Behaviors
Sample
Students in our samples look similar to those in many urban districts in the United States, where roughly 68% are eligible for free or reduced-price lunch, 14% are classified as in need of special education services, and 16% are identified as limited English proficient; roughly 31% are African American, 39% are Hispanic, and 28% are white ( Council of the Great City Schools, 2013 ). We do observe some statistically significant differences between student characteristics in the full sample versus our analytic subsample. For example, the percentage of students identified as limited English proficient was 20% in the full sample compared to 14% in the sample of students who ever were part of analyses drawing on our survey measures. Although variation in samples could result in dissimilar estimates across models, the overall character of our findings is unlikely to be driven by these modest differences.
As part of the expansive data collection effort, researchers administered a student survey with items (N = 18) that were adapted from other large-scale surveys including the TRIPOD, the MET project, the National Assessment of Educational Progress (NAEP), and the Trends in International Mathematics and Science Study (TIMSS) (see Appendix Table 1 for a full list of items). Items were selected based on a review of the research literature and identification of constructs thought most likely to be influenced by upper-elementary teachers. Students rated all items on a five-point Likert scale where 1 = Totally Untrue and 5 = Totally True.
We identified a parsimonious set of three outcome measures based on a combination of theory and exploratory factor analyses (see Appendix Table 1 ). 3 The first outcome, which we call Self-Efficacy in Math (10 items), is a variation on well-known constructs related to students’ effort, initiative, and perception that they can complete tasks. The second related outcome measure is Happiness in Class (5 items), which was collected in the second and third years of the study. Exploratory factor analyses suggested that these items clustered together with those from Self-Efficacy in Math to form a single construct. However, post-hoc review of these items against the psychology literature from which they were derived suggests that they can be divided into a separate domain. As above, this measure is a school-specific version of well-known scales that capture students’ affect and enjoyment ( Diener, 2000 ). Both Self-Efficacy in Math and Happiness in Class have relatively high internal consistency reliabilities (0.76 and 0.82, respectively) that are similar to those of self-reported attitudes and behaviors explored in other studies ( Duckworth et al., 2007 ; John & Srivastava, 1999 ; Tsukayama et al., 2013 ). Further, self-reported measures of similar constructs have been linked to long-term outcomes, including academic engagement and earnings in adulthood, even conditioning on cognitive ability ( King, McInerney, Ganotice, & Villarosa, 2015 ; Lyubomirsky, King, & Diener, 2005 ).
The third and final construct consists of three items that were meant to hold together and which we call Behavior in Class (internal consistency reliability is 0.74). Higher scores reflect better, less disruptive behavior. Teacher reports of students’ classroom behavior have been found to relate to antisocial behaviors in adolescence, criminal behavior in adulthood, and earnings ( Chetty et al., 2011 ; Segal, 2013 ; Moffitt et al., 2011 ; Tremblay et al., 1992 ). Our analysis differs from these other studies in the self-reported nature of the behavior outcome. That said, other studies also drawing on elementary school students found correlations between self-reported and either parent- or teacher-reported measures of behavior that were similar in magnitude to correlations between parent and teacher reports of student behavior ( Achenbach, McConaughy, & Howell, 1987 ; Goodman, 2001 ). Further, other studies have found correlations between teacher-reported behavior of elementary school students and either reading or math achievement ( r = 0.22 to 0.28; Miles & Stipek, 2006 ; Tremblay et al., 1992 ) similar to the correlation we find between students’ self-reported Behavior in Class and our two math test scores ( r = 0.24 and 0.26; see Table 2 ). Together, this evidence provides both convergent and consequential validity evidence for this outcome measure. For all three of these outcomes, we created final scales by reverse coding items with negative valence and averaging raw student responses across all available items. 4 We standardized these final scores within years, given that, for some measures, the set of survey items varied across years.
Descriptive Statistics for Students' Academic Performance, Attitudes, and Behaviors
Univariate Statistics | Pairwise Correlations | |||||||
---|---|---|---|---|---|---|---|---|
Mean | SD | Internal Consistency Reliability | High- Stakes Math Test | Low- Stakes Math Test | Self- Efficacy in Math | Happiness in Class | Behavior in Class | |
High-Stakes Math Test | 0.10 | 0.91 | -- | 1.00 | ||||
Low-Stakes Math Test | 0.61 | 1.1 | 0.82 | 0.70 | 1.00 | |||
Self-Efficacy in Math | 4.17 | 0.58 | 0.76 | 0.25 | 0.22 | 1.00 | ||
Happiness in Class | 4.10 | 0.85 | 0.82 | 0.15 | 0.10 | 0.62 | 1.00 | |
Behavior in Class | 4.10 | 0.93 | 0.74 | 0.24 | 0.26 | 0.35 | 0.27 | 1.00 |
For high-stakes math test, reliability varies by district; thus, we report the lower bound of these estimates. Self-Efficacy in Math, Happiness in Class, and Behavior in Class are measured on a 1 to 5 Likert Scale. Statistics were generated from all available data.
Student demographic and achievement data came from district administrative records. Demographic data include gender, race/ethnicity, free- or reduced-price lunch (FRPL) eligibility, limited English proficiency (LEP) status, and special education (SPED) status. These records also included current- and prior-year test scores in math and English Language Arts (ELA) on state assessments, which we standardized within districts by grade, subject, and year using the entire sample of students.
The project also administered a low-stakes mathematics assessment to all students in the study. Internal consistency reliability is 0.82 or higher for each form across grade levels and school years ( Hickman, Fu, & Hill, 2012 ). We used this assessment in addition to high-stakes tests given that teacher effects on two outcomes that aim to capture similar underlying constructs (i.e., math achievement) provide a unique point of comparison when examining the relationship between teacher effects on student outcomes that are less closely related (i.e., math achievement versus attitudes and behaviors). Indeed, students’ high- and low-stake math test scores are correlated more strongly ( r = 0.70) than any other two outcomes (see Table 1 ). 5
Teachers’ mathematics lessons were captured over a three-year period, with an average of three lessons per teacher per year. 6 Trained raters scored these lessons on two established observational instruments, the CLASS and the MQI. Analyses of these same data show that items cluster into four main factors ( Blazar et al., 2015 ). The two dimensions from the CLASS instrument capture general teaching practices: Emotional Support focuses on teachers’ interactions with students and the emotional environment in the classroom, and is thought to increase students’ social and emotional development; and Classroom Organization focuses on behavior management and productivity of the lesson, and is thought to improve students’ self-regulatory behaviors ( Pianta & Hamre, 2009 ). 7 The two dimensions from the MQI capture mathematics-specific practices: Ambitious Mathematics Instruction focuses on the complexity of the tasks that teachers provide to their students and their interactions around the content, thus corresponding to the set of professional standards described by NCTM (1989 , 2014 ) and many elements contained within the Common Core State Standards for Mathematics ( National Governors Association Center for Best Practices, 2010 ); Mathematical Errors identifies any mathematical errors or imprecisions the teacher introduces into the lesson. Both dimensions from the MQI are linked to teachers’ mathematical knowledge for teaching and, in turn, to students’ math achievement ( Blazar, 2015 ; Hill et al., 2008 ; Hill, Schilling, & Ball, 2004 ). Correlations between dimensions range from roughly 0 (between Emotional Support and Mathematical Errors ) to 0.46 (between Emotional Support and Classroom Organization ; see Table 3 ).
Descriptive Statistics for CLASS and MQI Dimensions
Univariate Statistics | Pairwise Correlations | ||||||
---|---|---|---|---|---|---|---|
Mean | SD | Adjusted Intraclass Correlation | Emotional Support | Classroom Organization | Ambitious Mathematics Instruction | Mathematical Errors | |
Emotional Support | 4.28 | 0.48 | 0.53 | 1.00 | |||
Classroom Organization | 6.41 | 0.39 | 0.63 | 0.46 | 1.00 | ||
Ambitious Mathematics Instruction | 1.27 | 0.11 | 0.74 | 0.22 | 0.23 | 1.00 | |
Mathematical Errors | 1.12 | 0.09 | 0.56 | 0.01 | 0.09 | −0.27 | 1.00 |
Intraclass correlations were adjusted for the modal number of lessons. CLASS items (from Emotional Support and Classroom Organization) were scored on a scale from 1 to 7. MQI items (from Ambitious Instruction and Errors) were scored on a scale from 1 to 3. Statistics were generated from all available data.
We estimated reliability for these metrics by calculating the amount of variance in teacher scores that is attributable to the teacher (the intraclass correlation [ICC]), adjusted for the modal number of lessons. These estimates are: 0.53, 0.63, 0.74, and 0.56 for Emotional Support, Classroom Organization, Ambitious Mathematics Instruction , and Mathematical Errors , respectively (see Table 3 ). Though some of these estimates are lower than conventionally acceptable levels (0.7), they are consistent with those generated from similar studies ( Kane & Staiger, 2012 ). We standardized scores within the full sample of teachers to have a mean of zero and a standard deviation of one.
