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.
The effect of teacher caring behavior and teacher praise on students’ engagement in efl classrooms.
The emergent respect for the prominence of engagement in the present education has made it one of the most widespread inquiry issues that it has been regarded as the ultimate target of learning. In the language teaching field, the idea of student activities for learning is intensely rooted in the prevailing standards of effective language learning, which considers language communication and interaction as analytical for language improvement. Moreover, teachers as center of learning process is the most prominent research attention, and teachers play a key role in regulating the education process as well as students’ learning achievement. However, there is an absence of research which have considered teachers’ care and praise among all positive interpersonal behavior and its significant effect on students’ engagement. So, the present review attempts to focus on teacher care and praise, and their effects on student engagement in EFL classrooms. Subsequently, some implications are presented to clarify the practice of teachers, students, teacher educators, and materials developers.
In language learning, some students are not motivated enough, so they lose their primary attentiveness that ultimately results in dropping out and quitting without graduation ( Finn and Zimmer, 2012 ), and lack of their engagement has a remarkable role in this way ( Fredricks et al., 2004 ; Ladd and Dinella, 2009 ). That is to say, students give up studying English because they become less engaged; hence, they might lose their initial interest gradually, which can result in dropping out.
Student engagement, as a strategic factor of learner achievement in higher education, has been at the center of the attention of directors, experts, and scholars in the previous decade ( Kahu and Nelson, 2018 ). In language learning, some researchers have paid attention to engagement in the classroom and have been of service to this domain to date ( Philp and Duchesne, 2016 ; Oga-Baldwin, 2019 ). Second language acquisition (SLA) has undertaken a shift toward Positive Psychology (PP; MacIntyre et al., 2019 ), which has fortified studies on engagement, focusing on some vital PP aspects embedded in the heart of positive language learning ( Mercer and Dörnyei, 2020 ; Wang et al., 2021 ).
Engagement is two sides of the coin; one side is disengagement that is what learners do to elude attending to learning tasks, while the other side is the way a student is involved in these tasks ( Lei et al., 2018 ). Engagement arises moderately out of “bright side” precursors, such as teacher care which derives from self-determination theory (SDT; Ryan and Deci, 2017 ). In line with SDT, all learners have a series of three fundamental widespread spiritual needs that is those for autonomy (need to encounter preference and self-authorization in individual’s performance), competence (need to sustain progress and a nous of reflectance in one’s communications with the situation), and relatedness (need to go through deep, approachable, and mutual care within individual’s interactions; Mercer, 2019 ; Reeve et al., 2019 ). The fulfillment of these needs throughout language learning regulates the degree to which learners flourish and show adaptive functioning, namely, inherent motivation and engagement ( Reeve et al., 2018 ). The relationships between teacher and student, which can be associated with learners’ basic emotional needs, are among the levels of encouragement on learner improvement ( Froiland et al., 2019 ; Xie and Derakhshan, 2021 ) and are the outcomes of a continuing interaction between the educator and the learner’s features ( Sabol and Pianta, 2012 ). Relatedness can envisage learner behavioral, emotional, and agentic engagement since good teacher-student relations can boost learners’ participation (behavioral engagement), nurture students’ optimistic outlooks toward the course and its tasks (emotional engagement), provide learners the self-confidence to work on difficult actions (cognitive engagement), and boost learners to speak out concerning their education requirements (agentic engagement; Ruzek et al., 2016 ; Vollet et al., 2017 ).
Student engagement is noteworthy since it can envisage learners’ success or academic advancement ( Ladd and Dinella, 2009 ). Also, student engagement is by some issues, such as teacher support or social experiences that provide teachers with instant feedback and praise on their efforts to inspire learners during the teaching process for assessment determinations ( Reeve, 2012 ). It is maintained that engagement would be the best educational indicator of students’ motivation and its dynamic and flexible features ( Appleton et al., 2006 ) entail that it can be reformed as a result of various intrapersonal and interpersonal ecological elements ( Fredricks et al., 2004 ). Teachers’ interpersonal relationship is the main element in the academic setting that exerts a central effect on students’ engagement ( Jiang and Zhang, 2021 ). Likewise, student engagement is inclined by relative variants, such as learning situations or approaches and techniques utilized by teachers ( Fredricks et al., 2019 ). Some longitudinal research ( Jang et al., 2016 ; Reeve et al., 2018 ) within the SDT framework indicated that modifications in student engagement are affected by their learning milieu and such relation is mutual; when situating in classroom circumstances, this ecological element is hypothesized as their teachers’ diverse appealing practice. In previous theories, student engagement has been proposed to be mutually a procedure and a consequence ( Reschly and Christenson, 2012 ). It might be perceived as an interpersonal development triggered by reciprocal interpersonal interactions ( Pianta et al., 2012 ), while as a consequence, student engagement refers to what learners do in the process of intervention. Furthermore, it is adjudged as a moderator between learners’ academic settings and student consequences ( Appleton et al., 2006 ).
