ArticlePDF Available

To What Extent Do Teacher-Student Interaction Quality and Student Gender Contribute to Fifth Graders' Engagement in Mathematics Learning?

Article

To What Extent Do Teacher-Student Interaction Quality and Student Gender Contribute to Fifth Graders' Engagement in Mathematics Learning?

Abstract and Figures

This study examines concurrent teacher-student interaction quality and 5th graders' (n = 387) engagement in mathematics classrooms (n = 63) and considers how teacher-student interaction quality relates to engagement differently for boys and girls. Three approaches were used to measure student engagement in mathematics: Research assistants observed engaged behavior, teachers reported on students' engagement, and students completed questionnaires. Engagement data were conducted 3 times per year concurrent with measures of teacher-student interaction quality. Results showed small but statistically significant associations among the 3 methods. Results of multilevel models showed only 1 significant finding linking quality of teacher-student interactions to observed or teacher-reported behavioral engagement; higher classroom organization related to higher levels of observed behavioral engagement. However, the multilevel models produced a rich set of findings for student-reported engagement. Students in classrooms with higher emotional support reported higher cognitive, emotional, and social engagement. Students in classrooms higher in classroom organization reported more cognitive, emotional, and social engagement. Interaction effects (Gender X Teacher-student interaction quality) were present for student-reported engagement outcomes but not in observed or teacher-reported engagement. Boys (but not girls) in classrooms with higher observed classroom organization reported more cognitive and emotional engagement. In classrooms with higher instructional support, boys reported higher but girls reported lower social engagement. The discussion explores implications of varied approaches to measuring engagement, interprets teacher-student interaction quality and gender findings, and considers the usefulness of student report in understanding students' math experiences.
Content may be subject to copyright.
Journal of Educational Psychology
To What Extent Do Teacher–Student Interaction Quality
and Student Gender Contribute to Fifth Graders’
Engagement in Mathematics Learning?
Sara E. Rimm-Kaufman, Alison E. Baroody, Ross A. A. Larsen, Timothy W. Curby, and Tashia
Abry
Online First Publication, July 28, 2014. http://dx.doi.org/10.1037/a0037252
CITATION
Rimm-Kaufman, S. E., Baroody, A. E., Larsen, R. A. A., Curby, T. W., & Abry, T. (2014, July
28). To What Extent Do Teacher–Student Interaction Quality and Student Gender Contribute
to Fifth Graders’ Engagement in Mathematics Learning?. Journal of Educational Psychology.
Advance online publication. http://dx.doi.org/10.1037/a0037252
To What Extent Do Teacher–Student Interaction Quality and Student
Gender Contribute to Fifth Graders’ Engagement in Mathematics Learning?
Sara E. Rimm-Kaufman
University of Virginia Alison E. Baroody
San Francisco State University
Ross A. A. Larsen
Virginia Commonwealth University Timothy W. Curby
George Mason University
Tashia Abry
Arizona State University
This study examines concurrent teacher–student interaction quality and 5th graders’ (n387) engage-
ment in mathematics classrooms (n63) and considers how teacher–student interaction quality relates
to engagement differently for boys and girls. Three approaches were used to measure student engagement
in mathematics: Research assistants observed engaged behavior, teachers reported on students’ engage-
ment, and students completed questionnaires. Engagement data were conducted 3 times per year
concurrent with measures of teacher–student interaction quality. Results showed small but statistically
significant associations among the 3 methods. Results of multilevel models showed only 1 significant
finding linking quality of teacher–student interactions to observed or teacher-reported behavioral en-
gagement; higher classroom organization related to higher levels of observed behavioral engagement.
However, the multilevel models produced a rich set of findings for student-reported engagement.
Students in classrooms with higher emotional support reported higher cognitive, emotional, and social
engagement. Students in classrooms higher in classroom organization reported more cognitive, emo-
tional, and social engagement. Interaction effects (Gender Teacher–student interaction quality) were
present for student-reported engagement outcomes but not in observed or teacher-reported engagement.
Boys (but not girls) in classrooms with higher observed classroom organization reported more cognitive
and emotional engagement. In classrooms with higher instructional support, boys reported higher but
girls reported lower social engagement. The discussion explores implications of varied approaches to
measuring engagement, interprets teacher–student interaction quality and gender findings, and considers
the usefulness of student report in understanding students’ math experiences.
Keywords: engagement, teacher–student interactions, mathematics, classrooms, fifth grade
There have been remarkable shifts in mathematics education in
the past 2 decades. The stance advanced by the National Council
for Teachers of Mathematics (NCTM, 2000) and codified in the
U.S. Common Core State Standards Initiative (CCSSI, 2014) de-
scribes learning mathematics as a dynamic, exploratory process
focused on creating opportunities for students to develop a con-
ceptual understanding of mathematics. Students are expected to
identify and describe patterns in mathematics, participate in con-
versations about mathematical problem solving, use mathematics
to think and reason, and justify their mathematical thinking. The
new emphasis contrasts with a traditional view describing mathe-
matics education as a static set of facts, concepts and procedures to
Sara E. Rimm-Kaufman, Curry School of Education & Center for
Advanced Study of Teaching and Learning, University of Virginia; Alison
E. Baroody, Department of Child and Adolescent Development, San Fran-
cisco State University; Ross A. A. Larsen, Department of Foundations of
Education, Virginia Commonwealth University; Timothy W. Curby, De-
partment of Applied Developmental Psychology, George Mason Univer-
sity; Tashia Abry, T. Denny Sanford School of Social and Family Dynam-
ics, Arizona State University.
The research reported here is based upon work supported by the National
Science Foundation under Grant DRL-0814872. The work was also sup-
ported by training grants from the Institute of Education Sciences, U.S.
Department of Education, through Grant R305B040049 and Grant
R305B060009 to the University of Virginia. Any opinions, findings, and
conclusions or recommendations expressed in this material are those of the
authors and do not necessarily reflect the views of the National Science
Foundation or U.S. Department of Education. We gratefully acknow-
ledge the contributions of Sandra Christenson, Julia Thomas, Michelle Ko,
Claire Cameron, Eileen Merritt, Abigail Moncrief, Jennifer Williams, and
the administrators, teachers, families and students in our collaborating
school district.
Correspondence concerning this article should be addressed to Sara E.
Rimm-Kaufman, Curry School of Education and Center for Advanced
Study of Teaching and Learning, University of Virginia, Ruffner Hall,
Emmet Street South, Charlottesville, VA 22904. E-mail: serk@
virginia.edu
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Journal of Educational Psychology © 2014 American Psychological Association
2014, Vol. 106, No. 3, 000 0022-0663/14/$12.00 http://dx.doi.org/10.1037/a0037252
1
be learned and memorized (Henningsen & Stein, 1997; Hiebert &
Grouws, 2007; Schoenfeld, 1992).
Standards-based mathematics education heightens the demand
for students to be actively engaged in learning. Imagine the chal-
lenge from the perspective of a fifth grade teacher striving to
produce high levels of engagement in her classroom. The teacher
may have been handed clear guidelines on what to teach; for
instance, CCSSI guidelines emphasize multiplication and division
of fractions and fluency in operations with multidigit whole num-
bers and decimals to the hundredths (CCSSI, 2014). However, how
to teach in a way that fully engages students is much less clear.
Fifth graders are experienced students and often know how to
comply and appear engaged in learning. However, learning math-
ematics requires much more than simply the appearance of en-
gagement. Students need to feel engaged in math learning for the
instruction to take hold. Students need to pay attention, become
interested in the mathematical ideas, and even work together with
others on math problems. The heightened demands establish the
need for research that identifies conditions that foster student
engagement.
Despite the large body of research on engagement, few studies
focus exclusively on math, little work measures teacher behaviors
and student engagement concurrently, and work using student-
report data is scarce (Christenson, Reschly, & Wylie, 2012;
Fredricks, Blumenfeld, & Paris, 2004). Finn and Zimmer (2012)
have stated a need for research on classroom contexts that support
and threaten student engagement: “A package of assessments for
this purpose would involve observations of students in the school
setting, observations of teacher–student interactions (with specific
foci), and reactions from students themselves” (p. 125).
The present study addresses this stated need. Five unique con-
tributions stand out. First, we gather student engagement data
based on observational, teacher-report, and student-report mea-
sures. Second, we measure teacher–student interaction quality and
engagement concurrently to understand the temporal coupling
between teachers’ behaviors and students’ experience. Third, we
gather data in mathematics classrooms only, whereas most re-
search on teacher–student interaction quality is not content spe-
cific. Fourth, we consider various facets of teacher–student quality.
We examine teacher sensitivity and supportiveness, the approach
to behavior management, and opportunities for higher order think-
ing and back-and-forth conversation between teachers and stu-
dents. Fifth, we examine the extent to which classroom conditions
are equivalently important for girls and boys. Thus, our goal is to
identify immediate classroom conditions that enhance and dimin-
ish engagement for fifth grade boys and girls. The work was
designed to provide basic research insights for mathematics edu-
cators concerned with leveraging teacher–student interaction qual-
ity to improve engagement in math learning.
Theoretical Perspective
The work was guided by an integrative framework of motivation
(Skinner, Kindermann, Connell, & Wellborn, 2009) that explains
how characteristics of children’s contexts contribute to self-
systems and self-perceptions, which lead to action (engagement or
disaffection) and ultimately to social, emotional, and academic
outcomes (Skinner et al., 2009). Skinner et al. (2009) defined
children’s contexts as settings composed of peers, teachers, family
members and others with whom children engage in social interac-
tions and activities. Self-systems and self-perceptions refer to
children’s beliefs, cognitive appraisals, and perceptions of them-
selves that develop in children as a result of their past experiences,
mold children’s interpretation of their experiences, and play an
important role in motivating children’s behavior. Action refers to
engagement versus disaffection; each reflects an outward signal of
motivational state and describes the quality of children’s interac-
tions with their physical and social world. The outcomes include
social, cognitive and personality development.
The integrative framework of motivation describes motivation
as a dynamic, developing characteristic that is sensitive to contexts
external to the child (e.g., interactions with teachers). Further, the
framework introduces the utility of measuring a child’s engage-
ment at a particular point in time as one way to “capture the target
definitional manifestations of motivation—namely, energized, di-
rected, and sustained action” (Skinner et al., 2009, p. 225). This
view applies to students in elementary math classrooms. Most fifth
grade students do not come to math class as “engaged” or “disen-
gaged.” Students’ engagement in math class varies depending on
their interactions with teachers, peers, and materials (Connell &
Wellborn, 1991; Skinner & Belmont, 1993). Further, students’
engagement varies across days, weeks, and months—a student
who appears engaged in mathematics instruction one day may be
less engaged in math class on a day 1 full month later.
We apply the integrative framework of motivation to understand
day-to-day interactions between teachers and students. Each year
of math instruction is composed of daily experiences that vary in
quality and accrue to create a cumulative experience for students.
We disaggregate the year of math instruction by sampling specific
days and assessing the immediate correspondence between teach-
ers’ interactions with students and students’ engagement. The
work is situated at an important point developmentally; fifth grad-
ers are capable of reflecting upon and reporting their engagement
in learning, and fifth grade marks a turning point when boys begin
to outperform girls in mathematics (Robinson & Lubienski, 2011).
Engagement in Learning
Engagement has been described as “the glue, or mediator, that
links important contexts—home, school, peers, and communi-
ty—to students and, in turn, to outcomes of interest” (Reschly &
Christenson, 2012, p. 3). Existing research establishes that engage-
ment is critical for learning and that engagement forecasts school
success. Students who stay on task, attend to learning goals, and
participate actively in the learning experience show better aca-
demic achievement in elementary school (Fredricks et al., 2004;
Greenwood, Horton, & Utley, 2002; Hughes & Kwok, 2007; Ladd,
Birch, & Buhs, 1999; Ponitz, Rimm-Kaufman, Grimm, & Curby,
2009; Reyes, Brackett, Rivers, White, & Salovey, 2012; Tucker et
al., 2002).
Definitions of engagement vary considerably; a three-part def-
inition of engagement that includes behavioral, cognitive, and
emotional engagement is most prevalent (Reschly & Christenson,
2012; Fredricks et al., 2004). Behavioral engagement refers to
paying attention, completing assigned work, participating in
teacher-sanctioned learning opportunities, and showing an absence
of disruptive behaviors. Cognitive engagement refers to a willing-
ness to exert effort to understand content, work through difficult
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
2RIMM-KAUFMAN, BAROODY, LARSEN, CURBY, AND ABRY
problems, and manage and direct their attention toward the task at
hand. Emotional engagement refers to feelings of connection to
content, interest in learning, and enjoyment of solving problems
and thinking about content (Fredricks et al., 2004). A fourth
construct, social engagement, is also fundamental. Social engage-
ment (termed “task-related interaction” by Patrick, Ryan, & Ka-
plan, 2007) refers to students’ day-to-day social exchanges with
peers that are tethered to the instructional content. Standards-based
math instruction emphasizes activities involving small groups of
students and mathematical discourse among students (Fuson, Kal-
chman, & Bransford, 2005; NCTM, 2000).
