Where Low and High Inference Data Converge: Validation of CLASS Assessment of Mathematics Instruction Using Mobile Eye Tracking with Expert and Novice Teachers
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Classroom observation research and research on teacher expertise are similar in their reliance on observational data with high-inference procedure to assess the quality of instruction. Expertise research usually uses low-inference measures like eye tracking to identify qualitative difference between expert and novice behaviors and cognition. In this study, we used mobile eye-tracking technology to create a low inference quality indicator for the comparison of experienced and student teachers. The distribution of visual fixations on students was measured using Gini coefficients based on the observation of van den Bogert, van Bruggen, Kostons, and Jochems (Teacher and Teacher Education, 37, 208–216, 2014) that expert teachers show better classroom monitoring. Results confirm that student teachers have a higher Gini coefficient than experienced teachers indicating weaker classroom monitoring. However, the Gini coefficient did not correlate in the predicted way with trained observer coding of video footage of the same classrooms using the Classroom Assessment Scoring System (CLASS) (Pianta, Hamre, Haynes, Mintz, & La Paro, 2007) although the mean differences in behavioral management were higher for the experienced teachers as expected. The CLASS dimension Quality of Feedback was significantly related to the Gini coefficient as an interaction with expertise: Only for novice teachers that a high quality of feedback was negatively associated with monitoring of the classroom.
KeywordsMobile eye tracking Classroom observational assessment CLASS Expert–novice paradigm Quality of feedback
- Aiken, L. S. & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage.Google Scholar
- Brophy, J. E & Good, T. L. (1969). Teacher–child dyadic interaction: A manual for coding classroom behavior. Report Series No. 27. Retrieved from ERIC database. (ED042688)Google Scholar
- Brophy, J. E. & Good, T. L. (1974). Teacher-student relationships: Causes and consequences. New York, NY: Holt, Rinehart and Winston.Google Scholar
- Brophy, J. E. & Good, T. L. (1986). Teacher behavior and student achievement. In M. C. Wittrock (Ed.), Handbook of research on teaching (3rd ed., pp. 328–375). New York, NY: MacMillan.Google Scholar
- Bromme, R. & Dobslaw, G. (2003). Teachers’ instructional quality and their explanation of students’ understanding. In M. Kompf & P. Denicolo (Eds.), Teacher thinking twenty years on: Revisiting persisting problems and advances in education (pp. 25–36). Liss, NL: Swets & Zeitlinger.Google Scholar
- Duchowski, A. T. (2007). Eye tracking methodology: Theory and practice. London, England: Springer.Google Scholar
- Ericsson, K. A. (2006). Development of professional expertise: Toward measurement of expert performance and design of optimal learning environments. Cambridge, UK: Cambridge University Press.Google Scholar
- Flanders, N. A. (1970). Analyzing teaching behavior. Reading, MA: Addison-Wesley.Google Scholar
- Fraser, B. J. (1991). Two decades of classroom environment research. In B. J. Fraser & H. J. Walberg (Eds.), Educational environments: Evaluation, antecedents and consequences (pp. 3–27). Elmsford, NY: Pergamon Press.Google Scholar
- Hill, H. C., Rowan, B. & Ball, D. B. (2005). Effect of teachers’ mathematical knowledge for teaching on student achievement. Americal Educational Research Journal, 42, 371-406. Jarodzka, H., Scheiter, K., Gerjets, P., & Van Gog, T. (2010). In the eyes of the beholder: How experts and novices interpret dynamic stimuli. Learning and Instruction, 20, 146–154.Google Scholar
- Miller, K. F. & Zhou, X. (2007). Learning from classroom video: What makes it compelling and what makes it hard. In R. Goldman, R. Pea, B. Barron & S. J. Derry (Eds.), Video research in the learning sciences (pp. 321–334). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
- Miller, K. F. & Correa, C. (2010, June). Attention in the classroom. Teacher eye movements as an index of situation awareness. Poster presented at the 5. IES research conference, National Harbor, MD.Google Scholar
- Muijs, D. & Reynolds, D. (2001). Effective teaching: Evidence and practice. London, England: Sage.Google Scholar
- Pianta, R. C., Hamre, B. K., Haynes, N. J., Mintz, S. L. & La Paro, K. M. (2007). Classroom assessment scoring system manual, middle/secondary version. Charlottesville, VA: University of Virginia.Google Scholar
- Pianta, R. C., La Paro, K. M. & Hamre, B. K. (2008). Classroom assessment scoring system, manual, K–3. Baltimore, MD: Brookes.Google Scholar
- Polikoff, M. S. & Porter, A. C. (2014). Instructional alignment as a measure of teaching quality. Educational Evaluation and Policy Analysis. Advance online publication. doi: 10.3102/0162373714531851.
- Sabers, D. S., Cushing, K. S. & Berliner, D. C. (1991). Differences among teachers in a task characterized by simultaneity, multidimensionality, and immediacy. American Educational Research Journal, 28(1), 63–88.Google Scholar
- Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15, 4-14.Google Scholar
- Wang, Z., Miller, K. F., & Cortina, K. S. (2013). Using the LENA in teacher training: promoting student involvement through automated feedback. Unterrichtswissenschaft, 41, 290-305.Google Scholar
- Yamamoto, T. & Imai-Matsumura, K. (2012). Teachers’ gaze and awareness of students’ behavior: Using an eye tracker. Innovative Teaching, 2. Retrieved from http://www.amsciepub.com/doi/full/10.2466/01.IT.2.6.