Moreover, the success of language learning in the classroom depends on the teacher, and it is stated that students’ achievement or failure in learning can rely on the efficacy of the teachers ( Luz, 2015 ). Sarter (2012) declared that human emotions have been brought to light recently. Although among the emotions that have been tackled so far, anxiety, depression, and stress have been the most prominent, during two previous decades, positive emotions, like love, pride, hope, and enjoyment, have been brought into view ( MacIntyre and Mercer, 2014 ), as generated by the advent of PP into second language learning ( MacIntyre and Mercer, 2014 ; MacIntyre et al., 2019 ; Wang et al., 2021 ). Correspondingly, there has been growing consideration to the role of emotions in teachers’ lives, and emotion is a crucial element of teaching ( Samier and Schmidt, 2009 ). It has been revealed that most teachers all around the world feel negative in language learning classrooms, so it can be proposed that teachers’ emotions should go beyond emotional issues and should turn into social aspects. Thus, sociology can be considered as an agenda to be aware of the social foundations of teachers’ feelings ( Tsang, 2015 ). Lacking constructive emotions, teachers may not be interested or motivated to develop students’ academic, social, and emotional progress ( Day and Qing, 2009 ). The significant role of positive emotions is assured as it helps to foster students’ emotional powers and wellbeing, and encourages social manners and it organizes the social properties for the students’ achievement in language learning ( Zhang et al., 2019 ). Prosocial behavior is a premeditated action to assist others when it is done intentionally instead of reacting to another’s command or by the expectancy of a reward or reprimand ( Grusec et al., 2011 ). Prosocial manners happen as peers support, care, collaborate, and demonstrate respect for each other, and variations in learners’ prosocial behavior are more receptive to sympathetic interactions and involving students in a caring situation ( Cheon et al., 2018 ).
Furthermore, the relations between teachers and learners and teacher manners can meaningfully influence student engagement ( Groves et al., 2015 ). Through interactions with teachers, the students encounter emotional and attitudinal stability and obtain satisfactory emotional support from their teachers which results in effective learning ( Pekrun and Schutz, 2007 ). As stated by Malaimakuni (2016) , effective teachers must have adequate knowledge of the subject matter and in giving their knowledge to students, they routinely should have noble relational communication. Among numerous issues, which provide emotional support for language learners, the relationship between teacher and learner is prominent which is actualized as the most authoritative tool that teachers have, when trying to cultivate a satisfactory learning setting ( Strachan, 2020 ; Li and Yang, 2021 ; Xie and Derakhshan, 2021 ). Because the teacher-learner relationship is dominant to the satisfaction of learners’ emotional needs, scholars have emphasized its quality and nature ( Pishghadam and Khajavy, 2014 ). This relationship in the classroom is essential for not only teachers’ progress but also students’ progress ( Delos Reyes and Torio, 2020 ). Therefore, they must cooperate together to build worthwhile learning circumstances in which the teacher motivates the formation of such situations by taking on relational performances that are related to students’ positive involvements ( Bolkan et al., 2015 ).
A positive relationship between the teacher and learners may be thoroughly associated with the passions, particularly the positive ones emphasized in PP, that students may face within the route of language learning ( Dewaele et al., 2019 ; McIntyre et al., 2019 ). A positive relationship between teacher and learner is acknowledged by empathy, caring, participation, hope, and esteem, and all examples of teacher positive behaviors investigated to date are teacher care, stroke, immediacy, credibility, simplicity, approval, and praise ( Frisby, 2019 ). It is hypothesized that for increasing student engagement, teachers should be friendly, sympathetic, sociable to students’ distinctiveness, support learners’ independence, and be eager about their careers ( Frisby, 2019 ). To this end, teachers can take on varied roles, such as taking care of their conversation, being cautious about feedback to learners, listening to them, providing inquiries to involve learners, and reconsidering classroom management to control relations ( Mercer and Dörnyei, 2020 ). In academic state, the construct of “care” has been broadly investigated and is evolving as an essential element of successful education ( Velasquez et al., 2013 ; Pishghadam et al., 2019 ). In this situation, caring encompasses presenting emotional provision and venture in the rapport with learners, and it is similar to what individuals do and say in their performances and communications ( Davis et al., 2012 ).