Although conceptualizations of engagement vary, there are four
common themes: (a) Engagement is a critical mediator for learn-
ing; (b) engagement is multifaceted with behavioral, cognitive,
emotional, and social elements; (c) different sources of data are
necessary depending on the type of engagement measured; and (d)
students show a decrease in engagement in learning as they prog-
ress from elementary school into the middle school years (Furrer &
Skinner, 2003; Marks, 2000; Reschly & Christenson, 2012; Reyes
et al., 2012). Engagement theories describe dynamics within the
engagement system (e.g., emotional engagement stimulates behav-
ioral engagement) and outside of the engagement system (e.g.,
social context contributes to behavioral engagement; Reschly &
Christenson, 2012; Skinner et al., 2009). We focus on a factor
outside of the engagement system, teacher–student interaction
quality, and consider the extent to which it contributes to behav-
ioral, cognitive, emotional, and social engagement.
Teacher–Student Interactions
Teachers’ interactions with students vary in quality and have
appreciable effects on math achievement outcomes (Martin, An-
derson, Bobis, Way, & Vellar, 2012; Reyes et al., 2012). Teacher–
student interactions are malleable features of classroom environ-
ments and have been the focus of national efforts to raise
mathematics achievement (Pianta & Hamre, 2009; Rimm-
Kaufman & Hamre, 2010).
Quality of Teacher–Student Interactions
Teacher–student interaction quality can be described in relation
to three domains: emotional, organizational, and instructional sup-
port (Pianta & Hamre, 2009). Emotional support refers to the
teachers’ connection to and responsiveness toward students,
awareness of students’ individual differences and needs, and will-
ingness to incorporate students’ point of view into learning activ-
ities. Classroom organization refers to the teachers’ tendency to
use proactive rather than reactive supports to foster classroom
routines and guide classroom behavior, use instructional ap-
proaches that make learning objectives clear, and use a variety of
modalities to engage students in learning. Instructional support
refers to the presence of feedback loops in teacher–student com-
munication and provision of opportunities to engage in higher
order thinking and learn new language and vocabulary (Pianta, La
Paro, & Hamre, 2008).
Research links teachers’ emotional support (i.e., positive class-
room social climate, teacher sensitivity toward students) to en-
hanced engagement in kindergarten (Rimm-Kaufman et al., 2002)
and third grade classrooms (NICHD Early Child Care Research
Network, 2005). Teacher efforts to reinforce prosocial behavior in
sixth and seventh grade contribute to enhanced behavioral and
social engagement (Matsumura, Slater, & Crosson, 2008). Meta-
analytic work has demonstrated associations between positive
teacher affect and engagement and between negative teacher affect
and disengaged behavior (Roorda, Koomen, Spilt, & Oort, 2011).
Engagement plays a mediational role linking emotional support to
achievement in both upper elementary (Reyes et al., 2012) and
middle school grades (Voelkl, 1995).
High quality classroom organization has been linked to engage-
ment in kindergarteners, first graders (Ponitz, Rimm-Kaufman,
Brock, & Nathanson, 2009; Rimm-Kaufman, Curby, Grimm, Na-
thanson, & Brock, 2009), and third graders (NICHD Early Child
Care Research Network, 2005). Teachers who establish clear rou-
tines in the fall appear to increase the self-regulated behavior of
their students throughout the school year (Bohn, Roehrig, & Press-
ley, 2004; Cameron, Connor, & Morrison, 2005). Third graders in
classrooms with higher levels of productivity and more opportu-
nities to engage in academic instruction spend more time behav-
iorally engaged (NICHD Early Child Care Research Network,
2005).
Instructionally rich learning environments are also likely to
support engagement. The presence of authentic learning experi-
ences (i.e., provision of interesting questions and opportunities for
in-depth learning) relates to increased student engagement in math
learning in elementary and middle school years (Marks, 2000). In
sixth and seventh grade classrooms, teachers who asked students
challenging questions and encouraged students to explain the
evidence behind their statements in classroom discussions en-
hanced the quality of the discourse (Matsumura et al., 2008), and
thus, participatory engagement. Middle school teachers who were
observed showing high expectations for student work, monitoring
student progress and providing scaffolding, and challenging stu-
dent thinking produced higher levels of student engagement (Ra-
phael, Pressley, & Mohan, 2008; Woodward et al., 2012).
Gender
Student gender has been linked to engagement; boys show lower
levels of behavioral and emotional engagement than girls in ele-
mentary and middle school (Kindermann, 2007; Marks, 2000).
Most work demonstrating gender differences in engagement gen-
eralizes across content areas and is not specific to math. The fifth
grade year appears to be an important time to compare the engage-
ment of boys and girls in math because it marks an inflection point
in achievement. From kindergarten to fifth grade, math achieve-
ment disparities between boys and girls increase, with boys show-
ing more achievement growth than girls. In middle school, the
reverse is true, and girls show larger achievement increases than
boys (Robinson & Lubienski, 2011). Gender disparities in engage-
ment and achievement warrant further investigation in math class-
rooms.
Simply comparing engagement between boys and girls does not
fully recognize the role of teacher–student interactions in the
facilitation of engagement. In a study of elementary and middle
school students, the higher level of engagement in girls than boys
was attenuated in the presence of social support (i.e., student-
reported teacher respect, feelings of safety at school, the presence
of high expectations from their teachers, and opportunities to
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
3
ENGAGEMENT IN FIFTH GRADE MATHEMATICS
discuss academic issues with their families; Marks, 2000). Other
work has described boys as having more frequent and academi-
cally challenging interactions with their teachers than girls (Na-
tional Research Council, 2001). Too little is known about how
teachers’ interactions with students are differentially important to
boys’ versus girls’ concurrent engagement in fifth grade math.
Informants of Engagement
We used three categories of informants to assess engagement.
Observers measured behavioral engagement; teachers reported on
behavioral engagement; and students reported on their cognitive,
emotional, and social engagement. Each information source pro-
vides a unique perspective. Classroom observational methods mea-
sure observable indicators of engagement, such as behavioral
engagement (Brophy & Good, 1986; NICHD Early Child Care
Research Network, 2005) but do not capture students’ internal
psychological experience. Teachers’ ratings provide global indi-
cators of students’ engagement and provide cumulative reports of
engagement over a year but also tap teachers’ subjective beliefs
about students (Gest, Domitrovich, & Welsh, 2005; Mashburn,
Hamre, Downer, & Pianta, 2006). Student-report methods provide
students’ own perspective of their psychological experience and
may be better for measuring intrapsychic experiences; however,
student-report methods may be sensitive to social desirability bias.
We included three informants because each reporter provides a
unique perspective on students’ engagement.
Other Factors
Several student attributes with theoretical or empirical links to
mathematics engagement were included as covariates. Age was
included because of its association with self-regulatory abilities in
school settings (Bronson, 2001). Eligibility for free or reduced
priced lunch (FRPL) was used as an indicator of low income;
elementary school-aged children living in impoverished environ-
ments experience confrontation with chronic stressors linked to
lower self-regulatory abilities and engagement (Evans & English,
2002; Evans & Rosenbaum, 2008). Initial achievement was in-
cluded because of associations between higher math achievement
and emotional engagement, and because rate of growth in math
learning differs for students with preexisting academic difficulty
compared to typical students (Bodovski & Farkas, 2007; Crosnoe
et al., 2010; Dotterer & Lowe, 2011). Self-efficacy in math,
defined as students’ perception of their capacity to learn or per-
form in math, was included because of established links to en-
gagement (Linnenbrink & Pintrich, 2003; Schunk & Pajares,
2005). Time of year was included because of changes in student
experience over the year (Curby, Rimm-Kaufman, & Abry, 2013).
The Present Study
We address three questions. First, to what extent do observa-
tionally based, teacher-reported, and student-reported measures of
engagement show concordance and discordance? We hypothesized
stronger associations within informants than among informants.
We expected that varied approaches to measurement would pro-
vide different lenses on engagement. Second, to what extent do the
quality of teacher–student interactions and student gender contrib-
ute to engagement? We expected higher engagement among girls
than boys and expected higher quality teacher–student interactions
to relate to higher engagement. Third, does the quality of teacher–
student interactions predict student engagement differentially for
boys and girls? We expected higher quality teacher–student inter-
actions would be more important for engaging boys than girls.
Method
Participants
All schools were located in a single suburban district in a
Mid-Atlantic state. Schools and fifth grade teachers were recruited
by the research team through in-person meetings with principals
and teachers. Response rates were 83% and 79% for schools and
teachers, respectively. The selected schools (N20) were socio-
economically and linguistically diverse; 33% of students qualified
for FRPL, and 31% were English language learners (ELL). Sixty-
three fifth grade mathematics teachers participated. Teachers had,
on average, 12.49 years of experience (range 1–38). Most
teachers were Caucasian (n48); five were Hispanic, one was
African American, one was Native American, and two were mul-
tiracial. Six teachers did not report their race/ethnicity. All teachers
held bachelor’s degrees; 38 had master’s degrees. All teachers
reported having a full state certification. Teachers received finan-
cial remuneration for participating.
Fifth grade students were recruited via mailings sent home to all
parents by participating teachers in the fall of students’ fifth grade
year. Family recruitment practices followed customary district
procedures for family communication, involving translation into
seven commonly spoken languages. Parents of 479 students signed
consent forms and received gift certificates for participating.
Approximately five students per classroom (mode 5) were
selected from the 479 consented students, resulting in the sample
of 387. Selection was conducted randomly for each classroom
bounded by two constraints: (a) maintenance of equal number of
girl and boy participants, and (b) demographic match to the whole
school (based on ethnicity, FRPL, and ELL percentages). The final
sample of student participants (n387; 203 girls) were 10.47
years old (SD 0.37) in September 2010. School records showed
that 21% of students qualified for FRPL (income of $40,793 for a
family of four, roughly below 180% of the federal poverty guide-
line). Parent-report questionnaires (described below) showed that
55% of students spoke primarily English at home, 28% spoke a
non-English language (22 different languages reported), and 17%
had missing data. Of the 321 parents reporting mothers’ education,
7.2% did not have a high school diploma, 21.5% had a high school
diploma, and 71.3% had an associate’s degree or above.
Procedures
The research team began by conducting extensive, iterative pilot
work in 2009–2010 with 33 fifth grade students and six fifth grade
teachers. Existing, well-validated measures were used to measure
engagement, when available. The research team reviewed existing
measures and found that typical engagement measures were not
necessarily well suited for fifth graders, math, or reflections on one
specific day of class. Pilot work was conducted to adapt existing
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
4RIMM-KAUFMAN, BAROODY, LARSEN, CURBY, AND ABRY
observational and student-report engagement measures to meet
high levels of rigor.
Data were gathered from five sources: (a) district data, (b) parent-
report questionnaire, (c) classroom observations, (d) student-report
questionnaires, and (e) teacher-report questionnaires. All data were
gathered while students were enrolled in fifth grade except for initial
student achievement data, which was gathered by the district as part
of the end-of-the-year fourth grade standardized testing. Data were
gathered between May 2010 and May 2011, as shown in Table 1.
Data gathered by the district were used to measure student
FRPL and initial achievement. Parents completed a demographic
questionnaire at fall recruitment to describe student and sample
characteristics (e.g., gender, age, mother’s education). Pairs of
research assistants conducted classroom observations in math
classes at three times during the school year, corresponding to
three windows (Window 1: late September to late November;
Window 2: late November to mid-February; and Window 3: late
February to late April). At each observation, one research assistant
videotaped the classroom to gather teacher–student interaction
quality data and a second research assistant live-coded student
engagement (as described below). The research assistants admin-
istered engagement questionnaires to student participants immedi-
ately after each observation. After the fall observation only, stu-
dents completed a questionnaire about their math self-efficacy. In
spring, teachers completed a teacher demographic questionnaire
and questionnaires about each participant to measure student en-
gagement in mathematics learning.
All classroom visits were scheduled and conducted following a
specific protocol. Research assistants scheduled classroom obser-
vations of teachers and students on days that teachers deemed
“typical days” of math instruction. Observations were scheduled
for 3 different days during the school year to sample typical
practices from over 3 hr of observation. Classroom observations
were conducted for the full length of the math lesson (M63 min,
range 15 to 135). One research assistant began videotaping the
classroom prior to the transition to math instruction and ended at
the end of the math lesson. The second research assistant (child
observer) conducted two 4-min observations of engagement for
each child participant during the same time in which the teacher
was observed. Child observers followed a protocol in which they
would watch one student for 4 min, complete ratings, watch a
second student for 4 min, complete ratings, and so on, until all
student participants had been observed and rated once. Then, the
child observer would cycle through the student participants a
second time, observing each student for 4 min and completing
ratings again. Child observations resulted in 24 min of observed
engagement per child. Child observers made efforts so that stu-
dents were unaware that they were being observed. When the math
lesson observation was complete, the research assistants distrib-
uted student-report engagement questionnaires to student partici-
pants to measure their engagement in mathematics on that specific
day. All classroom videotapes were sent to the laboratory for
subsequent coding.
