Sentences with phrase «student outcome variable»

Inter-correlations among the intersections between teacher and student outcome variables were also subjected to factor analysis achieved through step-wise regression modeling techniques to determine the most potent predictors of student arts and academic learning outcomes.

Not exact matches

Correlational evidence shows that sizable changes in teacher - related variables are associated with much smaller changes in student learning outcomes (Hill, Rowan, and Ball, 2005; Hanushek and Rivkin, 2012).

In order to remove the confounding influence of unobserved factors that have an impact on both school spending and student outcomes, we calculate how much spending in a given school district would have been predicted to change due solely to the passage of an SFR, and use that prediction, rather than the spending change the district actually experienced, as our key variable.

Phase 1 trials would be small, nongenerablizable empirical studies whose dependent variable is not year - end test scores, but «next - day or next - week outcomes: measurable effects on student behavior, effort, or short - term learning.»

Using a rich set of control variables, the report generates a ranking that shows which states are «breaking the curve» — producing stronger academic outcomes for their students compared to demographically similar students across the US.

From predicting the outcomes to identifying variables and finally scaffolding students learning so they can make informed conclusions, this investigation writing frame will encourage your students to focus on the specific scientific skills needed for any investigation.

They need to be taught that, though showing students a movie or an animation of something happening can be more instructive than just reading about it, true simulation (which is even more instructive) means that students can change variables and affect the outcome.

Our primary outcome variable is student achievement as measured by performance on standardized tests.

The outcome of the lottery, a random event, was used to create what statisticians refer to as an instrumental variable, which obtains unbiased estimates of the effects of attending private school on students» test scores.

This would be a time - variable system of education rooted in the outcomes or competencies students are expected to achieve.

In either case, these «unobserved» variables get in the way because students using vouchers may have had different academic outcomes even if there were no voucher program.

Although a seemingly common sense concept, researchers have had difficulty substantiating the connection between educator professional learning and student outcomes because of the host of potential variables and the overall complexity of the research.

In her 2011 memoir, A Chance to Make History, Kopp wrote that «education can trump poverty» as long as a teacher accepts her responsibility as the «key variable» driving measurable student outcomes.

Although a host of variables intervene between educator professional learning activities and student learning, Guskey and Sparks noted three major characteristics of professional development that have a direct influence on educator learning, which indirectly leads to student outcomes.

We treat Instrumental Variables Analysis (IV) estimates of the impact of private schooling on student outcomes, some of which are being presented for the first time in this study, as the causal «benchmark» estimate.

The key assumption for identification underlying all of our estimates is the conditional independence assumption (CIA), which assumes that there are no variables omitted from our matching process that affect both the choice of textbook and the outcome (student achievement).

Due to the complexity of the study, the fact that many of the classroom variables focus on grades 1 - 3 (e.g., student level of engagement, time spent in small - or whole - group instruction, preferred interaction style), and the use of different outcome measures, the kindergarten classrooms were dropped from the analysis.

Thus, when either high quality instructional leadership or high quality instruction does not occur, student achievement outcomes can be variable as a result.

In a nutshell, she points out that the MET study asked whether actual observation of teaching, student surveys, or VAM test score measures did a better job of predicting future student test score growth, which «privileges» test scores by using it both as a variable being tested and as the outcome reflecting gains.

With respect to the absence of a relationship between teachers» beliefs and student motivation, although there is good theoretical and empirical evidence to suggest that these variables could predict student outcomes, it is also true that linking teacher - level beliefs to student outcomes is not a clear and straight path (Holzberger et al. in press; Klassen et al. 2011).

A Value - Added Model (VAM) is a multivariate (multiple variable) student growth model that attempts to account or statistically control for all potential student, teacher, school, district, and external influences on outcome measures (i.e., growth in student achievement over time).

In addition, teacher - level variables did not have an effect on student outcomes.

Teachers are the most important in - school variable that influences student outcomes, yet our schools vary widely in their teacher quality.

I found that few variables from a principal's background had a statistically significant relationship with outcomes such as student math and reading scores, attendance, and principal retention at the school or district level.

Most analysts in the education policy conversation agree that teacher quality is the most important in - school variable shaping students» educational outcomes.

Studies have found that the most critical variable affecting the educational outcomes of students is the effectiveness of classroom teachers.

At the secondary level, an additional outcome, or dependent variable predicted is the number of Regents Exams a student passed for the first time in the current year.

An unconditional HLM is one without an explanatory variable that allows us to answer the question: how much variance in student outcome can be attributed to systematic differences between classrooms and schools on specific factors?

For school communities already committed to providing arts integration practice, the alternative methods and tools developed in the PAIR project demonstrate how to qualitatively and quantitatively assess the impact of individual teacher arts integration professional development variables on individual student arts integration and academic learning outcomes.

One indicator that this study may sufficiently account for both selection and omitted variable bias, is that its results are consistent with randomized studies on schools choice that also find no relationship between choice and student outcomes 7 8 9.

These systems included measures of school context (resources, student background variables, and so on); processes (curriculum coherence, leadership and teaching, and so on); and outcomes (student achievement, graduation rate, school safety, and so on).

When students are not progressing or mastering curricular content or skills, functional, curriculum - based assessments are conducted whereby teachers identify and analyze (a) relevant curricular and instructional variables and their relationship to student achievement outcomes; (b) assess curricular (i.e., scope and sequence) placement and performance expectations and outcomes; and (c) complete curricular task analyses and student mastery checks.

Thus, each outcome variable was regressed on number of years (at t1, t2... t5) among the SET students and the No - SET students separately.

(i) For each of the outcome variables, a linear regression was performed for each student group, which provides measures of the linear trends as effects of the intervention.

(iii) Analyses of variance (ANOVAs)(or multivariate analyses of variance, MANOVAs, when we analysed an instrument with subscales, such as the YSR and the ITIA) were run on the outcome scale (or subscales), with intervention or not (SET or No - SET), number of years (t1, t2... t5) and student gender as independent variables.

Garrard and Lipsey (2007) included additional tests — called moderator analyses — to see whether any factors, such as student variables, strengthened the likelihood that conflict resolution education (CRE) programs improved outcomes.