Applications Of Psychological Science To Teaching And Learning Gaps In The Literature

Applications of Psychological Science to Teaching
and Learning:
Gaps in the Literature
Prepared by an American Psychological Association Task Force
March, 2010
Task Force Members:
Chair: Mary Brabeck, New York University
Carol Dwyer, Educational Testing Service
Sandra Graham, University of California - Los Angeles
Thomas Kratochwill, University of Wisconsin - Madison
Joan Lucariello, City University of New York
Barbara McCombs, University of Denver
Sara Rimm-Kaufman, University of Virginia
Margaret Semrud-Clikeman, Michigan State University
APA Center for Psychology in Schools and Education Staff
Rena F. Subotnik
Rochelle Rickoff

Applications of Psychological Science to
Teaching and Learning:
Gaps in the Literature
Abstract
The authors identified gaps in the literature in the course of preparing 10 evidence-based
modules for teachers on topics related to cognitive, behavioral and affective dimensions
of the teaching and learning process. This document provides a summary of those gaps
and suggests potential topics for RFPs, dissertations, or collaborative projects to close the
gaps in our knowledge in areas classroom teachers say are important to their practice.
The purpose and process of the project
Teachers adopt psychological principles everyday when making instructional, assessment
and classroom management decisions. However, novice teachers, to say nothing of
policy makers and the general public, are often unfamiliar with the research that supports
these decisions, the context in which the research was conducted, and how the outcomes
of research affect different subsets of teachers and students.
Building on APA’s commitment to application of psychological science to high quality
instruction at the Pre-K-12 level, APA’s Board of Educational Affairs sponsored a task
force on Translating Psychological Science into Classroom Practice. The goals of the
work were to:
• Create a product that was teacher friendly (as so much psychological
science research is not)
• Use a pragmatic approach, not espousing a particular theory of learning
• Be grounded in the empirical work of psychology.
The task force examined factors that affect learning and found that courses provided to
teaching candidates rarely emphasize the interactions between child development,
learning theory, motivation, cultural and other individual differences. Members of the
task force were selected based on their expertise in several sub-disciplines of psychology
including: motivation, development, assessment, behavior management, social
psychology, and learning and instruction. Separately and together, members of the task
force translated literature from psychological science on 10 topics useful for teachers in
schools www.apa.org/ed/schools/cpse/activities/apstl.aspx.
The focus of the work was to integrate developmental and contextual concerns with
research on instruction. Based on the task force’s expertise, the available psychological
literature, and recent findings from the APA Teacher Needs Survey,
http://www.apa.org/ed/schools/coalition/teachers-needs.pdf, the 10 teaching strategies
were identified and developed into modules.

Modules on Primarily Cognitive Topics:
• Practice for Knowledge Acquisition (Not Drill and Kill)
• Using Classroom Data to Give Systematic Feedback to Students in order to Improve
Learning
• How Do My Students Think: Diagnosing Student Thinking
• How Do I Get My Students Over Their Alternative Conceptions (Misconceptions)?
• Using Praise to Enhance Student Resilience and Learning Outcomes
• Research in Brain Function and Learning
Modules on Primarily Behavioral Topics:
• Classroom Management
• Bullying in School
Modules on Primarily Affective Topics:
• Improving Students’ Relationships with Teachers to Provide Essential Supports for
Learning
• Developing Responsible and Autonomous Learners: A Key to Motivating Students
Each module provides information on:
• Why the topic is important
• Recommendations for teachers (Dos and Don’ts)
• Evidence and Explanation (why and how these teaching strategies work)
• Frequently Asked Questions and Answers
• Where teachers can get more information
• References
In the course of preparing each module the authors were able to explore whether there
were serious gaps in the literature that, if they were filled, would enhance the
effectiveness of these strategies for wider audiences, whether by virtue of age, ethnicity
or disability. This document provides a summary of some of the gaps in the literature that
Task Force members discovered in the course of module preparation.
Primarily Cognitive Topics
Practice for Knowledge Acquisition (Not Drill and Kill)
(prepared by Mary Brabeck, PhD, New York University)
It is an old adage that “practice makes perfect” and indeed, learning benefits from
deliberate practice. Rote repetition or simply repeating a task will not automatically
improve performance. Effective practice is deliberate (Ericsson, Krampe, & Clemens,
1993); involves attention, rehearsal and repetition; and leads to new knowledge or skills
that can be subsequently developed into more complex knowledge and skills (Anderson,
Reder & Simon, 1996). Across subject areas, differences in performance are affected by
the amount of deliberate practice in which individuals engage.
Research suggests that teachers should carefully design opportunities for students to
practice solving problems by keeping human memory limitations in mind. Our brains
have three memory stores: (1) sensory memory (lasts milliseconds), (2) short-term