Measures
District data.
Student demographic data. District records were used to de-
termine eligibility for FRPL.
Initial achievement. The paper version of the state standard-
ized test, the Standard of Learning (SOL), was administered by the
district to assess fourth grade mathematics achievement (Virginia
Department of Education [VDOE], 2008). The test was composed
of 50 multiple choice items tapping students’ procedural knowl-
edge and conceptual understanding of four skill categories: (a)
number and number sense, (b) computation and estimation, (c)
measurement and geometry, and (d) probability, statistics, pat-
terns, functions and algebra (VDOE, 2010). The state computed
the total number of items correct and converted the number to a
scaled score ranging from 0 to 600. A scaled score of 400 indicates
pass/proficient, and 500 indicates pass/advanced. The Virginia
Standards of Learning Technical Report (VDOE, 2008) describes
test development, calibration, and validity. Test items were devel-
oped through a collaborative process among Virginia educators,
Table 1
Timeline for Data Collection From Five Data Sources
Data source and type May 2010 Sept.–Nov.
2010 Nov. 2010
Feb. 2011 Feb.–April
2011 April–May
2011
District data
Student demographic information x
Initial achievement test (4th grade) x
Parent-report questionnaire
Student demographic questionnaire x
Classroom observations
Teacher–student interaction quality x x x
Observed engagement x x x
Student-report questionnaires
Math self-efficacy x
Engagement in mathematics x x x
Teacher-report questionnaires
Engagement questionnaire x
Teacher demographic questionnaire x
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
5
ENGAGEMENT IN FIFTH GRADE MATHEMATICS
VDOE, Educational Testing Service, Pearson, and content experts
based upon test blueprints. Calibration was established using Ra-
sch modeling and the partial credit model. Test validity was
established by gathering empirical evidence supporting the face
validity, intrinsic rational validity, content validity, and construct
validity (VDOE, 2008). Students deemed not proficient in English
were administered the Plain English Math version that equates to
the standard math assessment.
Parent-report questionnaires.
Student demographic information. Parents completed cus-
tomized questionnaires to describe sociodemographic characteris-
tics. Gender was coded as 1 for female. Child age in September
2010 was computed based on birthdate. Parents reported primary
language spoken at home and level of maternal education.
Classroom observations.
Teacher–student interaction quality. Quality of teacher–
student interactions was assessed using the Classroom Assessment
Scoring System (CLASS; Pianta et al., 2008). There are 10 mea-
sured dimensions that correspond to three domains (emotional
support, classroom organization, and instructional support). Emo-
tional support was measured using dimensions of positive climate,
negative climate, teacher sensitivity, and regard for student per-
spectives (␣⫽.83). Positive climate referred to a positive emo-
tional tone among teachers and students and referred to respect,
enthusiasm, and evidence of enjoyment. Negative climate (re-
versed for analysis) tapped teachers’ evidence of sarcasm, anger,
aggression and/or harshness. Teacher sensitivity measured evi-
dence of the teacher providing comfort, reassurance and encour-
agement in relation to students’ academic and social needs. Regard
for students’ perspectives referred to situations in which teachers’
choice of classroom activities demonstrated emphasis on students’
motivation, interests, and point of view.
Classroom organization was assessed using scales of behavior
management, productivity, and instructional learning formats (␣⫽
.82). Behavior management measured teachers’ use of effective
methods to prevent students’ behavior problems and redirect stu-
dents, as needed. Productivity referred to the teachers’ use of
instructional time and routines enabling appropriate learning op-
portunities for students. Instructional learning formats referred to
teachers’ use of materials and activities to facilitate learning op-
portunities.
Raters assessed three dimensions in relation to instructional
support for learning (␣⫽.72). Concept development measured the
teachers’ use of strategies to promote students’ higher order think-
ing. Quality of feedback assessed specificity of teachers’ verbal
interaction pertaining to student work, ideas and comments (e.g.,
did teacher comments create communication loops between the
teacher and students). Language modeling measured the extent to
which teachers facilitated, encouraged and modeled students’ use
of advanced language. Each dimension was rated on a 7-point
Likert scale. For analyses predicting observed and student-reported
engagement, CLASS domains collected concurrently with the en-
gagement outcome were used in models. For analyses predicting
teacher-reported student engagement, mean levels of domains
were calculated for each teacher across the three observation
windows.
Prior to CLASS training, coders read manuals and additional
readings and conducted practice observations. Training involved
the time equivalent of a 2-day small group interactive training
followed by paired observations with an expert. Reliability tests
involved rating ten 15-min segments for CLASS. Ratings were
compared to a gold standard, prepared by the instruments’ authors.
To be considered reliable, each coder’s responses had to be within
1 scale point of the gold standard on 80% of the responses.
Reliability exceeded these levels prior to data collection. Calibra-
tion involved independent coding (once or twice per month) in a
small group session followed by reliability checks and discussion
of coding rationales plus double coding of more than 10% of tapes
selected randomly. In addition, master coders conducted audits by
coding one tape coded by each coder every 12 weeks. Measures of
teacher–student interaction quality were based on two segments
(minutes 0–15 and 3045).
Observed behavioral engagement. Research assistants as-
sessed student engagement using time-sampling and global rating
systems adapted from the NICHD Early Childcare Research Net-
work (2005) Classroom Observation Scale (COS). The COS had
been used in fifth grade classrooms (Pianta et al., 2008) but
required honing, revised documentation, and testing, all of which
were conducted during the pilot year. The time sampling measure
was a low-inference measure and required an observer to note the
presence or absence of disengagement in 1-min intervals. Disen-
gagement included wandering, looking away from instructional
opportunities, behaviors disruptive to learning, and similar behav-
iors listed in a manual. Students were observed and coded for four
consecutive 1-min intervals, twice during each math lesson. Ob-
served on-task behavior was calculated as minutes observed minus
minutes of disengagement.
The global rating was a high-inference measure composed of
three rating scales: (a) participation in learning opportunities (e.g.,
duration and interest of involvement), (b) disruptive behavior
(reversed; e.g., excessive out-of-turn talking, sustained noise), and
(c) self-reliance (e.g., self-management of materials and responsi-
bilities). Research assistants took notes related to global codes
during the 4-min time-sampling period. Then, the research assis-
tant used the notes and a scoring rubric to rate behavior from 1
(low) to 7 (high) on each scale.
Reliability training involved following a protocol with a four-
phase process (preparation, training, reliability, and ongoing cali-
bration) to attain and maintain reliability. The process was com-
parable to that described for the CLASS. Reliability values prior to
data collection (based on eight segments) and during monthly
calibration (based on eight segments), respectively, showed an
intraclass correlation of .65 and .95 for the time sampling measure
and 75% and 90% within one match for the global rating. Initial
values were lower than desired, but later tests of reliability indi-
cated substantial improvements. Research assistants conducted
paired coding during initial visits until reliability improved. The
time sampling score and global ratings of behavioral engagement
were correlated (r.70). All four scores were included in a
confirmatory factor analysis to create a factor score.
Student-report questionnaires.
Students’ feelings of math efficacy. The Academic Efficacy
subscale of the Patterns of Adaptive Learning Scale (Midgley et
al., 2000) was used to measure students’ perception of their com-
petence. The subscale was modified to apply to a math context and
piloted and validated in a sample of 39 students (pilot ␣⫽.89).
Students rated items such as, “I’m certain I can master the skills
taught in math this year,” and “I can do almost all of the work in
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
6RIMM-KAUFMAN, BAROODY, LARSEN, CURBY, AND ABRY
math if I work at it” on a scale from 1 (almost never)to4(all the
time). The five items were averaged to create a composite value of
math efficacy where higher values indicated higher efficacy (␣⫽
.81).
Student-reported engagement in math class. The student-
reported measures of cognitive and emotional engagement were
developed based upon measures created by Meece (2009); Kong,
Wong, and Lam (2003); Rowley, Kurtz-Costes, Meyer, and Kizzie
(2009); and Skinner and Belmont (1993) to assess students’ report
of cognitive and emotional engagement in relation to a specific day
and the math context. Development and piloting of student-report
measures of cognitive and emotional engagement involved selec-
tion of items by two mathematics education experts, adjustment of
wording to measure a single day in math, and review by a research
team. This process was followed by piloting the measure with
students immediately after their math instruction. Researchers
observed the students and then distributed measures. Students
rated their engagement and identified confusing items. Research
assistants engaged in conversations about cognitive and emotional
engagement with selected students for content validation. This
process was conducted serially following protocols.
The student-report measure of social engagement developed and
used by Patrick et al. (2007) was adopted in its existing form (with
the only modification involving addition of the phrase “in math
class”). The measure of social engagement was composed of five
items that measured the extent to which students explained aca-
demic content to one another and discussed ideas with other
students in class.
Students reported on engagement in a 15-item questionnaire
with a scale from 1 (no, not at all true)to4(yes, very true). The
questionnaire was piloted in a sample of 33 fifth graders in three
schools prior to use, resulting in an alpha for the complete measure
of .90 and correlations exceeding .50 (pvalues .01), with
analogous measures given to teachers simultaneously. Using data
from the present study (n387), a confirmatory factor analysis
resulted in a three-factor solution representing subconstructs of
cognitive engagement, emotional engagement, and social engage-
ment, with alphas of .78, .91, and .74, respectively. The alpha
values for cognitive and social engagement were not as high as
desired; however, we chose to use these factors in subsequent
analyses because of solid factor loadings and high fit indices (see
Table 3). Factor scores for each subconstruct were used in analy-
ses. Further validation stems from relatedness of the three subcon-
structs to other constructs. Factor scores for cognitive, emotional,
and social engagement correlated .56, .67, and .49, respectively,
with student-reported feelings about school.
Teacher-report questionnaires.
Teacher-reported engagement. Teachers reported on behav-
ioral engagement in math using an eight-item version of the
student engagement questionnaire used by Wu, Hughes, and Kwok
(2010) and Skinner, Furrer, Marchand, and Kindermann (2008),
and adapted to include the phrase, “in math class.” Teachers rated
each item from 1 (strongly disagree)to4(strongly agree). Items
included “This student pays attention in math class” and “This
student participates in discussion in math class.” Factor analysis
confirmed a one-factor solution. This is consistent with previous
use of the measure as a single scale and is supported by a high
internal consistency-reliability estimate (␣⫽.92). This factor
score correlated significantly with fourth grade achievement (r
.29, p.01) and showed predictive validity to fifth grade achieve-
ment based on other analyses conducted as part of this study.
Teacher demographic questionnaire. Teachers reported gen-
der, ethnicity, education, years of experience, certification, and
other demographic characteristics in a questionnaire.
Analytic approach. The initial step involved the reduction
of engagement data. We conducted separate confirmatory factor
analyses (CFA) for each measure of engagement using Mplus
6.12 (Muthén & Muthén, 2010). The CFA utilized a priori
decisions about factors drawn from theory, previous research,
and item source (Kong et al., 2003; Meece, 2009; Rowley et al.
2009; Skinner & Belmont, 1993). Observed behavioral engage-
ment and teacher-reported engagement were hypothesized to
have only one factor based upon theory and study design,
whereas student-reported engagement was hypothesized to have
three factors. Following each CFA, a factor score was generated
for use in analyses.
Observed behavioral engagement and student-reported en-
gagement were collected three times per year, whereas the
teacher-reported engagement measure was collected only once.
For Question 1 only, we aggregated observed behavioral and
student-reported engagement measures across the three obser-
vations, resulting in one value per student for each of the
following: observed behavioral engagement, self-reported cog-
nitive engagement, emotional engagement, and social engage-
ment. Descriptive statistics were calculated to understand basic
data patterns.
Question 1 examined concordance and discordance between
observationally based, teacher-reported, and student-reported
math engagement. Bivariate correlation coefficients were com-
puted and examined. Questions 2 and 3 involved multilevel
modeling to account for clustering effects (observations nested
within students, students nested within teachers, and teachers
nested within schools) using PROC MIXED in SAS (Version
9.2). Questions 2 and 3 used the five dependent variables
(created from the abovementioned CFA): (a) observed behav-
ioral engagement, (b) teacher-reported behavioral engagement,
(c) student-reported cognitive engagement, (d) student-reported
emotional engagement, and (e) student-reported social engage-
ment. Both observed and student-reported engagement data
were gathered at three time points. Instead of aggregating the
data across time for these outcomes, we handled the longitudi-
nal nature of the data via random effects in SAS PROC MIXED.
Model assumptions. Multilevel modeling assumes normal-
ity of the residuals, linear relationships between variables, no
outliers, and an appropriate method for handling missing data
(Kline, 2011; Little & Rubin 1987). Data were examined
through residuals plots, histograms, and scatterplots. Assump-
tions were met for normality of the residuals and linear rela-
tionships between variables. No outliers were apparent.
Roughly 5% of the data were missing for all the covariates.
