Teaching practices and student engagement in early adolescence: A longitudinal study using the Classroom Assessment Scoring System

Teaching and Teacher Education xxx (xxxx) xxx

Contents lists available at ScienceDirect

Teaching and Teacher Education journal homepage: www.elsevier.com/locate/tate

Teaching practices and student engagement in early adolescence: A longitudinal study using the Classroom Assessment Scoring System Sarah E. McKellar*, Kai S. Cortina, Allison M. Ryan University of Michigan, United States

h i g h l i g h t s
Quality Feedback was the strongest predictor of behavioral engagement.
In addition to Quality Feedback, other practices matter for behavioral engagement.
Regard for Student Perspective was the only predictor of emotional engagement.
No gender and race differences were found among students for changes in engagement.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 13 January 2019 Received in revised form 9 September 2019 Accepted 18 September 2019 Available online xxx

Student engagement in schoolwork is crucial for positive academic adjustment, particularly during early adolescence. We investigated how observations of teaching practices predicted change in student behavioral and emotional engagement. In the fall, we examined teacher behavior in 54 fifth and sixth grade classrooms through external observers' reports of 11 dimensions of teachers’ practices (Classroom Assessment Scoring System, CLASS). Students reported on their behavioral and emotional engagement in the fall and spring. We found quality feedback was the strongest predictor of behavioral engagement and regard for student perspective was the strongest emotional engagement. Our findings were more nuanced for what predicts behavioral engagement, as there is evidence that other teaching practices work in conjunction with quality feedback to predict behavioral engagement. © 2019 Published by Elsevier Ltd.

Keywords: Engagement Adolescence Teachers Classroom observations

1. Introduction Student engagement in schoolwork is crucial for positive academic adjustment, particularly during early adolescence (Lawson & Lawson, 2013; Lei, Cui, & Zhou, 2018). The construct of student engagement has received considerable attention over the past few decades because it robustly predicts numerous outcomes and is responsive to intervention (Eccles, 2016; Finn & Zimmer, 2012; €, & Pietarinen, 2014). In particular, early Ulmanen, Soini, Pyh€ alto adolescent engagement has been linked with academic achievement, psychosocial adjustment, and long-term occupational success (Abbott-Chapman et al., 2014; Chase, Warren, & Lerner, 2015; Reyes, Brackett, Rivers, White, & Salovey, 2012). Student reports of teacherestudent relationships have been consistently associated with students’ enhanced engagement in school (Quin, 2017; Quin,

* Corresponding author. E-mail address: [email protected] (S.E. McKellar).

Hemphill, & Heerde, 2017). However, teachers may seek to tap into different facets of student engagement through taking different approaches in their teaching practices and interactions with students (Pianta, Hamre, & Allen, 2012). The aim of the current study was to examine a range of observed teaching practices in early adolescent classrooms and assess which are most strongly linked with changes in students’ emotional and behavioral engagement. Student engagement is viewed as a multifaceted construct most often assessed by asking students about what they do, think, and/or feel in relation to academic tasks (Eccles, 2016; Fredricks, Reschly, & Christenson, 2019). According to Finn’s (1989) participationidentification model, students who engage in learning are those who are emotionally invested and participate in actions that align with their investment. In the present study and as in previous studies (see, for example, Gonida, Voulala, & Kiosseoglou, 2009; Salmela-Aro & Upadaya, 2012; Skinner, Kindermann, & Furrer, 2009), student engagement was theorized as active behavior exhibited by the students in the classroom and as positive emotion

https://doi.org/10.1016/j.tate.2019.102936 0742-051X/© 2019 Published by Elsevier Ltd.

Please cite this article as: McKellar, S. E et al., Teaching practices and student engagement in early adolescence: A longitudinal study using the Classroom Assessment Scoring System, Teaching and Teacher Education, https://doi.org/10.1016/j.tate.2019.102936

2

S.E. McKellar et al. / Teaching and Teacher Education xxx (xxxx) xxx

experienced during class work. While behavioral and emotional engagement are conceptually and empirically correlated, they are nevertheless theoretically distinct (Skinner et al., 2009). This means that while students who are enjoying activities in math are likely to be putting forth effort into these activities, some may be more likely to report that they are enjoying it without showing much active engagement, and others are visibly engaged but are not particularly fond of the activities they are engaged in. Given this distinction in two correlated types of engagement, further investigation is needed to understand the extent to which specific teacher practices promote behavioral and/or emotional engagement. While both facets of engagement tend to be associated with the same student outcomes, they may differ in the way they respond to the learning context. Less is known about the extent to which specific observed teaching practices e or teacher-student interactions e differentially predict student behavioral and emotional engagement. According to Brophy (2010), teachers often overemphasize catering instruction to student enjoyment, interest, or emotions as a means of engaging students in learning. Instead, he asserted that the appropriate amount of instructional rigor, support, and feedback over time would foster students’ investment in learning, even if students do not initially see tasks as relevant or interesting. In contrast, recent work has stressed how teachers’ emotional support, or facilitating of student enjoyment in classroom activities, drives positive student outcomes (Ruzek et al., 2016; Weyns, Colpin, De Laet, Engels, & Verschueren, 2018). A teacher might be able to enlist students in class activities through subtle pressures that cause negative or mixed emotions; another teacher might grab students’ attention and make them feel welcome using an entertaining teaching style at the expense of academic focus and pace. There is a wealth of studies looking at adolescents’ reports of general teachers’ support in relation to their engagement (Quin, 2017), but studies are just beginning to use standardized observation of these practices (Rimm-Kaufman, Baroody, Larsen, Curby, & Abry, 2015). Trained observers of real-time or videotaped teaching practices assess teachers’ behavior apart from perception biases that are inherent in student reports (Pianta et al., 2012; Pianta & Hamre, 2009). These third-person observations can capture aspects of teaching largely outside of teacher and student awareness in real time. This allows for more effective teacher training and coaching because educators, particularly novice teachers, can better understand what concrete practices are helpful for student engagement regardless of their own perception (Rimm-Kaufman et al., 2015). The current study examined whether systematic observations of specific teaching practices predict student behavioral and emotional engagement. 1.1. Defining student engagement The study of motivation and engagement in the classroom has contributed to our understanding of effective teaching practices at the intersection of research in psychology and education (Brophy & Good, 1986; Pianta et al., 2012). Specifically, motivational theories such as self-determination theory (Ryan & Deci, 2000), expectancyvalue theory (Eccles et al., 1993), and goal theory (Ames, 1992; Covington, 2000) have been used to frame the ways in which teachers can engage students. For example, self-determination theory posits that teachers’ autonomy supportive practices, messages about competence, and facilitation of student belongingness in the classroom can engage and motivate students (see Pianta & Hamre, 2009, for a review). According to expectancy-value theory and goal theory, teachers’ messages about students’ abilities and feedback about learning shape students’ beliefs (i.e., learning goals and expectations about their success); in turn, students’ beliefs

