Advertisement

Identifying effective teachers: The relation between teaching profiles and students’ development in achievement and enjoyment

  • Doris HolzbergerEmail author
  • Anna-Katharina Praetorius
  • Tina Seidel
  • Mareike Kunter
Article
  • 121 Downloads

Abstract

Teaching effectiveness has often been described from a variable-centered perspective according to instructional, organizational, and emotional teaching characteristics and their prediction of students’ outcomes. Adopting a person-centered approach, the present study analyzed how multiple variables of teaching quality co-occur simultaneously within teachers and how these teaching profiles are related to students’ development in achievement and enjoyment. Data from 3483 secondary students and their 155 mathematics teachers were analyzed at two measurement points. A latent profile analysis identified high-, medium-, and low-quality teaching profiles. Multilevel analyses revealed that the high-quality profile—as compared to the medium-quality profile—was positively related to achievement gains, whereas no significant difference was found for students’ development in enjoyment. The findings reveal quantitative instead of qualitative teaching profiles and challenge the implicit assumption the higher the better. In particular, effective teachers may not need to display the highest levels in all teaching aspects. Instead, different thresholds for teaching effectiveness may apply for students’ achievement gains and emotional development, respectively.

Keywords

Teaching quality Effectiveness Student cognitive and emotional development Person-centered approach Latent profile analysis 

Notes

Funding

The COACTIV study was supported by the German Research Foundation (BA 1461/2-1 and BA 1461/2-2).