Analyses were conducted to determine the type of missing data
via bivariate correlations and logistic regression. Considering
the exhaustive nature of the covariates and the fact that missing
data analyses revealed no systematic trends, the data were
determined to be most likely missing at random. Subsequently,
data were imputed in Mplus (Muthén & Muthén, 2010) while
accounting for the clustering of the data.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
7
ENGAGEMENT IN FIFTH GRADE MATHEMATICS
Multilevel model building. Models were built incrementally
in three steps. First, we created a basic four-level model that
included gender and all covariates. Second, to address Question 2,
we added one teacher–student interaction quality variable at a time
to the basic model. Finally, to address Question 3, we generated
models with one teacher–student interaction quality variable (e.g.,
emotional support) plus its corresponding interaction with gender
(e.g., Gender Emotional support). The decision to include one
teacher–student interaction variable at a time versus all three
teacher–student interaction variables simultaneously involved ad-
ditional consideration. Next, we describe the model building pro-
cess and then we describe the decision about how we handled
teacher–student interaction variables.
The basic model involved two observation-level variables
(weeks in the year, weeks in the year squared), five student-
level variables (gender, age, FRPL, initial achievement, self-
efficacy), two teacher-level variables (master’s degree, years of
experience), and no school-level variables. To address Question
2, examining gender and teacher–student interaction quality
main effects, we added one teacher-level variable (emotional
support, classroom organization, or instructional support) to the
basic model. To address Question 3, examining gender by
teacher–student interactions, we used the basic model plus a
cross-level interaction between gender and one domain of
teacher–student interaction quality (Gender Emotional sup-
port, Gender Classroom organization, or Gender Instruc-
tional support). Each model included all the predictors that were
part of the basic model. Centering was unnecessary because
gender was binary at the student level. We conducted post hoc
contrasts to test significance of the slopes for boys and girls
separately.
This approach to model building was the same for four of five
engagement outcomes. Observed behavioral engagement and
the three student-reported engagement constructs were mea-
sured at three points during the year. Thus, we tested change
over time as a linear term (weeks from the start of school) and
curvilinear term (weeks from the start of school squared) to
estimate slight curvature. Teacher-reported behavioral engage-
ment was measured once. For that outcome, we used three-level
models that excluded the observation level.
The data measured longitudinally were analyzed using a
repeated measure analysis with random effects in SAS PROC
MIXED. This procedure allows time to be a within-subject
factor because different measurements on the same student are
at different times (which we refer to as observation level). Time
was tested as a main effect in the model; in other words, the
model was designed to answer the question of how a student
changes as time progresses (with potential linear and curvilin-
ear effects). The student was entered as a random effect
permitting inferences to be made to the entire population of
students who could have been in the study. The clustering
effects of classrooms and schools were handled similarly as
random effects. The multilevel models all had the same general
form.
Level 1 : Observed Behavioral Engagement ijkt ⫽␲
0ijk
⫹␲
1ijk (Weeks)⫹␲
2ijk (Weeks)2e,
Level 2 : 0ijk ⫽␤
00ij ⫹␤
10ij (Gender)⫹␤
20ij (Age)
⫹␤
30ij (FRPL)⫹␤
40ij (Initial Achievement)
⫹␤
50ij (SelfEfficacy) r,
Level 3 : 00ij ⫽␥
000i ⫹␥
100i(Masters Degree)t
⫹␥
200i (Years of Experience)t
⫹␥
300i (Teacher –Student Interaction Quality Domain)tu,
Level 4 : Y000i ⫽␩
0000 ε,
where
i1,...,20(schools)
j1,...,63(classrooms)
k1,...,387(students)
t1,...,3(time points).
Levels 1, 2, 3, and 4 correspond to time (observation), student,
teacher, and school-levels, respectively. Weeks and Weeks
2
were
set as fixed effects. Level 1 was omitted for the teacher-reported
behavioral engagement model.
Handling of correlated CLASS domains. Correlation coef-
ficients showed associations among the CLASS domains (emo-
tional support, classroom organization, instructional support) with
coefficients ranging from .58 to .62. Including all three domains in
the same model raises multicollinearity concerns. However, ana-
lyzing each domain separately means that model results for each
domain also contain information about the portion of variance
shared across domains. Resolution involved a two-part approach:
First, we analyzed each domain alone in separate models (keeping
all covariates the same); second, we computed each model with all
three domains entered simultaneously. We compared results and
considered trade-offs. Results for emotional support and classroom
organization were comparable regardless of analytic approach.
However, in the model with all domains entered simultaneously,
instructional support was negatively associated with each outcome.
The negative association contradicted theory, hypotheses, and the
positive relations evident in the zero order correlations. As a result,
we decided to report results from models that included each
CLASS domain separately, in keeping with work elsewhere
(Avant, Gazelle, & Faldowski, 2011; Rudasill, Gallagher, &
White, 2010).
Results
Factor Analysis
The CFA for observed behavioral engagement (based on time-
sampled frequency of engagement and global engagement ratings)
revealed an excellent model fit for the hypothesized one-factor
solution (CFI .99, TLI .97, RMSEA .09, SRMR .02).
Table 2 shows factor loadings for observer-reported engagement.
We hypothesized a one-factor solution for teacher-reported behav-
ioral engagement. The CFA was conducted and the resulting fit
statistics were excellent (CFI .95, TLI .93, RMSEA .04,
SRMR .04). Results are shown in Table 3. Three types of
student engagement were hypothesized for the student-report mea-
sure: cognitive, emotional, and social engagement. For cognitive
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
8RIMM-KAUFMAN, BAROODY, LARSEN, CURBY, AND ABRY
engagement, two items (“I thought about other things instead of
math in math class today” and “Today I only paid attention in math
when it was interesting”) had relatively weak loadings compared
to other items but were included because all factor loadings were
significant (p.01) and the model fit was excellent (CFI .96,
TLI .96, RMSEA .03, SRMR .04). For emotional engage-
ment and social engagement, the factor loadings were all relatively
strong, as shown in Table 4.
Descriptive Statistics and Correlations
Mean values suggest that, on average, students were highly
engaged in math class regardless of informant. Students were
observed as behaviorally engaged 3.28 min of the 4.0 min ob-
served and were rated by observers as 5.66 on behavioral engage-
ment on a 1–7 scale (based on values obtained prior to the CFA).
On average, student-reported engagement ranged from 3.01 to 3.42
and teacher-reported student engagement was 3.05 on 1- to 4-point
scales, indicating high engagement in learning. Table 5 shows
descriptive statistics based on factor scores.
Concordance and Discordance Between Measures of
Engagement
Addressing Question 1, correlation coefficients between pairs of
engagement variables showed (a) all correlations were positive,
statistically significant, and ranged from .11 to .68; (b) correlation
coefficients were higher within each measure (range from .49 to
.68) than between measures (range from .08 to .24); (c) lowest
correlation values were between student-reported and teacher-
reported engagement values (range from .11 to .24); and (d)
consistent with the CFA results, correlation values confirm that
students’ view of their cognitive, emotional, and social engage-
ment are related (r.49 to .68) but have distinct characteristics
(see Table 5).
Main Effect of Teacher–Student Interaction Quality
and Student Gender on Engagement
Question 2 examined the extent to which quality of teacher–
student interactions and student gender contributed to engagement.
As a preliminary step, descriptive statistics for covariates and
teacher–student interaction quality, as well as their correlation with
engagement, were computed (see Table 6). Mean levels of emo-
tional support and classroom organization appeared higher than
those for instructional support. Teachers showed a slightly more
limited range of emotional support and classroom organization
compared to instructional support.
To address Question 2, we used the basic four-level model
(labeled as Model 1 in Tables 7 and 8). We added one teacher-level
variable (concurrent emotional support [Model 2A], classroom
organization [Model 2B], or instructional support [Model 2C]) to
the basic model. Table 7 shows results of the multilevel models for
Table 2
Confirmatory Factor Analysis for Observer-Reported
Behavioral Engagement
Item Standardized factor
loading
Observed on-task behavior (based on frequency of
engaged behavior) 0.81
Participation in learning opportunities (based on
global rating) 0.92
Disruptive behavior (based on global rating) 0.56
Self-reliance (based on global rating) 0.90
Note. CFI .99, TLI 97, RMSEA .09, SRMR .02.
Table 3
Confirmatory Factor Analysis for Teacher-Reported
Behavioral Engagement
Item Standardized factor
loading
This student concentrates on doing his/her work
during math class. 0.89
This student works as hard as he/she can during
math class. 0.90
This student pays attention in math class. 0.89
This student tries to learn as much as he/she can
about math. 0.89
This student’s attention seems to wander during math
class (reversed). 0.52
This student participates in discussions in math class. 0.72
This student asks off-topic questions during math
class (reversed). 0.48
This student doesn’t try very hard in math class
(reversed). 0.79
Note. CFI .95, TLI 93, RMSEA .04, SRMR .04.
Table 4
Confirmatory Factor Analysis for Student-Reported Engagement
Item Standardized factor
loading
Cognitive engagement
Today in math class I worked as hard as I
could. 0.58
I thought about other things instead of math in
math class today. 0.33
Today I only paid attention in math when it
was interesting. 0.31
Today it was important to me that I
understood the math really well. 0.68
I tried to learn as much as I could in math
class today. 0.75
I did a lot of thinking in math class today. 0.64
Emotional engagement
Math class was fun today. 0.80
Today I felt bored in math class. 0.63
I enjoyed thinking about math today. 0.81
Learning math was interesting to me today. 0.82
I liked the feeling of solving problems in math
today. 0.70
Social engagement
Today I talked about math to other kids in
class. 0.59
Today I helped other kids with math when
they didn’t know what to do. 0.72
Today I shared ideas and materials with other
kids in math class. 0.65
Students in my math class helped each other
learn today. 0.59
Note. CFI .96, TLI 96, RMSEA .03, SRMR .04.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
9
ENGAGEMENT IN FIFTH GRADE MATHEMATICS
observed and teacher-reported behavioral engagement. Students in
classrooms with higher levels of classroom organization appeared
more behaviorally engaged (b.13, p.01) than students in
classrooms with lower levels of organization. Girls were observed
to be more behaviorally engaged than boys (b.19, p.01).
Pertaining to teacher-reported behavioral engagement, the quality
of teacher–student interactions did not relate to teachers’ report of
students’ engaged behavior. Teachers rated girls as more engaged
than boys (b.11, p.10).
Table 8 shows results of the multilevel models for student-
reported engagement. A consistent pattern of findings emerged:
Students in classrooms with teachers who provided more emo-
tional support and higher quality classroom organization reported
higher cognitive engagement (b.03, p.01; b.03, p.01,
respectively), higher emotional engagement (b.03, p.01, b
.03, p.01, respectively), and higher social engagement (b.03,
p.01, b.03, p.01, respectively). Girls reported higher
cognitive and social engagement than boys (b.07, p.01, b
.14, p.01, respectively).
Statistical Interactions Between Gender and
Teacher–Student Interaction Quality
Question 3 queried the statistical interaction between quality of
teacher–student interactions and gender in predicting engagement.
The multilevel models included the basic model; the main effects
(tested in Question 2, i.e., concurrent emotional support [Model
2A], classroom organization [Model 2B], or instructional support
[Model 2C]); and one of three statistical interactions (Gender
Emotional support [Model 3A], Gender Classroom organization
[Model 3B], or Gender Instructional support [Model 3C]). As
shown in Table 7, none of the interactions between gender and
CLASS domains were statistically significant for observed or
teacher-reported behavioral engagement. However, one Gender
Teacher–student interaction quality effect emerged for each of the
student-reported engagement outcomes, as shown in Models 3A,
3B, and 3C in Table 8. Analyses showed a small interaction effect
between gender and classroom organization for student-reported
cognitive engagement (b⫽⫺0.06, p.01). As classroom orga-
Table 5
Intercorrelations and Descriptive Statistics for Engagement Variables
Variable 1 2 3 4 5
1. Observed behavioral engagement
2. Teacher-reported behavioral engagement .23
ⴱⴱ
(356)
3. Student-reported cognitive engagement .17
ⴱⴱ
(384) .21
ⴱⴱ
(356)
4. Student-reported emotional engagement .16
ⴱⴱ
(384) .11
(356) .68
ⴱⴱ
(384)
5. Student-reported social engagement .18
ⴱⴱ
(384) .24
ⴱⴱ
(356) .57
ⴱⴱ
(384) .49
ⴱⴱ
(384)
M0.00 3.05 3.42 3.30 3.01
SD 0.83 0.64 0.43 0.62 0.60
Min 0.94 1.00 1.50 1.30 1.38
Max 3.04 4.00 4.00 4.00 4.00
N384 359 384 384 384
Note. Sample sizes appear in parentheses.
p.05.
ⴱⴱ
p.01.