predict engagement and achievement outcomes (Wang & Eccles, 2012). Skinner and Belmont’s (1993) seminal study employed motivational theory to assess the reciprocal relationship between teacher involvement, structure, and autonomy support on the one hand and student engagement on the other hand. This approach was based on the understanding that engagement is being optimized when the social context of the classroom fulfills children’s and adolescents’ basic psychological needs (Connell & Wellborn, 1991). These are just a few examples of measures of teaching practices that have been developed or aligned with those targeting students’ psychological needs, engagement, and achievement. In order to better understand the relationship between teaching practices and student engagement, both must be conceptually and empirically defined. In the past few decades, several common conceptualizations of engagement have emerged, which include academic, agentic, affective, social, and cognitive engagement (Jimerson, Campos, & Greif, 2003; Reschly & Christenson, 2012). However, with greater specificity in measuring engagement and a proliferation of constructs, Eccles (2016) cautions that researchers may lose sight of engagement as a more holistic concept. We therefore adopted Finn’s (1989) parsimonious two-dimensional approach to understanding engagement and its antecedents. According to this model and subsequent empirical conceptualizations (Skinner et al., 2009), engagement has two dimensions: behavioral and emotional. Behavioral engagement refers to students’ active engagement (e.g., a student pays attention, participates, listens, and is involved in class activities), while emotional engagement refers to the internal state (e.g., a student is interested, having fun, and enjoying class activities). While adolescent engagement may also be assessed via teacher or observer reports, students’ self-report of engagement taps at the students’ state of mind which include cognitive engagement and emotional state that is not necessarily expressed in observable behavior. Consistently, student self-reports tend to correlate with observer rating but are better predictors of students' learning and academic achievement (Lei, Cui, & Zhou, 2018). In line with theoretical and empirical work on student motivation and engagement, different teaching practices align with different students’ adaptive classroom behaviors (Pianta, Belsky, et al., 2008; Pianta et al., 2012). While it is assumed that positive teaching practices support all types of engagement, theory suggests that certain teaching strategies are more effective at engendering specific types of student engagement (Pianta et al., 2012; RimmKaufman et al., 2015). 1.2. Defining teacher practices Similar to student engagement, there have been attempts to create frameworks and standardized measures surrounding teacher practices that predict positive outcomes. Building on prior work, Pianta and Hamre (2009) developed observational measure and a corresponding frameworkdTeaching Through Interactions frameworkdfor assessing teacher-student interactions or teaching practices. In line with other categories of teaching practices from educational and psychological literature (e.g., Eccles & Roeser, 2011; Pressley et al., 2003; Roehrig, Johnson, Moore, & Bryan, 2015), the Teaching Through Interactions model describes three broad domains teaching practices: Emotional Support, Instructional Support, and Classroom Organization (Hafen et al., 2015; Hamre et al., 2013). Each of these domains describes teaching practices more generally, and contains dimensions within the larger domains that outline more specifically what teachers are doing to support students. The Emotional Support domain encompasses four meaningful dimensions: Positive Climate, Negative Climate, Teacher Sensitivity, and Regard for Student Perspective. The Instructional

Please cite this article as: McKellar, S. E et al., Teaching practices and student engagement in early adolescence: A longitudinal study using the Classroom Assessment Scoring System, Teaching and Teacher Education, https://doi.org/10.1016/j.tate.2019.102936

S.E. McKellar et al. / Teaching and Teacher Education xxx (xxxx) xxx

Support domain captures four teachers’ practices that support cognitive and academic development, including the promotion of Problem Solving, Content Understanding, Substantive Dialogue, and teachers’ Quality of Feedback. The Classroom Organization domain includes teachers’ abilities to manage classroom structures and routines to maximize students actively participating, specifically Behavior Management, Productivity, and Instructional Learning Formats. While teaching practices are defined by these three larger domains, efforts to support teachers in improving their practices have leveraged the specificity of the 11 teaching dimensions of the CLASS that fall under the three broader domains (Hafen et al., 2015). 1.3. Assessing teaching practice: standardized observational protocols While student reports of teaching practices are the most common way to assess teaching practices, this method has limitations. The vast majority of studies have used aggregated student perceptions to understand teaching practices due to proximity to student outcomes and the ease of collection compared with other sources (Burniske & Meibaum, 2012). Overreliance on student reports to assess teaching practices may be problematic for several reasons. First, the validity of student self-reports has been called into question, among others, for halo effects (Nisbett & Wilson, 1977; Wetzel, Wilson, & Kort, 1981) and social desirability bias (Fisher, 1993). In other words, relying solely on student reports of their teacher may cause issues because students have individual biases that affect their general preferences and the extent to which they can parse out the effectiveness of teachers’ specific practices. In predicting other questionnaire variables, common method bias becomes a major threat to the validity of the findings (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). This means that student reports of their teachers' practices and their individual engagement may be related based on student reporters as common data sources rather than teachers affecting students' outcomes. Furthermore, there is some evidence that student perceptions of the classroom are shaped to a certain extent by their experiences in prior course or other concurrent courses (e.g., Friedel, Cortina, Turner, & Midgley, 2010). Apart from these methodological aspects, there are several advantages of trained observers of teaching practices in comparison to student reports. Trained observers can be aware of teaching aspects that students are not aware. Because the trained observer focuses on concrete behavior of teachers and students seen in class, the results lend themselves to be used for teacher training purposes in particular if combined with observations and video footage of the class period assessed. A limitation of third-person observations apart from the higher costs of the classroom is that they only capture one (or a few) class periods and cannot assess students’ actual cognitive processes unless they are verbalized (e.g., a misconception in a science class). Accounting for these weaknesses, observing teaching practice can offer an effective alternative lens to understanding teaching practices. This is especially true if observers are trained to use an accepted rubric and adhere closely to this training. Over the past few decades, there has been increasing attention and progress in developing standardized observation protocols to better understand e and fairly asses e teaching practices (Danielson, 2007; 2011; Pianta et al., 2012). Calls for standardized protocols come out of efforts to better identify which classroom teaching processes (e.g., management, formats, interactions) are most closely linked with student achievement outcomes; referred to as the process-product paradigm (Brophy, 2010; Brophy & Good, 1986; Cochran-Smith & Lytle, 1990). The process-product approach

3

is linked with efforts to identify “best,” highly effective, or quality teaching practices (Cochran-Smith, 2016; Doyle, 1977). The CLASS, developed at the University of Virginia, has become a widely used protocol aimed at measuring aspects of instruction and teaching quality (Pianta et al., 2012). Growing use of the CLASS has also been accompanied by research and calls for additional work to further assess the predictive validity of the domains and dimensions (Bihler, Agache, Kohl, Willard, & Leyendecker, 2018; Hafen et al., 2015; Hamre et al., 2013; Westergård, Ertesvåg, & Rafaelsen, 2018). Some work revealed moderate to high correlation across the three domains, including discussions of multicollinearity among its three larger domains when predicting student engagement (See RimmKaufman et al., 2015). Despite support for a three-domain structure for the 11 teaching dimensions of the framework, a more practical focus, in particular from a teacher training perspective, favors a more differentiated model because the 11 dimensions (three to four dimensions per domain) are clearly defined and can be isolated in video footage for training purposes. 1.4. Observational assessment of teaching practices (the CLASS) and engagement In our review of the literature, we identified three studies to date that examined student engagement during early adolescence in relation to teaching practices using at least one of the three domains of the Classroom Assessment Scoring System (CLASS Emotional Support, Instructional Support, and Classroom Organization). The first of these studies, by Reyes and colleagues (2012), examined the extent to which the three domains of the CLASS predicted engagement e focusing solely on emotional engagement e and achievement in 5th and 6th grade English and Language Arts (ELA) classes. They found that emotional engagement mediated the relationship between CLASS Emotional Support and student achievement. However, they did not find any association between the other two CLASS teaching domains (i.e., Instructional Support and Classroom Organization) and student engagement or achievement. They focused on a more general construct of engagement using Furrer and Skinner’s (2003) Engagement vs. Disaffection Scale (e.g., “I feel good when I’m in my ELA class”). While longitudinal in design, this study did not assess how teaching practices predicted changes in student engagement over the course of the year and did not assess student behavioral engagement. Our second identified study, Ruzek et al. (2016), examined behavioral engagement instead of emotional engagement, focusing on only one domain of the CLASS, Emotional Support, as a predictor. This study was centered around Emotional Support because it was designed to assess whether the three facets of self-determination theory (students’ sense of autonomy support, competence, and peer belongingness) mediated observed Emotional Support and self-reported behavioral engagement. They found Emotional Support predicted behavioral engagement. They also found students sense of autonomy support and peer belongingness mediated the relationship between Emotional Support and behavioral engagement. These mediators closely align with the CLASS dimension of Regard for Student Perspective, which is one component of the Emotional Support domain. For example, Regard for Student Perspective includes indicators such as “relaxed structure for movement about the classroom” and “peer sharing and group work” paralleling autonomy support and peer relatedness respectively (Ruzek et al., 2016, p. 98). Our third identified study, Rimm-Kaufman et al. (2015), examined how several types of engagement were related to all three domains of the CLASS. They focused on a sample of 5th grade students and their math classrooms, using student engagement at