4.1. estimating teacher effects on students’ attitudes and behaviors.
Like others who aim to examine the contribution of individual teachers to student outcomes, we began by specifying an education production function model of each outcome for student i in district d , school s , grade g , class c with teacher j at time t :
OUTCOME idsgict is used interchangeably for both math test scores and students’ attitudes and behaviors, which we modeled in separate equations as a cubic function of students’ prior achievement, A it −1 , in both math and ELA on the high-stakes district tests 8 ; demographic characteristics, X it , including gender, race, FRPL eligibility, SPED status, and LEP status; these same test-score variables and demographic characteristics averaged to the class level, X ¯ it c ; and district-by-grade-by-year fixed effects, τ dgt , that account for scaling of high-stakes test. The residual portion of the model can be decomposed into a teacher effect, µ j , which is our main parameter of interest and captures the contribution of teachers to student outcomes above and beyond factors already controlled for in the model; a class effect, δ jc , which is estimated by observing teachers over multiple school years; and a student-specific error term,. ε idsgjct 9
The key identifying assumption of this model is that teacher effect estimates are not biased by non-random sorting of students to teachers. Recent experimental ( Kane, McCaffrey, Miller, & Staiger, 2013 ) and quasi-experimental ( Chetty et al., 2014 ) analyses provide strong empirical support for this claim when student achievement is the outcome of interest. However, much less is known about bias and sorting mechanisms when other outcomes are used. For example, it is quite possible that students were sorted to teachers based on their classroom behavior in ways that were unrelated to their prior achievement. To address this possibility, we made two modifications to equation (1) . First, we included school fixed effects, ω s , to account for sorting of students and teachers across schools. This means that estimates rely only on between-school variation, which has been common practice in the literature estimating teacher effects on student achievement. In their review of this literature, Hanushek and Rivkin (2010) propose ignoring the between-school component because it is “surprisingly small” and because including this component leads to “potential sorting, testing, and other interpretative problems” (p. 268). Other recent studies estimating teacher effects on student outcomes beyond test scores have used this same approach ( Backes & Hansen, 2015 ; Gershenson, 2016 ; Jackson, 2012 ; Jennings & DiPrete, 2010 ; Ladd & Sorensen, 2015 ; Ruzek et al., 2015 ). Another important benefit of using school fixed effects is that this approach minimizes the possibility of reference bias in our self-reported measures ( West et al., 2016 ; Duckworth & Yeager, 2015 ). Differences in school-wide norms around behavior and effort may change the implicit standard of comparison (i.e. reference group) that students use to judge their own behavior and effort.
Restricting comparisons to other teachers and students within the same school minimizes this concern. As a second modification for models that predict each of our three student survey measures, we included OUTCOME it −1 on the right-hand side of the equation in addition to prior achievement – that is, when predicting students’ Behavior in Class , we controlled for students’ self-reported Behavior in Class in the prior year. 10 This strategy helps account for within-school sorting on factors other than prior achievement.
Using equation (1) , we estimated the variance of µ j , which is the stable component of teacher effects. We report the standard deviation of these estimates across outcomes. This parameter captures the magnitude of the variability of teacher effects. With the exception of teacher effects on students’ Happiness in Class , where survey items were not available in the first year of the study, we included δ jc in order to separate out the time-varying portion of teacher effects, combined with peer effects and any other class-level shocks. The fact that we are able to separate class effects from teacher effects is an important extension of prior studies examining teacher effects on outcomes beyond test scores, many of which only observed teachers at one point in time.
Following Chetty et al. (2011) , we estimated the magnitude of the variance of teacher effects using a direct, model-based estimate derived via restricted maximum likelihood estimation. This approach produces a consistent estimator for the true variance of teacher effects ( Raudenbush & Bryk, 2002 ). Calculating the variation across individual teacher effect estimates using Ordinary Least Squares regression would bias our variance estimates upward because it would conflate true variation with estimation error, particularly in instances where only a handful of students are attached to each teachers. Alternatively, estimating the variation in post-hoc predicted “shrunken” empirical Bayes estimates would bias our variance estimate downward relative to the size of the measurement error (Jacob & Lefgren, 2005).
We examined the contribution of teachers’ classroom practices to our set of student outcomes by estimating a variation of equation (1) :
This multi-level model includes the same set of control variables as above in order to account for the non-random sorting of students to teachers and for factors beyond teachers’ control that might influence each of our outcomes. We further included a vector of their teacher j ’s observation scores, OBSER VAT ^ ION l J , − t . The coefficients on these variables are our main parameters of interest and can be interpreted as the change in standard deviation units for each outcome associated with exposure to teaching practice one standard deviation above the mean.
One concern when relating observation scores to student survey outcomes is that they may capture the same behaviors. For example, teachers may receive credit on the Classroom Organization domain when their students demonstrate orderly behavior. In this case, we would have the same observed behaviors on both the left and right side of our equation relating instructional quality to student outcomes, which would inflate our teaching effect estimates. A related concern is that the specific students in the classroom may influence teachers’ instructional quality ( Hill et al., 2015 ; Steinberg & Garrett, 2016 ; Whitehurst, Chingos, & Lindquist, 2014 ). While the direction of bias is not as clear here – as either lesser- or higher-quality teachers could be sorted to harder to educate classrooms – this possibility also could lead to incorrect estimates. To avoid these sources of bias, we only included lessons captured in years other than those in which student outcomes were measured, denoted by – t in the subscript of OBSER VAT ^ ION l J , − t . To the extent that instructional quality varies across years, using out-of-year observation scores creates a lower-bound estimate of the true relationship between instructional quality and student outcomes. We consider this an important tradeoff to minimize potential bias. We used predicted shrunken observation score estimates that account for the fact that teachers contributed different numbers of lessons to the project, and fewer lessons could lead to measurement error in these scores ( Hill, Charalambous, & Kraft, 2012 ). 11
An additional concern for identification is the endogeneity of observed classroom quality. In other words, specific teaching practices are not randomly assigned to teachers. Our preferred analytic approach attempted to account for potential sources of bias by conditioning estimates of the relationship between one dimension of teaching practice and student outcomes on the three other dimensions. An important caveat here is that we only observed teachers’ instruction during math lessons and, thus, may not capture important pedagogical practices teachers used with these students when teaching other subjects. Including dimensions from the CLASS instrument, which are meant to capture instructional quality across subject areas ( Pianta & Hamre, 2009 ), helps account for some of this concern. However, given that we were not able to isolate one dimension of teaching quality from all others, we consider this approach as providing suggestive rather than conclusive evidence on the underlying causal relationship between teaching practice and students’ attitudes and behaviors.
In our third and final set of analyses, we examined whether teachers who are effective at raising math test scores are equally effective at developing students’ attitudes and behaviors. To do so, we drew on equation (1) to estimate µ̂ j for each outcome and teacher j . Following Chetty et al., 2014 ), we use post-hoc predicted “shrunken” empirical Bayes estimates of µ̂ j derived from equation (1) . Then, we generated a correlation matrix of these teacher effect estimates.
Despite attempts to increase the precision of these estimates through empirical Bayes estimation, estimates of individual teacher effects are measured with error that will attenuate these correlations ( Spearman, 1904 ). Thus, if we were to find weak to moderate correlations between different measures of teacher effectiveness, this could identify multidimensionality or could result from measurement challenges, including the reliability of individual constructs ( Chin & Goldhaber, 2015 ). For example, prior research suggests that different tests of students’ academic performance can lead to different teacher rankings, even when those tests measure similar underlying constructs ( Lockwood et al., 2007 ; Papay, 2011 ). To address this concern, we focus our discussion on relative rankings in correlations between teacher effect estimates rather than their absolute magnitudes. Specifically, we examine how correlations between teacher effects on two closely related outcomes (e.g., two math achievement tests) compare with correlations between teacher effects on outcomes that aim to capture different underlying constructs. In light of research highlighted above, we did not expect the correlation between teacher effects on the two math tests to be 1 (or, for that matter, close to 1). However, we hypothesized that these relationships should be stronger than the relationship between teacher effects on students’ math performance and effects on their attitudes and behaviors.
We begin by presenting results of the magnitude of teacher effects in Table 4 . Here, we observe sizable teacher effects on students’ attitudes and behaviors that are similar to teacher effects on students’ academic performance. Starting first with teacher effects on students’ academic performance, we find that a one standard deviation difference in teacher effectiveness is equivalent to a 0.17 sd or 0.18 sd difference in students’ math achievement. In other words, relative to an average teacher, teachers at the 84 th percentile of the distribution of effectiveness move the medium student up to roughly the 57 th percentile of math achievement. Notably, these findings are similar to those from other studies that also estimate within-school teacher effects in large administrative datasets ( Hanushek & Rivkin, 2010 ). This suggests that our use of school fixed effects with a more limited number of teachers observed within a given school does not appear to overly restrict our identifying variation. In Online Appendix A , where we present the magnitude of teacher effects from alternative model specifications, we show that results are robust to models that exclude school fixed effects or replace school fixed effects with observable school characteristics. Estimated teacher effects on students’ self-reported Self-Efficacy in Math and Behavior in Class are 0.14 sd and 0.15 sd, respectively. The largest teacher effects we observe are on students’ Happiness in Class , of 0.31 sd. Given that we do not have multiple years of data to separate out class effects for this measure, we interpret this estimate as the upward bound of true teacher effects on Happiness in Class. Rescaling this estimate by the ratio of teacher effects with and without class effects for Self-Efficacy in Math (0.14/0.19 = 0.74; see Online Appendix A ) produces an estimate of stable teacher effects on Happiness in Class of 0.23 sd, still larger than effects for other outcomes.