memory, also called working memory (lasts less than a minute), and (3) long-term
memory (can last indefinite periods of time). Learning occurs when students move
information from working memory to long-term memory and practice makes this
transition more likely (See module on Research on Brain Functioning and Learning
www.apa.org/ed/schools/cpse/activities/apstl.aspx).
Extensive, deliberate practice makes it possible for students to access and apply
increasingly complex information without explicitly thinking about it. This process frees
working memories to process new information (Kotovsky, Hayes, & Simon, 1985).
When information is too concentrated, working memory will fail so learning needs to
apply a distributed process (information repeated with time spaces in between) rather
than by crammed practice (longer practice times occurring “all at once”) (Bahrick &
Hall, 2005).
There are many things teachers can do to enhance the effects of practice on learning.
Teachers can increase students’ ability to transfer existing problem-solving knowledge to
new problems when they prompt students to reflect on their problem-solving processes.
(Rosenshine & Meister, 1992; Stark, Mandl, Gruber, & Renkl., 2002). Reviews and tests
can improve learning and can serve as practice activities. These activities are more
effective when they are given at spaced intervals and when they are given frequently
(Dempster, 1991). Similarly, homework that involves practice is more effective when
assignments are shorter, more frequent, and distributed over longer periods of time
(Cooper, 2001).
All populations of students benefit from practice. If teachers design appropriate practice
activities, students can learn to compensate for special learning difficulties (Merzenich et
al., 1996). Cooper’s (2001) examination of homework practice suggests that students
with learning disabilities benefit more from short, skill reinforcing, carefully-monitored
activities.
Students’ memories and attention spans develop over time (Lechuga, Moreno, Pelegrina,
Gomez-Ariza, & Bajo, 2006). That is, older students will have greater attention spans
and greater memory capacity than will younger students. This finding may account for
developmental differences in the benefits of homework (Cooper, 2001; Muhlenbrook,
Cooper, Nye, & Lindsay, 1999) and age differences in the amount of practice learners
engage in (Ericsson et al., 1993).
Although research has discovered many of the conditions that enhance learning through
practice, there is still much that remains open for study. Gaps, conflicts, and ambiguities
include:
• Some have argued that researchers overemphasize the role of practice in expert
performance at the expense of other important variables such as ability and critical
developmental periods (See Gobet & Campitelli, 2007 for an overview of this
debate).