Please cite this article as: McKellar, S. E et al., Teaching practices and student engagement in early adolescence: A longitudinal study using the Classroom Assessment Scoring System, Teaching and Teacher Education, https://doi.org/10.1016/j.tate.2019.102936

4

S.E. McKellar et al. / Teaching and Teacher Education xxx (xxxx) xxx

three time points over the course of the year. They confirmed that students had higher cognitive, social, and emotional engagement in classrooms with greater Emotional Support and Classroom Organization. They also found only one of the three domains of the CLASS, Classroom Organization, predicted observer reports of student behavioral engagement. Rimm-Kaufmann and colleagues were surprised that Instructional Support did not predict behavioral engagement and hypothesized a few possible reasons, calling future studies to examine the role of differences in student ability levels and the role of specific dimensions within the domain of Instructional Support. Overall, more research is needed to understand the relationships between the CLASS domains of Instructional Support and Classroom Organization and different types of engagement. Nonetheless, when taken together, these three studies provide evidence that the Emotional Support domain of the CLASS predicts student emotional and behavioral engagement in secondary ELA and math classrooms. While these studies used the three broad domains of the CLASS to assess the relationships between teaching practices and student engagement, they ultimately used the CLASS’s 11 teaching dimensions to explain the implications of their findings. For example, when discussing findings related to the Emotional Support domain of the CLASS, Ruzek et al. (2016) and RimmKaufman et al. (2015) suggested ways in which student engagement may be shaped by practices that demonstrate Regard for Student Perspective (e.g., providing students with autonomy and opportunities to connect with classmates). Similarly, when discussing findings about the Classroom Organization domain of the CLASS, Rimm-Kaufman and colleagues explained how classrooms that have “interesting learning formats, clear statement of expectations, high productivity” enhance students’ behavioral engagement (p. 182). Despite these strides in understanding how domains of observed teaching practices are related to student engagement, the strength of the CLASS’s ability to record 11 specific teaching practices has not fully been utilized. Few studies have parsed out these 11 dimensions of the CLASS to outline how different teacherstudent interactions may differentially predict distinct types of student engagement, despite the fact that from the practical perspective of teacher training it is particularly beneficial to be as specific as possible. Teachers may be able to improve their instructional approaches with greater insights on the extent to which student emotional and behavioral engagement are related to each of the 11 teaching dimensions of the CLASS (e.g., Teacher Sensitivity, Content Understanding, and Productivity). Deemer and Hanich (2005) stressed the benefits of this kind of specificity, arguing that a framework including detailed dimensions of teaching (i.e., Epstein’s (1988) and Ames’ (1992) TARGET framework) can help instructors improve student motivation and engagement. For example, they describe how understanding teacher recognition of student effort and appropriate evaluation of student learningdcomponents in the CLASS’s dimension Quality of Feedbackdcan support teachers in increasing student engagement, especially when used as part of a multidimensional framework. Moreover, the developers of the CLASS and their colleagues, Hafen et al. (2015), created an intervention program, called MyTeachingPartner, to improve teaching practices based on the Teaching Through Interactions framework and the CLASS. While MyTeachingPartner uses all 11 teaching dimensions of the CLASS to offer feedback, this specificity has not been used to understand secondary student reports of their engagement in prior empirical studies. Although a domain approach to understanding engagement is useful, studying the specific dimensions of the CLASS may better support teachers in their efforts to improve their practices. There has also been some debate about whether these 11

dimensions actually fall under three domains or whether a different factor or domain structure is best, such as one or two domains (Allen et al., 2013; Bihler et al., 2018; Hafen et al., 2015; Virtanen et al., 2018). A dimensional approach instead of a domain approach to predict engagement may help address some of these recent questions. Pianta et al. (2012) also recommended that future studies examine teaching practices at a “granular level” in relation to student engagement (p. 380). Researchers and teachers will easily agree that Emotional Support, Instructional Support, and Classroom Organization will all be positively associated with desirable student outcomes, but the present study built upon prior work by more closely examining the extent to which the 11 CLASS teaching dimensions predict two types of student engagement, behavioral and emotional engagement, in early adolescence. 1.5. Purpose and hypotheses In classrooms, teachers may take different approaches to engaging students and tap into different facets of student engagement. For example, one might imagine a classroom where students are behaviorally engaged due to the high expectations and academic rigor rather than emotional support. Conversely, one can imagine a classroom where students are emotionally engaged as a result of teacher warmth and positive relational climate rather than instructional supports. While teachers use a variety of practices to engage students, perhaps specific practices promote specific types of student engagement. The purpose of this study was to systematically examine the ways in which specific observed teaching practices predict specific forms of engagement. The present study used the 11 teaching dimensions of the CLASS to understand behavioral and emotional engagement. While this study was largely exploratory, especially related to understanding which dimensions of the CLASS predict changes in behavioral engagement, we had two broad hypotheses based on Pianta and colleagues' (2012) theoretical review and the three studies linking the CLASS to engagement during early adolescence (Reyes et al., 2012; Rimm-Kaufman at al., 2015; Ruzek et al., 2016). Based on Reyes et al.’s (2012) and Rimm-Kaufman at al. ’s (2015) findings, our first hypothesis was that one or more dimensions within the domain of Emotional Support would be the strongest predictor of emotional engagement. Second, we hypothesized that one of the domains of Classroom Organization would also strongly predict emotional engagement. There was insufficient prior research to make dimensional-based hypotheses related to behavioral engagement, though Rimm-Kaufman et al.’s and Ruzek et al.’s work discussed Productivity and Regard for Student Perspective as possible dimensional predictors. Lastly, in light of Rimm-Kaufman at al. ’s (2015) findings that the relationship between Classroom Organization and behavioral engagement was stronger for 5th grade boys than girls, we also explored gender differences among students. 2. Method 2.1. Procedure and participants Data were collected as part of a larger longitudinal study examining early adolescent social and academic adjustment in school. Schools were recruited from three school districts located in small urban communities in the Midwest. To provide a common reference point across the different school settings, we focused on the classroom context in the domains of math and science. Prior work (Allen et al., 2013) found that the CLASS was related to gains in math and science achievement similarly, providing support for including both subjects. All math and science teachers in the 6th

Please cite this article as: McKellar, S. E et al., Teaching practices and student engagement in early adolescence: A longitudinal study using the Classroom Assessment Scoring System, Teaching and Teacher Education, https://doi.org/10.1016/j.tate.2019.102936