Furthermore, caring has been clarified as those feelings, activities, and thoughts that emanate from an educator’s aspiration to stimulate, help, engage, or motivate their learners ( O’Connor, 2008 ). Concentrating on the mutual nature of this construct, it is claimed that “caring education is the performance that arises from a reciprocal caring relationship between learner and teacher, where learning takes place through modeling, discourse, and approval at the social levels” ( Velasquez et al., 2013 ). Teacher care inspires learner-related capabilities, such as engagement, self-confidence, wellbeing, feeling appreciated, and achievement ( Derakhshan et al., 2019 ; Havik and Westergård, 2020 ).
In addition, praise has been reflected as providing encouragement, self-confidence, and good teacher-learner relationships and it is believed that in educational psychology, teacher praise is an essential basis of support for effective student presentation and an indispensable and influential part of teaching and it is a noteworthy strategy in engaging student in the route of learning and praising students in the class fosters language students’ learning motivation and behaviors ( Guilloteaux and Dörnyei, 2008 ). Praising is a technique to reward students who involve in echoing good behaviors or accomplishing better presentations to take advantage of praise ( Brophy, 2004 ).
Undoubtedly, the reward of the teacher is a type of gratefulness of the work of learners which can be done through phrasing praise proclamations as a statement of educational response and feedback instead of appraisal and assessment ( Brophy, 2004 ). This is congruent with Deci et al. (2001) who pinpointed that verbal rewards should be explanatory more willingly than regulatory since the regulatory types are inclined to challenging motivation. Verbal praise should embrace gratefulness of the students’ presentation because the teacher is acting two things equivalently; he is gratifying learners for their performance while teaching them how to allocate their determinations to their inherent enthusiasm rather than to extrinsic motivations provided by the teacher ( Brophy, 2004 ), and it is evinced that the use of praise brings about inspiring learners of having a feeling of superiority and self-assurance in their abilities and accomplishments. As Firdaus (2015) stated, the use of praise will be influential if the teachers discern about it well and how to employ it. Moreover, Hodgman (2015) claimed that praise can be positive support toward students’ manners and confronts the students in a challenging setting to be involved in the educational inquiries and decrease the learners’ problems. Teacher praise is frequently acclaimed as behavior management preparation, which is maintained by some studies ( Conroy et al., 2008 ; Epstein et al., 2008 ).
Taken together the significance of teacher caring behavior and praise as types of positive interpersonal interactions in language education, on the one hand, and learner engagement, on the other hand, it seems required that investigating the rapport between these two constructs has superiority. Furthermore, the rapport between student engagement and teacher caring behavior and teacher praise has not been much investigated in the language education field thus far. Accordingly, the aim of the present study was to bridge this lacuna by investigating the connection between student engagement and teacher praise and caring behavior in the EFL context.
As stated by Dörnyei (2018) , student engagement as a whole relates to involvement in educational tasks and activities. More accurately, engagement can be illuminated as the level of a student’s enthusiastic participation in instructional activities ( Reeve, 2012 ), or a person’s extreme participation in an action ( Reeve et al., 2004 ). Concerning the dimensions of learner engagement, Appleton et al. (2006) utilized the cognitive and emotional modules to measure student engagement. Hart et al. (2011) focused on the three sub-constituents to measure student engagement, namely, emotional, cognitive , and behavioral , and another component which is agentic was added by Reeve (2013) .
The behavioral engagement is elucidated as the noticeable educational presentation and sharing activities and tasks which is evaluated through visible educational act containing: student’s positive behavior; participation; attempt to focus on activities; involvement in class negotiations; contribution in educational and co-curricular tasks; persistence; and resiliency, when confronted with challenging actions ( Khademi Ashkzari et al., 2018 ).