Table 6
Correlations and Descriptive Statistics of Covariates With Engagement
Engagement Child
gender Child
age Child
FRPL Initial
achievement Self-
efficacy Master’s
degree Years
exp. CLASS
ES CLASS
CO CLASS
IS
Observed factor score
(behavioral) .21
ⴱⴱ
.06 .02 .20
ⴱⴱ
.08 .03 .06
.16
ⴱⴱ
.26
ⴱⴱ
.08
Teacher-reported
(behavioral) .16
ⴱⴱ
.08 .03 .29
ⴱⴱ
.18
ⴱⴱ
.03 .01 .07 .11
.03
Student-reported
(cognitive) .14
.06 .06 .11
.32
ⴱⴱ
.06
ⴱⴱ
.15
ⴱⴱ
.10
.04 .00
Student-reported
(emotional) .05 .02 .13
.03 .24
ⴱⴱ
.03 .01 .05 .00 .02
Student-reported
(social) .10
.07 .04 .18
ⴱⴱ
.40
ⴱⴱ
.06
.11
ⴱⴱ
.09 .03 .05
M0.53 10.47 0.22 506.38 3.30 0.63 12.34 5.16 5.99 3.37
SD 0.50 0.38 0.41 70.68 0.57 0.47 8.54 0.55 0.41 0.62
Min 0.00 8.24 0.00 284.00 1.40 0.00 1.00 3.83 4.39 1.83
Max 1.00 11.77 1.00 600.00 4.00 1.00 35.00 6.67 6.67 5.17
N386 315 386 332 379 59 59 59 59 59
Note. Student gender (0 male, 1 female); FRPL (0 no, 1 yes). ES Emotional support; CO Classroom organization; IS Instructional
support. Sample size for correlations ranged from 300 to 382.
p.10.
p.05.
ⴱⴱ
p.01.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
10 RIMM-KAUFMAN, BAROODY, LARSEN, CURBY, AND ABRY
nization increased, boys reported higher levels of cognitive en-
gagement, but there was no comparable association evident for
girls. Likewise, findings showed a statistically significant interac-
tion effect between classroom organization and gender for student-
reported emotional engagement (b⫽⫺0.13, p.01). As class-
room organization increased, boys reported higher emotional
engagement, but girls did not. Post hoc analyses were conducted
for both interaction effects. The slope of classroom organization
was statistically significant for boys (p.01) but not girls (p
.05) for both outcomes. There was an interaction effect between
instructional support and gender for student-reported social en-
gagement (b⫽⫺0.06, p.05). As instructional support in-
creased, social engagement decreased for girls but not for boys.
Post hoc analyses revealed a significant negative slope for girls
(p.01) but a nonsignificant slope (p.05) for boys. (See Figure
1 for a description of these interactions.)
Table 7
Multilevel Model Results for Observed and Teacher-Reported Behavioral Engagement
Measure
Observed Teacher-reported
bSEbSE
Observation level (Model 1)
Weeks of the year 0.00 0.01 0.01
Weeks of the year
2
0.00 0.00 0.00
Student level
Child gender (female) 0.19
ⴱⴱ
0.05 0.22 0.11
0.06 0.17
Child age 0.08 0.07 0.03 0.03 0.09 0.02
Free/Reduced Price Lunch 0.07 0.06 0.08 0.14
0.07 0.17
Initial achievement (Math) 0.00
ⴱⴱ
0.00 0.14 0.00
ⴱⴱ
0.00 0.24
Self-efficacy 0.02 0.04 0.01 0.10
0.05 0.09
Teacher level (Models 1, 2A, 2B, 2C)
Master’s degree (Model 1) 0.06 0.06 0.10 0.04 0.08 0.03
Years of experience (Model 1) 0.00 0.00 0.08 0.00 0.00 0.03
Concurrent emotional support (2A) 0.02 0.03 0.02 0.04 0.06 0.05
Concurrent classroom organization (2B) 0.13
ⴱⴱ
0.04 0.06 0.09 0.10 0.06
Concurrent instructional support (2C) 0.01 0.02 0.00 0.02 0.06 0.03
Note. Teacher-reported behavioral engagement was collected only once and thus the model does not include
the observation level. Results of analyses addressing Research Question 3 showed no significant interactions and
therefore the interaction term is not reported.
p.10.
p.05.
ⴱⴱ
p.01.
Table 8
Multilevel Model Results for Student-Reported Cognitive, Emotional, and Social Engagement
Measure
Cognitive Emotional Social
bSEbSEbSE
Observation level (Model 1)
Weeks of the year 0.01
ⴱⴱ
0.00 0.02 0.02
ⴱⴱ
0.00 0.03 0.01 0.00 0.01
Weeks of the year
2
0.00
ⴱⴱ
0.00 0.00 0.00
ⴱⴱ
0.00 0.00 0.00
0.00 0.02
Student level
Child gender (female) 0.07
ⴱⴱ
0.03 0.17 0.09 0.05 0.24 0.14
ⴱⴱ
0.04 0.24
Child age 0.03 0.04 0.02 0.02 0.08 0.01 0.04 0.06 0.03
Free/Reduced Price Lunch 0.06 0.04 0.14 0.17
ⴱⴱ
0.07 0.05 0.04 0.06 0.06
Initial achievement (math) 0.00 0.00 0.02 0.00 0.00 0.01 0.00 0.00 0.03
Self-efficacy 0.17
ⴱⴱ
0.03 0.23 0.26
ⴱⴱ
0.05 0.24 0.30
ⴱⴱ
0.04 0.28
Teacher level (Models 1, 2A, 2B, 2C)
Master’s degree (Model 1) 0.07
ⴱⴱ
0.03 0.20 0.05 0.05 0.13 0.08
0.05 0.13
Years of experience (Model 1) 0.00 0.00 0.06 0.00 0.00 0.06 0.01
ⴱⴱ
0.00 0.22
Concurrent emotional support (2A) 0.03
ⴱⴱ
0.01 0.04 0.03
ⴱⴱ
0.02 0.03 0.03
ⴱⴱ
0.01 0.02
Concurrent classroom organization (2B) 0.03
ⴱⴱ
0.01 0.03 0.03
ⴱⴱ
0.01 0.02 0.03
ⴱⴱ
0.01 0.02
Concurrent instructional support (2C) 0.01 0.02 0.01 0.00 0.01 0.01 0.01 0.01 0.01
interactions (Models 3A, 3B, 3C)
Gender Emotional Support (3A)
Gender Classroom Organization (3B) 0.06
ⴱⴱ
0.02 0.08 0.13
ⴱⴱ
0.04 0.09 — — —
Gender Instructional Support (3C) 0.06
ⴱⴱ
0.02 0.06
Note. Student-reported engagement measures were collected in the fall, winter, and spring. Children 387, teachers 63, schools 20. Interactions
that were not statistically significant were not included in the final models and are not shown.
p.10.
p.05.
ⴱⴱ
p.01.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
11
ENGAGEMENT IN FIFTH GRADE MATHEMATICS
Changes in Engagement Over Time
Although not the study’s focus, results showed that the linear
and curvilinear trends of time (weeks in the year) were not signif-
icant for observed behavioral engagement. Linear and curvilinear
trends for time were present for all student-reported outcomes
(cognitive, emotional, social engagement). For each student-
reported outcome, the linear trend of time was significant (bfrom
–.01 to –.02, p.01) with a slight curvilinear relation (b.01,
p.01), indicating that engagement decreased over time, but the
decrease was steeper between Time 1 and 2 than 2 and 3.
Discussion
Three main findings emerged. First, the fifth graders, on average,
showed high levels of math engagement, regardless of informant.
Correlations between informants were lower than anticipated given
the simultaneity of the data collection, a finding that suggests the
unique vantage point of each informant. Second, the most systematic
finding from the multilevel models was the link from teacher–student
interaction quality to student-reported engagement. That is, students
in classrooms with teachers who show warmth, caring, and individual
responsiveness to their students reported working hard, enjoying
learning about math, and sharing ideas and materials with other
students in their classroom. Similarly, students in classroom with
teachers who used proactive approaches to behavior management,
facilitated smooth transitions between activities, and made learning
objectives clear prior to learning also reported feeling greater cogni-
tive, emotional, and social engagement in their math learning. Third,
results showed higher engagement for girls than boys on three of the
five engagement measures. Boys’ report of their cognitive and emo-
tional engagement was more closely coupled to the classroom con-
ditions (emotional and organizational support) than girls. An unex-
pected finding was that boys reported higher social engagement but
girls reported lower social engagement in the presence of higher
instructional support.
Measurement Concordance and Discordance
Correlation coefficients between different informants of student
engagement were statistically significant, but small (1% to 11%
shared variance). In contrast, associations within informants were
high (24% to 49% of shared variance), even when comparing the
same informant on different subconstructs of engagement. Findings
match other literature showing modest cross-informant agreement
(Gresham, Elliott, Cook, Vance, & Kettler, 2010; Konold & Pianta,
2007; Renk & Phares, 2004). Comparisons of informants can be
considered in light of the integrative framework of motivation (Skin-
ner et al., 2009). In theory, contexts, self-systems, and action are
conceptually distinct, but in practice, accurate measurement of action
(engagement) is challenging because it is tinged by characteristics of
students’ self-systems (goals, expectancies, perceived task value) and
contexts (classroom interactions) depending on informant. The results
provide researchers with new understanding as they consider mea-
surement trade-offs.
We posit that low correlations among informants represent dispar-
ities in perspectives on the classroom and cannot be dismissed as
error. For example, correlations between the observer’s perception of
behavioral engagement and students’ feelings of engagement were
low (r.24 to .26), although the data were collected simultaneously.
Behavioral engagement can be observed reliably by a research assis-
tant and therefore may provide a more objective standpoint for un-
derstanding engagement. However, observed behavioral engagement
may reflect superficial signs of engagement, whereas cognitive and
Figure 1. Interactions between teacher–student interaction quality (classroom organization and instructional
support) and gender predicting student-reported engagement (cognitive, emotional, and social engagement).
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
12 RIMM-KAUFMAN, BAROODY, LARSEN, CURBY, AND ABRY
emotional engagement assessed by student ratings may reflect intra-
psychic processes that, in part, are influenced by students’ self-
systems. This difference is important by the time students reach fifth
grade because children have become accustomed to the student
“script” and may show signs of behavioral engagement without gen-
uine feelings of connection to learning. Researchers evaluating math
curricula and interventions may benefit from gathering information
about students’ perception of engagement.
The majority of engagement research relies on teacher-reported
behavioral engagement at the end of the year. Despite research linking
teacher-reported behavioral engagement to achievement (e.g., Hughes
& Kwok, 2007; Valiente, Lemery-Chalfant, Swanson, & Reiser,
2008), the present findings suggest that researchers should be cautious
about overreliance on teacher-reported data. The correlations between
observed behavioral engagement at three time points and teacher-
reported behavioral engagement at the end of the year were low (from
.29 to .33). Teacher-reported engagement is multidetermined and
reflects students’ actual engagement as well as teachers’ attributes and
perceptions (Mashburn et al. 2006). Teachers’ rating tendencies may
be systematic; for instance, teachers’ ratings of fifth graders show
greater inflation of girls’ scores compared to boys (Robinson &
Lubienski, 2011), and teachers appear to be better reporters of exter-
nalizing than internalizing problems (Konold & Pianta, 2007).
Contribution of Gender and Interaction
Quality on Engagement
Girls were more engaged than boys for three of the five mea-
sured engagement constructs: observed behavioral engagement,
student-reported cognitive engagement, and student-reported so-
cial engagement. Gender differences on self-reported emotional
engagement and teacher-reported behavioral engagement ap-
proached statistical significance. Girls’ higher observed behavioral
engagement is consistent with other research suggesting higher
behavioral engagement among girls than boys in late elementary
and middle school (Marks, 2000; Wang, Willett, & Eccles, 2011).
By definition, behavioral engagement involves the absence of
disruptive behavior (Finn, Pannozzo, & Voelkl, 1995; Wang et al.,
2011). The gender difference in observed behavioral engagement
fits with other work describing more disruptive behavior in boys
than girls (Finn et al., 1995). Girls reported higher cognitive
engagement in math, comparable to findings in seventh graders
(Wang et al., 2011).
The presence of emotional support was linked to students’
own perception of their engagement (cognitive, emotional, and
social). By definition, emotionally supportive teachers show
warm and responsive behavior toward students and facilitate a
classroom climate in which students exhibit positive, prosocial
behavior (Pianta et al., 2008). Emotional support signals a sense
of security to students that permits full attention to the academic
work. It also fosters a classroom environment with positive
communication and respect among peers (Luckner & Pianta,
2011). Both factors may be important for fifth graders facing
challenging math learning. Findings match research pointing to
the importance of the affective qualities of school, positive
classroom climate, and teacher–student relationship for promot-
ing engagement and learning (Borman & Overman, 2004;
Decker, Dona, & Christenson, 2007; Dotterer & Lowe, 2011;
Roorda et al., 2011; Stronge, Ward, & Grant, 2011; Reyes et al.,
2012). The finding that teachers’ facilitation of a warm and
supportive environment relates to students’ perceived engage-
ment but not higher observed or teacher-reported engagement
underscores the point that student-reported engagement taps
intrapsychic processes. The result also emphasizes the impor-
tance of emotionally supportive interactions between teachers
and students in fifth grade, an issue that is crucial to convey to
late elementary school math educators who are pressed for time
or may perceive that relationship-building efforts are less es-
sential for older students.