S.E. McKellar et al. / Teaching and Teacher Education xxx (xxxx) xxx

grade at the middle schools agreed to participate, and we chose one of their classes to observe and administer surveys. For the teachers from the feeder elementary schools, we aimed to focus on math or science in equal proportions. Observations were conducted in October and November. As the school year commenced in mid-August in these school districts, this was about two or three months into the school year. Research assistants scheduled classroom observations on days that the teachers deemed “typical” days of math or science instruction. About a week or two following observations, two trained research assistants administered surveys to students in their classrooms. Four classrooms did not complete the student survey aspect of the project due to scheduling conflicts and were not included in our study. The Upper Elementary Version of the CLASS certification (grades 4e6; Pianta, Hamre, & Mintz, 2010) was obtained by our six classroom observers; the Collaborative Institutional Training Initiative (CITI) Human Subject Protection trained university researchers. Certification on the CLASS observation protocol meant that all six coders had to achieve at least 80% correct on the test at the end of the CLASS course. To obtain this, score codes must be within one point (on a seven point continuum) from what the developers consider the correct code when coding segments of videos of classrooms. Furthermore, 20% of the classroom observations in our sample were conducted in pairs. When we employed the same criteria in the field as the CLASS certification test, 94% of the time our observation pairs were coded within one point of each other. For the present study, students came from 54 classrooms (27 5th grade classrooms, 18 math and 9 science; and 27 6th grade classrooms, 16 math and 11 science). The 5th grade teachers were predominantly female (N ¼ 25, 93%) and White (N ¼ 23, 85%). The 6th grade teachers were predominantly female (N ¼ 21, 78%) and White (N ¼ 25, 93%). Only students who returned signed permission from parents were allowed to participate. The percentage of students with parental consent averaged 84% and was similar at both grade levels. The total sample (N ¼ 860) was comprised of students who had data at both waves. Roughly half of our student sample was female (51.1%) and there was heterogeneity of student ethnicity across our sample (36% African American, 47% European American,

5

7% Hispanic and 9% other ethnic groups). For all of the variables in our study, no differences were found between students who left after the fall (i.e., missing data in the spring) compared with students who participated in the fall and the spring. Instructions and items were read aloud while students read along and responded. Students were assured that the information in the survey would be kept confidential. In addition, students were told that filling out the survey was voluntary and that they could stop at any time. We visited the schools one additional day to administer make-ups for students who were absent for survey administration. Survey administration was repeated about six months later in the spring of the school year. 2.2. Measures Classroom Assessment Scoring System (CLASS). The CLASS is a well-established observational measure of teaching practices in the classroom (Pianta, La Paro, & Hamre, 2008). The CLASS is comprised of three domains each with three or four dimensions, so 11 dimensions total related to teaching, each with a set of criteria to score (see Table 1). The first domain of Emotional Supports (a ¼ 0.83) contains three dimensions of Positive Climate (relationships, affect, respect, communication), Negative Climate (punitiveness, sarcasm/disrespect, negativity), Sensitivity (awareness, responsiveness, action to address problems, comfort), and Regard for Student Perspectives (flexibility, support for autonomy, connections to current life, and meaningful peer interactions). The second domain of Instructional Supports (a ¼ 0.77) is comprised of four dimensions: Content Understanding (teachers help students understand both the broad framework and key ideas in an academic discipline), Analysis and Problem Solving (promotion of higherorder thinking skills, opportunities for application), Quality of Feedback (feedback loops, encouragement of responses, explanation of performance), and Instructional Dialogue (content-driven exchanges, distributed talk and facilitation strategies). The third domain of Classroom Organization (a ¼ .78) includes three dimensions: Behavior Management (clear expectations, proactiveness and redirection), Productivity (maximized time use, efficient routines and transitions), and Instructional Learning Formats (engaging approach that maximizes learning opportunities). The CLASS

Table 1 Classroom assessment scoring system domains and (class) dimensions (upper elementary version, grades 4e6; Pianta, Hamre & Mintz, 2010). Domain

Dimension

Indications/Description

Emotional Support

Positive Climate Negative Climatea Teacher Sensitivity Regard for Student Perspectives

Relationships, Positive affect, Positive communications, and Respect Negative affect, Punitive control, and Disrespect Awareness, Responsiveness, Effectiveness in addressing problems, and Student comfort Flexibility and student focus, Connections to current life, Support for student autonomy and leadership, and Meaningful peer interactions

Instructional Support

Content Understanding Depth of understanding, Communication of concepts and procedures, Background knowledge and misconceptions, Transmission of content knowledge and procedures, and Opportunity for practice of procedures and skills Analysis and Problem Inquiry & Analysis, Opportunities for novel application, and Metacognition Solvingb Quality of Feedback Feedback loops, Scaffolding, Building on student responses, and Encouragement and affirmation Instructional Dialogue Cumulative, content-driven exchanges, Distributed talk, and Facilitation strategies

Classroom Organization

Behavior Management Clear expectations, Proactive, Effective redirection of misbehavior, and Student behavior Productivity Maximizing learning time, Routines, Transitions, and Preparation Instructional Learning Learning targets/organization, Variety of modalities, strategies, and materials, Active facilitation, and Effective engagement Formats*

Note 1. The domain and dimension changes outlined above have been made to the CLASS since Hafen and colleagues' (2015) study. This table represents the CLASS teaching dimensions during our data collection, and we used Pianta, Hamre & Mintz's (2010) conceptualization of the CLASS dimensions and domains. Note 2. In addition to the 11 dimensions outlined above, there is one CLASS dimension, Student Engagement, that does not fall under any other domains and is not considered an indicator of teaching practices. a Domain Changes: Negative Climate became part of Classroom Organization and Instructional Learning Formats became part of Instructional Supports. b Dimension Change: Analysis and Problem Solving has been renamed to Analysis and Inquiry.

Please cite this article as: McKellar, S. E et al., Teaching practices and student engagement in early adolescence: A longitudinal study using the Classroom Assessment Scoring System, Teaching and Teacher Education, https://doi.org/10.1016/j.tate.2019.102936

6

S.E. McKellar et al. / Teaching and Teacher Education xxx (xxxx) xxx

Table 2 Mean, Standard Deviation, and Correlations between Student Gender, Race, and Student Behavioral and Emotional Engagement.

D Emotional

Emotional Engagement Correlations M Gender (male students ¼ 1) Black students White students Latino students Asian students D Emotional Engagement D Behavioral Engagement

SD

Fall

0.49

0.50

-.01

0.38 0.49 0.08 0.06 0.21 0.15

0.49 0.50 0.27 0.24 1.02 0.77

.06y -.08* .00 .02 -.38*** -.11**

Engagement Correlations Spring .00 .00 -.03 -.01 .05 .50*** .34***

D Behavioral Engagement Correlations

Behavioral Engagement Correlations Fall

Spring

.05

-.09**

-.08*

-.06y .05 -.02 .03

-.02 .01 .02 .00 -.17*** -.36***

-.03 .02 -.03 .04 .31*** .58***

.02 -.01 .00 -.03 .05 .51***

Note 1. D represents the difference between fall and spring engagement, M represents mean, and SD represents standard deviation. Note 2. “D Emotional Engagement Correlation” and “D Behavioral Engagement Correlation” show the correlations between demographic variables and the difference between mean fall and spring emotional engagement. Note 3. Correlations between emotional and behavioral engagement and changes in engagement ranged from .40 to .65, and are all p<.001. yp<.1, *p<.05, **p<.01, ***p<.001