The affective or emotional aspect of engagement relates to the cumulative and permanent degrees of emotions encountered by learners and gains the level of desire learners perceive toward the tertiary knowledge ( Bowden, 2013 ), and this type of engagement evinced through intensified levels of positive emotions during activities, which may be presented through pleasure, superiority, enjoyment, eagerness, and interest ( Klem and Connell, 2004 ). Students who are passionately involved in academic activities are capable of detecting the objective of the tasks and social communications ( Schaufeli et al., 2002 ).
Social engagement examines the links of belongingness shaped between students and their classmates, educational staff, and other pertinent facts in their tertiary practice ( Pekrun and Linnenbrink-Garcia, 2012 ). It engenders feelings of determination, socialization, and association to the tertiary source ( Eldegwy et al., 2018 ) that are noticeable in language learning contexts through collaboration with speakers ( Mercer, 2019 ). Social engagement in the classroom is operationally defined as directions of the learning setting, such as assistance, listening to others, taking part in a class on time, and preserving a sensible teacher-learner power construction ( Pekrun and Linnenbrink-Garcia, 2012 ), while out of the class, it is presented through students’ involvement in groups, where ties are molded with others founded on shared principles, wellbeing, or perseverance ( Wentzel, 2012 ). Students without social engagement are ready to undergo isolation and loneliness bringing about condensed wellbeing ( MacIntyre et al., 2018 ).
Cognitive engagement talks about active mental states and goal-oriented learning strategies that students employ in educational activities during learning developments ( Lei et al., 2018 ). Learners who are cognitively involved reveal a better understanding of educational work through their opinions, theories, and approaches implemented during educational activities ( Khademi Ashkzari et al., 2018 ).
Agentic engagement refers to the learners’ participation in the current teaching that is thoughtful and originated by the student ( Reeve, 2013 ) and this dimension of engagement is close to the other three, as it is also a practical student-originated route to educational development.
Teacher caring behavior.
Caring is a passion, a connection, and a behavioral sign that can be theorized as an emotion, an inspiration, and/or behavior, displaying an apprehension about other individual’s emotional states and desires ( Mayseless, 2015 ). Teacher care refers to teachers’ performances to fulfill students’ spiritual and passionate desires through running a positive, caring, and nurturing setting ( Laletas and Reupert, 2016 ). In the educational setting, teacher care represents a noteworthy facet of teacher-student interpersonal relationships ( Gasser et al., 2018 ) and teacher care affects teachers’ establishment of support to students, demonstrating awareness in students’ learning ( Gabryś-Barker, 2016 ). Caring was hypothesized to be an essential component for generating and preserving influential teacher-student interactions ( Noddings, 2006 ), permitting teachers to concede and react to their students’ desires and provide them with safety and care ( Mayseless, 2015 ). It has been proposed that caring is advantageous for both the care receiver and the care provider, as it stimulates the care provider’s joy, pleasure, self-assessment, social relations, and ties between them ( Lavy and Bocker, 2018 ). In fact, teachers can stimulate the positive emotions of learners by involving them in meaningful tasks, providing a milieu that boosts their contribution in classroom negotiations, and presenting empathy ( Gedzune, 2015 ). Some issues related to students, such as engagement, self-confidence, wellbeing, and achievement, are encouraged by teacher care ( Derakhshan et al., 2019 ; Lavy and Naama-Ghanayim, 2020 ).
Baumeister et al. (1990) as cited in Abbasi et al. (2015) declared that praise is encouraging interpersonal feedback . Positive feedback is categorized as a dynamic element in nurturing learners’ educational success and strengthening the preferred classroom behavior. Nicols (1995) as cited in Abbasi et al. (2015) pinpointed that positive feedback is perceived as an attractive corresponding that is in line with the learner’s self-image. Praise is generally regarded as positive feedback since it has the same meaning as it makes students feeling reinforced and meaningful and praise can be universal or specific ( Moffat, 2011 ). The former refers to as behavior-specific praise ( Hawkins and Heflin, 2011 ), while the latter type of praise is a well-organized and constructive educational tactic that can surge an extensive range of proper behaviors ( Jenkins et al., 2015 ). Teacher praise is a manifestation of support or appreciation that goes further than feedback for an accurate reaction ( Reinke et al., 2007 ). Teacher praise is regarded as a classroom strategy as dependent or a result of suitable student behaviors. In academic settings, praise should be associated with the performances or skills that the teacher desires to develop ( Partin et al., 2009 ). Praise makes the students feel respectable, and it increases student-teacher relations through constructing a positive learning setting, diminishes troubles in the classroom, and makes learning promising ( Rathel et al., 2014 ). To develop student engagement and success, teachers use praise regularly to reassure suitable behavior, while it reduces problematic behaviors in the classroom ( Reinke et al., 2007 ).