Teacher Effects on Students' Academic Performance, Attitudes, and Behaviors
Observations | SD of Teacher- Level Variance | ||
---|---|---|---|
Teachers | Students | ||
High-Stakes Math Test | 310 | 10,575 | 0.18 |
Low-Stakes Math Test | 310 | 10,575 | 0.17 |
Self-Efficacy in Math | 108 | 1,433 | 0.14 |
Happiness in Class | 51 | 548 | 0.31 |
Behavior in Class | 111 | 1,529 | 0.15 |
Notes: Cells contain estimates from separate multi-level regression models.
All effects are statistically significant at the 0.05 level.
Next, we examine whether certain characteristics of teachers’ instructional practice help explain the sizable teacher effects described above. We present unconditional estimates in Table 5 Panel A, where the relationship between one dimension of teaching practice and student outcomes is estimated without controlling for the other three dimensions. Thus, cells contain estimates from separate regression models. In Panel B, we present conditional estimates, where all four dimensions of teaching quality are included in the same regression model. Here, columns contain estimates from separate regression models. We present all estimates as standardized effect sizes, which allows us to make comparisons across models and outcome measures. Unconditional and conditional estimates generally are quite similar. Therefore, we focus our discussion on our preferred conditional estimates.
Teaching Effects on Students' Academic Performance, Attitudes, and Behaviors
High- Stakes Math Test | Low- Stakes Math Test | Self- Efficacy in Math | Happiness in Class | Behavior in Class | |
---|---|---|---|---|---|
Panel A: Unconditional Estimates | |||||
Emotional Support | 0.012 (0.013) | 0.018 (0.014) | 0.142 (0.031) | 0.279 (0.082) | 0.039 (0.027) |
Classroom Organization | −0.017 (0.014) | −0.010 (0.014) | 0.065 (0.038) | 0.001 (0.090) | 0.081 (0.033) |
Ambitious Mathematics Instruction | 0.017 (0.015) | 0.021 (0.015) | 0.077 (0.036) | 0.082 (0.068) | 0.004 (0.032) |
Mathematical Errors | −0.027 (0.013) | −0.009 (0.014) | −0.107 (0.030) | −0.164 (0.076) | −0.027 (0.027) |
Panel B: Conditional Estimates | |||||
Emotional Support | 0.015 (0.014) | 0.020 (0.015) | 0.135 (0.034) | 0.368 (0.090) | 0.030 (0.030) |
Classroom Organization | −0.022 (0.014) | −0.018 (0.015) | −0.020 (0.042) | −0.227 (0.096) | 0.077 (0.036) |
Ambitious Mathematics Instruction | 0.014 (0.015) | 0.019 (0.016) | −0.006 (0.040) | 0.079 (0.068) | −0.034 (0.036) |
Mathematical Errors | −0.024 (0.013) | −0.005 (0.014) | −0.094** (0.033) | −0.181 (0.081) | −0.009 (0.029) |
Teacher Observations | 196 | 196 | 90 | 47 | 93 |
Student Observations | 8,660 | 8,660 | 1,275 | 517 | 1,362 |
In Panel A, cells contain estimates from separate regression models. In Panel B, columns contain estimates from separate regression models, where estimates are conditioned on other teaching practices. All models control for student and class characteristics, school fixed effects, and district-by-grade-by-year fixed effects, and include and teacher random effects. Models predicting all outcomes except for Happiness in Class also include class random effects.
We find that students’ attitudes and behaviors are predicted by both general and content-specific teaching practices in ways that generally align with theory. For example, teachers’ Emotional Support is positively associated with the two closely related student constructs, Self-Efficacy in Math and Happiness in Class . Specifically, a one standard deviation increase in teachers’ Emotional Support is associated with a 0.14 sd increase in students’ Self-Efficacy in Math and a 0.37 sd increase in students’ Happiness in Class . These finding makes sense given that Emotional Support captures teacher behaviors such as their sensitivity to students, regard for students’ perspective, and the extent to which they create a positive climate in the classroom. As a point of comparison, these estimates are substantively larger than those between principal ratings of teachers’ ability to improve test scores and their actual ability to do so, which fall in the range of 0.02 sd and 0.08 sd ( Jacob & Lefgren, 2008 ; Rockoff, Staiger, Kane, & Taylor, 2012 ; Rockoff & Speroni, 2010 ).
We also find that Classroom Organization , which captures teachers’ behavior management skills and productivity in delivering content, is positively related to students’ reports of their own Behavior in Class (0.08 sd). This suggests that teachers who create an orderly classroom likely create a model for students’ own ability to self-regulate. Despite this positive relationship, we find that Classroom Organization is negatively associated with Happiness in Class (−0.23 sd), suggesting that classrooms that are overly focused on routines and management are negatively related to students’ enjoyment in class. At the same time, this is one instance where our estimate is sensitive to whether or not other teaching characteristics are included in the model. When we estimate the relationship between teachers’ Classroom Organization and students’ Happiness in Class without controlling for the three other dimensions of teaching quality, this estimate approaches 0 and is no longer statistically significant. 12 We return to a discussion of the potential tradeoffs between Classroom Organization and students’ Happiness in Class in our conclusion.
Finally, we find that the degree to which teachers commit Mathematical Errors is negatively related to students’ Self-Efficacy in Math (−0.09 sd) and Happiness in Class (−0.18 sd). These findings illuminate how a teacher’s ability to present mathematics with clarity and without serious mistakes is related to their students’ perceptions that they can complete math tasks and their enjoyment in class.
Comparatively, when predicting scores on both math tests, we only find one marginally significant relationship – between Mathematical Errors and the high-stakes math test (−0.02 sd). For two other dimensions of teaching quality, Emotional Support and Ambitious Mathematics Instruction , estimates are signed the way we would expect and with similar magnitudes, though they are not statistically significant. Given the consistency of estimates across the two math tests and our restricted sample size, it is possible that non-significant results are due to limited statistical power. 13 At the same time, even if true relationships exist between these teaching practices and students’ math test scores, they likely are weaker than those between teaching practices and students’ attitudes and behaviors. For example, we find that the 95% confidence intervals relating Classroom Emotional Support to Self-Efficacy in Math [0.068, 0.202] and Happiness in Class [0.162, 0.544] do not overlap with the 95% confidence intervals for any of the point estimates predicting math test scores. We interpret these results as indication that, still, very little is known about how specific classroom teaching practices are related to student achievement in math. 14
In Online Appendix B , we show that results are robust to a variety of different specifications, including (1) adjusting observation scores for characteristics of students in the classroom, (2) controlling for teacher background characteristics (i.e., teaching experience, math content knowledge, certification pathway, education), and (3) using raw out-of-year observation scores (rather than shrunken scores). This suggests that our approach likely accounts for many potential sources of bias in our teaching effect estimates.
In Table 6 , we present correlations between teacher effects on each of our student outcomes. The fact that teacher effects are measured with error makes it difficult to estimate the precise magnitude of these correlations. Instead, we describe relative differences in correlations, focusing on the extent to which teacher effects within outcome type – i.e., teacher effects on the two math achievement tests or effects on students’ attitudes and behaviors – are similar or different from correlations between teacher effects across outcome type. We illustrate these differences in Figure 1 , where Panel A presents scatter plots of these relationships between teacher effects within outcome type and Panel B does the same across outcome type. Recognizing that not all of our survey outcomes are meant to capture the same underlying construct, we also describe relative differences in correlations between teacher effects on these different measures. In Online Appendix C , we find that an extremely conservative adjustment that scales correlations by the inverse of the square root of the product of the reliabilities leads to a similar overall pattern of results.
Scatter plots of teacher effects across outcomes. Solid lines represent the best-fit regression line.
Correlations Between Teacher Effects on Students' Academic Performance, Attitudes, and Behaviors
High-Stakes Math Test | Low-Stakes Math Test | Self- Efficacy in Math | Happiness in Class | Behavior in Class | |
---|---|---|---|---|---|
High-Stakes Math Test | 1.00 -- | ||||
Low-Stakes Math Test | 0.64 (0.04) | 1.00 -- | |||
Self-Efficacy in Math | 0.16 (0.10) | 0.19 (0.10) | 1.00 -- | ||
Happiness in Class | −0.09 (0.14) | −0.21 (0.14) | 0.26~ (0.14) | 1.00 -- | |
Behavior in Class | 0.10 (0.10) | 0.12 (0.10) | 0.49 (0.08) | 0.21 (0.14) | 1.00 -- |
Standard errors in parentheses. See Table 4 for sample sizes used to calculate teacher effect estimates. The sample for each correlation is the minimum number of teachers between the two measures.