• The empirical research on practice is skewed toward achievement gains that are
easier to quantify, such as math problem solving accuracy, athletic performance,
and phonemic awareness. More research on the relationship between practice and
student achievement in areas such as the humanities and higher level skills in all
subjects is needed.
• Though researchers acknowledge that motivation is an important factor for effective
practice, they offer few suggestions on how to engage students in practice activities
that are not inherently enjoyable. Teachers are encouraged to motivate students with
the promise of future performance gains. This suggestion does not address the
difficulties classroom teachers face in engaging students in practice activities. More
research on what motivates students to practice is needed.
• There is little advice on how to differentiate practice activities. Though researchers
point out that demands on working memory associated with a particular task will
vary significantly between individual students, they offer little advice on how to
design flexible practice activities for differentiated instruction. Because there is a
lack of research on specific guidelines for appropriate spacing and duration of
practice, teachers must determine the amount and timing of practice most beneficial
to learning based on their own assessments.
• Neuro-imaging studies are needed to examine further the optimal conditions for
practice to be effective in long term memory gains.
References
Anderson, J. R., Reder, L. M., & Simon, H. A. (1996). Situated learning in education.
Educational Researcher, 25(4), 5-11.
Bahrick, H. P., & Hall, L. K. (2005). The importance of retrieval failures to long-term
retention: A metacognitive explanation of the spacing effect. Journal of Memory
and Language, 52, 566-577.
Cooper, H. (2001). The battle over homework: Common ground for administrators,
teachers, and parents (2nd ed.). Thousand Oaks, CA: Corwin Press.
Dempster, F. (1991). Synthesis of research on reviews and tests. Educational
Leadership, 48(7), 71-76.
Ericsson, A. K., Krampe, R. T., & Clemens, T.-R. (1993). The role of deliberate practice
in the acquisition of expert performance. Psychological Review, 100(3), 363-406.
Gobet, F., & Campitelli, G. (2007). The role of domain-specific practice, handedness,
and starting age in chess. Developmental Psychology, 43(1), 159-72.
Kotovsky, K., Hayes, J. R., & Simon, H. A. (1985). Why are some problems hard?
Evidence from the Tower of Hanoi. Cognitive Psychology, 17, 248-294.
Lechuga, M.T., Moreno, V., Pelegrina, S., Gomez-Ariza, C.J., & Bajo, M.T. (2006). Age
differences in memory control: Evidence from updating and retrieval-practice
tasks. Acta Psychologica, 123, 279-298.
Merzenich, M. M., Jenkins, W. M., Johnston, P., Schreiner, C., Miller, S. L., & Tallal, P.
(1996). Temporal processing deficits of language-learning impaired children
ameliorated by training. Science, 271, 77-81.

Muhlenbruck, L., Cooper, H., Nye, B., & Lindsay, J. J. (1999). Homework and
achievement: Explaining the different relations at the elementary and secondary
school levels. Social Psychology of Education, 3, 295-317.
Rosenshine, B., & Meister, C. (1992). The use of scaffolds for teaching higher-level
cognitive strategies. Educational Leadership, 49(7), 26-33.
Stark, R., Mandl, M., Gruber, H., & Renkl, A. (2002). Conditions and effects of example
elaboration. Learning and Instruction, 12, 39-60.
Using Classroom Data to Give Systematic Feedback to Students in
order to Improve Learning
(prepared by Carol Dwyer, Educational Testing Service)
Research by Dylan Wiliam and his colleagues (e.g., Black, Harrison, Lee, Marshall, &
Wiliam, 2003; Black & Wiliam, 2004a, 2004b; Wiliam, 2005) has shown that student
learning increases when teachers:
• Clearly delineate the purposes of each lesson that they teach;
• Use lessons to collect evidence on how students learn; and
• Use collected evidence and promptly re-direct students as needed.
Effective feedback is an important way for teachers to collect and improve student
learning. Unfortunately, feedback opportunities are scarce in most classrooms (Bransford,
Brown, & Cocking, 1999). Teachers can, however, generate more opportunities for
effective feedback and use the data collected formatively to improve their students’
learning.
The results or evidence from almost any assessment can be used to great effect, provided
they are used to make instructional adjustments – the shorter the teaching-assessment-
analysis-reteaching cycle, the more powerful the effect on learning.
Wiliam (2005) has identified five key strategies of assessment for learning:
1. For each important new concept or assignment, on a daily basis, teachers should
make learning expectations clear and share with students the criteria for meeting
those expectations successfully. Revisit these learning intentions and criteria at
the end of the class to evaluate the class’s progress toward these goals.
2. Use effective classroom discussions, questions, and learning tasks to revise
lessons and activities. Teachers can use classroom techniques that engage all
students in discussion, and use evidence of student thinking from these
discussions in their planning of future instruction.
3. Provide feedback that identifies clearly and explicitly what needs to be improved
to move learners forward and promote students’ thinking about the concept.
Teachers use this evidence to adapt instruction in real time to meet students’
immediate learning needs.
4. Encourage students to serve as instructional and learning resources for one
another on daily basis.
5. Motivate students to take responsibility for their own learning.