The teacher-student interactions, which occur through supportive and approachable relationships in addition to positive and promising behaviors of teachers toward students, affect foreign language satisfaction ( Pishghadam et al., 2021 ). As stated by Mercer and Dörnyei (2020) , and Li et al. (2018) , teachers and some of their features, such as care, respect, helpfulness, and positive attitude, seem to be among the factors that play a prominent role in foreign language interest. When the positive teacher-student interactions are shaped, learners’ motivation, learning achievement, and engagement are developed ( Henry and Thorsen, 2018 ). The results of the study by Royer et al. (2019) about the role of teacher praise in educational situations proved the declines of unsuitable behaviors. Teacher praise is spontaneously associated with the eminence of the student-teacher relations as Cook et al. (2018) indicated that providing praise to students can support the improvement of positive relations with learners. In the same vein, Epstein et al. (2008) acknowledged teacher praise as one of the operational approaches that support student performance and involvement and consequently undergo social and behavioral accomplishment. Rahimi and Karkami (2015) stated that teachers commonly use reward strategies in general and praise, in particular, to elude reprimand and violence strategies since these types of strategies have a destructive impact on their motivation and commitment. The study conducted by Awang et al. (2013) indicated that teachers praise is a common management strategy that is used in the classroom to manage behavior and upsurge student learning engagement in the classroom.
While there are quite a few empirical researches on Positive Psychology worldwide, there is a relatively small number of researches on Positive Psychology in China. Li (2020) has hosted a column in Journal of the Foreign Language World , one of the top linguistic journals in China, featuring the study of emotions in SLA. In this column, Dewaele and Li (2020) contributed a critical review on the previous theories and practices in emotion studies in SLA, proposing that the future of emotion studies can be combined with the control-value theory from the perspective of educational Positive Psychology. The next three empirical studies in the column echo with this proposal. Han and Xu (2020) investigated cases of EFL college students’ academic emotions after receiving written corrective feedback in learning second language writing and their emotion-oriented‚appraisal-oriented‚and situation-oriented self-regulation strategies. The study has generated implications for using Positive Psychology to facilitate students’ wellbeing. Another empirical study conducted by Jiang (2020) used focused essay technique to examine teacher-related factors in affecting EFL classroom enjoyment. Jiang’s research has implications for promoting Positive Psychology in China’s EFL classrooms. The last empirical research article by Li (2020) is also conducted in the realm of Positive Psychology. The method of questionnaire and self-rating test is used to investigate students’ emotional intelligence, emotions, and their relationship with English achievement. Emotions, such as enjoyment, anxiety, and burnout, exert influence on students’ emotional intelligence in general. Emotions and EI have correlations with English achievement. Li’s (2020) research offers a unique perspective of understanding emotion intervention in L2 pedagogy. This column has demonstrated a variety of research methodologies and perspectives, covering a wide range of emotions in EFL learning, such as enjoyment and emotional intelligence, and has implications for using Positive Psychology in EFL setting.
Li’s research interest remains in Positive Psychology of SLA. Li (2021) critically reviewed the researches in PP from the past to the present, advocating that the conception of positive language education will promote language learning emotions as well as the wellbeing of the students. Positive Psychology in SLA has attracted research interest not only in China, but also in worldwide. Empirical studies are still in urgent need to investigate the role of PP in language teaching and learning.
The current review may have some implications for researchers, teachers, and teacher educators in the EFL context. From the operational viewpoint, developing the interrelations of teacher and student features in the EFL milieu can be significant in developing learner engagement. Likewise, this study can be of help to the language teacher in search of ways to improve student engagement.
This review can help teachers to be acquainted with the status of some individual aspects like teacher caring behavior and praise in inspiring positive results concerning student engagement. Thus, teachers and teacher trainers keen on increasing the positive upshots of EFL classes can meditate on the findings of this study and make enhancements in their works. Based on the low degrees of learner engagement, it is necessary for teachers to scrutinize methods to enthusiastically involve learners in the classroom ( Nguyen et al., 2018 ). Teachers evinced that distractions, rebellion, and disengagement are among the most reliably demanding and unsatisfying manners with which they face in their life ( Alter et al., 2013 ). Learner engagement is promoted and nurtured with the mastery of the use of motivating teaching behaviors ( Nicholson and Putwain, 2018 ). More precisely, the teaching behaviors should embrace implementing great levels of relatedness care from the outset and teachers need to engross learners in the syllabus with learning tasks, while witnessing the learners with endurance and providing regulation with positive feedback, namely, praise and support during interventions. Based on the literature review, the authors concluded that teachers’ classroom management and learner engagement, and the relations between learner and teacher are significantly integrated.