Examining the correlations of teacher effect estimates reveals that individual teachers vary considerably in their ability to impact different student outcomes. As hypothesized, we find the strongest correlations between teacher effects within outcome type. Similar to Corcoran, Jennings, and Beveridge (2012) , we estimate a correlation of 0.64 between teacher effects on our high- and low-stakes math achievement tests. We also observe a strong correlation of 0.49 between teacher effects on two of the student survey measures, students’ Behavior in Class and Self-Efficacy in Math . Comparatively, the correlations between teacher effects across outcome type are much weaker. Examining the scatter plots in Figure 1 , we observe much more dispersion around the best-fit line in Panel B than in Panel A. The strongest relationship we observe across outcome types is between teacher effects on the low-stakes math test and effects on Self-Efficacy in Math ( r = 0.19). The lower bound of the 95% confidence interval around the correlation between teacher effects on the two achievement measures [0.56, 0.72] does not overlap with the 95% confidence interval of the correlation between teacher effects on the low-stakes math test and effects on Self-Efficacy in Math [−0.01, 0.39], indicating that these two correlations are substantively and statistically significantly different from each other. Using this same approach, we also can distinguish the correlation describing the relationship between teacher effects on the two math tests from all other correlations relating teacher effects on test scores to effects on students’ attitudes and behaviors. We caution against placing too much emphasis on the negative correlations between teacher effects on test scores and effects on Happiness in Class ( r = −0.09 and −0.21 for the high- and low-stakes tests, respectively). Given limited precision of this relationship, we cannot reject the null hypothesis of no relationship or rule out weak, positive or negative correlations among these measures.
Although it is useful to make comparisons between the strength of the relationships between teacher effects on different measures of students’ attitudes and behaviors, measurement error limits our ability to do so precisely. At face value, we find correlations between teacher effects on Happiness in Class and effects on the two other survey measures ( r = 0.26 for Self-Efficacy in Math and 0.21 for Behavior in Class ) that are weaker than the correlation between teacher effects on Self-Efficacy in Math and effects on Behavior in Class described above ( r = 0.49). One possible interpretation of these findings is that teachers who improve students’ Happiness in Class are not equally effective at raising other attitudes and behaviors. For example, teachers might make students happy in class in unconstructive ways that do not also benefit their self-efficacy or behavior. At the same time, these correlations between teacher effects on Happiness in Class and the other two survey measures have large confidence intervals, likely due to imprecision in our estimate of teacher effects on Happiness in Class . Thus, we are not able to distinguish either correlation from the correlation between teacher effects on Behavior in Class and effects on Self-Efficacy in Math .
6.1. relationship between our findings and prior research.
The teacher effectiveness literature has profoundly shaped education policy over the last decade and has served as the catalyst for sweeping reforms around teacher recruitment, evaluation, development, and retention. However, by and large, this literature has focused on teachers’ contribution to students’ test scores. Even research studies such as the Measures of Effective Teaching project and new teacher evaluation systems that focus on “multiple measures” of teacher effectiveness ( Center on Great Teachers and Leaders, 2013 ; Kane et al., 2013 ) generally attempt to validate other measures, such as observations of teaching practice, by examining their relationship to estimates of teacher effects on students’ academic performance.
Our study extends an emerging body of research examining the effect of teachers on student outcomes beyond test scores. In many ways, our findings align with conclusions drawn from previous studies that also identify teacher effects on students’ attitudes and behaviors ( Jennings & DiPrete, 2010 ; Kraft & Grace, 2016 ; Ruzek et al., 2015 ), as well as weak relationships between different measures of teacher effectiveness ( Gershenson, 2016 ; Jackson, 2012 ; Kane & Staiger, 2012 ). To our knowledge, this study is the first to identify teacher effects on measures of students’ self-efficacy in math and happiness in class, as well as on a self-reported measure of student behavior. These findings suggest that teachers can and do help develop attitudes and behaviors among their students that are important for success in life. By interpreting teacher effects alongside teaching effects, we also provide strong face and construct validity for our teacher effect estimates. We find that improvements in upper-elementary students’ attitudes and behaviors are predicted by general teaching practices in ways that align with hypotheses laid out by instrument developers ( Pianta & Hamre, 2009 ). Findings linking errors in teachers’ presentation of math content to students’ self-efficacy in math, in addition to their math performance, also are consistent with theory ( Bandura et al., 1996 ). Finally, the broad data collection effort from NCTE allows us to examine relative differences in relationships between measures of teacher effectiveness, thus avoiding some concerns about how best to interpret correlations that differ substantively across studies ( Chin & Goldhaber, 2015 ). We find that correlations between teacher effects on student outcomes that aim to capture different underlying constructs (e.g., math test scores and behavior in class) are weaker than correlations between teacher effects on two outcomes that are much more closely related (e.g., math achievement).
These findings can inform policy in several key ways. First, our findings may contribute to the recent push to incorporate measures of students’ attitudes and behaviors – and teachers’ ability to improve these outcomes – into accountability policy (see Duckworth, 2016 ; Miller, 2015 ; Zernike, 2016 for discussion of these efforts in the press). After passage of the Every Student Succeeds Act (ESSA), states now are required to select a nonacademic indicator with which to assess students’ success in school ( ESSA, 2015 ). Including measures of students’ attitudes and behaviors in accountability or evaluation systems, even with very small associated weights, could serve as a strong signal that schools and educators should value and attend to developing these skills in the classroom.
At the same time, like other researchers ( Duckworth & Yeager, 2015 ), we caution against a rush to incorporate these measures into high-stakes decisions. The science of measuring students’ attitudes and behaviors is relatively new compared to the long history of developing valid and reliable assessments of cognitive aptitude and content knowledge. Most existing measures, including those used in this study, were developed for research purposes rather than large-scale testing with repeated administrations. Open questions remain about whether reference bias substantially distorts comparisons across schools. Similar to previous studies, we include school fixed effects in all of our models, which helps reduce this and other potential sources of bias. However, as a result, our estimates are restricted to within-school comparisons of teachers and cannot be applied to inform the type of across-school comparisons that districts typically seek to make. There also are outstanding questions regarding the susceptibility of these measures to “survey” coaching when high-stakes incentives are attached. Such incentives likely would render teacher or self-assessments of students’ attitudes and behaviors inappropriate. Some researchers have started to explore other ways to capture students’ attitudes and behaviors, including objective performance-based tasks and administrative proxies such as attendance, suspensions, and participation in extracurricular activities ( Hitt, Trivitt, & Cheng, 2016 ; Jackson, 2012 ; Whitehurst, 2016 ). This line of research shows promise but still is in its early phases. Further, although our modeling strategy aims to reduce bias due to non-random sorting of students to teachers, additional evidence is needed to assess the validity of this approach. Without first addressing these concerns, we believe that adding untested measures into accountability systems could lead to superficial and, ultimately, counterproductive efforts to support the positive development of students’ attitudes and behaviors.
An alternative approach to incorporating teacher effects on students’ attitudes and behaviors into teacher evaluation may be through observations of teaching practice. Our findings suggest that specific domains captured on classroom observation instruments (i.e., Emotional Support and Classroom Organization from the CLASS and Mathematical Errors from the MQI) may serve as indirect measures of the degree to which teachers impact students’ attitudes and behaviors. One benefit of this approach is that districts commonly collect related measures as part of teacher evaluation systems ( Center on Great Teachers and Leaders, 2013 ), and such measures are not restricted to teachers who work in tested grades and subjects.
Similar to Whitehurst (2016) , we also see alternative uses of teacher effects on students’ attitudes and behaviors that fall within and would enhance existing school practices. In particular, measures of teachers’ effectiveness at improving students’ attitudes and behaviors could be used to identify areas for professional growth and connect teachers with targeted professional development. This suggestion is not new and, in fact, builds on the vision and purpose of teacher evaluation described by many other researchers ( Darling-Hammond, 2013 ; Hill & Grossman, 2013 ; Papay, 2012 ). However, in order to leverage these measures for instructional improvement, we add an important caveat: performance evaluations – whether formative or summative – should avoid placing teachers into a single performance category whenever possible. Although many researchers and policymakers argue for creating a single weighted composite of different measures of teachers’ effectiveness ( Center on Great Teachers and Leaders, 2013 ; Kane et al., 2013 ), doing so likely oversimplifies the complex nature of teaching. For example, a teacher who excels at developing students’ math content knowledge but struggles to promote joy in learning or students’ own self-efficacy in math is a very different teacher than one who is middling across all three measures. Looking at these two teachers’ composite scores would suggest they are similarly effective. A single overall evaluation score lends itself to a systematized process for making binary decisions such as whether to grant teachers tenure, but such decisions would be better informed by recognizing and considering the full complexity of classroom practice.