Formative feedback is essential to the assessment process as it allows teachers to collect
the evidence they need to address their students’ learning needs immediately. Leahy,
Lyon, Thompson, and Wiliam (2005) give a brief, teacher-oriented description of each of
these strategies.
Despite the advances in this area of research, important gaps remain. The following are
some examples of areas in which more research is needed:
• Science and mathematics, particularly at the postsecondary level. There appears
to be some promise of decreased drop-out from difficult mathematics and science
courses when faculty engage in professional development that is aimed at
increasing their pedagogical skills, including the five key strategies identified by
Wiliam. Another potential research direction for this work would thus be to
conduct larger-scale trials in high schools and in especially difficult mathematics
and science post-secondary courses on the use of feedback for effort and
strategies.
• Teacher differences and contexts. Wiliam and colleagues believe that strategies of
formative assessment must be tailored by individual teachers to meet the needs of
their students and contexts. For example, teachers can use the “find and fix”
technique, in which they ask pairs of students to work together to identify and
correct errors on an assessment. Younger students can use this technique only
with short assessments, such as two or three problems; older students can use the
technique with much longer assessments. Teachers’ knowledge of their students
and their students’ needs and contexts is essential, but they currently lack
accessible exemplars of how this is done with students like theirs. Developing and
field-testing prototypes of such exemplars would be a first step toward scaling up
this important aspect of formative assessments.
• Enhancing students’ motivation and resilience through formative assessments.
Most of the research on formative assessment has focused on teachers’ actions
and students academic outcomes. Much remains unknown about how to integrate
proven affective and motivational strategies into the formative assessment
process. Studies of the most effective use of praise as formative feedback would be
a useful step toward enhancing the effects of formative assessment.
References
Black, P., Harrison, C., Lee, C., Marshall, B., & Wiliam, D. (2003). Assessment for
learning: Putting it into practice. Buckingham, UK: Open University Press.
Black, P. J., & Wiliam, D. (2004a). Classroom assessment is not (necessarily) formative
assessment (and vice-versa). In M. Wilson (Ed.), Towards coherence between
classroom assessment and accountability: 103rd yearbook of the National Society
for the Study of Education (Pt. 2, pp. 183-188). Chicago: University of Chicago
Press.
Black, P. J., & Wiliam, D. (2004b). The formative purpose: Assessment must first
promote learning. In M. Wilson (Ed.), Towards coherence between classroom
assessment and accountability: 103rd yearbook of the National Society for the
Study of Education (Pt. 2, pp. 20-50). Chicago: University of Chicago Press.

Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (1999). How people learn:
Brain, mind, experience, and school. Washington, DC: National Academies Press.
Leahy, S., Lyon, C., Thompson, M., & Wiliam, D. (2005). Classroom assessment, minute
by minute, day by day. Educational Leadership, 63(3), 19-24.
Wiliam, D. (2005). Keeping learning on track: Formative assessment and the regulation
of learning. In M. Coupland, J. Anderson, & T. Spencer (Eds.), Making
mathematics vital: Proceedings of the twentieth biennial conference of the
Australian Association of Mathematics Teachers (pp. 26–40). Adelaide, Australia:
Australian Association of Mathematics Teachers.
How Do My Students Think?: Diagnosing Student Thinking
(prepared by Joan Lucariello, City University of New York)
Diagnosing student thinking and knowledge is a critical area of teacher functioning. We
know that students come to school with considerable knowledge (pre-instructional
knowledge) about the topics they will encounter. Learning depends on and is related to
prior knowledge (Bransford, Brown, & Cocking, 2000; Gelman & Lucariello, 2002;
Piaget & Inhelder, 1969; Resnick, 1983). Hence, it makes sense for teachers to figure out
what students know before beginning instruction on a topic. First, learning processes
vary considerably, depending on whether the teacher is confronting student alternative
conceptions (misconceptions) or correct (anchoring) conceptions about the concepts or
theories to be taught (Carey, 1985; 1986; Posner, Strike, Hewson, & Gertzog, 1982;
Strike & Posner, 1985, 1992).
All of us interpret incoming information in terms of our current knowledge and cognitive
organizations. Learners try to link new information to what they already know (Resnick,
1983). When the information to-be-taught is consistent with what learners already know,
learners can add this information to their knowledge base. This kind of learning is known
as assimilation or conceptual growth. However, when new information is inconsistent
with what learners already know, it cannot be assimilated. Rather, the learner’s
knowledge will have to change or be altered because of the new information and
experience. This kind of learning is known as accommodation (of knowledge/mental
structures) or conceptual change. There are different instructional strategies to achieve
conceptual growth and conceptual change, respectively. Accordingly, teachers need to
know what instructional context they are facing and utilize the appropriate respective
strategies. Teaching has a greater chance of effectiveness when instructional strategies
are informed by students’ current understandings of the concepts and theories to be
taught (e.g., Carpenter, Fennema, Peterson, Chiang, & Loef, 1989; Fennema et al., 1996).
Diagnostic teaching strategies are needed, as well, because teacher (pre- and in-service)
assessment of student academic skills and prediction of student knowledge may not
accurately reflect pupil skill and knowledge (Diakidoy & Iordanou, 2003; Eckert, Dunn,
Codding, Begeny, & Kleinmann, 2006).
There has not been a cohesive literature developed on the topic of diagnostic teaching
strategies. Rather, researchers from widely different disciplines have developed a strategy
or two that meets their own research goals (e.g., developed student questionnaires on