Well-organized classrooms appear to support students’ en-
gagement in math learning, as evidenced by higher observed
behavioral engagement and student-reported cognitive and
emotional engagement. The finding linking classroom organi-
zation to observed behavioral engagement is not surprising;
interesting learning formats, clear statement of expectations,
high productivity are teaching practices that have been linked to
observed student behavioral engagement in classic (Brophy,
1983) and recent work (Downer, Rimm-Kaufman, & Pianta,
2007). However, the result that higher classroom organization
relates to students’ perception of their cognitive engagement
(commitment to paying attention, desire to understand compli-
cated material) and emotional engagement (enjoyment of math
class and problem solving) stands out as important new contri-
bution. By fifth grade, teachers may perceive students’ need for
more autonomy. Effective practices attuned to fifth graders’
developmental needs involve fostering autonomy while main-
taining clear objectives and minimizing classroom chaos
(Eccles, 2004).
Counter to expectation, results showed no main effects of
instructional support on students’ engagement in learning. This
is a surprising finding. One possible explanation stems from the
reliance on the CLASS, a global measure of instructional sup-
port that reflects teachers’ interactions with their whole class-
room of students. Students within a single classroom show a
wide range of abilities. Although teachers may be providing
even amounts of concept development or high quality feedback
to students across the classroom, the level of instruction may be
too hard for some students, too easy for others, and just right for
others. Another explanation pertains to the reliance on an
observational measure of instructional support. Gathering in-
formation on each student’s perception of instructional support
from their teacher may increase accuracy. Future work is
needed that considers students’ ability level relative to the level
of the mathematical tasks and taps students’ perception of
teachers’ instructional support.
None of the three domains of teacher–student interaction quality
related to teacher-reported behavioral engagement. Measuring
teacher–student interactions and student engagement concurrently
reveals associations between teacher and student behavior that
otherwise may be masked in an end-of-the-year, teacher-reported
measure. Teachers’ report of engagement may reflect teacher
attributes as well as student engagement (Mashburn et al., 2006).
Interactions Between Teacher–Student Interaction
Quality and Gender
Classroom organization was associated with students’ percep-
tion of their engagement more for boys than girls. Boys may be
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
13
ENGAGEMENT IN FIFTH GRADE MATHEMATICS
more distracted by chaotic learning environments and thus show
more difficulty engaging in learning (Ponitz, Rimm-Kaufman,
Brock, & Nathanson, 2009). Girls may have better developed
self-regulatory skills and require fewer external structures to sup-
port their engagement.
Instructional support findings were surprising. Girls in class-
rooms with higher instructional support reported lower social
engagement, whereas there was no relation between instructional
support and social engagement in boys. The finding may reflect
complementarity between teacher–student and student–student in-
teractions. High levels of instructional support may be more evi-
dent in classrooms where teachers engage in frequent, high quality
interactions with students but facilitate fewer peer interactions. In
fact, two of the three instructional support CLASS dimensions
(quality of feedback, language modeling) involve verbal interac-
tions between teachers and students. Frequent teacher–student
interactions may supplant peer-to-peer interactions that would
occur otherwise (Patrick et al., 2007). It is unclear why this
association was present among girls but not boys.
Limitations
Several limitations require mention. First, we did not emphasize
peer interactions. By fifth grade, students are increasingly aware
and influenced by peers. Peer liking and acceptance contribute to
teacher–student interaction quality, peer nominations of academic
competence, and students’ perception of their achievement self-
efficacy (Hughes & Chen, 2011). Fifth graders may be sensitive to
classroom composition. The engagement of a student’s peer group
links to their engagement over the course of the year, net of other
factors—a consideration not included in the present work (Kin-
dermann, 2007). Second, data were gathered in the context of a
descriptive study; therefore, the work does not support causal
inferences. Future research using an experimental design is
needed. Third, the three approaches to measuring engagement
reflect different time sampling. Observational data were based on
24 min spread across 3 days; student-reported data were based on
students’ reflection on the full period of math class across those
same 3 days. Teacher-reported measures were based on reflections
of students over the course of the year. Fourth, the data collection
did not include students’ reports of their teachers’ quality of
interactions. Fifth, some of the measures have lower than ideal
reliability.
Closing Comments
Recommendations for improving mathematics achievement
hinge on teachers’ ability to engage students in learning in the
classroom (CCSSI, 2014; NCTM, 2000; National Research Coun-
cil, 2005), raising questions about the extent to which different
types of teacher–student interactions contribute to enhanced en-
gagement in the math classroom. On a daily basis, teachers rely on
their perception of students to know whether to adjust the content
and pace of learning to keep students engaged. However, by fifth
grade students know how to appear interested and engaged, leav-
ing teachers with questions about what they can do to be sure that
students are putting forth their best effort and are truly curious and
interested in the math. Findings lead to at least two implications.
Teachers having difficulty gauging students’ interest, curiosity and
attention toward math may want to rely less on their own insights
or observations of an observer and generate strategies for receiving
direct and honest feedback from their students. Despite the fact
that fifth graders are not young children, the students, especially
boys, appear to be well attuned to the warmth and responsiveness
of their teacher and clarity of expectations in the classroom.
References
Avant, T. S., Gazelle, H., & Faldowski, R. (2011). Classroom emotional
climate as a moderator of anxious solitary children’s longitudinal risk for
peer exclusion: A Child Environment model. Developmental Psychol-
ogy, 47, 1711–1727. doi:10.1037/a0024021
Bodovski, K., & Farkas, G. (2007). Mathematics growth in early elemen-
tary school: The roles of beginning knowledge, student engagement, and
instruction. The Elementary School Journal, 108, 115–130. doi:10.1086/
525550
Bohn, C. M., Roehrig, A. D., & Pressley, M. (2004). The first days of
school in the classrooms of two more effective and four less effective
primary-grades teachers. The Elementary School Journal, 104, 269–287.
doi:10.1086/499753
Borman, G. D., & Overman, L. T. (2004). Academic resilience in mathe-
matics among poor and minority students. The Elementary School Jour-
nal, 104, 177–195. doi:10.1086/499748
Bronson, M. B. (2001). Self-regulation in early childhood: Nature and
nurture. New York, NY: Guilford Press.
Brophy, J. (1983). Classroom organization and management. The Elemen-
tary School Journal, 83, 265–286. doi:10.1086/461318
Brophy, J., & Good, T. (1986). Teacher behavior and student achievement.
In M. C. Whitrock (Ed.), The handbook of research on teaching (3rd ed.,
pp. 328–375). New York, NY: Macmillan.
Cameron, C. E., Connor, C. M. D., & Morrison, F. J. (2005). Effects of
variation in teacher organization on classroom functioning. Journal of
School Psychology, 43, 61–85. doi:10.1016/j.jsp.2004.12.002
Christenson, S. L., Reschly, A. L., & Wylie, C. (2012). Handbook of
research on student engagement. New York, NY: Springer.
Common Core State Standards Initiative. (2014). Mathematics standards.
Retrieved from http://www.corestandards.org/math
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: The
Minnesota Symposia on Child Psychology (Vol. 23, pp. 43–77). Hills-
dale, NJ: Erlbaum.
Crosnoe, R., Morrison, F., Burchinal, M., Pianta, R., Keating, D., Fried-
man, S. L., & Clarke-Stewart, K. A. (2010). Instruction, teacher–student
relations, and math achievement trajectories in elementary school. Jour-
nal of Educational Psychology, 102, 407–417. doi:10.1037/a0017762
Curby, T. W., Rimm-Kaufman, S. E., & Abry, T. (2013). Do emotional
support and classroom organization earlier in the year set the stage for
higher quality instruction? Journal of School Psychology, 51, 557–569.
doi:10.1016/j.jsp.2013.06.001
Decker, D. M., Dona, D. P., & Christenson, S. L. (2007). Behaviorally
at-risk African American students: The importance of student–teacher
relationships for student outcomes. Journal of School Psychology, 45,
83–109. doi:10.1016/j.jsp.2006.09.004
Dotterer, A. M., & Lowe, K. (2011). Classroom context, school engage-
ment, and academic achievement in early adolescence. Journal of Youth
and Adolescence, 40, 1649–1660. doi:10.1007/s10964-011-9647-5
Downer, J. T., Rimm-Kaufman, S. E., & Pianta, R. C. (2007). How do
classroom conditions and children’s risk for school problems contribute
to children’s behavioral engagement in learning? School Psychology
Review, 36, 413–432.
Eccles, J. S. (2004). Schools, academic motivation, and stage-environment
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
14 RIMM-KAUFMAN, BAROODY, LARSEN, CURBY, AND ABRY
fit. In R. M. Lerner & L. D. Steinberg (Eds.), Handbook of adolescent
psychology (pp. 125–153). Hoboken, NJ: Wiley.
Evans, G. W., & English, K. (2002). The environment of poverty: Multiple
stressor exposure, psychophysiological stress, and socioemotional ad-
justment. Child Development, 73, 1238–1248. doi:10.1111/1467-8624
.00469
Evans, G. W., & Rosenbaum, J. (2008). Self-regulation and the income–
achievement gap. Early Childhood Research Quarterly, 23, 504–514.
doi:10.1016/j.ecresq.2008.07.002
Finn, J. D., Pannozzo, G. M., & Voelkl, K. E. (1995). Disruptive and
inattentive–withdrawn behavior and achievement among fourth graders.
The Elementary School Journal, 95, 421–434. doi:10.1086/461853
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. 97–131). New York,
NY: Springer. doi:10.1007/978-1-4614-2018-7_5
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engage-
ment: Potential of the concept, state of the evidence. Review of Educa-
tional Research, 74, 59–109. doi:10.3102/00346543074001059
Furrer, C., & Skinner, E. (2003). Sense of relatedness as a factor in
children’s academic engagement and performance. Journal of Educa-
tional Psychology, 95, 148–162. doi:10.1037/0022-0663.95.1.148
Fuson, K., Kalchman, M., & Bransford, J. (2005). Mathematical under-
standing: An introduction. In M. S. Donovan & J. D. Bransford (Eds.),
How students learn: History, mathematics, and science in the classroom
(pp. 217–256). Washington, DC: National Academies Press.
Gest, S. D., Domitrovich, C. E., & Welsh, J. A. (2005). Peer academic
reputation in elementary school: Associations with changes in self-
concept and academic skills. Journal of Educational Psychology, 97,
337–346. doi:10.1037/0022-0663.97.3.337
Greenwood, C. R., Horton, B. T., & Utley, C. A. (2002). Academic
engagement: Current perspectives on research and practice. School Psy-
chology Review, 31, 328–349.
Gresham, F. M., Elliott, S. N., Cook, C. R., Vance, M. J., & Kettler, R.
(2010). Cross-informant agreement for ratings for social skill and prob-
lem behavior ratings: An investigation of the Social Skills Improvement
System—Rating Scales. Psychological Assessment, 22, 157–166. doi:
10.1037/a0018124
Henningsen, M., & Stein, M. K. (1997). Mathematical tasks and student
cognition: Classroom-based factors that support and inhibit high-level
mathematical thinking and reasoning. Journal for Research in Mathe-
matics Education, 28, 524–549. doi:10.2307/749690
Hiebert, J., & Grouws, D. (2007). The effects of classroom mathematics
teaching on students’ learning. In F. K. Lester (Ed.), Second handbook
of research on mathematics teaching and learning (Vol. 1, pp. 371–
404). Charlotte, NC: Information Age.
Hughes, J., & Chen, Q. (2011). Reciprocal effects of student-teacher and
student-peer relatedness: Effects on academic self-efficacy. Journal of
Applied Developmental Psychology, 32(5),278–298.
Hughes, J., & Kwok, O. (2007). Influence of student–teacher and parent–
teacher relationships on lower achieving readers’ engagement and
achievement in the primary grades. Journal of Educational Psychology,
99, 39–51. doi:10.1037/0022-0663.99.1.39
Kindermann, T. A. (2007). Effects of naturally existing peer groups on
changes in academic engagement in a cohort of sixth graders. Child
Development, 78, 1186–1203. doi:10.1111/j.1467-8624.2007.01060.x
Kline, R. B. (2011). Principles and practice of structural equation mod-
eling (3rd ed.). New York, NY: Guilford Press.
Kong, Q. P., Wong, N. Y., & Lam, C. C. (2003). Student engagement in
mathematics: Development of instrument and validation of construct.
Mathematics Education Research Journal, 15, 4–21. doi:10.1007/
BF03217366
Konold, T. R., & Pianta, R. C. (2007). The influence of informants on
ratings of children’s behavioral functioning. Journal of Psychoeduca-
tional Assessment, 25, 222–236. doi:10.1177/0734282906297784
Ladd, G. W., Birch, S. H., & Buhs, E. S. (1999). Children’s social and
scholastic lives in kindergarten: Related spheres of influence? Child
Development, 70, 1373–1400. doi:10.1111/1467-8624.00101
Linnenbrink, E. A., & Pintrich, P. R. (2003). The role of self-efficacy
beliefs in student engagement and learning in the classroom. Reading &
Writing Quarterly, 19, 119–137. doi:10.1080/10573560308223
Little, R., & Rubin, D. (1987). Statistical analysis with missing data (2nd
ed.). New York, NY: Wiley.