We also see opportunities to maximize students’ exposure to the range of teaching skills we examine through strategic teacher assignments. Creating a teacher workforce skilled in most or all areas of teaching practice is, in our view, the ultimate goal. However, this goal likely will require substantial changes to teacher preparation programs and curriculum materials, as well as new policies around teacher recruitment, evaluation, and development. In middle and high schools, content-area specialization or departmentalization often is used to ensure that students have access to teachers with skills in distinct content areas. Some, including the National Association of Elementary School Principals, also see this as a viable strategy at the elementary level ( Chan & Jarman, 2004 ). Similar approaches may be taken to expose students to a collection of teachers who together can develop a range of academic skills, attitudes and behaviors. For example, when configuring grade-level teams, principals may pair a math teacher who excels in her ability to improve students’ behavior with an ELA or reading teacher who excels in his ability to improve students’ happiness and engagement. Viewing teachers as complements to each other may help maximize outcomes within existing resource constraints.
Finally, we consider the implications of our findings for the teaching profession more broadly. While our findings lend empirical support to research on the multidimensional nature of teaching ( Cohen, 2011 ; Lampert, 2001 ; Pianta & Hamre, 2009 ), we also identify tensions inherent in this sort of complexity and potential tradeoffs between some teaching practices. In our primary analyses, we find that high-quality instruction around classroom organization is positively related to students’ self-reported behavior in class but negatively related to their happiness in class. Our results here are not conclusive, as the negative relationship between classroom organization and students’ happiness in class is sensitive to model specification. However, if there indeed is a negative causal relationship, it raises questions about the relative benefits of fostering orderly classroom environments for learning versus supporting student engagement by promoting positive experiences with schooling. Our own experience as educators and researchers suggests this need not be a fixed tradeoff. Future research should examine ways in which teachers can develop classroom environments that engender both constructive classroom behavior and students’ happiness in class. As our study draws on a small sample of students who had current and prior-year scores for Happiness in Class , we also encourage new studies with greater statistical power that may be able to uncover additional complexities (e.g., non-linear relationships) in these sorts of data.
Our findings also demonstrate a need to integrate general and more content-specific perspectives on teaching, a historical challenge in both research and practice ( Grossman & McDonald, 2008 ; Hamre et al., 2013 ). We find that both math-specific and general teaching practices predict a range of student outcomes. Yet, particularly at the elementary level, teachers’ math training often is overlooked. Prospective elementary teachers often gain licensure without taking college-level math classes; in many states, they do not need to pass the math sub-section of their licensure exam in order to earn a passing grade overall ( Epstein & Miller, 2011 ). Striking the right balance between general and content-specific teaching practices is not a trivial task, but it likely is a necessary one.
For decades, efforts to improve the quality of the teacher workforce have focused on teachers’ abilities to raise students’ academic achievement. Our work further illustrates the potential and importance of expanding this focus to include teachers’ abilities to promote students’ attitudes and behaviors that are equally important for students’ long-term success.
Acknowledgments.
The research reported here was supported in part by the Institute of Education Sciences, U.S. Department of Education, through Grant R305C090023 to the President and Fellows of Harvard College to support the National Center for Teacher Effectiveness. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education. Additional support came from the William T. Grant Foundation, the Albert Shanker Institute, and Mathematica Policy Research’s summer fellowship.
Factor Loadings for Items from the Student Survey
Year 1 | Year 2 | Year 3 | ||||
---|---|---|---|---|---|---|
Factor 1 | Factor 2 | Factor 1 | Factor 2 | Factor 1 | Factor 2 | |
Eigenvalue | 2.13 | 0.78 | 4.84 | 1.33 | 5.44 | 1.26 |
Proportion of Variance Explained | 0.92 | 0.34 | 0.79 | 0.22 | 0.82 | 0.19 |
Self-Efficacy in Math | ||||||
I have pushed myself hard to completely understand math in this class | 0.32 | 0.18 | 0.43 | 0.00 | 0.44 | −0.03 |
If I need help with math, I make sure that someone gives me the help I need. | 0.34 | 0.25 | 0.42 | 0.09 | 0.49 | 0.01 |
If a math problem is hard to solve, I often give up before I solve it. | −0.46 | 0.01 | −0.38 | 0.28 | −0.42 | 0.25 |
Doing homework problems helps me get better at doing math. | 0.30 | 0.31 | 0.54 | 0.24 | 0.52 | 0.18 |
In this class, math is too hard. | −0.39 | −0.03 | −0.38 | 0.22 | −0.42 | 0.16 |
Even when math is hard, I know I can learn it. | 0.47 | 0.35 | 0.56 | 0.05 | 0.64 | 0.02 |
I can do almost all the math in this class if I don't give up. | 0.45 | 0.35 | 0.51 | 0.05 | 0.60 | 0.05 |
I'm certain I can master the math skills taught in this class. | 0.53 | 0.01 | 0.56 | 0.03 | ||
When doing work for this math class, focus on learning not time work takes. | 0.58 | 0.09 | 0.62 | 0.06 | ||
I have been able to figure out the most difficult work in this math class. | 0.51 | 0.10 | 0.57 | 0.04 | ||
Happiness in Class | ||||||
This math class is a happy place for me to be. | 0.67 | 0.18 | 0.68 | 0.20 | ||
Being in this math class makes me feel sad or angry. | −0.50 | 0.15 | −0.54 | 0.16 | ||
The things we have done in math this year are interesting. | 0.56 | 0.24 | 0.57 | 0.27 | ||
Because of this teacher, I am learning to love math. | 0.67 | 0.26 | 0.67 | 0.28 | ||
I enjoy math class this year. | 0.71 | 0.21 | 0.75 | 0.26 | ||
Behavior in Class | ||||||
My behavior in this class is good. | 0.60 | −0.18 | 0.47 | −0.42 | 0.48 | −0.37 |
My behavior in this class sometimes annoys the teacher. | −0.58 | 0.40 | −0.35 | 0.59 | −0.37 | 0.61 |
My behavior is a problem for the teacher in this class. | −0.59 | 0.39 | −0.38 | 0.60 | −0.36 | 0.57 |
Notes: Estimates drawn from all available data. Loadings of roughly 0.4 or higher are highlighted to identify patterns.
1 Although student outcomes beyond test scores often are referred to as “non-cognitive” skills, our preference, like others ( Duckworth & Yeager, 2015 ; Farrington et al., 2012 ), is to refer to each competency by name. For brevity, we refer to them as “attitudes and behaviors,” which closely characterizes the measures we focus on in this paper.
2 Analyses below include additional subsamples of teachers and students. In analyses that predict students’ survey response, we included between 51 and 111 teachers and between 548 and 1,529 students. This range is due to the fact that some survey items were not available in the first year of the study. Further, in analyses relating domains of teaching practice to student outcomes, we further restricted our sample to teachers who themselves were part of the study for more than one year, which allowed us to use out-of-year observation scores that were not confounded with the specific set of students in the classroom. This reduced our analysis samples to between 47 and 93 teachers and between 517 and 1,362 students when predicting students’ attitudes and behaviors, and 196 teachers and 8,660 students when predicting math test scores. Descriptive statistics and formal comparisons of other samples show similar patterns as those presented in Table 1 .
3 We conducted factor analyses separately by year, given that additional items were added in the second and third years to help increase reliability. In the second and third years, each of the two factors has an eigenvalue above one, a conventionally used threshold for selecting factors ( Kline, 1994 ). Even though the second factor consists of three items that also have loadings on the first factor between 0.35 and 0.48 – often taken as the minimum acceptable factor loading ( Field, 2013 ; Kline, 1994 ) – this second factor explains roughly 20% more of the variation across teachers and, therefore, has strong support for a substantively separate construct ( Field, 2013 ; Tabachnick & Fidell, 2001 ). In the first year of the study, the eigenvalue on this second factor is less strong (0.78), and the two items that load onto it also load onto the first factor.
4 Depending on the outcome, between 4% and 8% of students were missing a subset of items from survey scales. In these instances, we created final scores by averaging across all available information.
5 Coding of items from both the low- and high-stakes tests also identify a large degree of overlap in terms of content coverage and cognitive demand ( Lynch, Chin, & Blazar, 2015 ). All tests focused most on numbers and operations (40% to 60%), followed by geometry (roughly 15%), and algebra (15% to 20%). By asking students to provide explanations of their thinking and to solve non-routine problems such as identifying patterns, the low-stakes test also was similar to the high-stakes tests in two districts; in the other two districts, items often asked students to execute basic procedures.
6 As described by Blazar (2015) , capture occurred with a three-camera, digital recording device and lasted between 45 and 60 minutes. Teachers were allowed to choose the dates for capture in advance and directed to select typical lessons and exclude days on which students were taking a test. Although it is possible that these lessons were unique from a teachers’ general instruction, teachers did not have any incentive to select lessons strategically as no rewards or sanctions were involved with data collection or analyses. In addition, analyses from the MET project indicate that teachers are ranked almost identically when they choose lessons themselves compared to when lessons are chosen for them ( Ho & Kane, 2013 ).