different topics). A comprehensive survey across these literatures has identified several
pre-instructional diagnostic strategies found to be successful for getting at pupil
knowledge. These appear in a module entitled How Do My Students Think: Diagnosing
Student Thinking on the APA Web site www.apa.org/ed/schools/cpse/activities/apstl.aspx
and also in Lucariello (2008). They include the following:
• Conduct quizzes (Pretests)
• Learn HOW students are solving problems or arriving at their answers.
The ideas and strategies – the means by which students arrive at an answer
(be it right or wrong) - can be very informative as to their thinking.
• Give students an opportunity to ask questions and then examine the
content of those questions.
• Ask for student definitions of major concepts and analyze any errors for
the kind of thinking (the rule) that students follow in making mistakes.
• Use the technique of “differential diagnosis” (Brown & Burton, 1978).
• Use the technique of DFA (difficulty factors assessment) (see Rittle-
Johnson & Koedinger, 2005). DFA is a way to identify what features of a
problem are causing student difficulty or facilitating the student’s learning.
• Present problems for students to solve where you vary the problems on
systematic features (Brown & Burton, 1978).
The following strategies for getting at student thinking are not dependent on student
language and verbal skills. They may be most effective for students whose current verbal
skills are not strong:
• Give quizzes or ask questions wherein you provide misconceptions among
the answer options to choose from (e.g., multiple-choice format).
• Give “implicit-confidence” tests. These tests entail a simple modification
of the multiple-choice test (see Klymkowsky, Taylor, Spindler, & Garvin-
Doxas, 2006).
• Administer questionnaires in particular subject matter areas
• Administer categorization/sorting tasks – In this activity, one presents the
students with some model objects (or pictures of objects) to sort into
different categories. One instructs students to “put the things together that
go together” or to “put the things that are alike (or “the same kind of
thing”) together.
• Have students model (draw, use props) their solutions and analyze these
for error patterns, strategies, and solution-kinds.
• Use student memory/recall of problems and definitions as a window into
their thinking.
Since these strategies derive from widely different sources and literatures and have
mainly been utilized by researchers to ascertain the knowledge state of participants in
their experiments, there has been little systematic research on the effects of these
strategies in the classroom. Accordingly, future research is needed on links between the
use of these strategies and teaching quality and student learning. More specifically:

