Presentation at AERA's annual meeting, Chicago. 2023.

1. MULTILEVEL MODELLING OF CHINESE
PRIMARY CHILDREN’S METACOGNITIVE
STRATEGIES IN MATHEMATICS
Dr. Christian Bokhove
University of Southampton
AERA 2023 annual meeting - Chicago

2. Prof Christian Bokhove Dr Zhenzhen Miao Prof David Reynolds

3. The MasterMT project
https://www.youtube.com/watch?v=Y5hVYQHaxOE

4. Research questions
What role do metacognitive strategies play in Chinese
primary pupil’s mathematical learning.
1. To what extent do metacognitive abilities predict
mathematics achievement in Chinese primary
classrooms?
2. Is there a difference between regulation and knowledge
of cognition with regard to this?
3. Are there individual characteristics that influence this
metacognitive relationship?

5. Background
• Metacognition
• Flavell (1979) in the 1970s as the knowledge of “one’s own
cognitive processes and products or anything related to them” and
“the active monitoring of these processes” (p.232).
• Knowledge of cognition and the regulation of cognition (e.g.
Sperling et al., 2002; Veenman et al., 2006).
• Mathematical learning
• Metacognitive training can improve students’ academic outcomes,
both in primary and in secondary education (e.g. Schneider &
Artelt, 2010).
• Master teachers in China
• Provincial Teaching Research Officials (TROs) asked to recruit
mathematics teachers who demonstrated best teaching practice in
primary mathematics and were full-time classroom teachers.

6. Methodology
• 70 Master teachers and their pupils in cities or
socioeconomically equivalent cities from five Chinese
provinces/municipalities: Anhui, Beijing, Jiangsu, Jiangxi,
and Tianjin.
• 2,642 pupils in Grades 5 and 6.

7. Instruments
• Junior Metacognitive Awareness Inventory (Jr MAI), which
is a self-report inventory and an efficient screening
instrument to assess students’ metacognitive abilities
(Sperling et al., 2002; Sperling et al., 2012; Kim et al.,
2017; Ning, 2019).
• 9 items measure Knowledge of Cognition (KoC) and 9 items
measure Regulation of Cognition (RoC). The only change we
made to the inventory questions is that we included ‘mathematics’
as the target subject.
• Mathematics attainment was measured with 28 items on
rational and proportional reasoning adapted from the
Concepts in Secondary Mathematics and Science
(CSMS) project (Hart et al., 1981).

8. Instruments
Classroom strategies.
We use the component
‘Promoting active
learning and developing
metacognitive skills’ from
the International
System for Teacher
Observation and
Feedback (ISTOF, Muijs
et al., 2018)

9. Analytical approach
• Two-level multilevel models: pupils in classrooms
(teachers).
• lme4 package in R.
• Five models
• Model 0: null
• Model 1 + Age + Gender + SES (pupil level)
• Model 2 + ISTOF (classroom level)
• Model 3 + MC (pupil level) whole scale
• Model 4 + KC + RC (pupil level) separate scales

10. Results
These tables are in the paper, so what are the headlines
related to the research questions?

11. Conclusions
• To the first question to what extent metacognitive abilities
predict mathematics achievement in Chinese primary
classrooms, we can see that overall self-reported
metacognitive abilities are a significant, positive
predictor of mathematics achievement.
• However, answering the second question, in this sample
the contribution of ‘Knowledge of Cognition’ is very
positive, while the contribution of ‘Regulation of
Cognition’ is somewhat negative.
• Finally, the third and final question, age did not
significantly predict mathematics achievement, while
Gender and SES did, with girls’ achievement significantly
lower.

12. Discussion
• Limited explained variance.
• Other classroom factors, like
disciplinary climate.
• Self-report – to what extent
does introspection work?
• Not an experiment 
correlational.

13. Further work
Relate findings to other data
sources, including engagement,
observations and teachers’
professional development, and
more.
https://mastermt.org/masterly-mathematics-teaching/

14. THANK YOU
@cbokhove
More info see http://mastermt.org
https://orcid.org/0000-0002-4860-8723

15. Selected references
Hart, K., Brown, M., Kerslake, D., Küchemann, D. E., & Ruddock, G. (1981). Chelsea diagnostic
mathematics tests. NFER-NELSON.
Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental
inquiry. American Psychologist, 34(10), 906–911. https://doi.org/10.1037/0003-066X.34.10.906
Kim, B., Zyromski, B., Mariani, M., Lee, S. M., & Carey, J. C. (2017). Establishing the factor structure of the
18-item version of the junior metacognitive awareness inventory. Measurement and Evaluation in
Counseling and Development, 50(1-2), 48–57. https://doi.org/10.1177%2F0748175616671366
Ning, H. K. (2019). The Bifactor model of the junior metacognitive awareness inventory (Jr. MAI). Current
Psychology, 38(2), 367–375. https://doi.org/10.1007/s12144-017-9619-3
Schneider, W., & Artelt, C. (2010). Metacognition and mathematics education. ZDM Mathematics
Education, 42, 149–161. https://doi.org/10.1007/s11858-010-0240-2
Sperling, R. A., Howard, B. C., Miller, L. A., & Murphy, C. (2002). Measures of children's knowledge and
regulation of cognition. Contemporary Educational Psychology, 27(1), 51–79.
https://doi.org/10.1006/ceps.2001.1091
Sperling, R. A., Richmond, A. S., Ramsay, C.M., & Klapp, M. (2012). The measurement and predictive
ability of metacognition in middle school learners. Journal of Educational Research, 105(1), 1–7.
https://doi.org/10.1080/00220671.2010.514690
Tsai, C. C. (2001). Relationships between student scientific epistemological beliefs and perceptions of
constructivist learning environments. Educational Research, 42(2), 193–205.
https://doi.org/10.1080/001318800363836