7 Developers of the CLASS instrument identify a third dimension, Classroom Instructional Support . Factor analyses of data used in this study showed that items from this dimension formed a single construct with items from Emotional Support ( Blazar et al., 2015 ). Given theoretical overlap between Classroom Instructional Support and dimensions from the MQI instrument, we excluded these items from our work and focused only on Classroom Emotional Support.
8 We controlled for prior-year scores only on the high-stakes assessments and not on the low-stakes assessment for three reasons. First, including prior low-stakes test scores would reduce our full sample by more than 2,200 students. This is because the assessment was not given to students in District 4 in the first year of the study (N = 1,826 students). Further, an additional 413 students were missing fall test scores given that they were not present in class on the day it was administered. Second, prior-year scores on the high- and low-stakes test are correlated at 0.71, suggesting that including both would not help to explain substantively more variation in our outcomes. Third, sorting of students to teachers is most likely to occur based on student performance on the high-stakes assessments since it was readily observable to schools; achievement on the low-stakes test was not.
9 An alternative approach would be to specify teacher effects as fixed, rather than random, which relaxes the assumption that teacher assignment is uncorrelated with factors that also predict student outcomes ( Guarino, Maxfield, Reckase, Thompson, & Wooldridge, 2015 ). Ultimately, we prefer the random effects specification for three reasons. First, it allows us to separate out teacher effects from class effects by including a random effect for both in our model. Second, this approach allows us to control for a variety of variables that are dropped from the model when teacher fixed effects also are included. Given that all teachers in our sample remained in the same school from one year to the next, school fixed effects are collinear with teacher fixed effects. In instances where teachers had data for only one year, class characteristics and district-by-grade-by-year fixed effects also are collinear with teacher fixed effects. Finally, and most importantly, we find that fixed and random effects specifications that condition on students’ prior achievement and demographic characteristics return almost identical teacher effect estimates. When comparing teacher fixed effects to the “shrunken” empirical Bayes estimates that we employ throughout the paper, we find correlations between 0.79 and 0.99. As expected, the variance of the teacher fixed effects is larger than the variance of teacher random effects, differing by the shrinkage factor. When we instead calculate teacher random effects without shrinkage by averaging student residuals to the teacher level (i.e., “teacher average residuals”; see Guarino et al, 2015 for a discussion of this approach) they are almost identical to the teacher fixed effects estimates. Correlations are 0.99 or above across outcome measures, and unstandardized regression coefficients that retain the original scale of each measure range from 0.91 sd to 0.99 sd.
10 Adding prior survey responses to the education production function is not entirely analogous to doing so with prior achievement. While achievement outcomes have roughly the same reference group across administrations, the surveys do not. This is because survey items often asked about students’ experiences “in this class.” All three Behavior in Class items and all five Happiness in Class items included this or similar language, as did five of the 10 items from Self-Efficacy in Math . That said, moderate year-to-year correlations of 0.39, 0.38, and 0.53 for Self-Efficacy in Math , Happiness in Class , and Behavior in Class , respectively, suggest that these items do serve as important controls. Comparatively, year-to-year correlations for the high- and low-stakes tests are 0.75 and 0.77.
11 To estimate these scores, we specified the following hierarchical linear model separately for each school year: OBSER VAT ^ ION lj , − t = γ j + ε ljt The outcome is the observation score for lesson l from teacher j in years other than t ; γ j is a random effect for each teacher, and ε ljt is the residual. For each domain of teaching practice and school year, we utilized standardized estimates of the teacher-level residual as each teacher’s observation score in that year. Thus, scores vary across time. In the main text, we refer to these teacher-level residual as OBSER VAT ^ ION l J , − t rather than γ ̂ J for ease of interpretation for readers.
12 One explanation for these findings is that the relationship between teachers’ Classroom Organization and students’ Happiness in Class is non-liner. For example, it is possible that students’ happiness increases as the class becomes more organized, but then begins to decrease in classrooms with an intensive focus on order and discipline. To explore this possibility, we first examined the scatterplot of the relationship between teachers’ Classroom Organization and teachers’ ability to improve students’ Happiness in Class . Next, we re-estimated equation (2) including a quadratic, cubic, and quartic specification of teachers’ Classroom Organization scores. In both sets of analyses, we found no evidence for a non-linear relationship. Given our small sample size and limited statistical power, though, we suggest that this may be a focus of future research.
13 In similar analyses in a subset of the NCTE data, Blazar (2015) did find a statistically significant relationship between Ambitious Mathematics Instruction and the low-stakes math test of 0.11 sd. The 95% confidence interval around that point estimate overlaps with the 95% confidence interval relating Ambitious Mathematics Instruction to the low-stakes math test in this analysis. Estimates of the relationship between the other three domains of teaching practice and low-stakes math test scores were of smaller magnitude and not statistically significant. Differences between the two studies likely emerge from the fact that we drew on a larger sample with an additional district and year of data, as well as slight modifications to our identification strategy.
14 When we adjusted p -values for estimates presented in Table 5 to account for multiple hypothesis testing using both the Šidák and Bonferroni algorithms ( Dunn, 1961 ; Šidák, 1967 ), relationships between Emotional Support and both Self-Efficacy in Math and Happiness in Class , as well as between Mathematical Errors and Self-Efficacy in Math remained statistically significant.
David Blazar, Harvard Graduate School of Education.
Matthew A. Kraft, Brown University.
In a series of interviews with master teachers, a reporter finds that certain intangible qualities matter more than the best tactics.
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Good teaching isn’t about following a “rigid list of the most popular evidence-based tools and strategies,” veteran high school English teacher Renee Moore tells Kristina Rizga for The Atlantic ’s On Teaching series. The most effective teaching tools, Moore suggests, are intangible qualities that directly address the fundamental human needs of a diverse classroom community—traits like empathy, kindness, and a deep respect for the lives and interests of individual students.
Working from a place of caring, Rizga reports, the best teachers establish deep connections with students, and then build up to a “daily commitment to bringing in well-considered, purposeful practices and working child by child.” For master teachers, then, the person precedes the pedagogy—and finding the right mix of practices, at least to some extent, is contingent on knowing what each child needs.
Rizga travelled across the country for two years for the series, interviewing some of America’s most accomplished veteran teachers in an effort to collect their wisdom and discover “what has helped them bring out the best in their students.” The result is an edifying collection of stories that touch on issues from race and culture to advice about how to teach remotely.
We pulled out some of the most constructive, foundational ideas that informed teacher mindsets through decades of work in the classroom, and helped them inspire even the most reticent students to grow and learn.
Part of getting to know students, says high school English teacher Pirette McKamey, involves watching and listening as students speak in class or in the hallway, and observing how they express themselves in their work. “Every time a student does an assignment, they are communicating something about their thinking,” says McKamey, who is now the principal at Mission High School in San Francisco. “There are so many opportunities to miss certain students and not see them, not hear them, shut them down.”
It also means finding opportunities to connect with each child individually. Moore recalls a 17-year-old student who, in spite of excelling in math class, struggled with writing in her English class. After spending time with the child after school, she found he lit up when discussing sports and family—subjects she encouraged him to write about, resulting in more complex, lively writing. She also recorded their conversations and asked the student to transcribe the recordings—without worrying too much about spelling and grammar—an exercise that allowed him to see proof of his “capacity for unique ideas and analysis,” and opened the door for Moore to begin teaching him grammar and composition. The student became the first of his six siblings to graduate with a high school diploma.
The experience “taught me the power of getting to know your students well enough to teach,” says Moore, illuminating the powerful but not always intuitive connection between relationship-building and improving academic outcomes. Instead of designing pedagogy around individual student needs, “we’re shuffling kids through a system designed on a factory model, and we often give up too soon, because they don’t get to grade level by the time the system says they should. When they don’t, we say they’re not ready to learn or are hopeless. But they are just not on our schedule; it has nothing to do with their innate potential or ability.”
When Moore surveyed her students for a research project in 2000 about best practices for teaching English, students confirmed what she’d long suspected: They learned best when teachers “saw and heard them as individuals, helped them understand their strengths, and connected what they were learning with their future ambitions.” When, instead of recognizing and supporting student effort, teachers focused on minor issues like lateness or poor grammar, students reported feeling discouraged.
Finding time and head space for reflection—especially after teaching all day, grading assignments, fielding student and family queries, and preparing for the next day’s lessons—is challenging but absolutely essential to good teaching. It’s also not just about reflecting on your pedagogy.
McKamey got in the habit of spending her commute going over what she’d observed about each student that day. “She noted, for example, any body language that might indicate disengagement, like expressionless faces, or heads on desks,” writes Rizga. She also tracked student engagement, going over in her mind instances when she saw, for example, students chatting spontaneously about assignments, or doing extra work. “The next day, McKamey would synthesize what she’d observed, and adjust her lesson plans for the day ahead.”