Teacher praise is a widespread classroom-managing approach that successfully impacts student learning ( Floress et al., 2017 ). Praise is an active and concrete policy that is employed to surge learners’ prosocial behaviors ( Dufrene et al., 2014 ). Teacher praise and overt inspiration can also be salient for constructing students’ self-confidence ( Dweck, 2007 ). Assisting teachers to utilize praise in the classrooms is prominent as it can prevent student problems. However, the fact that several teachers do not logically arranged for more positive than negative in their classrooms ( Reinke et al., 2013 ; Derakhshan et al., 2021 ) specifies that it is required to conduct further study to investigate what teaching approaches and types of praise will result in successful teacher praise. Undoubtedly, having a more detailed perception of how praise functions in EFL classrooms will assist all learners to be efficacious in the EFL classroom. Although the significant effect of teacher praise is certified in language learning, studying the kinds of praise can be most efficient will be worthwhile in evolving proper proficient progress for teachers in further studies.
Teachers can encourage positive relations through activities that convey hope, compassion, and care and in which they identify the independence and individuality of students ( Gkonou and Mercer, 2018 ). All learners have strong points, and it can be essential for them to be able to ascertain these and use them ( Mercer, 2019 ). Teacher care speaks of teacher-originated actions that cultivate positive social ties with learners which demands sustaining a classroom setting in which the learners feel appreciated and are simultaneously respectful of the teacher as the power character ( Ware, 2006 ). The caring relation constructed between teacher and student through interactions appeared to manage their conception and creations of themselves. When learners feel that the teacher is caring for them, they become more self-confident and regard themselves as superior learners. Sequentially, for the teachers, students’ mutuality permitted them to perceive the positive effect of their caring for most learners leading to their engagement in the process of learning. Teacher caring behavior has been related to a widespread series of positive results comprising higher presence, enhanced academic success, and decreased drop-out proportion ( Foster, 2008 ).
Additionally, material developers can take advantage of this route of study by considering them when adjusting teacher and learner books. In the same vein, syllabus designers are supposed to reflect effective teacher-student relations as a basis of learning to design tasks and activities in a way to uphold teacher-student negotiations and interactions to undertake tasks and engage them. However, there is an absence of inquiries on how teacher’s positive behaviors, such as teacher praise and caring behavior, can be enhanced in the training courses to be efficient in engaging learners and further consideration should be given to different dimensions of engagement.
The author confirms being the sole contributor of this work and has approved it for publication.
This study is sponsored by the China Postdoctoral Science Foundation (Project No. 2018M642877) and MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Project No. 18YJC740088).
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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Keywords: teacher caring behavior, teacher praise, students’ engagement, EFL classroom, teacher’s effect on language teaching
Citation: Sun Y (2021) The Effect of Teacher Caring Behavior and Teacher Praise on Students’ Engagement in EFL Classrooms. Front. Psychol . 12:746871. doi: 10.3389/fpsyg.2021.746871
Received: 25 July 2021; Accepted: 11 August 2021; Published: 14 September 2021.
Reviewed by:
Copyright © 2021 Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yadi Sun, [email protected]
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HORRY COUNTY, SC (WMBF) - The parent of the Horry County student allegedly dragged across the floor by his special education teacher has filed a lawsuit.
The suit against former St. James Intermediate special education teacher Gabriel Hernandez and Horry County Schools was filed on Aug. 14.
Horry County police charged Hernandez with cruelty to children in April 2023 for allegedly dragging the plaintiff’s son, a nonverbal student at St. James Intermediate identified in the lawsuit as John Doe.
PREVIOUS COVERAGE: Warrant: St. James Intermediate special education teacher dragged student across floor
But the parent alleges that wasn’t the first time Hernandez inappropriately touched his son or other students.
STORY CONTINUES BELOW VIDEO
In January 2020, Hernandez started working in the district as a special education teacher at Waccamaw Elementary School.
