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First published online June 5, 2017

Teachers’ Mathematical Knowledge, Cognitive Activation in the Classroom, and Student Progress

Abstract

In both the United States and Europe, concerns have been raised about whether preservice and in-service training succeeds in equipping teachers with the professional knowledge they need to deliver consistently high-quality instruction. This article investigates the significance of teachers’ content knowledge and pedagogical content knowledge for high-quality instruction and student progress in secondary-level mathematics. It reports findings from a 1-year study conducted in Germany with a representative sample of Grade 10 classes and their mathematics teachers. Teachers’ pedagogical content knowledge was theoretically and empirically distinguishable from their content knowledge. Multilevel structural equation models revealed a substantial positive effect of pedagogical content knowledge on students’ learning gains that was mediated by the provision of cognitive activation and individual learning support.

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Figure and Tables

Figure 1 Hierarchical linear model to test the significance of teachers’ pedagogical content knowledge (PCK) and content knowledge (CK) for instructional quality and student learning.
Note. SES = socioeconomic status; - - - = no relation expected.
Table 1 Descriptive Findings at Student Level (N = 4,353)
VariablesTotalNonacademic TracksAcademic TrackΔtracks
MSDMSDMSDtp
Mathematics achievement, end of Grade 100.050.98–0.310.900.530.86–30.3.000
Mathematical literacy, end of Grade 90.040.97–0.320.920.530.87–31.6.000
Reading literacy, end of Grade 90.060.96–0.250.920.470.85–19.3.000
Mental ability, end of Grade 90.050.96–0.220.960.430.81–23.4.000
Age, years15.70.6615.80.7115.60.6710.4.000
Socioeconomic statusa53.416.048.614.659.915.4–24.4.000

 %%%χ2, (df = 1)p
Parents with university degree32.319.150.740.9.000
Immigrant background19.321.416.65.7.000
a
As operationalized by the International Socio-Economic Index. Specifically, the analysis used the higher-status occupation between the two parents.
Table 2 Descriptive Findings at Class Level (n = 194) and Teacher Level (n = 181)
Constructs and VariablesTotalNonacademic TracksAcademic TrackΔtracks
MaSDMaSDMaSDtp
Class Level
Cognitive level of tasks
  Type of mathematical taskb1.580.251.570.241.590.26–0.68.50
  Level of mathematical argumentationc0.070.100.040.060.120.13–5.50.000
  Innermathematical translationc0.380.300.380.340.380.300.02.98
Curricular level of tasks
  Alignment to grade 10 curriculumb2.720.192.680.202.780.15–3.38.000
Individual learning support
  Adaptive explanationsd2.820.432.840.432.780.440.95.34
  Constructive response to errorsd2.940.442.910.442.970.43–0.84.40
  Patienced2.760.532.760.502.750.580.07.95
  Adaptive pacingd2.200.442.200.442.210.44–0.21.84
  Respectful treatment of studentsd3.220.493.160.503.310.47–2.10.04
  Caring ethosd2.660.502.690.502.610.481.20.23
Quality of classroom management
  Prevention of disruptiond2.530.622.520.612.560.63–0.47.64
  Effective use of timed2.660.582.660.562.650.600.21.84
Teacher Level
  Age (in years)48.158.048.517.847.468.40.79.43
  Gender (male)52.1% 49.5% 57.1% χ2 = 0.85.36
  Years of service22.19.422.79.520.99.31.13.26
  Grade point averageeMdn = 2.6 Mdn = 2.8 Mdn = 2.4 5.66.35
a
Unless noted otherwise.
b
1 = low, 3 = high.
c
0 = low, 3 = high.
d
1 = low, 4 = high.
e
1 = highest, 6 = lowest.
Table 3 Teachers’ Content Knowledge and Pedagogical Content Knowledge by Type of Certification
VariablesTotalType 1: AcademicType 2: NonacademicType 3: IntegratedaΔ(Type 1 – Type 2)Δ(Type 1 – Type 3)Δ(Type 2 – Type 3)
M (SD)M (SD)M (SD)M (SD)t (p)t (p)t (p)
CK–0.139 (0.99)0.737 (0.91)–0.524 (0.82)–0.446 (0.73)7.537 (.000)6.099 (.000)–0.465 (.643)
PCK–0.027 (0.99)0.427 (1.10)–0.007 (0.83)–0.596 (0.77)2.301 (.023)4.442 (.000)3.329 (.001)
PCK|CKb–0.083–0.0970.256–0.407–1.909 (.058)1.531 (.128)4.064 (.000)
Note. Excluding the 14 teachers who completed a 5-year training program in the former German Democratic Republic. CK = content knowledge; PCK = pedagogical content knowledge.
a
Training in former German Democratic Republic.
b
PCK estimated after controlling for CK; PCK estimated at CK = −0.139.
Table 4 Teachers’ Content Knowledge and Pedagogical Content Knowledge by Track
VariablesTotalNonacademic TracksAcademic TrackΔtracks
MSDMSDMSDtp
CK–0.111.00–0.580.840.730.68–10.2.000
PCK
  Tasks–0.040.97–0.240.890.311.00–3.46.001
  Students–0.021.00–0.240.980.450.864.68.000
  Instruction–0.030.97–0.310.930.480.85–5.37.000
  Overall–0.050.98–0.330.960.490.76–5.65.000
Note. CK = content knowledge; PCK = pedagogical content knowledge.
Table 5 Predicting Mathematics Achievement at the End of Grade 10 by Dimensions of Instructional Quality, Content Knowledge, and Pedagogical Content Knowledge, Controlling for Individual Selection Variables
Predictor/ParameterIndividualDimensions of Instructional QualityBlack Box (PCK)Mediation (PCK)Black Box (CK)Mediation (CK)
Model 1Model 2Model 3Model 4Model 5Model 6Model 7Model 8
Individual Levela
Mathematics achievement, end of Grade 10 on:
  Prior knowledge of mathematics, end of Grade 9.49.49.49.49.49.48.49.49
  Reading literacy, end of Grade 9.21.21.20.21.20.20.20.20
  Mental ability, end of Grade 9.24.23.24.24.24.24.24.24
  Socioeconomic statusb.01.00.00.00.00–.01–.01–.01
Parental education (6 dummies)
  Hauptschule, no apprenticeship (0/1)–.02–.01–.02–.02–.02–.02–.02–.02
  Hauptschule and apprenticeship (0/1)–.02–.02–.02–.02–.02–.02–.02–.02
  Realschule and apprenticeship (0/1).03–.03–.03–.03–.03–.03–.03–.03
Realschule and professional training (reference)
  Gymnasium (0/1)–.02–.02–.02–.02–.02–.02–.02–.02
  University (0/1).03.04.02.03.02.02.02.02
  Immigration status (0/1)–.03–.03–.03–.03–.03–.03–.03–.03
R2.64.64.62.62.62.62.62.62
Class Level
Mathematics achievement, end of Grade 10 on:
  Track (nonacademic/academic)  .58 .42.56.42.60
  PCK (CK)   .62.42 .30 
  Cognitive level of tasks .32.32  .32 .31
  Curricular level of tasks .30.17  .17 .18
  Individual learning support .08.11  .10 .10
  Effective classroom management .33.30  .31 .32
Cognitive level (MV1) on PCK (CK)     .24 .01
Curricular level (MV2) on PCK (CK)     .33 .32
Individual learning support (MV4) on PCK (CK)     .26 –.06
Effective classroom management (MV3) on PCK (CK)     .14 –.01
R2 .37.68.39.54.69.44.65