References

  1. Bauer, D. J., & Shanahan, M. J. (2007). Modeling complex interactions: person-centered and variable-centered approaches. In T. D. Little, J. A. Bovaird, & N. A. Card (Eds.), Modeling contextual effects in longitudinal studies (pp. 255–283). Mahwah: Lawrence Erlbaum Associates.Google Scholar
  2. Baumert, J., Kunter, M., Blum, W., Brunner, M., Voss, T., Jordan, A., et al. (2010). Teachers’ mathematical knowledge, cognitive activation in the classroom and student progress. American Educational Research Journal, 47, 133–180.  https://doi.org/10.3102/0002831209345157.CrossRefGoogle Scholar
  3. Bennett, A. A., Gabriel, A. S., Calderwood, C., Dahling, J. J., & Trougakos, J. P. (2016). Better together? Examining profiles of employee recovery experiences. Journal of Applied Psychology, 101, 1635–1654.  https://doi.org/10.1037/apl0000157.CrossRefGoogle Scholar
  4. Blazar, D., & Kraft, M. (2017). Teacher and teaching effects on students’ attitudes and behaviors. Educational Evaluation and Policy Analysis, 39, 146–170.  https://doi.org/10.3102/0162373716670260.CrossRefGoogle Scholar
  5. Brophy, J. (1986). Teacher influences on student achievement. American Psychologist, 41, 1069–1077.  https://doi.org/10.1037//0003-066x.41.10.1069.CrossRefGoogle Scholar
  6. Bryk, A. S., & Raudenbush, S. W. (1987). Application of hierarchical linear models to assessing change. Psychological Bulletin, 101, 147–158.  https://doi.org/10.1037/0033-2909.101.1.147.CrossRefGoogle Scholar
  7. Burke, M. J., Finkelstein, L. M., & Dusig, M. S. (1999). On average deviation indices for estimating interrater agreement. Organizational Research Methods, 2, 49–68.  https://doi.org/10.1177/109442819921004.
  8. Celeux, G., & Soromenho, G. (1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification, 13, 195–212.  https://doi.org/10.1007/BF01246098.CrossRefGoogle Scholar
  9. Cohen, J. (2015). Challenges in identifying high-leverage practices. Teachers College Record, 117, 1–41.Google Scholar
  10. Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis—with applications in the social, behavioral, and health sciences. Hoboken: Wiley.Google Scholar
  11. Curby, T. W., LoCasale-Crouch, Konold, R, T., Pianta, R. C., Howes, C., Burchinal, M., et al. (2009). The relations of observed pre-k classroom quality profiles to children’s achievement and social competence. Early Education and Development, 20, 346–372.  https://doi.org/10.1080/10409280802581284.CrossRefGoogle Scholar
  12. Ehmke, T., Blum, W., Neubrand, M., Jordan, A., & Ulfig, F. (2006). Wie verändert sich die mathematische Kompetenz von der neunten zur zehnten Klassenstufe [How does mathematical competence change between grade nine and ten?]. In M. Prenzel, J. Baumert, W. Blum, R. Lehmann, D. Leutner, M. Neubrand, R. Pekrun, J. Rost, & U. Schiefele (Eds.), PISA 2003: Untersuchungen zur Kompetenzentwicklung im Verlauf eines Schuljahres (pp. 63–86). Münster: Waxmann.Google Scholar
  13. Evertson, C. M., & Weinstein, C. S. (2006). Classroom management as a field of inquiry. In C. M. Evertson & C. S. Weinstein (Eds.), Handbook of classroom management (pp. 3–15). Mahwah: Lawrence Erlbaum Associates.Google Scholar
  14. Fauth, B., Decristan, J., Rieser, S., Klieme, E., & Büttner, G. (2014). Student ratings of teaching quality in primary school: dimensions and prediction of student outcomes. Learning and Instruction, 29, 1–9.  https://doi.org/10.1016/j.learninstruc.2013.07.001.CrossRefGoogle Scholar
  15. Frenzel, A. C., Pekrun, R., & Goetz, T. (2007). Perceived learning environment and students’ emotional experiences: a multilevel analysis of mathematics classrooms. Learning and Instruction, 17, 478–493.  https://doi.org/10.1016/j.learninstruc.2007.09.001.CrossRefGoogle Scholar
  16. Frenzel, A. C., Goetz, T., Lüdtke, O., Pekrun, R., & Sutton, R. E. (2009). Emotional transmission in the classroom: exploring the relationship between teacher and student enjoyment. Journal of Educational Psychology, 101, 705–716.  https://doi.org/10.1037/a0014695.CrossRefGoogle Scholar
  17. Goetz, T., Frenzel, A. C., Pekrun, R., & Hall, N. C. (2006). The domain specificity of academic emotional experiences. The Journal of Experimental Education, 75, 5–29.  https://doi.org/10.3200/JEXE.75.1.5-29.CrossRefGoogle Scholar