observational measure can be used for any subject matter and across elementary school subjects. Each dimension of the CLASS is rated on a scale from 1 to 7 by certified observers. The scoring does not factor in aspects of the classroom outside of the teaching practices in relation to their students (e.g., there are no scores for instructional materials, physical environment, or curriculum adoption; Pianta & Hamre, 2009). For the current study, observers visited classrooms on two different days (but at the same time). During these observations the entire lesson was observed and between four to six rating cycles were completed. Each rating cycle consisted of a 15 minute observation of classroom practices followed by a 5 to 10 minute time period where the observers rated the 11 CLASS dimensions. The length of the lessons varied from 48 min to an hour. The eleven dimensions were averaged to produce scores on the three domains as outlined above. Student Engagement. We adapted an established 10-item selfreport measure of student engagement to assess behavioral and emotional engagement in the classroom (Skinner et al., 2009). For behavioral engagement, items assessed the extent to which students try hard, participate in classroom activities, exert effort, pay attention, and persist. For example, items included “I try hard to do well in math/science class” and “I pay attention in my math/science class.” For emotional engagement, items assessed the extent to which students enjoyed and had positive feelings related to their experiences in their class. For example, items included, “When we work on something in math/science class, I feel interested” and “I enjoy learning new things in my math/science class.” All items were answered on a 5-point Likert response scale (1 ¼ not at all true of me, 3 ¼ somewhat true, and 5 ¼ very true of me). The scores ranged from 1 to 5 with a 5 indicating more or positive behavioral or emotional engagement. The validity of this measure has been demonstrated in research showing that students who rate themselves high on engagement are more likely to be identified by their teacher as engaged (Skinner et al., 2009). Our measure of behavioral engagement was reliable in the current sample (six items, Cronbach’s alpha ¼ .86 at Wave 1 and 0.89 at Wave 2) as well as the measure of emotional engagement (four items, Cronbach’s alpha ¼ .89 at Wave 1 and 0.92 at Wave 2). 2.3. Analytical procedure Given that the students were nested within classrooms, and therefore dependent observations, we used multilevel modeling, specifically two-level Hierarchical Linear Modeling (HLM; Raudenbush & Bryk, 2002). First, we ran a fully unconditional

model with spring engagement as the outcome to calculate the intraclass correlation (ICC) as a measure of the amount of variance that existed between classrooms. Next, in Model 1 (see Table 4), we introduced fall engagement in order to model change, and student demographics (gender and race). In subsequent models, we added class-level predictors using a stepwise approach in order to avoid instability due to multicollinearity as reported in prior research (Rimm-Kaufman et al., 2015). We included in the model the dimension of CLASS that showed the highest, significant effect if added to the model as a single additional predictor, hence reflecting the strongest predictor of change. In a subsequent step, we inspected the potential of further CLASS dimensions to significantly add to the prediction of change after the first predictor was included. This process of adding predictors continued until additional predictors did not significantly predict engagement and the model fit did not improve (e.g., the Chi-square change between the prior and current model was non-significant), emulating standard “stepwise” procedures in regression analysis in software packages (e.g., SPSS). This analytic approach has the advantage that it documents at every step the potential instability of regression coefficients for predictors already in the model. We ran these stepwise models separately for behavioral engagement and emotional engagement.

3. Results 3.1. Descriptive statistics and correlations The means, standard deviations, and correlations are displayed in Table 2 (student level variables) and Table 3 (classroom level variables). For student level variables, gender and race were not correlated with changes in either type of engagement from fall to spring. For classroom level variables, eight of the 11 dimensions of the CLASS across all three broader domains were significantly correlated with classroom aggregates of student behavioral engagement in the spring. Quality of Feedback and Regard for Student Perspectives were significantly correlated with the change in classroom aggregates of student behavioral engagement from fall to spring. For emotional engagement, three dimensions of the CLASS observations (Positive Climate, Teacher Sensitivity, Regard for Student Perspectives) were correlated with aggregates of student emotional engagement in the spring. Regard for Student Perspectives was significantly correlated with the change in classroom aggregates of student emotional engagement from fall to spring.

Please cite this article as: McKellar, S. E et al., Teaching practices and student engagement in early adolescence: A longitudinal study using the Classroom Assessment Scoring System, Teaching and Teacher Education, https://doi.org/10.1016/j.tate.2019.102936

S.E. McKellar et al. / Teaching and Teacher Education xxx (xxxx) xxx

7

Table 3 Mean, Standard Deviation and Correlations between the 11 CLASS Dimensions of Teaching Practices and Classroom Aggregates of Student Engagement. Emotional Engagement Correlations

Emotional Support Positive Climate Negative Climate Teacher Sensitivity Regard for Student Perspectives Classroom Organization Behavior Management Productivity Instructional Learning Formats Classroom Organization Content Understanding Analysis and Problem Solving Quality of Feedback Instructional Dialogue

M

SD

Fall

Spring

4.57 1.94 3.88 2.76

1.09 0.95 1.07 1.13

.22 -.13 .19 .13

.32* -.21 .29* .42**

4.88 4.53 3.76

1.10 0.96 0.75

.15 .16 .01

3.52 1.98 3.11 2.79

1.04 0.93 0.84 0.99

.06 .16 .08 .23y

D Emotional Engagement Correlations

Behavioral Engagement Correlations

D Behavioral Engagement Correlations

Fall

Spring

-.11 .09 -.11 -.34*

.15 -.20 .15 .26y

.34* -.38** .34** .45**

-.27y .26y -.27 -.28*

.21 .27y .08

-.07 -.12 -.08

.31* .22 -.02

.41** .40** .13

-.18 -.27y -.19

.06 .20 .25y .27y

.01 -.04 -.21 -.03

.05 .20 .12 .27*

.24y .171 .38** .38**

-.25y .01 -.36** -.19

Note. D represents the difference between fall and spring engagement, M represents mean, and SD represents standard deviation. yp < .1, * p < .05, **p < .01, ***p < .001.

3.2. Multilevel analyses results Null and Covariate Model: For spring engagement, 5% of the variance of behavioral engagement and 9% of the variance of emotional engagement was attributable to between-classroom differences. The covariate model (see Models 1 in Table 4) refers to models with spring engagement predicted by fall engagement, gender, and race. In the covariate model, we established that fall engagement predicted spring engagement for both types (behavioral engagement g ¼ 0.70, p < .001 and emotional engagement g ¼ 0.64, p < .001). The proportion of classroom variance was accounted for by covariates was 62%. Gender and race did not significantly predict spring engagement when controlling for fall levels; however, we kept them in subsequent models as controls. Behavioral engagement. Our HLM exploratory analysis run for Model 1 of behavioral engagement the CLASS dimension with the highest t-value was for Quality of Feedback (b ¼ 0.04, SE ¼ 0.01, t ¼ 3.581; see Table S1 in the online Supporting Information). As shown in Table 4 Model 2, teachers’ Quality of Feedback was hence included in the next step and is a strong predictor of behavioral engagement, b ¼ 0.12, SE ¼ 0.04, t ¼ 3.43, p < .001. After including Quality of Feedback, Productivity, the second highest original potential predictor with b ¼ 0.01, SE ¼ 0.00, t ¼ 1.56,1 was not a significant predictor of behavioral engagement above and beyond teachers’ Quality of Feedback; however, there was a marginally significant trend, b ¼ 0.05, SE ¼ 0.03, t ¼ 1.87, p ¼ .068. Emotional engagement. For emotional engagement, the Model 1 HLM exploratory analysis revealed Regard for Student Perspective as the CLASS dimension with the highest t-value (b ¼ 0.06, SE ¼ 0.02, t ¼ 3.321; see Table S2 in the online Supporting Information). As shown in Table 4 Model 2, Regard for Student Perspective significantly predicts emotional engagement in the spring when controlling for fall engagement. Instructional Learning Formats, which had the second highest t-value as potential additional predictor in Model 1 was not a significant predictor of emotional engagement above and beyond Regard for Student Perspective. Consistently, as seen in Model 3, adding Instructional Learning Formats did not improve the model fit. We conclude that there were no additional dimensions of the CLASS that significantly predict emotional engagement above and beyond Regard for

1

p values are not generated in HLM exploratory analyses.

