24 SES 08 A, Teaching Profesional Development Part 2
Paper Session continued from 24 SES 02, to be continued in 24 SES 13 A
Findings from several educational studies reveal that a competent teacher is very important for students’ learning (Timberley & Lee, 2008; Nordenbo, et al. 2010; Hattie 2009). Klette (2013) argues that it is particularly the teacher’s use of high quality instructional strategies that seems to be a critical factor. This is supported by findings from several international studies that report a positive association between various aspects of instruction of high quality and student outcome measures (Creemers & Kyriakides, 2008; Baumert et al. 2010; Kane & Cantrell, 2012).
In a video study of 39 mathematics classrooms in Germany and Switzerland, researchers investigated if different aspects of Instructional Quality (IQ) had positive associations with students’ motivation for mathematics and achievement gains. The gains were measured using a pretest-posttest study design. The researchers reported that high quality classroom management and cognitive challenging instruction were both positively associated with student outcome measures (Lipowski et al., 2009). In another report that explores the relationships between mathematics teachers’ use of teaching strategies and student outcome measures, combining information from the two OECD studies PISA and TALIS, researchers likewise found that cognitive activation strategies had a strong association with students’ achievement in mathematics. These strategies were defined as stimulating students’ critical thinking, encourage students to find more than one solution to a problem, asking students to be creative and explain their solutions. The researchers found that teachers who were able to maintain a disciplined and well-functioning classroom environment, tended to use cognitive activation strategies more frequently.
These findings point to two important dimensions of IQ; classroom management and cognitive activation. Researchers have also investigated if other aspects of IQ have positive association with student outcome measures. This include among others the use of clear learning goals (Scherer & Gustavsson, 2015), making learning interesting and establishing a classroom culture of mutual learning support (Ferguson, 2011). IQ can thus be said to include a range of different dimensions. In a European context, the works of Baumert et al. (2010) and Klieme et al. (2009) have been particularly influential in developing four scales of IQ that have been used in several studies, among others PISA 2012/2015. These scales are: Classroom Management, Cognitive Activation, Clarity of Instruction and Supportive Teacher. Norway along with a few other countries used the opportunities of national options given by TIMSS, to extend the TIMSS 2015 Student Questionnaire to include these four scales. Based on the analyses of these scales from the Norwegian data set in TIMSS 2015, the aim of this paper is to investigate the association of IQ with student achievement in low SES and high SES student groups in 5th and 9th grade. We are particularly interested in investigating if high IQ, as measured through the four mentioned scales is more important for low SES student groups than for high SES student groups.
Our research question is:
- Based on data from TIMSS 2015; How is Instructional Quality associated with student achievement score for low SES as compared to high SES student groups in 5th grade and 9th grade Norwegian classrooms?
Many studies have investigated the association of IQ with student outcome measures. Fewer studies, however, have investigated if these associations are the same for all student groups. Our contribution to this field of study is thus to investigate if IQ are more important for low SES student groups than for high SES student groups.
As previously mentioned, our study is based on the Norwegian results from an extension of the TIMSS 2015 Student Questionnaire. In total, our data consists of responses from 4329 5th grade students and 4697 9th grade students. We used “Number of books at home” as an indicator of SES. This is a five dimensional scale in the TIMSS 2015 Student Questionnaire. Due to small sample sizes in some of these SES-groups, the two lowest (0-10 books and 11-25 books) and two highest categories (101-200 books and >201 books) were collapsed into respectively low SES (0-25 books) and high SES groups (>101 books). Thus we had three SES groups, Low-Medium-High. In the TIMSS 2015 Student Questionnaire students were asked to respond to a set of items constituting the four dimensions of IQ. For these items a four level Likert scale was used, and mean values were calculated for each dimension on a student level. To obtain a measure of instructional quality for each individual mathematics teacher, the mean values were then aggregated to class level. This generated data of instructional quality for 222 classrooms in 5th grade and 216 classrooms in 9th grade. Furthermore, we defined low, medium and high instructional quality normatively, with the cut-off points being 25th and 75th percentile. There were an uneven amount of students in these three groups, but all groups in both 5th and 9th grade consist of more than 1250 students. Combining these to categorizations (SES & IQ), generates 3x3=9 groups of students for both grades. In line with our research questions, we limited our analyses to the following four student groups in both 5th and 9th grade: 1. High SES & High IQ, 2. High SES & Low IQ, 3. Low SES & High IQ, 4. Low SES & Low IQ. Most of these groups consist of between 300 and 350 students, the exception being one group with 295 students, and two groups with around 500 students. Having completed this categorisation, we analysed how High IQ and Low IQ influenced the achievement scores in the two student groups, Low SES and High SES. The analysis of achievement-level was done using IEA IDB Analyzer (Version 4.0.13) together with IBM SPSS Statistics v. 24 and using TIMSS plausible values for the student achievements.