When thinking about productive relationships, teachers should think laterally too: acknowledging and tapping into the strengths of colleagues was a trait of master teachers. Peer networks allow educators to learn from each other, enrich their practice, and access a valuable support network that helps teachers feel connected and more likely to stay in the field.
For many seasoned educators, peer networks are “the main mechanism for transferring collective wisdom and acquiring tacit knowledge that can’t be learned by reading a book or listening to a lecture—skills such as designing a strong lesson plan with precise pacing, rhythm, and clear focus, for instance, or building positive relationships among students,” Rizga writes in another piece in the collection.
“When they struggled—and all of them told me they did—they conferred with colleagues at the school, or teachers in professional associations, or online communities. And together, these teacher groups acted intentionally to identify the challenges students were facing and come up with personalized plans,” Rizga reports.
When teachers were able to share insights and intentionally plan together, they collaborated across academic subjects in new and creative ways, Rigza writes, coming up with valuable lessons and programs that were “more likely to be culturally specific, speaking to the realities of their students’ lives.”
Former high school English teacher Judith Harper, for example, worked with her teaching colleagues in Mesa, Arizona, to help boost students’ public speaking, interviewing, and college-essay-writing skills. Many of her students came from “working-class and Latino families who didn’t always speak English at home,” and building these skills opened up new opportunities for them. Rebecca Palacios, an early-childhood educator in Corpus Christi, Texas, worked with her teaching colleagues to launch a coaching program to help the Latino parents of her preschool students learn how to support their children’s reading skills at home.
It’s important for teachers to be able to use the latest evidence from research in their classroom practice, but how can they use that research well to create meaningful impact?
Researchers from the Monash Q project provide some tips and resources for educators.
Almost any professional accreditation document or school improvement framework has an expectation that teachers use research in their practice. At first glance, these statements appear simple, however the process of sourcing, interpreting and using research in practice is more complex.
At the Monash Q Project, we are interested in understanding how teachers use research and supporting them to use it well. In a recent survey of nearly 500 Australian educators, we found:
This article discusses four key considerations for using research well in the classroom, along with initial resources and practical guides to support teachers to engage with research.
The educators in our survey told us about the challenges they face in accessing research. For instance, 68% indicated they didn’t have sufficient access to research, and 76% couldn’t keep up with new and emerging research.
While issues of access cannot be easily resolved without system-wide changes, there are a number of tricks that can make accessing research easier.
Check out our Q Data Insight for a round-up of ideas.
Before you consider implementing research in your classroom, you need to evaluate and assess the research to determine whether it is suitable for your context. The educators in our survey were more confident to critique research in this manner if they were a school leader, held post-graduate qualifications and/or had more than five years of experience.
However, as research can be valuable for all educators, there are a number of guides to scaffold the assessment process.
Research cannot simply be dropped into your classroom to solve all of your problems. After critiquing and interpreting the research, teachers should spend some time developing a plan to adapt the research, trial it in their classroom and then reflect on whether it worked or not.
For the educators in our survey, the most important considerations to keep in mind were whether the research had directly and sufficiently addressed a pre-identified problem (1st ranked importance) and whether it was compatible with their current teaching practices (2nd ranked importance).
Teachers don’t have to embark on the journey of engaging with research alone. In fact, 76% of educators in our survey used research as a prompt to discuss best practices with their colleagues.
Our most recent Q Data Insight also highlighted how collaborative learning environments can support educators’ beliefs about research as well as their capacities to source, critique and implement it.
These four considerations aim to provide educators with a springboard to explore how they can use research well so it has a meaningful impact in their classrooms. We hope that the resources provided assist teachers to:
These suggestions provide an important first step, but there are important systemic issues that also need to be addressed, such as the lack of dedicated time to engage with research.
These concerns are continuing to drive our work at the Monash Q Project, and we explore them in an upcoming discussion paper.
For more information about the Monash Q Project, visit our website or join the conversation with us on Twitter @MonashQProject .
With thanks to the additional Q Project researchers who also contributed to this article: Mandy Salisbury, Joanne Gleeson , and Connie Cirkony.
Tuesday 15 October 2024, 7pm-8pm A one-hour online session designed for forward-thinking educational leaders ready to question the status quo. Register now
Four ways to support teachers to use research in their practice, how do australian teachers feel about their work in 2022, the role of research in the professional development of graduate teachers, bringing future of education into the classroom, what do australians really think about teachers and schools.
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Home > ETD > Doctoral > 6047
Examining the impact of special education teacher attrition on student performance: a causal-comparative study.
Kiandra Dane Jones , Liberty University Follow
School of Education
Doctor of Philosophy in Education (PhD)
Rebecca Lunde
attrition, prosocial classroom, student achievement, students with disabilities, burnout
Education | Special Education and Teaching
Jones, Kiandra Dane, "Examining the Impact of Special Education Teacher Attrition on Student Performance: A Causal-Comparative Study" (2024). Doctoral Dissertations and Projects . 6047. https://digitalcommons.liberty.edu/doctoral/6047
The purpose of this quantitative, causal-comparative study was to determine if there was a difference between the achievement of Georgia special education students on the Ninth Grade Literature and American Literature Georgia Milestones Test in school districts with high and low special education teacher (SET) attrition rates. This study provided quantifiable data that measured the impact of teacher burnout on student achievement. This research further supported the literature in this field by documenting the consequences of increasing teacher turnover rates. Participants in this study included Georgia Milestones student achievement data from approximately 180 Georgia school districts from 2019–2022. The state’s SET attrition data accounts for approximately 114,800 teachers. By providing quantifiable data reflecting the impact of teacher burnout on student achievement, the literature documenting the consequences of growing teacher attrition rates was supported. Two independent samples t-tests were conducted to determine if there was a difference between student achievement scores and school districts with high or low teacher attrition rates. The researcher determined that there was no significant difference between Georgia Literature Milestones student achievement scores and SET rates between school districts with high and low attrition rates. Results from this study may also assist leaders by providing a different perspective and strategic approach when seeking to improve Georgia Milestones student achievement data.
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In his new book, BU’s Anthony Abraham Jack says that colleges’ commendable efforts to admit more disadvantaged students aren’t matched by support for those students once they’re in the alien culture of higher education.
Rich barlow, michael d. spencer.
As an Amherst College freshman in the early 2000s, Anthony Abraham Jack had to deal with a problem that never blips affluent students’ radars: the closing of the dining hall during spring break. Coming from a low-income Miami family, Jack couldn’t afford the plane fare home and was staying on campus. How would he eat? He lucked out, getting a job at the college’s gym to earn money for meals—and for his mother, who asked him for whatever he could spare to help pay bills back home.
“While Amherst had opened its doors to welcome poor students like me, they forgot to keep the doors open for those of us who couldn’t afford to leave,” Jack writes in his new book Class Dismissed (Princeton University Press). As part of his research for the book, Jack, faculty director of Boston University’s Newbury Center , which supports first-generation college students, interviewed 125 Harvard University undergraduates, “from families across the economic spectrum,” to reveal the daunting problems—aggravated, but not created by COVID-19 campus closures—that still confront disadvantaged students.
While commendably diversifying admissions beyond traditionally wealthy, white students, Jack writes, academia too often fails to help disadvantaged students—for whom college often seems as culturally alien as Mars—succeed in school and after graduation: “It is not just about financial aid. Colleges remain woefully unprepared to support the students who make it in.”
The Brink spoke with Jack, a BU Wheelock College of Education & Human Development associate professor of higher education leadership, about Class Dismissed and how universities can better support students from disadvantaged backgrounds.
Jack will read from Class Dismissed on Saturday, September 28 from 3 to 4:30 pm in the George Sherman Union Conference Auditorium, 775 Commonwealth Ave., as part of Alumni Weekend .
The brink: can you summarize the major inequalities that colleges ignored before covid, and which linger after the pandemic.
Jack: One of the biggest is that universities take a very hands-off approach on how students get into campus jobs. This is a problem because [for] many jobs on campus, you can only apply to them if you know the professor. For a lot of these jobs, like teaching assistant, it’s about who you know. Students who are comfortable gaining rapport with the faculty member are much more likely to not only apply for that job, but to [also] become a course assistant or research assistant. Who is more comfortable engaging with faculty? More privileged students, or students for whom college is not new [to their families]. They are more likely to apply for those jobs and get those jobs. Lower-income students disproportionately are more likely to withdraw from faculty. To not feel comfortable with faculty members means that you are not likely to get one of those jobs. Why does this matter? Well, some jobs only give you a paycheck. If you’re working as a barista, as grounds crew, you get a paycheck at the end of the week or every two weeks. If you’re working as a research assistant, you get a paycheck and a letter of recommendation. In not just addressing the immediate needs of today, but [also in] taking concrete steps to have a better shot at a career after college, the jobs are unequal. COVID revealed what I say is a segregated labor market on campus. My research was the first to actually make explicit just how different the work experiences of lower-income versus more affluent students are on campus. COVID closures led to a removal of almost all the work hours for lower-income students. You had to be on campus and present to do them. But if you were a research assistant or course assistant, your services were actually needed in higher demand. Unless we understand how students are funneled into different parts of campus, we will still be ignorant of just how our practices exacerbate the inequality in their lives. Lower-income students not only have to work because their financial aid letter says so, but [also] to support their families back home.
