The definition and domains of the field: Association for Educational Communications and Technology.

  • Simon, M. A. (1995). Reconstructing mathematics pedagogy from a constructivist perspective. Journal for Research in Mathematics Education, 26(2), 114–145. https://doi.org/10.2307/749205.

    Article  Google Scholar 

  • Song, L., Wang, W., & Dai, H. (2016). Overall and item fitting indicators under the cognitive diagnostic model. Psychological Exploration, 36(1), 79–83.

    Google Scholar 

  • Stohl, H. (2005). Probability in teacher education and development. In Exploring probability in school (pp. 345-366): Springer.

  • Tatsuoka, K. K. (1984). Analysis of errors in fraction addition and subtraction problems: Computer-based education research laboratory, University of Illinois.

  • Tatsuoka, K. K. (2009). Cognitive assessment: An introduction to the rule space method: Routledge.

  • Templin, J., & Bradshaw, L. (2013). Measuring the reliability of diagnostic classification model examinee estimates. Journal of Classification, 30(2), 251–275. https://doi.org/10.1007/s00357-013-9129-4.

    Article  Google Scholar 

  • Templin, J., & Henson, R. A. (2010). Diagnostic measurement: Theory, methods, and applications. Guilford Press.

  • Templin, J. L., & Henson, R. A. (2006). Measurement of psychological disorders using cognitive diagnosis models. Psychological Methods, 11(3), 287–305. https://doi.org/10.1037/1082-989X.11.3.287.

    Article  PubMed  Google Scholar 

  • Tu, D. (2019). Cognitive diagnostic analysis (flexCDMs) platform. Retrieved from http://www.psychometrics-studio.cn

  • von Davier, M. (2008). A general diagnostic model applied to language testing data. British Journal of Mathematical and Statistical Psychology, 61(2), 287–307. https://doi.org/10.1002/j.2333-8504.2005.tb01993.x.

    Article  Google Scholar 

  • von Davier, M. (2010). Hierarchical mixtures of diagnostic models. Psychological Test and Assessment Modeling, 52(1), 8–28.

    Google Scholar 

  • von Davier, M., & Yamamoto, K. (2004). A class of models for cognitive diagnosis. In Paper presented at the 4th spearman conference.

    Google Scholar 

  • Vrieze, S. I. (2012). Model selection and psychological theory: A discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychological Methods, 17(2), 228–243. https://doi.org/10.1037/a0027127.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang, W., Song, L., & Ding, S. (2018). The index and application of cognitive diagnostic test from the perspective of classification. Psychological Science, 41(2), 475–483.

    Google Scholar 

  • Wolff, A., Montaner, J. J. C., & Kortuem, G. (2016). Urban data in the primary classroom: Bringing data literacy to the UK curriculum. The Journal of Community Informatics, 12(3), 57–82.

    Article  Google Scholar 

  • Wu, X., Wu, R., Chang, H.-H., Kong, Q., & Zhang, Y. (2020). International comparative study on PISA mathematics achievement test based on cognitive diagnostic models. Frontiers in Psychology, 11, 1–15. https://doi.org/10.3389/fpsyg.2020.02230.

    Article  Google Scholar 

  • Zhang, Q., Bian, Y., Chen, P., & Zhang, J. (2019). The cognitive diagnostic assessment of lower primary school Students' Chinese character learning. Educational Research.

Download references

Acknowledgments

This work was support by China Scholarship Council (No. 201906140104); 2020 Academic Innovation Ability Enhancement Plan for outstanding doctoral Students of East China Normal University (No. YBNLTS2020-003) and Peak Discipline Construction Project of Education at East China Normal University.

Author information

Authors and Affiliations

  1. Faculty of Education, Shaanxi Normal University, **an, China

    **aopeng Wu

  2. College of Teacher Education, Faculty of Education, East China Normal University, Shanghai, China

    **aopeng Wu & Tianshu Xu

  3. College of Education, Purdue University, West Lafayette, IN, USA

    **aopeng Wu

  4. School of Mathematics and Statistics, Qiannan Normal University for Nationalities, Qiannan, China

    Yi Zhang

  5. School of Mathematic Science, East China Normal University, Shanghai, China

    Yi Zhang

Authors
  1. **aopeng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tianshu Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to **aopeng Wu or Yi Zhang.

Ethics declarations

Ethics Statements

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants’ legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements. No animal studies are presented in this manuscript. No potentially identifiable human images or data is presented in this study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, X., Xu, T. & Zhang, Y. Research on the data analysis knowledge assessment of pre-service teachers from China based on cognitive diagnostic assessment. Curr Psychol 42, 4885–4899 (2023). https://doi.org/10.1007/s12144-021-01836-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12144-021-01836-y

Keywords

Search

Navigation