Luckner, A. E., & Pianta, R. C. (2011). Teacher–student interactions in
fifth grade classrooms: Relations with children’s peer behavior. Journal
of Applied Developmental Psychology, 32, 257–266. doi:10.1016/j
.appdev.2011.02.010
Marks, H. M. (2000). Student engagement in instructional activity: Patterns
in the elementary, middle, and high school years. American Educational
Research Journal, 37, 153–184. doi:10.3102/00028312037001153
Martin, A. J., Anderson, J., Bobis, J., Way, J., & Vellar, R. (2012).
Switching on and switching off in mathematics: An ecological study of
future intent and disengagement among middle school students. Journal
of Educational Psychology, 104, 1–18. doi:10.1037/a0025988
Mashburn, A. J., Hamre, B. K., Downer, J. T., & Pianta, R. C. (2006).
Teacher and classroom characteristics associated with teachers’ ratings
of prekindergartners’ relationships and behaviors. Journal of Psychoe-
ducational Assessment, 24, 367–380. doi:10.1177/0734282906290594
Matsumura, L. C., Slater, S. C., & Crosson, A. (2008). Classroom climate,
rigorous instruction and curriculum, and students’ interactions in urban
middle schools. The Elementary School Journal, 108, 293–312. doi:
10.1086/528973
Meece, J. (2009). Measure of Student Cognitive Engagement. Unpublished
measure, University of North Carolina.
Midgley, C., Maehr, M. L., Hruda, L. Z., Anderman, E., Anderman, L.,
Freeman, K. E.,...Urdan, T. (2000). Manual for the Patterns of
Adaptive Learning Scale. Ann Arbor: University of Michigan.
Muthén, L. K., & Muthén, B. O. (2010). Mplus user’s guide (6th ed.). Los
Angeles, CA: Muthén & Muthén.
National Council of Teachers of Mathematics. (2000). Principles and
standards for school mathematics (Vol. 1). Ann Arbor, MI: Author.
National Research Council. (2001). Adding it up: Helping children learn
mathematics. Washington, DC: The National Academies Press.
National Research Council. (2005). How students learn: History, mathe-
matics, and science in the classroom. Washington, DC: The National
Academies Press.
NICHD Early Child Care Research Network. (2005). A day in third grade:
Classroom quality, teacher, and student behaviors. The Elementary
School Journal, 105, 305–323. doi:10.1086/428746
Patrick, H., Ryan, A. M., & Kaplan, A. (2007). Early adolescents’ percep-
tions of the classroom social environment, motivational beliefs, and
engagement. Journal of Educational Psychology, 99, 83–98. doi:
10.1037/0022-0663.99.1.83
Pianta, R. C., & Hamre, B. K. (2009). Conceptualization, measurement,
and improvement of classroom processes: Standardized observation can
leverage capacity. Educational Researcher, 38, 109–119. doi:10.3102/
0013189X09332374
Pianta, R. C., La Paro, K. M., & Hamre, B. K. (2008). Classroom
Assessment Scoring System (CLASS: PreK-3). Baltimore, MD: Brookes.
Ponitz, C. C., Rimm-Kaufman, S. E., Brock, L. L., & Nathanson, L. (2009).
Early adjustment, gender differences, and classroom organizational cli-
mate in first grade. The Elementary School Journal, 110, 142–162.
doi:10.1086/605470
Ponitz, C. C., Rimm-Kaufman, S. E., Grimm, K. J., & Curby, T. W. (2009).
Kindergarten classroom quality, behavioral engagement, and reading
achievement. School Psychology Review, 38, 102–120.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
15
ENGAGEMENT IN FIFTH GRADE MATHEMATICS
Raphael, L. M., Pressley, M., & Mohan, L. (2008). Engaging instruction in
middle school classrooms: An observational study of nine teachers. The
Elementary School Journal, 109, 61–81. doi:10.1086/592367
Renk, K., & Phares, V. (2004). Cross-informant ratings of social compe-
tence in children and adolescents. Clinical Psychology Review, 24,
239–254. doi:10.1016/j.cpr.2004.01.004
Reschly, A., & 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. 3–19). New York, NY: Springer.
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, 700–712. doi:
10.1037/a0027268
Rimm-Kaufman, S. E., Curby, T. W., Grimm, K. J., Nathanson, L., &
Brock, L. L. (2009). The contribution of children’s self-regulation and
classroom quality to children’s adaptive behaviors in the kindergarten
classroom. Developmental Psychology, 45, 958–972. doi:10.1037/
a0015861
Rimm-Kaufman, S. E., Early, D. M., Cox, M., Saluja, G., Pianta, R.,
Bradley, R., & Payne, C. (2002). Early behavioral attributes and teach-
ers’ sensitivity as predictors of competent behavior in the kindergarten
classroom. Journal of Applied Developmental Psychology, 23, 451–470.
Rimm-Kaufman, S. E., & Hamre, B. K. (2010). The role of psychological
and developmental science in efforts to improve teacher quality. Teach-
ers College Record, 112, 2988–3023.
Robinson, J. P., & Lubienski, S. T. (2011). The development of gender
achievement gaps in mathematics and reading during elementary and
middle school. American Educational Research Journal, 48, 268–302.
doi:10.3102/0002831210372249
Roorda, D. L., Koomen, H. M. Y., Spilt, J. L., & Oort, F. J. (2011). The
influence of affective teacher–student relationships on students’ school
engagement and achievement. Review of Educational Research, 81,
493–529. doi:10.3102/0034654311421793
Rowley, S. J., Kurtz-Costes, B., Meyer, R., & Kizzie, K. (2009). Engage-
ment and self-concept during the transition to middle school: Gender
and domain-specific differences in change in African American youth.
Unpublished manuscript, University of Michigan.
Rudasill, K., Gallagher, K. C., & White, J. M. (2010). Temperamental
attention and activity, classroom emotional support, and academic
achievement in third grade. Journal of School Psychology, 48, 113–134.
doi:10.1016/j.jsp.2009.11.002
Schoenfeld, A. H. (1992). Learning to think mathematically: Problem
solving, metacognition, and sense making in mathematics. In D. Grouws
(Ed.), Handbook of research on mathematics teaching and learning (pp.
334–370). New York, NY: Macmillan.
Schunk, D., & Pajares, F. (2005). Competence perceptions and academic
functioning. In A. J. Elliot (Ed.), Handbook of competence and motiva-
tion (pp. 85–104). New York, NY: Guilford Press.
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, 571–581. doi:
10.1037/0022-0663.85.4.571
Skinner, E., Furrer, C., Marchand, G., & Kindermann, T. (2008). Engage-
ment and disaffection in the classroom: Part of a larger motivational
dynamic? Journal of Educational Psychology, 100, 765–781. doi:
10.1037/a0012840
Skinner, E. A., Kindermann, T. A., Connell, J. P., & Wellborn, J. G.
(2009). Engagement and disaffection as organizational constructs in the
dynamics of motivational development. In K. R. Wentzel & A. Wigfield
(Eds.), Handbook of motivation at school (pp. 223–245). New York,
NY: Routledge.
Stronge, J. H., Ward, T. J., & Grant, L. W. (2011). What makes good
teachers good? A cross-case analysis of the connection between teacher
effectiveness and student achievement. Journal of Teacher Education,
62, 339–355. doi:10.1177/0022487111404241
Tucker, C. M., Zayco, R. A., Herman, K. C., Reinke, W. M., Trujillo, M.,
Carraway, K.,...Ivery, P. D. (2002). Teacher and child variables as
predictors of academic engagement among low-income African Amer-
ican children. Psychology in the Schools, 39, 477–488. doi:10.1002/pits
.10038
Valiente, C., Lemery-Chalfant, K., Swanson, J., & Reiser, M. (2008).
Prediction of children’s academic competence from their effortful con-
trol, relationships, and classroom participation. Journal of Educational
Psychology, 100, 67–77. doi:10.1037/0022-0663.100.1.67
Virginia Department of Education. (2008). Virginia Standards of Learning
technical report: 2008 –2009 administration cycle. Retrieved from http://
www.doe.virginia.gov/testing/test_administration/technical_reports/
sol_technical_report_2008-09_administration_cycle.pdf
Virginia Department of Education. (2010). Virginia standards of learn-
ing assessment: Test blueprint, Grade 4 mathematics. Retrieved from
http://www.doe.virginia.gov/testing/sol/blueprints/mathematics_
blueprints/2009/blueprint_math4%20.pdf
Voelkl, K. E. (1995). School warmth, student participation, and achieve-
ment. Journal of Experimental Education, 63, 127–138. doi:10.1080/
00220973.1995.9943817
Wang, M. T., Willett, J. B., & Eccles, J. S. (2011). The assessment of
school engagement: Examining dimensionality and measurement invari-
ance by gender and race/ethnicity. Journal of School Psychology, 49,
465–480. doi:10.1016/j.jsp.2011.04.001
Woodward, J., Beckmann, S., Driscoll, M., Franke, M., Herzig, P., Jiten-
dra, A.,...Ogbuehi, P. (2012). Improving mathematical problem
solving in Grades 4 through 8: A practice guide (NCEE 2012–4055).
Retrieved from http://ies.ed.gov/ncee/wwc/pdf/practice_guides/
mps_pg_052212.pdf
Wu, J., Hughes, J., & Kwok, O. (2010). Teacher–student relationship
quality type in elementary grades: Effects on trajectories for achieve-
ment and engagement. Journal of School Psychology, 48, 357–387.
doi:10.1016/j.jsp.2010.06.004
Received December 22, 2012
Revision received May 2, 2014
Accepted May 12, 2014
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
16 RIMM-KAUFMAN, BAROODY, LARSEN, CURBY, AND ABRY
... Previous studies have also identified interpersonal movement coordination between learners and instructors 11,12 , but the relationship between synchronized body movement, learning, and instruction (e.g., academic performance, instructional approaches) is largely unknown, despite the fact that numerous studies have demonstrated a relationship between the quality of learning interactions and learning satisfaction. Positive instructor-learner interactions are known to contribute to students' learning engagement 13 , comfort 14 , school satisfaction 15 , and eventually to academic performance 16 . Conversely, insufficient learning interactions and/ or poor interaction quality are associated with low levels of emotional engagement 17 and negative test scores 18 . ...
... The results from the linear mixed-effects model on overall BtBC showed a main effect of Instructional Approach (β = 0.02, SE = 0.01, t = 2.25, p = 0.03, R 2 β = 0.19; Fig. 2b). Further analysis disclosed that the scaffolding approach (0.15 ± 0.01) was associated with significantly larger BtBC between instructors and learners than the explanation approach (0. 13 Relationship between motion quantity and Body-to-Body Coupling Based on our observation that instructor movement quantity differed between instructional approaches (scaffolding vs. explanation) and previous studies demonstrating the important role of instructor guidance in learning interaction 25 , we sought to examine whether instructor motion could explain the BtBC effect. ...
Article
Full-text available
It is widely accepted that nonverbal communication is crucial for learning, but the exact functions of interpersonal coordination between instructors and learners remain unclear. Specifically, it is unknown what role instructional approaches play in the coupling of physical motion between instructors and learners, and crucially, how such instruction-mediated Body-to-Body Coupling (BtBC) might affect learning. We used a video-based, computer-vision Motion Energy Analysis (MEA) to quantify BtBC between learners and instructors who used two different instructional approaches to teach psychological concepts. BtBC was significantly greater when the instructor employed a scaffolding approach than when an explanation approach was used. The importance of instructional approach was further underscored by the fact that an increase in motion in the instructor was associated with boosted BtBC, but only during scaffolding; no such relationship between the instructor movements and BtBC was found during explanation interactions. Finally, leveraging machine learning approaches (i.e., support vector and logistic regression models), we demonstrated that both learning outcome and instructional approaches could be decoded based on BtBC. Collectively, these results show that the real-time interaction of teaching and learning bodies is important for learning and that instructional approach matters, with possible implications for both in-person and online learning.
... Research has clearly shown that the relationship that students have with their teachers can have a significant impact on their engagement in school (e.g., Garcia-Reid et al., 2005;Lam et al., 2012;Wang and Eccles, 2012;Estell and Perdue, 2013;Rimm-Kaufman et al., 2014;Archambault et al., 2017;Gutiérrez et al., 2017). Adolescents who have close and caring relationships with teachers presented higher school engagement (e.g., Wang and Holcombe, 2010). ...
