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«RELATIONSHIP BETWEEN TEACHER PREPAREDNESS AND INQUIRY-BASED INSTRUCTIONAL PRACTICES TO STUDENTS’ SCIENCE ACHIEVEMENT: EVIDENCE FROM TIMSS 2007 A ...»

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This study examined the instructional experiences and science achievement of a national sample of a target population of 2007-2008 eighth grade science students in United States Schools. The information on instruction came from the TIMSS 2007-2008 survey of teachers, which collected questionnaires from the 687 science teachers of 7,377 eighth grade students enrolled in public and private schools across the United States. These questionnaires asked about characteristics of the classes tested in TIMSS 2007; pedagogic approach, instructional time, materials and activities for teaching science and promoting students’ interest in the subject; use of computers and the internet;

assessment practices; and home-school connections. They also asked teachers their views on their opportunities for collaboration with other teachers and professional development, and for information about themselves and their education and training.

The students, who participated in the TIMSS 2007, were carefully selected to represent all students in their respective nations. International technical review committees scrutinized the entire assessment process to ensure its adherence to established standards. An international curriculum analysis was carried out prior to the development of the assessments to ensure that the tests reflected the math and science curricula of the variety of TIMSS 2007 countries and did not over-emphasize information taught in only a few countries. International monitors carefully checked the test translations and visited many classrooms while the tests were being administered in the 48 countries to make sure the instructions were properly followed. Testing occurred two to three months before the end of the 2006-2007 school year. More elaborate information is provided in Olson, Martin, and Mullis (2008).

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The sample design was intended to ensure that the TIMSS 2007 survey data provide accurate and efficient estimates of national student populations. Three critical features in the TIMSS data collection were addressed when analyzing the data: (1) sample weights, (2) cluster sampling and design effect, and (3) plausible value and scaling procedures.

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The TIMSS 2007 National Research Coordinator (NRC) of each participating country was responsible for implementing the sample design, including documenting every step of the sampling procedure for approval by the TIMSS and PIRLS International Study Center prior to implementation (Williams et al., 2009). TIMSS 2007 participants were expected to ensure that the national defined populations included at least 95% of the national desired populations of students (Williams et al., 2009). TIMSS 2007 used a three-stage stratified cluster sampling technique, which resulted in students having an unequal, but known, probability of selection (Williams et al., 2009).

Therefore, any analysis of the data must use the proper weights to correct for these sampling biases and to enable the researcher to obtain sound, nationally representative estimates to draw conclusions about the population. Since this study is limited to only United States students, Houseweight, (HOUWGT) will be used (Williams et al., 2009).

This weight is calculated by taking into account the stratification or disproportional sampling of the subgroups, adjustment for non-response, and the selection probability of each student (Williams et al., 2009).

Failure to apply appropriate weights would result in biased population estimates. A detailed description is provided in Olson, Martin, and Mullis (2008).

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Standard statistical analysis programs, including SPSS, assume that data are collected from a simple random sampling design. TIMSS 2007 did not use a simple random sampling, but gathered data using a three-stage stratified cluster design (Joncas, 2008). The United States sample design within schools consisted of an equal probability sample (Joncas, 2008). Once the school was selected, scores from one classroom within that school were used in the study. All eligible students in the classroom were designated to be in the sample. In total, 239 eighth-grade schools and 7,377 eighth grade students participated for the United States (Joncas, 2008). This procedure ensured that all sub-populations in the sample were proportionately represented, however complex sampling designs make the task of computing standard errors to quantify sampling variance more difficult (Joncas, 2008). The result is a design effect. If this design effect is not considered in the analysis, erroneous decisions in inferential significance testing will result, because a smaller standard of error from simple random sample is used in analysis when a larger standard error from cluster sample is required (Joncas, 2008).

In order to correct for the design effect, a Jackknife Repeated Replication (JRR) developed through WESVAR was implemented. The JRR procedure provides an unbiased estimate of the statistic of interest by repeatedly selecting subsets of the sample from which to calculate the statistic (Williams et al., 2009). The variability of these estimates was used to estimate the sampling variance for the statistic. This variance was the sum of the squared differences of the weighted total for each of the replicates and the weighted total for the full sample (Williams et al., 2009). Seventy-five replicate weights were used to compute statistics of interest in TIMSS 2007 (Williams et al., 2009). Details on the procedures used can be found in the WesVar 4.3 User’s Guide (Westat, 2007).





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TIMSS 2007 was designed to produce population estimates on how various populations of students collectively performed on its proficiency scales and subscales in science. TIMSS 2007 did not estimate individual student scores in science. To keep the testing burden to a minimum, and to ensure broad subject-matter coverage, the TIMSS 2007 assessment design was based on Balanced Incomplete Block (BIB) spiraling of assessment items (Williams et al., 2009). Each student completed only a sample of items from the total collection of assessment items. No student responded to all of the assessment items. This permits an increase of content-area without increasing the students’ assessment time (Williams et al., 2009). Since not all students were given the same questions, during the scaling process, plausible values were estimated to account for the increased measurement error. A more technical description can be found in Olson, Martin, and Mullis (2008).

In TIMSS 2007, total and scale scores were estimated for each student using an item response theory (IRT) model.