About a year and a half later, a student at the school claimed Hernandez grabbed and squeezed his neck, making the student place his head down on his desk, according to the lawsuit.
Hernandez then made his first of several transfers to different schools in September 2021 before finally ending up at St. James Intermediate in August 2022, taking over John Doe’s classroom.
During his first month at the school, a paraprofessional working with Hernandez reported to school leaders that Hernandez possibly left a mark on a student’s leg, the lawsuit states.
That same paraprofessional reported Hernandez again in October 2022, when Hernandez allegedly held a student down by their shoulders in the cafeteria with the help of another adult holding the student by the waist.
Hernandez reportedly later told law enforcement this behavior was “not in keeping with CPI training he had received.”
The day after the cafeteria incident, Hernandez stepped on John Doe’s hand, making him wiggle his hand free to escape, according to the lawsuit.
Shortly after, the district placed Hernandez on administrative leave with full pay and benefits.
Around the same time, Hernandez‘s paraprofessional turned in his resignation because of “Hernandez’s persistent inappropriate physical contact with students,” the lawsuit states.
Horry County police began investigating the incident with John Doe but ultimately differed to HCS’ internal investigation.
Besides the paid administrative leave, the district did not discipline Hernandez for the incident with John Doe or any incident prior, the lawsuit claims.
Hernandez was back in the classroom a little over two weeks after being placed on leave and became “more aggressive when dealing with the undesirable behavior of students,” the suit states.
In March 2023, Hernandez allegedly let John Doe bang his head on the table at lunch, something the boy did when he was frustrated.
It wasn’t until another paraprofessional intervened that John Doe stopped banging his head.
The next day, March 16, John Doe wasn’t cooperating with being brought into the classroom, a common behavior for special needs students.
John Doe’s behavior caused Hernandez “to become very agitated,” the suit states.
“According to a paraprofessional who worked with Defendant Hernandez, Defendant Hernandez’s responses to such behavior often escalated the behavior and caused the students to become afraid,” the lawsuit reads.
John Doe began banging his head on a bookshelf, and that’s when Hernandez allegedly dragged the student by the wrists across the room.
Hernandez also is accused of pinning John Doe’s knees against his chest to where the student could not move.
The district placed Hernandez on paid leave again, but after police charged him in April, Hernandez resigned for “personal reasons,” according to the lawsuit.
John Doe’s parent is accusing Hernandez of causing physical and emotional harm to his son.
The parent is also accusing HCS of failing to properly discipline Hernandez.
WMBF News asked for comment on the lawsuit from HCS, and the district said it does not comment on pending litigation.
The parent is asking for actual, consequential and punitive damages in amounts to be determined by a jury.
A jury trial for Hernandez‘s cruelty to children charged is scheduled for Sept. 27, public records show.
Copyright 2024 WMBF. All rights reserved.
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and behaviors. These findings lend empirical evidence to well-established theory on the. multidimensional nature of teaching and the need to identify strategies for improving the full. range of teachers' skills. Keywords: teacher effectiveness, instruction, non-cognitive outcomes, self-efficacy, happiness, behavior. 1.
Hypothesis 5 was supported; the impact of teacher-student relationship quality at Wave I. on student educational expectations at Wave II was significantly stronger for students. whose parents indicated low educational expectations compared to high educational. expectations (B difference= -0.057, SE= 0.02, p= 0.001).
sample of 42 New Zealand teachers responding to a questionnaire rated classroom mismanagement as 'sometimes' or 'very often' a cause of problematic classroom behaviour (Johansen, Little & Akin-Little, 2011). Of concern was the fact that many of these teachers had had minimal pre-service training in behaviour management and in-
The purpose of this paper is to explore the research and implementation of Positive. Behavior Interventions and Supports (PBIS) and other related-based classroom strategies and school-wide behavior management tools. I will research the best approaches, strategies and. interventions used for behavioral issues.
On the basis of a new model of motivation, we examined the effects of 3 dimensions of teacher (n = 14) behavior (involvement, structure, and autonomy support) on 144 children's (Grades 3-5 ...
teacher's behaviour (clarity, interaction, pacing, disclosure, speech and rapport) have positive. relationship ( p <0.05) with academic performance of students while the two other components ...