χ225.478781931.833.688231.5874
df9190200151825314240
p.003.00.00.007.07.00.005.00
Comparative fit index.99.97.97.99.99.96.99.96
Root mean square error of approximation.02.03.03.02.01.02.02.03
Standardized root mean square residual between.001.04.04.06.01.08.01.09
Standardized root mean square residual within.005.03.03.005.004.02.004.02
Note. Multilevel structural equation models with latent variables, completely standardized solutions; coefficients in bold are significant at least at the 5% level. CK = content knowledge; PCK = pedagogical content knowledge; MV = mediator variable.
a
All individual variables centered at the grand mean.
b
As operationalized by the International Socio-Economic Index. Specifically, the analysis used the higher-status occupation between the two parents.

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Article first published online: June 5, 2017
Issue published: March 2010

Keywords

  1. teacher knowledge
  2. teacher education
  3. mathematics
  4. instruction
  5. cognitive activation
  6. hierarchical modeling with latent variables

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Copyright © 2010 by American Educational Research Association. All rights reserved.
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Authors

Affiliations

Mareike Kunter
Max Planck Institute for Human Development
Werner Blum
Martin Brunner
University of Luxembourg
Thamar Voss
Max Planck Institute for Human Development
Alexander Jordan
University of Bielefeld
Uta Klusmann
Max Planck Institute for Human Development
Stefan Krauss
Michael Neubrand
University of Oldenburg
Yi-Miau Tsai
University of Michigan

Notes

Jürgen Baumert is a co-director at Max Planck Institute for Human Development, Center for Educational Research, Lentzeallee 94, 14195 Berlin, Germany; e-mail: [email protected]. His research interests include research in teaching and learning, cultural comparisons, large-scale assessment, and cognitive and motivational development in adolescence.
Mareike Kunter is a research scientist at Max Planck Institute for Human Development, e-mail: [email protected]. Her research interests include teacher research, motivational processes in the classroom, and assessment of instructional processes.
Werner Blum is a professor of mathematics education at University of Kassel, e-mail: [email protected]. His research interests include empirical research on instructional quality in mathematics, national and international comparison studies in mathematics, approaches to application, modeling, and proofs in mathematics instruction.
Martin Brunner is an associate professor at University of Luxembourg, e-mail: [email protected]. His research interests include research on cognitive abilities, achievement, and achievement motivation by means of modern measurement models.
Thamar Voss is a predoctoral research fellow at Max Planck Institute for Human Development, e-mail: [email protected]. Her research interests include research on instruction and learning, teacher research, and teacher beliefs.
Alexander Jordan is an academic staff member at University of Bielefeld, e-mail: [email protected]. His research interests include empirical research on mathematics instruction, instructional quality development, professional knowledge of mathematics teachers, and national and international comparison studies in mathematics.
Uta Klusmann is a research scientist at Max Planck Institute for Human Development, e-mail: [email protected]. Her research interests include research in teaching and learning, teacher stress, and personal goals.
Stefan Krauss is a professor of mathematics education at University of Kassel, e-mail: [email protected]. His research interests include research on teaching and learning, didactics of mathematics, professional knowledge of mathematics teachers, and probabilistic reasoning.
Michael Neubrand is a professor of mathematics instruction at University of Oldenburg, e-mail: [email protected]. His research interests include teaching and learning mathematics in secondary school, didactics in geometry, mathematical literacy, professional knowledge of mathematics teachers, and international comparison studies in mathematics.
Yi-Miau Tsai is a research fellow at University of Michigan, e-mail: [email protected]. Her research interests include research in learning and instruction, achievement motivation and goals, and context and cultural effects on motivation.

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