Article
Full-text available
Promoting student’s school engagement is a major goal in our society. The literature has shown that students’ proximal sources of social support can play a fundamental role in facilitating this engagement. The purpose of this study was (1) to compare perceived support from four sources (mother, father, teacher, and peers) as a function of two different middle-school student backgrounds, a priority education area and a privileged area; (2) and (3) to examine the contribution of these main sources of social support, either directly or indirectly (through sense of school belonging) to school engagement; and (4) to test whether perceived social support is more strongly related to school engagement, directly or indirectly, among students from priority education school compared to students from the advantaged area. In all, 623 middle-school students (aged 11–16) from either a privileged or priority education area participated in this study. The results showed that the mother was perceived as providing more support, followed by the father, the teachers, and the peers. Students from the priority education area perceived more support from their teachers than their counterparts from the more privileged area did. A path analysis showed that each source of social support, except for maternal support, contributed to school engagement. Peers and teachers emerged as the best source of support for school engagement, having significant direct effects among students from the priority education area and both direct and indirect (through the sense of school belonging) effects among students from the advantaged area. Peer support also appears to have a double-edged effect on school engagement among students in the priority education area. This study contributes to enlightening the phenomenon of school engagement in adolescence by clarifying the role of social support and the related mediating process. Being perceived as an important source of social support by students is not enough to contribute to their sense of school belonging and school engagement.
Article
Full-text available
Introduction: Researchers note a consistent decline in adolescents' motivation and participation in science. It is important to examine factors vital to students' motivation in science, such as teacher-student relationships (TSRs). Limited research in science has examined TSRs from a multidimensional or person-centered perspective. The present investigation adopts Ang's tripartite relational framework to examine three dimensions of TSRs: socio-emotional support, instrumental help, and conflict. Such research is needed to better understand the diversity of relationships that exist within a science classroom and their impact on science motivation. Methods: This study examined N = 2669 Australian high school students (66% girls; Mage = 15.11 years; SD = 0.69). Data were collected via online sampling in the final quarter of 2020. The data are cross-sectional. Latent profile analysis was used to (1) determine if distinct student profiles based on the three dimensions of TSRs existed and (2) the extent to which these profiles were associated with varying levels of science motivation: self-efficacy, intrinsic value, utility value, and cost. Results: Four distinct profiles were identified: Positive, Complicated, Distant, and Negative. Students in the Negative TSR profile reported the lowest adaptive motivation and highest cost. The associations between profile membership and motivation were more varied for the Positive, Complicated, and Distant TSR profiles. Conclusions: Findings indicate that dichotomous perspectives (positive vs. negative) may be insufficient to describe the diversity of relationships within science classrooms. Results also suggest that concurrent attendance to all dimensions of TSRs is needed to improve relationships.
Article
Previous research studies about mathematics performance have continuously reported race/ethnic or gender gaps. Learners have different educational experiences depending on not only their ethnicity or gender, but also grade and sociocultural factors. However, only a few studies have considered all these factors integrally. Hence, the need of examining academic performance differences across ethnicity, gender, grade, and sociocultural variables led this study. The purpose of this study was to examine mathematics academic achievement of 4th and 8th grade African, Latinx, and Asian American students related to students’ ethnicity, gender, grade, and sociocultural variables such as student bullying, parental involvement, and engaging teaching. The guiding research question for this inquire was: Which factors (gender, student bullying, parental involvement, and engaging teaching) do predict the mathematics achievement of 4th and 8th grade African, Latinx, and Asian American students? The participants were total 9,605 fourth (n=4,785) and eighth grade (n=4,820) African, Latinx, and Asian American students in TIMSS 2015 U.S. national public-use data set. According to the results of multiple linear regression analyses, parental involvement was a significant predictor for all students across grade and ethnicity. In addition, engaging teaching from mathematics teachers significantly predicted 4th and 8th grade Latinx American students’ mathematics achievement. This study revealed that gender, student bullying, parental involvement, and engaging teaching had different level of impacts on mathematics achievement of each group of students.
Article
Full-text available
This study assesses the principles of good practice in the teaching and learning processes at the Faculty of Education in the University for Development Studies, Ghana. Student teachers are seemingly seen talking about the state of the culture of teaching and learning and the manner in which the principles governing good practice of teaching and learning in the faculty are inadequately realised. A qualitative approach using case study design was employed in the study. Purposive sampling technique was used to select twelve (12) Level 100 student teachers and twelve (12) Level 400 student teachers at the end of the 2016/ 2017 academic year. Data collected through face-to-face interviews were analysed thematically. Findings revealed the following as the factors hindering the realization of the principles governing good practice of teaching and learning in the faculty: inadequate student-lecturer contacts, lack of concerns for student teachers' educational and personal needs, ineffective collaboration and cooperation among student teachers in their learning experiences, ineffective feedback on student teachers' assignments and quizzes, inadequate time on task in the faculty and inadequate attention to students' diverse talents and ways of learning. Some recommendations made include: Heads of Departments organizing seminars on the principles governing good practice of teaching and learning for all lecturers, lecturers ensuring adequate contact sessions among student teachers in and outside the lecture halls, lecturers ensuring individual student teacher attention in class, Heads of Departments monitoring lecturers' feedback on students' assignments and quizzes and the faculty guidance and counseling coordinator organizing seminars for students on collaboration and cooperation in the learning process and experience.
Article
Math self-concept is strongly associated with a range of academic and career outcomes in math. The current research sought to identify factors that distinguish between undergraduates with particularly low or high math self-concept. A sample of 754 college students were asked to recall a low point they had with math as well as respond to questionnaires measuring math self-concept, value, and anxiety. Focal analyses were conducted on a subsample of participants who reported either high (n = 90) or low (n = 94) math self-concept. Relative to participants who were high in math self-concept, those who were low tended to be women, were higher in math anxiety, and valued math less. Thematic analysis also revealed similarities and differences in how undergraduates from these two groups appraised challenges, or low points, that they encountered in their history with math. Although there were similarities in the types of low points described by members of these two groups, these experiences were often appraised in distinct ways. Unique themes also emerged for each group, indicating that narrative interpretations of math experiences vary with current levels of math self-concept. Implications for future research and math education are discussed.
Article
Using a transactional framework, this study explored social relationships in the classroom as mediators of the association between ethnic-racial identity and academic-related outcomes. Participants were 101 fifth graders of diverse backgrounds who completed computer-based questionnaires about their friendships, ethnic-racial identity, and academic engagement. Teachers reported on closeness in their student-teacher relationships. Relationships in the expected direction were evident; positive associations were observed among public regard dimensions of ethnic-racial identity and cognitive engagement in the classroom. Correlational analyses demonstrated higher friendship quality was associated with cognitive engagement, indicating more self-regulated and strategic approaches to learning for both boys and girls. Further, path analyses revealed that the relationship between public regard and cognitive engagement was mediated by student-teacher closeness for the whole sample. Gender differences were evident; for boys, public regard was related indirectly to language arts and math grades through cognitive engagement whereas for girls this indirect effect was not present. Findings highlight the varied contribution of ethnic-racial identity and classroom relationships on achievement-related outcomes, particularly for boys.
Article
Student engagement is a pivotal contributor to academic achievement, retention, and well-being, and yet the role of teacher influence on engagement is poorly understood. This is in part due to the contextual and ‘hidden’ nature of student engagement, and as such, levels of student engagement are assumed through observable factors such as attendance and conduct. It is also due to the difficulty in mapping student engagement simultaneously with understanding the teacher practices used to influence it. This article reports on a pre-post case study in which student survey and teacher focus group data were analysed together, revealing the nature and depth of association between the practices adopted by teachers and student engagement. By comparing the change of engagement at a class or homegroup level, it was possible to identify how approaches used by teachers impacted various elements of engagement. Furthermore, it found a high correlation between teacher practices and change in student engagement at a class or homegroup level, providing the opportunity for teachers to learn what practices were effective in their specific context.
Article
Full-text available
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) behavioral and emotional engagement across a school year. Correlational and path analyses revealed that teacher involvement was central to children's experiences in the classroom and that teacher provision of both autonomy support and optimal structure predicted children's motivation across the school year. Reciprocal effects of student motivation on teacher behavior were also found. Students who showed higher initial behavioral engagement received subsequently more of all 3 teacher behaviors. These findings suggest that students who are behaviorally disengaged receive teacher responses that should further undermine their motivation. The importance of the student-teacher relationship, especially interpersonal involvement, in optimizing student motivation is highlighted.
Article
Full-text available
Theory, methods, and knowledge gained from years of study in psychological science and human development apply to the understanding and improvement of teacher quality and, ultimately, student achievement and social and emotional outcomes. With these applications, educational research has stronger potential to make more effective and systematic contributions to the improvement of teaching in American schools. This potential can be realized by linking the scientific study of psychology and teachers’ development (social, relational, psychological, and cognitive) to teachers’ classroom behaviors (the mechanisms and processes underlying quality) and student achievement of educational and social and emotional objectives (the outputs of quality). New funds of knowledge in developmental and psychological science can serve as a basis for future research on inputs into teacher quality.
Article
Full-text available
In this study we investigated the relation of rigorous instructional practices and teachers' efforts to create a respectful, collaborative learning environment to students' positive behavior toward one another and to the rate and quality of students' participation in classroom discussions. Full class period (i.e., 50-minute) observations of English language arts and mathematics lessons were conducted in 34 sixth- and seventh-grade classrooms in five high-poverty, urban, public middle schools (N = 608 students, 64 observations). Raters coded each lesson for the affective qualities of the classroom environment, the rigor of curricular tasks including guidelines for student work, and the quality of teacher-student verbal exchanges. We applied multiple regression techniques to explain predictive relations between classroom climate, instructional quality, and student behavior. Results indicated that the degree of respect that teachers showed students significantly predicted students' behavior toward one another. The presence of explicit rules in the classroom for respectful, prosocial behavior also significantly predicted the number of students who participated in discussions. Further, the quality of students' participation in class discussions-that is, the degree to which they built on other students' contributions and explained and supported their responses-was predicted by teachers pressing students to explain their thinking in discussions and by the rigor of the questions posed to students in the discussion.
Chapter
What is the role of sleep in children's behavioral, emotional, and cognitive regulation? This chapter considers theoretical and conceptual links between sleep and self-regulation, with special attention to sleep and self-regulation in early childhood. We selectively review the growing body of research on associations between sleep and self-regulation, mentioning some methodological issues. We also consider how child characteristics and sociocontextual factors may interact with sleep in the development of self-regulation in early childhood. We provide some relevant empirical examples from our own research.
Chapter
How do you get a fourth-grader excited about history? How do you even begin to persuade high school students that mathematical functions are relevant to their everyday lives? In this volume, practical questions that confront every classroom teacher are addressed using the latest exciting research on cognition, teaching, and learning. How Students Learn: History, Mathematics, and Science in the Classroom builds on the discoveries detailed in the bestselling How People Learn. Now, these findings are presented in a way that teachers can use immediately, to revitalize their work in the classroom for even greater effectiveness. Organized for utility, the book explores how the principles of learning can be applied in teaching history, science, and math topics at three levels: elementary, middle, and high school. Leading educators explain in detail how they developed successful curricula and teaching approaches, presenting strategies that serve as models for curriculum development and classroom instruction. Their recounting of personal teaching experiences lends strength and warmth to this volume. The book explores the importance of balancing students’ knowledge of historical fact against their understanding of concepts, such as change and cause, and their skills in assessing historical accounts. It discusses how to build straightforward science experiments into true understanding of scientific principles. And it shows how to overcome the difficulties in teaching math to generate real insight and reasoning in math students. It also features illustrated suggestions for classroom activities. How Students Learn offers a highly useful blend of principle and practice. It will be important not only to teachers, administrators, curriculum designers, and teacher educators, but also to parents and the larger community concerned about children’s education.
Chapter
From the time individuals first enter school until they complete their formal schooling, children and adolescents spend more time in schools than in any other place outside their homes. Exploring all of the possible ways in which educational institutions influence motivation and development during adolescence is beyond the scope of a single chapter. In this chapter I discuss the ways in which schools influence adolescents' social- emotional and behavioral development through organizational, social, and instructional processes ranging from those based in the immediate, proximal relation between students and the tasks they are asked to perform to the role that principals and the school boards play in setting school-level and district-level policies, which in turn influence the social organization of the entire school community. I discuss at length three examples of the ways in which these multiple organizational levels interact synergistically to influence adolescent development through their impact on the daily experiences that adolescents in the United States encounter as they move through the American school system. The first example focuses on the role of school transitions, the second on the role of curricular tracking, and the third on extracurricular activities. Few of these processes have been studied in countries other than the United States. I assume similar processes are true in other countries, but this remains to be demonstrated empirically.
Article
Although student engagement with the intellectual work of school is important to students' achievement and to their social and cognitive development, studies over a span of two decades have documented low levels of engagement, particularly in the classroom. Examining several theoretical perspectives that attempt to explain engagement through comprehensive frameworks, this study evaluates the effect on engagement of school reform initiatives that are consistent with the theories. The study also investigates whether patterns exist in students' engagement, whether the patterns are consistent across grade levels, and whether class subject matter (mathematics or social studies) differentially affects engagement. The sample includes 3.669 students representing 143 social studies and mathematics classrooms in a nationally selected sample of 24 restructuring elementary, middle, and high schools. Because of the nature of the nested data (students nested within classrooms nested within schools), the analysis is conducted using hierarchical linear modeling in its three-level application (HLM3L). The reform initiatives, which are consistent with the theories, eliminate personal background effects. Together with classroom subject matter, they substantially influence engagement. The results are generally consistent across grade levels.