In our analyses we find that the group of Low SES 5th grade students that receives Low IQ has a mean score of 511 points on the TIMSS achievement test, while the group of Low SES 5th grade students that receives High IQ has a mean score of 526 points. This difference of 15 score points is significant at the .05 level. We find no significant difference in score points between the High SES 5th grade students that receive Low IQ and High IQ respectively. In a similar analysis of the 9th graders we find a difference of 15 score points between the Low SES group that receive Low IQ and the Low SES group that receive High IQ. Among the 9th graders we also find a difference of 19 points between the High SES group that receive Low IQ and the High SES group that receive High IQ. Both these score differences among the 9th graders are significant at the .05 level. Based on these findings, our conclusion is that High IQ, as measured through the four scales of Classroom management, Supportive Teacher, Cognitive Activation and Clear Intentions is positively associated with higher achievement score for low SES groups both in 5th grade and 9th grade. The same positive influence of High IQ on achievement score is found for the 9th grade High SES group. However, for the High SES group of students in 5th grade we do not find any positive association between IQ and achievement score. This indicates that high instructional quality is particularly important for low SES groups in 5th grade and for students in both low SES and high SES groups in 9th grade. Further research is required to investigate why High IQ does not render higher achievement score for 5th grade High SES groups.
Baumert, J., Kunter, M., Blum, W., Brunner, M., Voss, T., et al. (2010). Teachers’ Mathematical Knowledge, Cognitive Activation in the Classroom, and Student Progress. American Educational Research Journal, 47(1), 133-180. Creemers, B., & Kyriakides, L. (2008). The dynamics of educational effectiveness. A contribution to policy, practice and theory in contemporary schools. Abingdon: Routledge. Ferguson, R. (2011). Tripod Classroom-Level Student Perceptions as Measures of Teaching Effectiveness. Paper presented at the NCTE, USA. Hattie, J. (2009). Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement. USA: Routledge. Kane, T., & Cantrell, S. (2010). Learning about teaching: Initial findings from the measures of effective teaching project. MET Project Research Paper, Bill & Melinda Gates Foundation, 9. Klette, K. (2013). What do we know about good instruction? Report from classroom research. [Hva vet vi om god undervisning? Rapport fra klasseromsforskningen.]. In R. J. Krumsvik & R. Säljö (Eds.), Eduction within Practise-Pedoagogy: an anthology. [Praktisk-pedagogisk utdanning: en antologi] (pp. 173-201). Bergen: Fagbokforlaget. Klieme, E., Pauli, C., & Reusser, K. (2009). The pythagoras study: Investigating effects of teaching and learning in Swiss and German mathematics classrooms. In T. Janik & T. Seidel (Eds.), The power of video studies in investigating teaching and learning in the classroom (pp. 137-160). New York: Waxmann Publicing Co. Le Donné, N., P. Fraser and G. Bousquet (2016). Teaching Strategies for Instructional Quality: Insights from the TALIS-PISA Link Data”. OECD Education Working Papers, No. 148. Paris: OECD Publishing. Lastet ned fra: http://dx.doi.org/10.1787/5jln1hlsr0lr-en Lipowsky, F., Rakoczy, K., Pauli, C., Drollinger-Vetter, B., Klieme, E., & Reusser, K. (2009). Quality of geometry instruction and its short-term impact on students' understanding of the Pythagorean Theorem. Learning and Instruction, 19(6), 527-537. Nordenbo, S. E., Holm, A., Elstad, E., Scheerens, J., Larsen, M. S., et al. (2010). Input, Process, and Learning in primary and lower secondary schools: A systematic review carried out for The Nordic Indicator Workgroup (DNI) (Vol. 2010): Danish Clearinghouse for Educational Research. Scherer, R. & Gustafsson, J.-E. (2015). Student assessment of teaching as a source of information about aspects of teaching quality in multiple subject domains: an application of multilevel bifactor structural equation modeling. Frontiers in Psychology, 6(1550). Timperley, H. & Altin-Lee, A. (2008). Reframing Teacher Professional Learning. Review of Research in Education, 32(1), s. 328-369.
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