• Research is needed to ascertain whether teachers’ use of these pre-instructional
strategies leads to more effective teaching behaviors or any change in teaching
behavior.
• Research is needed to address whether a link can be demonstrated between pupil
learning and achievement and teacher understanding (through use of these
strategies) of the following:
o Student knowledge (“preconceptions”)
o Student learning processes
o What is hard and easy for students to grasp
o Common student errors
• The effectiveness of these different diagnostic strategies for getting at student
thinking under different contextual conditions needs to be explored. Questions
include whether strategy effectiveness varies by the following variables and their
interaction:
o Student developmental level/grade
o Student achievement level
o Subject matter (e.g., math, science, language arts)
o Different populations (e.g., low-SES children)
o Teacher experience and training
References
Bransford, J. D., Brown, A. L., Cocking, R. R. (Eds.). (2000). How people learn: Brain,
mind, experience, and school. Washington, DC: National Academy Press.
Brown, J. S., & Burton, R.R. (1978). Diagnostic models for procedural bugs in basic
mathematical skills. Cognitive Science, 2, 155-192.
Carey, S. (1985). Conceptual change in childhood. Cambridge, MA: MIT Press.
Carey, S. (1986). Cognitive science and science education. American Psychologist, 41,
1123-1130.
Carpenter, T. P., Fennema, E., Peterson, P. L., Chiang, C. P., & Loef, M. (1989). Using
knowledge of children’s mathematics thinking in classroom teaching: An
experimental study. American Educational Research Journal, 26, 499-531.
Diakidoy, I. N., & Iordanou, K. (2003). Preservice teachers’ and teachers’ conceptions of
energy and their ability to predict pupils’ level of understanding. European
Journal of Psychology of Education, 18, 357-368.
Eckert, T. L., Dunn, E. K., Codding, R. S., Begeny, J. C., & Kleinmann, A. E. (2006).
Assessment of mathematics and reading performance: An examination of the
correspondence between direct assessment of student performance and teacher
report. Psychology in the Schools, 43, 247-265).
Fennema, E., Carpenter, T. P., Franke, M. L., Levi, L., Jacobs, V. R., & Empson, S. B.
(1996). A longitudinal study of learning to use children’s thinking in mathematics
instruction. Journal for Research in Mathematics Education, 27, 403-434.
Gelman, R., & Lucariello, J. (2002). Role of learning in cognitive development. In H.
Pashler (Series Ed.) & C. R. Gallistel (Vol. Ed.), Stevens’ Handbook of

Experimental Psychology: Vol. 3. Learning, motivation, and emotion (3rd ed., pp.
395-443). New York: Wiley.
Klymkowsky, M. W., Taylor, L. B., Spindler, S. R., & Garvin-Doxas, R. K. (2006).
Two-dimensional, implicit confidence tests as a tool for recognizing student
misconceptions, Journal of College Science Teaching, 36(3), 44-48.
Lucariello, J. (2010). Instructional strategies for diagnosing student thinking. Manuscript
in preparation.
Piaget, J., & Inhelder, B. (1969). The psychology of the child. Basic Books, New York.
Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Accommodation
of a scientific conception: Toward a theory of conceptual change. Science
Education, 66, 211-227.
Resnick, L. B. (1983). Mathematics and science learning: A new conception. Science,
220, 477-478.
Rittle-Johnson, B., & Koedinger, K. R. (2005). Designing knowledge scaffolds to support
mathematical problem solving. Cognition and Instruction, 23, 313-349.
Strike, K. A., & Posner, G. J. (1985). A conceptual change view of learning and
understanding. In L. West & L. Pines (Eds.), Cognitive structure and conceptual
change (pp. 211-231). San Diego, CA: Academic Press.
Strike, K. A., & Posner, G. J. (1992). A revisionist theory of conceptual change. In R. A.
Duschl & R. J. Hamilton (Eds.), Philosophy of science: Cognitive psychology,
and educational theory and practice (pp.147-176). Albany, NY: Statue University
of New York Press.
How Do I Get My Students Over Their Alternative Conceptions
(Misconceptions)?: Achieving Conceptual Change
(prepared by Joan Lucariello, City University of New York)
Students do not come to school as blank slates to be filled by instruction. Children are
active cognitive agents who arrive at school after years of cognitive growth (Committee
on Science Learning, Kindergarten through Eighth Grade, 2007). They come to the
classroom with considerable knowledge based on intuitions, every day experiences, or
what they have been taught in other contexts. This type of student knowledge is referred
to as preconceptions. A considerable amount of our knowledge is organized by subject
areas, such as mathematics, science, etc., so too are preconceptions.
It is important for teachers to know about the preconceptions of their students because
learning depends on and is related to student prior knowledge (Bransford, Brown, &
Cocking, 2000; Gelman & Lucariello, 2002; Piaget & Inhelder, 1969; Resnick, 1983).
Student preconceptions that are consistent with concepts in the assigned curriculum are
anchoring conceptions. In cases such as these, where teachers are presenting information
that is consistent with what learners already know, learning is a matter of assimilation or
conceptual growth. Learners can add this information to their knowledge base.
Student preconceptions that are inconsistent with concepts in the curriculum are known
as alternative conceptions or misconceptions. Misconceptions are very typical and both
children and adults possess them. Misconceptions interfere with learning for several
reasons. Students use these erroneous understandings to interpret new experiences,