  18. Grossman, P., Loeb, S., Cohen, J., & Wyckoff, J. (2013). Measure for measure: the relationship between measures of instructional practice in middle school English language arts and teachers’ value-added scores. American Journal of Education, 119, 445–470.  https://doi.org/10.1086/669901.CrossRefGoogle Scholar
  19. Grossman, P., Cohen, J., Ronfeldt, M., & Brown, L. (2014). The test matters: the relationship between classroom observation scores and teacher value added on multiple types of assessment. Educational Researcher, 43(6), 293–303.  https://doi.org/10.3102/0013189X14544542.CrossRefGoogle Scholar
  20. Halpin, P. F., & Kieffer, M. J. (2015). Describing profiles of instructional practice: a new approach to analyzing classroom observation data. Educational Researcher, 44(5), 263–277.  https://doi.org/10.3102/0013189X15590804.CrossRefGoogle Scholar
  21. Hamre, B. K., & Pianta, R. C. (2005). Can instructional and emotional support in the first-grade classroom make a difference for children at risk of school failure? Child Development, 76, 949–967.  https://doi.org/10.2307/3696607.CrossRefGoogle Scholar
  22. Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. New York: Routledge.Google Scholar
  23. Helmke, A., & Weinert, F. E. (1997). Unterrichtsqualität und Leistungsentwicklung: Ergebnisse aus dem SCHOLASTIK- Projekt [Quality of instruction and development of achievement: results from the SCHOKASTIK project]. In A. Helmke & F. E. Weinert (Eds.), Entwicklung im Grundschulalter (pp. 223–258). Weinheim: Psychologie Verlags Union.Google Scholar
  24. Hill, H. C., Umland, K., Litke, E., & Kapitula, L. R. (2012). Teacher quality and quality teaching: examining the relationship of a teacher assessment to practice. American Journal of Education, 118, 489–519.  https://doi.org/10.1086/666380.CrossRefGoogle Scholar
  25. Holzberger, D., Philipp, A., & Kunter, M. (2013). How teachers’ self-efficacy is related to instructional quality: A longitudinal analysis. Journal of Educational Psychology, 105, 774–786.  https://doi.org/10.1037/a0032198.
  26. Keeley, J. W., English, T., Irons, J., & Henslee, A. M. (2013). Investigating halo and ceiling effects in student evaluations of instruction. Educational and Psychological Measurement, 73, 440–457.  https://doi.org/10.1177/0013164412475300.CrossRefGoogle Scholar
  27. Klieme, E., Schümer, G., & Knoll, S. (2001). Mathematikunterricht der Sekundarstufe I: "Aufgabenkultur" und Unterrichtsgestaltung im internationalen Vergleich [Mathematics instruction at lower secondary level: “task culture” and quality of instruction]. In J. Baumert & E. Klieme (Eds.), TIMSS - Impulse für Schule und Unterricht. Forschungsbefunde, Reforminitiativen, Praxisberichte und Video-Dokumente (pp. 43–57). Bonn: Bundesministerium für Bildung und Forschung.Google Scholar
  28. Kunter, M., & Baumert, J. (2006). Who is the expert? Construct and criteria validity of student and teacher ratings of instruction. Learning Environments Research, 9, 231–251.  https://doi.org/10.1007/s10984-006-9015-7.
  29. Kunter, M., Tsai, Y.-M., Klusmann, U., Brunner, M., Krauss, S., & Baumert, J. (2008). Students’ and mathematics teachers’ perceptions of teacher enthusiasm and instruction. Learning and Instruction, 18, 468–482.  https://doi.org/10.1016/j.learninstruc.2008.06.008.CrossRefGoogle Scholar
  30. Kunter, M., Frenzel, A., Nagy, G., Baumert, J., & Pekrun, R. (2011). Teacher enthusiasm: Dimensionality and context specificity. Contemporary Educational Psychology, 36, 289–301.  https://doi.org/10.1016/j.cedpsych.2011.07.001.
  31. Kunter, M., Baumert, J., Blum, W., Klusmann, U., Krauss, S., & Neubrand, M. (2013a). Cognitive activation in the mathematics classroom and professional competence of teachers. Results from the COACTIV project. New York: Springer.CrossRefGoogle Scholar
  32. Kunter, M., Klusmann, U., Baumert, J., Richter, D., Voss, T., & Hachfeld, A. (2013b). Professional competence of teachers: effects on instructional quality and student development. Journal of Educational Psychology, 105, 805–820.  https://doi.org/10.1037/a0032583.CrossRefGoogle Scholar