Student Perspective. Additional findings. Across all models, we controlled for gender and race as fixed effect. After controlling for prior engagement, neither effect was significant. 4. Discussion The purpose of this study was to use standardized classroom observations of specific teaching practices to predict changes in early adolescent emotional and behavioral engagement from fall to spring. Overall, the Quality of Feedback was the strongest predictor of student behavioral engagement and Regard for Student Perspective was the strongest predictor of student emotional engagement. While several practices were positively associated with change in behavioral engagement, Quality of Feedback and Productivity explained nearly all of the predictive power of the CLASS dimensions for behavioral engagement. Given our exploratory approach and Brophy’s (2010) work on motivation to learn, it is not surprising that teachers’ Quality of Feedback, a dimension of Instructional Support, was the strongest predictor of behavioral engagement. Our findings align well with Pianta and Hamre’s (2009) framework that outlines Quality of Feedback as a teacher’s ability to scaffold content and build on student responses for student learning. It is also not surprising that teachers’ Productivity, a dimension of Classroom Organization, was the second strongest predictors of behavioral engagement. This parallels RimmKaufmann et al. s’ (2015) finding that Classroom Organization predicts observer reports of student behavioral engagement. The strong relationship between Quality of Feedback and behavioral engagement does not mean that other strengths of the instruction are irrelevant. However, for increasing behavioral engagement, our results suggest that when observers see high quality in the way teachers give feedback, the instruction tends to be strong in other observed dimensions as well. Overall, our findings for behavioral engagement suggest that teachers’ ability to scaffold lessons and offer constructive feedback is most likely to increase students’ participation, effort, and focus in classroom learning tasks from fall to spring. Along with these findings, Productivity and Regard for Student Perspective are also likely to predict behavioral engagement based on our exploratory analyses, and they are likely to be working in conjunction with Quality of Feedback.

Please cite this article as: McKellar, S. E et al., Teaching practices and student engagement in early adolescence: A longitudinal study using the Classroom Assessment Scoring System, Teaching and Teacher Education, https://doi.org/10.1016/j.tate.2019.102936

S.E. McKellar et al. / Teaching and Teacher Education xxx (xxxx) xxx

Table 4 Multilevel Models for the CLASS Teaching Practices Predicting Changes in Emotional and Behavioral Engagement from Fall to Spring.

8

Please cite this article as: McKellar, S. E et al., Teaching practices and student engagement in early adolescence: A longitudinal study using the Classroom Assessment Scoring System, Teaching and Teacher Education, https://doi.org/10.1016/j.tate.2019.102936

S.E. McKellar et al. / Teaching and Teacher Education xxx (xxxx) xxx

Change in student emotional engagement was largely predicted by teachers’ Regard for Student Perspective. Different from behavioral engagement, other dimensions of the CLASS did not predict emotional engagement above and beyond this critical predictor. Our results relate to Rimm-Kaufmann and colleagues' (2015) findings that Emotional Support predicted engagement, since teachers’ Regard for Student Perspectiveda dimension of Emotional Supportdwas the strongest and only significant predictor of emotional engagement when looking at multiple dimensions. According to Pianta et al. (2012), this association means that teachers’ consideration of students’ autonomy needs and promotion of meaningful peer interactions supports students’ enjoyment in classroom learning tasks. These findings for emotional engagement were expected, given the motivational needs outlined by Ryan and Deci’s (2000) self-determination theorydautonomy, competence, and belongingness dparallel the CLASS’s dimension of Regard for Student Perspective. However, we were surprised that we did not find links between dimensions within the Classroom Organization domain, since Rimm-Kaufmann et al. (2015) found emotional engagement was related to both Emotional Support and Classroom Organization. When interpreting these findings, it is important to consider the extent to which different types of engagement are amenable to teacher influence; to what extent can teachers shape students’ behaviors in their math class versus their feelings about their math class? Our preliminary analyses provided evidence that classroom differences in behavioral engagement were better explained by the CLASS than classroom difference in emotional engagement. An estimated 17% of the between-classroom variance for emotional engagement was explained by Regard for Student Perspective and Instructional Delivery Formats, while 30% of the betweenclassroom variance for behavioral engagement was explained by Quality of Feedback and Productivity. Moreover, the correlations of CLASS dimensions with emotional engagement are systematically lower than the correlations with behavioral engagement. From these findings, our study suggests that observed teaching practices are better designed to explain changes in behavioral engagement than changes in emotional engagement. Students’ behavioral engagement is part of the social interactions of the classroom itself and therefore logically related to the visible teaching practices or interactions with the students, while emotional engagement is a more private attitude towards the learning process that might not be immediately visible and hence less reliably observed. Overall, our study reaffirms the importance of specifically examining what teachers are doing to support student effort and enjoyment during learning activities. Most importantly, this supports the need to have observational tools to more clearly assess practices, as many student-report measures have a tendency to have a halo effect, where students will rate a teacher highly across all teaching practices neglecting to differentiate across aspects of teaching, whereas third-party observations delineate between teaching practices that support distinct types of student engagement. For example, if a teacher has students who appear to enjoy time in math but are failing to participate in more cognitively challenging ways, the teacher may want to consider focusing their attention on the types of feedback offered to students. Yet a teacher whose students are working hard but failing to enjoy learning tasks may want to focus primarily on greater attunement with students’ needs and taking students’ voices into consideration. While the three dimensions of the CLASS allow for parsimony in research studies, examining the 11 teaching dimensions of the CLASS allowed for a more nuanced understanding of what predicts behavioral and emotional engagement. Although our study does not address concerns over the validity of the three larger domains of the Teaching Through Interactions framework, a closer

9

examination of the dimensions of the CLASS may offer insights that can support educators in targeting their professional development goals towards bolstering behavioral and/or emotional engagement. Our findings support current intervention efforts to increase student engagement. For example, the developers of the CLASS and their colleagues’ intervention, MyTeachingPartner, could use these findings as they evaluate teachers across all 11 dimensions of the CLASS and suggests targeted areas for improvement (Hafen et al., 2015). 4.1. Strength and limitations Among the strengths of this study was our focus on understanding changes in student engagement over the course of a school year. This accounts for the individual student characteristics that predict student engagement prior to them entering the classroom. Another strength of the study was our racially diverse sample of students from a range of socio-economic backgrounds, predominantly middle to lower income students. Despite these strengths, the study was limited by our inability to assess student engagement more than twice in a given school year. We also did not collect data to assess the stability of the observed teaching practices, and this study was limited by only having fall observations of teaching practices. Nonetheless, our findings parallel Rimm-Kaufmann et al. s’ (2015) work looking at three time points. Further research should assess engagement and teaching practices several times throughout the year in order to test Finn’s (1989) participation-identification model because this would offer evidence for the reciprocal effects between behavioral and emotional engagement in relation to teacher practices. We were also limited in only assessing two types of engagement. Future work may also consider additional types of engagement, including academic, agentic, affective, social, and cognitive engagement (Reschly & Christenson, 2012). Prior work has noted the importance of peers for both behavioral and emotional engagement (Kindermann, 2016). Thus, additional research may also consider the role of peer dynamics alongside teaching practices. This is especially important during adolescence given the importance of peer influence for student engagement (Shin & Ryan, 2014). Perhaps the most important lesson to be learned from this study was that distinct practices can be leveraged by teachers to support distinct types of student engagement. Quality of Feedback and Productivity can be leveraged to support student behavioral engagement, and Regard for Student Perspective can be leveraged to support student emotional engagement. Depending upon teaching goals and learning objectives, a teacher may want to prioritize facilitation of student enjoyment in learning activities instead of prioritizing facilitation of student effort and focus on learning tasks. While these objectives are reciprocal according to Finn’s (1989) participation-identification model, our study can support teachers in targeting practices to areas that need the most attention as well as catering instruction to their engagement priorities for students. With this in mind, observed teaching practices are better able to measure behavioral engagement than emotional engagement. This work paves the way for intervention work to equip teachers with the tools that best align with their needs and explore less common ways of assessing teaching-practices (e.g., third party observations) as they relate to student engagement. Author note Thank you to all of the teachers and students who participated in the Classroom and Peer Ecologies (CAPE) project. We would like to acknowledge the original funding source provided by the Spender Foundation. Correspondence concerning this article