Unless we understand how students are funneled into different parts of campus, we will still be ignorant of just how our practices exacerbate the inequality in their lives. Lower-income students not only have to work because their financial aid letter says so, but [also] to support their families back home. Anthony Abraham Jack
It absolutely is. You can work for four years as a barista, as a groundskeeper, and your contact with university officials can be very minimal. If you work as a research assistant for the same faculty member, that faculty member has four years of interactions—research, travel, asking you to babysit their cat when they go away. You become someone in their life. [Students say], “This person took me under their wing when I went to office hours, they offered me a job in their lab, and that’s how I got interested in science.”
Elite schools have money to remove hurdles out of students’ way to graduation. Things that trip students up at Bunker Hill [Community College], or at UMass Amherst, are not as prevalent here at BU, Harvard, Amherst. When we think about housing, about the gap between your financial aid package and tuition, about commuters to campus, those are things that we have known for years that lower the odds of graduation. The longer your commute, the less connected you are to the university, the more likely you are to step out of study or drop out. The more debt you have to take on, [the more] you might think that it’s not worthwhile. The more you work off campus, with your schedule constantly changing, you are more likely to have to make the choice between going to work and going to class. Students at elite schools are the traditional age, 18 to 22. They are least likely to have kids, they are least likely to have familial responsibilities that require them to be away from campus. But I don’t believe that graduation rates tell the whole story. You can have a school that has a 98 percent graduation rate, but the trajectory after college is divided by class, where your wealthier students, five years out, are on the path they want to be on, but your lower-income students are not—because wealthier students are more likely to have letters of recommendation and the connections introducing them to headhunters and businesses. [Disadvantaged students] who were uncomfortable in college may have a good GPA, but they don’t have a network who can vouch for them and be able to support them. You need eight letters of recommendation for the Rhodes Scholarship. You can have two students, one with a 4.0 GPA and one a 3.8 GPA. But that 4.0 student has never connected with faculty members, has never been to office hours; that 3.8 student has been the research assistant for two faculty members, has babysat or pet-sat for [professors], is actually embedded within a group of faculty and staff. That person is more likely to get the nod. Some people say: Why study elite schools? Haven’t they gotten undue attention? As a sociologist, I push back against that, because so much mobility literature was not about elite schools. I want to study two things at one time: the mobility prospects of disadvantaged youth and the gap between proclamation and practice. How are you putting [disadvantaged students’ admission] into action? Are you just opening the doors, or opening the doors and changing your daily practices to prepare yourself for who you are now welcoming in?
It is not that I’m calling for colleges to fix the structural inequalities. I am asking them to prepare themselves. For those who have lived in poverty’s long shadow, that is incredibly important. If you are going to admit more lower-income students and go to those new zip codes to get students, you know that they’re bright, they’re ready to do the academic work. But the calls that those students get in the middle of the night are very different from their middle-class peers. If you’re going to go to a place that suffered tremendously due to the opioid epidemic, if you are going to recruit students who come from neighborhoods where joblessness is a modal life event for adults, if you are going to communities where disadvantage is the norm, you have to make sure that your resources on campus can help students bridge the gap.
One of the things I suggest in the book is: Why are the offices of career services fellowships, internships, and on-campus employment typically in separate places? We know from research that contact breeds trust, and trust breeds use of an office. Northeastern [University] has all of their employment-related offices in one office, so that students, from their first year to their last year, come to the same place with all questions related to work. We don’t want social class to dictate what opportunities students view as for them or not for them. It’s about demystifying the hidden curriculum. You [also] have to make sure that your mental health services are prepared to help students deal with that gap. It’s not just about what [services] you have, it’s about who is carrying out therapy on campus, and what training and skill sets they have. Quite frankly, a lot of campuses don’t know how to deal with and support students who come from a more diverse class. I’ve heard Native students say mental health services are not aware of how to deal with the trauma and the legacy of settler colonialism. Mental health services know how to help students through traditional life events for someone who’s [age] 18 to 21: the death of a grandparent, divorced parents. But when it comes to problems that are located in [a particular] place—like what happens on reservations, what happens in inner cities, and what happens in rural parts of America—mental health services are not prepared to deal with a lot of those. Right now, therapists are helping you like: “Let’s develop study skills and help you get through the semester, let’s help you focus on the present.” We’re talking about the legacy of generations of trauma and exploitation and exclusion that many students carry with them on their way to college, and health services are not prepared to deal with that. Do they have specific training to deal with colonialism? There are counselors who are trained to understand racial trauma, students who are the children of immigrants.
We need people who are more aware of the structural inequalities, and especially violence that happens in the country. It’s one thing to see it every day; you learn how to navigate it from [age] zero to 18. But it’s when you come to campus you have this freedom to actually think through things. But you sometimes need guidance on how to do that.
This interview was edited for brevity and clarity .
Jack will read from Class Dismissed on September 28 from 3 to 4:30 pm in the George Sherman Union Conference Auditorium, 775 Commonwealth Ave., as part of Alumni Weekend .
Jack’s research for Class Dismissed was funded by Harvard University and its Presidential Initiative on Harvard & the Legacy of Slavery.
Rich Barlow is a senior writer at BU Today and Bostonia magazine. Perhaps the only native of Trenton, N.J., who will volunteer his birthplace without police interrogation, he graduated from Dartmouth College, spent 20 years as a small-town newspaper reporter, and is a former Boston Globe religion columnist, book reviewer, and occasional op-ed contributor. Profile
Michael D. Spencer Profile
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Earth & climate, the solar system, the universe, aeronautics.
Nasa, us department of education bring stem to after-school programs.
Nasa headquarters.
NASA and the U.S. Department of Education are teaming up to engage students in science, technology, engineering, and math (STEM) education during after-school hours. The interagency program aims to reach approximately 1,000 students in more than 60 sites across 10 states to join the program, 21st Century Community Learning Centers.
“Together with the Education Department, NASA aims to create a brighter future for the next generation of explorers,” said NASA Deputy Administrator Pam Melroy. “We are committed to supporting after-school programs across the country with the tools they need to engage students in the excitement of NASA. Through STEM education investments like this, we aspire to ignite curiosity, nurture potential, and inspire our nation’s future researchers and explorers, and innovators.”
On Monday, NASA and the Education Department kicked off the program at the Wheatley Education Campus in Washington. Students had an opportunity to hear about the interagency collaboration from Kris Brown, deputy associate administrator, NASA’s Office of STEM Engagement, and Cindy Marten, deputy secretary, Education Department, as well as participate in an engineering design challenge.
“The 21st Century Community Learning Centers will provide a unique opportunity to inspire students through hands-on learning and real-world problem solving,” said Brown. “By engaging with in learning opportunities with NASA scientists and engineers, students will not only develop the critical thinking and creativity needed to tackle the challenges of tomorrow, but also discover the joy of learning.”
“Through this collaboration between the U.S. Department of Education and NASA, we are unlocking limitless opportunities for students to explore, innovate, and thrive in STEM fields,” said Marten. “The 21st Century Community Learning Centers play a pivotal role in making this vision a reality by providing essential after-school programs that ignite curiosity and empower the next generation of thinkers, problem-solvers, and explorers. Together, we are shaping the future of education and space exploration, inspiring students to reach for the stars.”
NASA’s Glenn Research Center in Cleveland will provide NASA-related content and academic projects for students, in-person staff training, continuous program support, and opportunities for students to engage with NASA scientists and engineers. Through engineering design challenges, students will use their creativity, critical thinking, and problem-solving skills to help solve real-world challenges that NASA engineers and scientists may face.
In May 2023, NASA and the Education Department signed a Memorandum of Understanding, strengthening collaboration between the two agencies, and expanding efforts to increase access to high-quality STEM and space education to students and schools across the nation. NASA Glenn signed a follow-on Space Act Agreement in 2024 to support the 21st Century Community Learning Centers. The program, managed by the Education Department and funded by Congress, is the only federal funding source dedicated exclusively to afterschool programs.
Learn more about how NASA’s Office of STEM Engagement is inspiring the next generation of explorers at:
https://www.nasa.gov/stem
Abbey Donaldson Headquarters, Washington 202-269-1600 [email protected]
Jacqueline Minerd Glenn Research Center, Cleveland 216-433-6036 [email protected]
COMMENTS
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The Brink: Can you summarize the major inequalities that colleges ignored before COVID, and which linger after the pandemic? Jack: One of the biggest is that universities take a very hands-off approach on how students get into campus jobs. This is a problem because [for] many jobs on campus, you can only apply to them if you know the professor. For a lot of these jobs, like teaching assistant ...
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