  33. LeBreton, J. M., & Senter, J. L. (2008). Answers to 20 questions about interrater reliability and interrater agreement. Organizational Research Methods, 11, 815–852.  https://doi.org/10.1177/1094428106296642.CrossRefGoogle Scholar
  34. Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88, 767–778.  https://doi.org/10.1093/biomet/88.3.767.CrossRefGoogle Scholar
  35. LoCasale-Crouch, J., Konold, T., Pianta, R. C., Howes, C., Burchinal, M., Bryant, D., et al. (2007). Observed classroom quality profiles in state-funded pre-kindergarten programs and associations with teacher, program, and classroom characteristics. Early Childhood Research Quarterly, 22, 3–17.  https://doi.org/10.1016/j.ecresq.2006.05.001.CrossRefGoogle Scholar
  36. Lüdtke, O., Trautwein, U., Kunter, M., & Baumert, J. (2006). Reliability and agreement of student ratings of the classroom environment: a reanalysis of TIMSS data. Learning Environments Research, 9, 215–230.  https://doi.org/10.1007/s10984-006-9014-8.CrossRefGoogle Scholar
  37. Molenaar, P. C. M., & Campbell, C. G. (2009). The new person-specific paradigm in psychology. Current Directions in Psychology, 18, 112–117.  https://doi.org/10.1111/j.1467-8721.2009.01619.x.CrossRefGoogle Scholar
  38. Morin, A. J. S., Morizot, J., Boudrias, J.-S., & Madore, I. (2011). A multifoci person-centered perspective on workplace affective commitment: a latent profile/factor mixture analysis. Organizational Research Methods, 14, 58–90.  https://doi.org/10.1177/1094428109356476.CrossRefGoogle Scholar
  39. Muthén, L. K., & Muthén, B. O. (2015). Mplus user’s guide. Seventh edition. Los Angeles: Muthén & Muthén.Google Scholar
  40. Nie, Y., & Lau, S. (2009). Complementary roles of care and behavioral control in classroom management: the self-determination theory perspective. Contemporary Educational Psychology, 34, 185–194.  https://doi.org/10.1016/j.cedpsych.2009.03.001.CrossRefGoogle Scholar
  41. Nisbett, R. E., & Wilson, T. D. (1977). The halo effect: evidence for unconscious alteration of judgments. Journal of Personality and Social Psychology, 35, 250–256.  https://doi.org/10.1037//0022-3514.35.4.250.CrossRefGoogle Scholar
  42. Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Structural Equation Modeling, 14, 535–569.  https://doi.org/10.1080/10705510701575396.CrossRefGoogle Scholar
  43. Organization for Economic Cooperation and Development. (2004). Learning for tomorrow’s world: First results from PISA 2003. Paris.Google Scholar
  44. Oser, F. K., & Baeriswyl, F. J. (2001). Choreographics of teaching: bridging instruction to learning. In V. Richardson (Ed.), Handbook of research on teaching (4th ed., pp. 1031–1065). Washington, DC: American Educational Research Association.Google Scholar
  45. Pakarinen, E., Kiuru, N., Lerkkanen, M.-K., Poikkeus, A.-M., Siekkinen, M., & Nurmi, J.-E. (2010). Classroom organization and teacher stress predict learning motivation in kindergarten children. European Journal of Psychology of Education, 25, 281–300.  https://doi.org/10.1007/s10212-010-0025-6.CrossRefGoogle Scholar
  46. Pastor, D. A., Barron, K. E., Miller, B. J., & Davis, S. L. (2007). A latent profile analysis of college students’ achievement goal orientation. Contemporary Educational Psychology, 32, 8–47.  https://doi.org/10.1016/j.cedpsych.2006.10.003.CrossRefGoogle Scholar
  47. Patrick, H., Ryan, A. M., & Kaplan, A. (2007). Early adolescents’ perceptions of the classroom social environment, motivational beliefs, and engagement. Journal of Educational Psychology, 99, 83–98.  https://doi.org/10.1037/0022-0663.99.1.83.CrossRefGoogle Scholar
  48. Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18, 315–341.  https://doi.org/10.1007/s10648-006-9029-9.
  49. Pekrun, R., Götz, T., Jullien, S., Frenzel, A. C., vom Hofe, R., & Blum, W. (2003). Skalenhandbuch PALMA (Projekt zur Analyse der Leistungsentwicklung in Mathematik) [Codebook for the PALMA study (Project for the analysis of learning and achievement in mathematics)]. Germany: Department of Psychology: University of Munich.Google Scholar
  50. Pianta, R. C., & Hamre, B. K. (2009). Conceptualization, measurement, and improvement of classroom processes: standardized observation can leverage capacity. Educational Researcher, 38, 109–119.  https://doi.org/10.3102/0013189X09332374.CrossRefGoogle Scholar