thereby interfering with the process of correctly grasping the new experiences. Moreover,
misconceptions can be entrenched and tend to be very resistant to instruction (Brewer &
Chinn, 1991; McNeil & Alibali, 2005). Hence, for concepts or theories in the curriculum
where students typically have misconceptions, learning is of a more difficult kind. Instead
of simply adding to student knowledge, learning is a matter of radically reorganizing or
replacing student knowledge. Conceptual change or accommodation has to occur for
learning to happen (Carey, 1985; 1986; Posner, Strike, Hewson, & Gertzog, 1982; Strike
& Posner, 1985, 1992). Teachers will need to bring about this conceptual change, and
teaching for such conceptual change requires different strategies than teaching for
conceptual growth.
A comprehensive survey across many diverse literatures has identified several
instructional strategies found to be successful at achieving conceptual change, that is, at
helping students overcome misconceptions. This literature and associated instructional
strategies are available in a module entitled “How Do I Get My Students Over Their
Alternative Conceptions (Misconceptions)?” on the APA Web site
www.apa.org/ed/schools/cpse/activities/apstl.aspx and also in Lucariello (2008). They
include the following:
• Present the new concepts or theories that you are teaching in a way that students
see these concepts or theories as plausible, of high quality, intelligible and
generative.
• Use students’ correct conceptions and build on those by creating a bridge of
examples to the new concept or theory that students are having trouble learning
because of alternative conceptions (misconceptions).
• Use model-based reasoning, which helps students construct new representations
that vary from their intuitive theories.
• Use “diverse instruction,” wherein a teacher presents a few examples that
challenge multiple assumptions rather than a larger number of examples that
challenge just one assumption.
• Help students become aware of (raise their meta-cognition about) their own
alternative conceptions (misconceptions).
• Present experiences that cause cognitive conflict in the students’ minds.
o Experiences that can cause cognitive conflict are those that get students to
consider their misconceptions (as in strategy 3 above) side-by-side with or
at the same time as the correct concept or theory.
• Engage in Interactive Conceptual Instruction (ICI)
o Develop students’ epistemological thinking -- beliefs and theories about
the nature of knowledge and the nature of learning -- in ways that will
facilitate conceptual change. The more naïve students’ beliefs are about
knowledge and learning, the less likely they are to revise their
misconceptions.
• Help students “self-repair” their misconceptions.
o Once students have overcome their alternative conceptions
(misconceptions), engage them in argument to help strengthen their new
knowledge representations.

The research base that has identified these instructional strategies for conceptual change has
been limited in range. It has targeted misconceptions mainly in science, and to a lesser extent
mathematics, primarily with high school and college students, with a small amount of work
conducted on middle school students. Even less work has been conducted with misconceptions
held by students in elementary school. Also, in large measure, the research has been conducted
on normative, not at-risk, populations of students. Hence, research is needed in the following
areas.
• Research at different developmental levels
Much of the research on effective instructional strategies is focused on high school and
college students. We need to know whether the same strategies are effective with middle
and elementary school children.
• Research with different populations
There are almost no studies on the effectiveness of instructional strategies that challenge
misconceptions held by low-SES or ethnically diverse children or low-achieving children.
Research needs to be done with these samples of children.
• Research with different subject-matter domains
Most of the research on effective instructional strategies is focused on the domain of
science and to a much lesser (and not sufficient) extent on mathematics. Hence we need
to do research on the following:
o Identifying the common student misconceptions in other subject matter
areas, including social studies, history, and language arts.
o Applying these instructional strategies to all other subject matter domains,
especially mathematics, and testing their effectiveness.
• Research to develop more instructional strategies
Fewer than ten teaching strategies to overcome misconceptions have been identified
(most of which are listed here). Research could be conducted to identify additional
effective strategies that teachers can use in this learning circumstance.
• Research linking teacher knowledge of student misconceptions to effects (or not)
on teaching and student learning and achievement.
References
Bransford, J. D., Brown, A. L., Cocking, R. R. (Eds.). (2000) How people learn: Brain,
mind, experience, and school. Washington, DC: National Academies Press.
Brewer, W. F., & Chinn, C. A. (1991). Entrenched beliefs, inconsistent information, and
knowledge change. In L. Birnbaum (Ed.), The International Conference of the
Learning Sciences: Proceedings of the 1991 conference (pp. 67-73).
Charlottesville, VA: Association for the Advancement of Computing in
Education.
Carey, S. (1985). Conceptual change in childhood. Cambridge, MA: MIT Press.
Carey, S. (1986). Cognitive science and science education. American Psychologist, 41,
1123-1130.