Please cite this article as: McKellar, S. E et al., Teaching practices and student engagement in early adolescence: A longitudinal study using the Classroom Assessment Scoring System, Teaching and Teacher Education, https://doi.org/10.1016/j.tate.2019.102936

10

S.E. McKellar et al. / Teaching and Teacher Education xxx (xxxx) xxx

should be addressed to Sarah E. McKellar, Combined Program in Education and Psychology, 610 E. University Ave, Ann Arbor, MI 48109; Email: [email protected]. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.tate.2019.102936. References Abbott-Chapman, J., Martin, K., Ollington, N., Venn, A., Dwyer, T., & Gall, S. (2014). The longitudinal association of childhood school engagement with adult educational and occupational achievement: Findings from an Australian national study. British Educational Research Journal, 40(1), 102e120. https:// doi.org/10.1002/berj.3031. Allen, J., Gregory, A., Mikami, A., Lun, J., Hamre, B. K., & Pianta, R. (2013). Observations of effective teacher-student interactions in secondary school classrooms: Predicting student achievement with the classroom assessment scoring system–secondary. School Psychology Review, 42(1), 76e97. Ames, C. (1992). Classrooms: Goals, structures, and student motivation. Journal of Educational Psychology, 84(3), 261e271. https://doi.org/10.1037/0022-0663.84. 3.261. Bihler, L.-M., Agache, A., Kohl, K., Willard, J. A., & Leyendecker, B. (2018). Factor analysis of the classroom assessment scoring system replicates the three domain structure and reveals no support for the bifactor model in German preschools. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.01232. Brophy, J. E. (2010). Motivating students to learn (3rd ed.). New York, NY: Routledge https://doi-org.proxy.lib.umich.edu/10.4324/9780203858318. Brophy, J., & Good, T. (1986). Teacher behavior and student achievement. In M. C. Wittrock (Ed.), Handbook of research on teaching (3rd ed., pp. 376e391) (New York: McMillan). Burniske, J., & Meibaum, D. (2012). The use of student perceptual data as a measure of teaching effectiveness (Briefing paper from the Texas Comprehensive Center). Austin, TX: SEDL. Retrieved from http://www.sedl.org/txcc/resources/briefs/ number_8/index.php. Chase, P. A., Warren, D. J. A., & Lerner, R. M. (2015). School engagement, academic achievement, and positive youth development. In P. E. Bowers, J. G. Geldhof, K. S. Johnson, J. L. Hilliard, M. R. Hershberg, V. J. Lerner, et al. (Eds.), Promoting positive youth development: Lessons from the 4-H study (pp. 57e70). Cham, Switzerland: Springer. Cochran-Smith, M. (2016). Teaching and teacher education: Absence and presence in AERA presidential addresses. Educational Researcher, 45(2), 92e99. https:// doi.org/10.3102/0013189X16639040. Cochran-Smith, M., & Lytle, S. L. (1990). Research on teaching and teacher research: The issues that divide. Educational Researcher, 19(2), 2e11. Connell, J. P., & Wellborn, J. G. (1991). Competence, autonomy, and relatedness: A motivational analysis of self-system processes. In M. R. Gunnar, & L. A. Sroufe (Eds.), Self processes and development (pp. 43e77). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. Covington, M. V. (2000). Goal theory, motivation, and school achievement: An integrative review. Annual Review of Psychology, 51(1), 171e200. https://doi.org/ 10.1146/annurev.psych.51.1.171. Danielson, C. (2007). Enhancing professional practice: A framework for teaching (2nd ed.). Alexandria, VA: Association for Supervision and Curriculum Development. Danielson, C. (2011). Evaluations that help teachers learn. Educational Leadership, 68(4), 35e39. Deemer, S., & Hanich, L. B. (2005). Using achievement goal theory to translate evidence-based principles into practice in educational psychology. The Clearing House, 78(5), 197e201. Doyle, W. (1977). Paradigms for research on teacher effectiveness. Review of Research in Education, 5(1), 163e198. https://doi.org/10.3102/ 0091732X005001163. Eccles, J. S. (2016). Engagement: Where to next? Learning and Instruction, 43, 71e75. https://doi.org/10.1016/j.learninstruc.2016.02.003. Eccles, J. S., Midgley, C., Wigfield, A., Buchanan, C. M., Reuman, D., Flanagan, C., et al. (1993). Development during adolescence: The impact of stage-environment fit on young adolescents’ experiences in schools and in families. American Psychologist, 48(2), 90e101. https://doi.org/10.1037/0003-066X.48.2.90. Eccles, J. S., & Roeser, R. W. (2011). Schools as developmental contexts during adolescence: Schools as developmental contexts. Journal of Research on Adolescence, 21(1), 225e241. https://doi.org/10.1111/j.1532-7795.2010.00725.x. Epstein, J. (1988). Effective schools or effective students? Dealing with diversity. In R. Haskins, & B. MacRae (Eds.), Policies for America’s public schools: Teacher equity indicators (pp. 89e126). Norwood, NJ: Ablex. Finn, J. D. (1989). Withdrawing from school. Review of Educational Research, 59(2), 117e142. https://doi.org/10.3102/00346543059002117. Finn, J. D., & Zimmer, K. S. (2012). Student engagement: What is it? Why does it matter? In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 97e131). New York, NY, US: Springer Science þ Business Media. https://doi.org/10.1007/978-1-4614-2018-7_5.