  51. Pianta, R. C., La Paro, K. M., & Hamre, B. K. (2008). Classroom assessment scoring system. Baltimore: Paul H. Brookes.Google Scholar
  52. Pianta, R. C., Hamre, B. K., & Allen, J. P. (2012). Teacher-student relationships and engagement: conceptualizing, measuring, and improving the capacity of classroom interactions. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 365–386). Boston: Springer.CrossRefGoogle Scholar
  53. Praetorius, A.-K., Klieme, E., Herbert, B., & Pinger, P. (2018). Generic dimensions of teaching quality: the German framework of Three Basic Dimensions. ZDM Mathematics Education, 50, 407–426.  https://doi.org/10.1007/s11858-018-0918-4.CrossRefGoogle Scholar
  54. Reyes, M. R., Brackett, M. A., Rivers, S. E., White, M., & Salovey, P. (2012). Classroom emotional climate, student engagement, and academic achievement. Journal of Educational Psychology, 104, 700–712.  https://doi.org/10.1037/a0027268.CrossRefGoogle Scholar
  55. Rost, J. (1990). Rasch models in latent classes: an integration of two approaches to item analysis. Applied Psychological Measurement, 14, 271–282.  https://doi.org/10.1177/014662169001400305.CrossRefGoogle Scholar
  56. Salminen, J., Lerkkanen, M.-K., Poikkeus, A.-M., Pakarinen, E., Siekkinen, M., Hännikäinen, M., et al. (2012). Observed classroom quality profiles in kindergarten classrooms in Finland. Early Education and Development, 23, 654–677.  https://doi.org/10.1080/10409289.2011.574267.CrossRefGoogle Scholar
  57. Salminen, J., Pakarinen, E., Poikkeus, A.-M., & Lerkkanen, M.-K. (2017). Development of pre-academic skills and motivation in kindergarten: a subgroup analysis between classroom quality profiles. Research Papers in Education, 0(0), 1–29.  https://doi.org/10.1080/02671522.2017.1353673.Google Scholar
  58. Seidel, T., & Shavelson, R. J. (2007). Teaching effectiveness research in the past decade: the role of theory and research design in disentangling meta-analysis results. Review of Educational Research, 77, 454–499.  https://doi.org/10.3102/0034654307310317.CrossRefGoogle Scholar
  59. Skinner, E. A., & Belmont, M. J. (1993). Motivation in the classroom: reciprocal effects of teacher behavior and student engagement across the school year. Journal of Educational Psychology, 85, 571–581.  https://doi.org/10.1037/0022-0663.85.4.571.CrossRefGoogle Scholar
  60. Tein, J.-Y., Coxe, S., & Cham, H. (2013). Statistical power to detect the correct number of classes in latent profile analysis. Structural Equation Modeling, 20, 640–657.  https://doi.org/10.1080/10705511.2013.824781.CrossRefGoogle Scholar
  61. Vansteenkiste, M., Sierens, E., Goossens, L., Soenens, B., Dochy, F., Mouratidis, A., et al. (2012). Identifying configurations of perceived teacher autonomy support and structure: associations with self-regulated learning, motivation and problem behavior. Learning and Instruction, 22, 431–439.  https://doi.org/10.1016/j.learninstruc.2012.04.002.CrossRefGoogle Scholar
  62. Vieluf, S., Kaplan, D., Klieme, E., & Bayer, S. (2012). Teaching practices and pedagogical innovation: Evidence from TALIS. Paris: OECD Publishing.CrossRefGoogle Scholar
  63. Wagner, W., Göllner, R., Werth, S., Voss, T., Schmitz, B., & Trautwein, U. (2015). Student and teacher ratings of instructional quality: consistency of ratings over time, agreement, and predictive power. Journal of Educational Psychology, 118, 705–721.  https://doi.org/10.1037/edu0000075.Google Scholar
  64. Wang, M. C., Haertel, G. D., & Walberg, H. J. (1993). Toward a knowledge base for school learning. Review of Educational Research, 63, 249–194.  https://doi.org/10.3102/00346543063003249.CrossRefGoogle Scholar
  65. Weinert, F. E., Schrader, F.-W., & Helmke, A. (1989). Quality of instruction and achievement outcomes. International Journal of Educational Research, 13, 895–914.  https://doi.org/10.1016/0883-0355(89)90072-4.CrossRefGoogle Scholar

Copyright information

© Instituto Superior de Psicologia Aplicada, Lisboa and Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.TUM School of EducationTechnical University of MunichMunichGermany
  2. 2.University of ZurichZürichSwitzerland
  3. 3.Goethe University FrankfurtFrankfurt am MainGermany

Personalised recommendations