IRT scaling provided estimates of item parameters, such as difficulty, that define the relationship between the item and the underlying variable measured by the test (Williams et al., 2009). IRT quantifies what the true performance of a student might have been, had it been observed.

Parameters of the IRT model are established as well as scales for each content area and cognitive domain specified in the assessment framework (Williams et al., 2009). With IRT, the difficulty of each item is deduced using information about how likely it is for students to get some items correct versus other items. Once the difficulty of each item is determined, the ability of each student can be estimated even when different students have been administered different items (Williams et al., 2009). A detailed description is provided in Olson, Martin, and Mullis (2008).

Variable Selection and Statistical Analysis of Data TIMSS 2007 teacher questionnaires contained numerous items representing constructs of primary concern to this study (Appendix B). TIMSS 2007 was not designed to specifically measure the preparedness, use, or beliefs of didactic and inquiry-based instruction of the teacher population. The specific instructional variables and relationships among variables examined in this report were selected because of the theoretical interests of this study. In order to eliminate bias in identification of variables for this study, 33 variables representing instructional practices that characterize both didactic and inquiry-based approaches, as recommended by the National Science Education Standards (NSES) and the framework outlined in the literature review were extracted from the teacher questionnaire. The instructional variables included various learning objectives, methods of instruction, and beliefs about learning science concepts.

Instructional methods were measured by responses to questions about the relative roles of computers, experiments, lectures, discussions, group work, individualization, homework, and assessment. For the purpose of content validity, nine secondary science teachers, with teaching experience, participated in the identification of the thirty-three extracted variables as either didactic or inquiry-based (Appendix C). The criteria for selection of variables were based on a 67% agreement of variables being identified as either didactic or inquiry-based. As a result, ten variables were identified from the teacher questionnaire as being representative of inquiry-based instruction and five variables were identified as being representative of didactic instruction.

A correlation analysis was used to determine if a relationship existed between eighth grade science teachers’ main area of study and their self-reported beliefs on preparedness to teach specific science content areas.

Another correlation analysis was used to determine if a relationship existed between eighth grade science teachers’ self-reported beliefs about their preparedness to teach specific science content areas and their self-reported instructional practices. Another correlation analysis was used to determine if a relationship existed between eighth grade science teachers’ self-reported beliefs about their preparedness to teach specific science content areas and eighth grade student science achievement. A final correlation analysis was used to determine if a relationship existed between teachers’ self-reported instructional practices in teaching science to eighth grade students and eighth grade science achievement.

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The research method and design, sampling frame, and data collection procedures have been presented in this chapter. In addition, the instrument that was used in the study and the data analysis process has been discussed.

Chapter 4 presents the data analysis procedures and results. The sections will include 5 purposes.

Quantitative data results from the descriptive analysis of teachers’ characteristics are examined. A correlation analysis is used to determine if a relationship existed between eighth grade science teachers’ main area of study and their self-reported beliefs on preparedness to teach specific science content areas. A second correlation analysis is used to determine if a relationship existed between eighth grade science teachers’ self-reported beliefs about their preparedness to teach specific science content areas and their self-reported instructional practices. A third correlation analysis is used to determine if a relationship existed between eighth grade science teachers’ self-reported beliefs about their preparedness to teach specific science content areas and eighth grade student science achievement. A fourth correlation analysis is used to determine if a relationship existed between teachers’ self-reported instructional practices in teaching science to eighth grade students and eighth grade science achievement.

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The purpose of this study was to examine the relationship between teachers’ self-reported preparedness for teaching science content and their instructional practices to the science achievement of eighth grade science students in the United States as demonstrated by TIMSS 2007. Six hundred eighty-seven eighth grade science teachers in the United States representing 7,377 students responded to the TIMSS 2007 questionnaire about their instructional preparedness and their instructional practices.

This quantitative study sought to answer the following

questions:

1. What is the orientation of science teachers, with respect to their preparedness to teach specific science content to eighth grade science students?

2. What is the orientation of science teachers, on a continuum from didactic to inquiry oriented, with respect to their self-reported instructional practices in teaching science to eighth grade science students?

3. What, if any, relationship exists between teachers’ beliefs about preparedness to teach science content and their self-reported instructional practices in teaching science to eighth grade science students?

4. What, if any relationship exists between student

achievement in science and:

a. Teachers’ beliefs about preparedness to teach

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Quantitative data were reported according to five purposes. Descriptive analysis of teachers’ characteristics examined several dimensions and provided an accurate showcase of the population from which the sample was derived. A correlation analysis was used to determine if a relationship existed between eighth grade science teachers’ main area of study and their self-reported beliefs on preparedness to teach specific science content areas. A second correlation analysis was used to determine if a relationship existed between eighth grade science teachers’ self-reported beliefs about their preparedness to teach specific science content areas and their self-reported instructional practices. A third correlation analysis was used to determine if a relationship existed between eighth grade science teachers’ self-reported beliefs about their preparedness to teach specific science content areas and eighth grade student science achievement. A final correlation analysis was used to determine if a relationship existed between teachers’ self-reported instructional practices in teaching science to eighth grade students and eighth grade science achievement.



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