Table 3.1 reveals that the theories/models and list in the state-of-the-art on teacher effectiveness refer to a different number of relevant factors/dimensions/domains, although three of them refer to three overarching factors. However, looking into more detail into these factors and their content, it is striking that there is much in common even though the different theories/models stem from ...
research study explored how teachers' engagement in school-based instructional leadership. initiatives influences self-efficacy and collective teacher efficacy (CTE). A teacher's self-. efficacy is a teacher's belief in facilitating education behaviors using a unique skill set that will. result in the desired outcome.
The program is used to create a school environment that works to improve positive change in student behavior (PBIS, 2012). Teachers and staff are taught the. program strategies to reduce the negative behavior presented by students. Tobin, Sugai, and Colvin (2000) stated students at risk for behavioral problems.
engagement in classroom activities (Broeckelman-Post et al., 2015; Johnson et al., 2019). Student behavior influences classroom culture, climate, and ultimately success. The purpose of this research study was to examine and improve educator support as they understand and respond to student behavior in the classroom. There is no one set ...
Cazden (2001) added that teacher-student relationship is one of the significant factors in the learning environment. Research conducted by Krane et al. (2017) revealed students develop positive relationship with their teachers when respect is exchanged between teachers and students. Moreover, negative behavior affects both teachers and students.
students, teaching experience, and knowledge of classroom management can all impact. the teacher's decision. This study attempts to determine how much influence these factors. have on a teacher's decisions and if there are differences in responses based on the type. of behavior exhibited by the student.
The study sought to find students' most. common behavioral problems inside the classroom, the greatest barrier that hinder discipline. implementation in class and identifies teachers'. commonly ...
the impact of classroom management on behavior regulation for students in early childhood and elementary school classrooms a master's thesis submitted to the faculty of bethel university by katherine s. winters in partial fulfillment of the requirement for the degree of master of arts august 2022
He found that teachers who practiced collaborative interactive teaching strategies promoted deep-level cognitive processing in their students. 2. Research Methodology The main purpose of this study is to evaluate and identify the impact of the teachers' behavior on students' self- regulation. This research is a casual-comparative research design.
student performance outcomes, behaviors contributing to classroom success, and the inhibition of impulses) and attention (as defined as on-task behavior) on written expression performance of. male and female students within the context of a Tier 1 class-wide writing intervention (e.g., performance feedback).
Theses/Dissertations from 2019. PDF. Uncovering One Teacher's Knowledge of Arts Integration for Developing English Learners' Reading Comprehension: A Self-Study, Tina RaLinn McCulloch. PDF. A Content Analysis of Scientific Practices in a Fourth-Grade Commercial Literacy Program, Hailey A. Oswald. PDF.
Teachers as professional leaders perform a crucial role in establishing positive behavior and qualities among learners. This descriptive research aimed to shed light to these questions determine the influence of teacher's personality and behavior on the respondents' character building in terms of their performance of their academic duties, acceptance of additional duties in class, and ...
Mihaly, McCaffrey, Staiger, and Lockwood (2013) found a correlation of 0.57 between middle school teacher effects on students' self-reported effort versus effects on math test scores. Our analyses extend this body of research by estimating teacher effects on additional attitudes and behaviors captured by students in upper-elementary grades.
Taken together the significance of teacher caring behavior and praise as types of positive interpersonal interactions in language education, on the one hand, and learner engagement, on the other hand, it seems required that investigating the rapport between these two constructs has superiority. ... (Master's thesis) Massachusetts: Bridgewater ...
processes (such as teacher assistance and classroom environment) are frequently used in student studies (Reddy et al., 2003). Behaviour is a reaction that a person displays at various times to his or her setting. It is fascinating to identify the appearances of the behaviour, attitudes, expertise, skills of teachers and their
Lolita A. Dulay. [email protected]. versity, Malaybalay City, Bukidnon, Mindanao 8700 Philippines ,Abstract This study described the classroom behavior and academic performance of Public Elementary School Pupils in San Gui. to Elementary School at Pangantucan district during the school year 2012-2013. One hund.
of teachers involve students in the decision -. making process regarding classr oom discip line. 75.6% of teachers always arrange a classr oom. that encourages p ositive behaviour. 80.1% of ...
The next day, March 16, John Doe wasn't cooperating with being brought into the classroom, a common behavior for special needs students. John Doe's behavior caused Hernandez "to become very ...
Student behavior problems, cellphones in class, anemic pay and AI-powered cheating are taking their toll on America's teachers. Many are demoralized or leaving the profession.