Fisher, R. J. (1993). Social desirability bias and the validity of indirect questioning. Journal of Consumer Research, 20(2), 303e315. https://doi.org/10.1086/209351. Fredricks, J. A., Reschly, A. L., & Christenson, S. L. (2019). Interventions for student engagement: Overview and state of the field. In J. A. Fredricks, A. L. Reschly, & S. L. Christenson (Eds.), Handbook of student engagement interventions (pp. 1e11). https://doi.org/10.1016/B978-0-12-813413-9.00001-2. Friedel, J. M., Cortina, K. S., Turner, J. C., & Midgley, C. (2010). Changes in efficacy beliefs in mathematics across the transition to middle school: Examining the effects of perceived teacher and parent goal emphases. Journal of Educational Psychology, 102(1), 102e114. https://doi.org/10.1037/a0017590. Furrer, C., & Skinner, E. (2003). Sense of relatedness as a factor in children’s academic engagement and performance. Journal of Educational Psychology, 95(1), 148e162. https://doi.org/10.1037/0022-0663.95.1.148. Gonida, E. N., Voulala, K., & Kiosseoglou, G. (2009). Students’ achievement goal orientations and their behavioral and emotional engagement: Co-examining the role of perceived school goal structures and parent goals during adolescence. Learning and Individual Differences, 19(1), 53e60. https://doi.org/10.1016/ j.lindif.2008.04.002. Hafen, C. A., Hamre, B. K., Allen, J. P., Bell, C. A., Gitomer, D. H., & Pianta, R. C. (2015). Teaching through interactions in secondary school classrooms: Revisiting the factor structure and practical application of the classroom assessment scoring systemesecondary. The Journal of Early Adolescence, 35(5e6), 651e680. https:// doi.org/10.1177/0272431614537117. Hamre, B. K., Pianta, R. C., Downer, J. T., DeCoster, J., Mashburn, A. J., Jones, S. M., et al. (2013). Teaching through interactions. The Elementary School Journal, 113(4), 461e487. Jimerson, S. R., Campos, E., & Greif, J. L. (2003). Toward an understanding of definitions and measures of school engagement and related terms. California School Psychologist, 8(1), 7e27. https://doi.org/10.1007/BF03340893. Kindermann, T. A. (2016). Peer group influences on students’ academic motivation. In K. R. Wentzel, & G. B. Ramani (Eds.), Handbook of social influences in school contexts: Social-emotional, motivation, and cognitive outcomes (pp. 31e47). NewYork, NY: Routledge. Lawson, M. A., & Lawson, H. A. (2013). New conceptual frameworks for student engagement research, policy, and practice. Review of Educational Research, 83(3), 432e479. Lei, H., Cui, Y., & Zhou, W. (2018). Relationships between student engagement and academic achievement: A meta-analysis. Social Behavior and Personality: International Journal, 46(3), 517e528. https://doi.org/10.2224/sbp.7054. Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84(3), 231e259. https://doi.org/10. 1037/0033-295X.84.3.231. Pianta, R. C., Belsky, J., Vandergrift, N., Houts, R., & Morrison, F. J. (2008). Classroom effects on children’s achievement trajectories in elementary school. American Educational Research Journal, 45(2), 365e397. https://doi.org/10.3102/ 0002831207308230. Pianta, R. C., & Hamre, B. K. (2009). Conceptualization, measurement, and improvement of classroom processes: Standardized observation can leverage capacity. Educational Researcher, 38(2), 109e119. https://doi.org/10.3102/ 0013189X09332374. Pianta, R. C., Hamre, B. K., & Allen, J. P. (2012). Teacher-student relationships and engagement: Conceptualizing, measuring, and improving the capacity of classroom interactions. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 365e386). New York, NY, US: Springer. https://doi.org/10.1007/978-1-4614-2018-7_17. Pianta, R. C., Hamre, B. K., & Mintz, S. (2010). Classroom assessment scoring system (CLASS): Elementary 4e6 manual. Baltimore, MD: Paul H. Brooks. Pianta, R. C., La Paro, K. M., & Hamre, B. K. (2008). Classroom Assessment Scoring System (CLASS). Charlottesville: VA: University of Virginia. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879e903. https:// doi.org/10.1037/0021-9010.88.5.879. Pressley, M., Roehrig, A. D., Raphael, L., Dolezal, S., Bohn, C., Mohan, L., & Hogan, K. (2003). Teaching processes in elementary and secondary education. In W. M. Reynolds, & G. E. Miller (Eds.), Handbook of psychology: Educational psychology (pp. 153e175). Hoboken, NJ, US: John Wiley & Sons Inc.. https://doi. org/10.1002/0471264385.wei0708 Quin, D. (2017). Longitudinal and contextual associations between teacherestudent relationships and student engagement: A systematic review. Review of Educational Research, 87(2), 345e387. https://doi.org/10.3102/0034654316669434. Quin, D., Hemphill, S. A., & Heerde, J. A. (2017). Associations between teaching quality and secondary students’ behavioral, emotional, and cognitive engagement in school. Social Psychology of Education: International Journal, 20(4), 807e829. https://doi.org/10.1007/s11218-017-9401-2. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. SAGE Publications Inc. Reschly, A. L., & Christenson, S. L. (2012). Jingle, jangle, and conceptual haziness: Evolution and future directions of the engagement construct. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 3e19). Retrieved from http://link.springer.com/10.1007/978-14614-2018-7. Reyes, M. R., Brackett, M. A., Rivers, S. E., White, M., & Salovey, P. (2012). Classroom emotional climate, student engagement, and academic achievement. Journal of Educational Psychology, 104(3), 700e712. https://doi.org/10.1037/a0027268.

Please cite this article as: McKellar, S. E et al., Teaching practices and student engagement in early adolescence: A longitudinal study using the Classroom Assessment Scoring System, Teaching and Teacher Education, https://doi.org/10.1016/j.tate.2019.102936

S.E. McKellar et al. / Teaching and Teacher Education xxx (xxxx) xxx Rimm-Kaufman, S. E., Baroody, A. E., Larsen, R. A. A., Curby, T. W., & Abry, T. (2015). To what extent do teacherestudent interaction quality and student gender contribute to fifth graders’ engagement in mathematics learning? Journal of Educational Psychology, 107(1), 170e185. https://doi.org/10.1037/a0037252. Roehrig, G. H., Johnson, C. C., Moore, T. J., & Bryan, L. A. (2015). Integrated STEM education. In C. C. Johnson, & T. A. Sondergeld (Eds.), STEM road map (pp. 35e50). Routledge. Ruzek, E. A., Hafen, C. A., Allen, J. P., Gregory, A., Mikami, A. Y., & Pianta, R. C. (2016). How teacher emotional support motivates students: The mediating roles of perceived peer relatedness, autonomy support, and competence. Learning and Instruction, 42, 95e103. https://doi.org/10.1016/j.learninstruc.2016.01.004. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68e78. https://doi.org/10.1037/0003-066X.55.1.68. Salmela-Aro, K., & Upadaya, K. (2012). The schoolwork engagement inventory. European Journal of Psychological Assessment, 28, 60e67. https://doi.org/10.1027/ 1015-5759/a000091. Shin, H., & Ryan, A. M. (2014). Early adolescent friendships and academic adjustment: Examining selection and influence processes with longitudinal social network analysis. Developmental Psychology, 50(11), 2462e2472. https://doi. org/10.1037/a0037922. Skinner, E. A., & Belmont, M. J. (1993). Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year. Journal of Educational Psychology, 85(4), 571e581. https://doi.org/10.1037/00220663.85.4.571. Skinner, E. A., Kindermann, T. A., & Furrer, C. J. (2009). A motivational perspective on

11

engagement and disaffection: Conceptualization and assessment of children’s behavioral and emotional participation in academic activities in the classroom. Educational and Psychological Measurement, 69(3), 493e525. https://doi.org/10. 1177/0013164408323233. €lto €, K., & Pietarinen, J. (2014). Strategies for academic Ulmanen, S., Soini, T., Pyha engagement perceived by Finnish sixth and eighth graders. Cambridge Journal of Education, 44(3), 425e443. https://doi.org/10.1080/0305764X.2014.921281. Virtanen, T. E., Pakarinen, E., Lerkkanen, M.-K., Poikkeus, A.-M., Siekkinen, M., & Nurmi, J.-E. (2018). A validation study of classroom assessment scoring systemesecondary in the Finnish school context. The Journal of Early Adolescence, 38(6), 849e880. https://doi.org/10.1177/0272431617699944. Wang, M. T., & Eccles, J. S. (2012). Social support matters: Longitudinal effects of social support on three dimensions of school engagement from middle to high school. Child Development, 83, 877e895. https://doi.org/10.1111/j.1467-8624. 2012.01745.x. Westergård, E., Ertesvåg, S. K., & Rafaelsen, F. (2018). A preliminary validity of the classroom assessment scoring system in Norwegian lower-secondary schools. Scandinavian Journal of Educational Research, 1e19. https://doi.org/10.1080/ 00313831.2017.1415964. Wetzel, C. G., Wilson, T. D., & Kort, J. (1981). The halo effect revisited: Forewarned is not forearmed. Journal of Experimental Social Psychology, 17(4), 427e439. https://doi.org/10.1016/0022-1031(81)90049-4. Weyns, T., Colpin, H., De Laet, S., Engels, M., & Verschueren, K. (2018). Teacher support, peer acceptance, and engagement in the classroom: A three-wave longitudinal study in late childhood. Journal of Youth and Adolescence, 47(6), 1139e1150. https://doi.org/10.1007/s10964-017-0774-5.

Please cite this article as: McKellar, S. E et al., Teaching practices and student engagement in early adolescence: A longitudinal study using the Classroom Assessment Scoring System, Teaching and Teacher Education, https://doi.org/10.1016/j.tate.2019.102936