Committee on Science Learning, Kindergarten through Eighth Grade (2007). Taking
science to school: Learning and teaching science in grades K-8. Washington, DC:
National Academies Press.
Gelman, R., & Lucariello, J. (2002). Role of learning in cognitive development. In H.
Pashler (Series Ed.) & C. R. Gallistel (Vol. Ed.), Stevens’ Handbook of
Experimental Psychology: Vol. 3. Learning, motivation, and emotion (3rd ed., pp.
395-443). New York: Wiley.
Lucariello, J. (2010). Instructional strategies for achieving conceptual change:
Overcoming students’ subject-matter misconceptions. Manuscript in preparation.
McNeil, N. M., & Alibali, M. W. (2005). Why won’t you change your mind?
Knowledge of operational patterns hinders learning and performance on
equations. Child Development, 76, 883-899.
Piaget, J., & Inhelder, B. (1969). The psychology of the child. New York: BasicBooks.
Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Accommodation
of a scientific conception: Toward a theory of conceptual change. Science
Education, 66, 211-227.
Resnick, L. B. (1983). Mathematics and science learning: A new conception. Science,
220, 477-478.
Strike, K. A., & Posner, G. J. (1985). A conceptual change view of learning and
understanding. In L. West & L. Pines (Eds.), Cognitive structure and conceptual
change (pp. 211-231). San Diego, CA: Academic Press.
Strike, K. A., & Posner, G. J. (1992). A revisionist theory of conceptual change. In R. A.
Duschl & R. J. Hamilton (Eds.), Philosophy of science: Cognitive psychology,
and educational theory and practice (pp.147-176). Albany, NY: Statue University
of New York Press.
Using Praise to Enhance Student Resilience and Learning Outcomes
(prepared by Carol Dwyer, Educational Testing Service)
Dweck (1999, 2000, 2006) and others have shown that holding the belief that intelligence
is unchangeable (called the “entity theory” of self) leads to withdrawal of effort and
avoidance of challenges. “Entity” theories lead to increased probability of academic
withdrawal and alienation, and decreased engagement with learning—all of which lead to
lower achievement.
In contrast, holding the belief that intelligence can be increased through one’s own efforts
(called the “incremental theory” of self) leads to increased effort and seeking out
challenging materials. This is part of the development of self-efficacy and resilience,
which are important in all facets of life. “Incremental” self theories lead to increased
resilience in the face of difficulties and setbacks, and to higher academic achievement.
How we talk to students about their performance and work products affects the self
theories they hold: Feedback for intelligence increases “entity” thinking; feedback for
effort and strategies decreases it. Fortunately, Dweck and her colleagues have also found
that entity theories are susceptible to change with relatively simple interventions related
to how praise is given for learning outcomes (e.g., Dweck, 2000; Dweck & Molden,

2005; Kamins & Dweck, 1999). This has been found among a wide age span and a wide
range of current academic achievement levels.
Another important discovery derived from this line of research has been that entity
theories of intelligence are more prevalent among women and under-represented
minorities. Thus, these findings relate to achievement gaps as well. Some of the reasons
students drop out of challenging programs or do not hold high educational aspirations are
due to their beliefs that they “don’t have what it takes” to succeed. In fact, the problem
may be that they need to apply more effort or employ different strategies in order to
succeed.
This program of research has demonstrated clearly that holding the mind set that you are
smart or that you are not has many negative consequences for learning. Fortunately,
teachers can, by being careful about how they give students praise for their work and
efforts, alter this mind set so that students view intelligence as something that can be
developed. A different mind set is fostered by praising students’ efforts and specific work
strategies and outcomes (“process praise”) when they do well, rather than praising them
for their intelligence (“person praise”); and by discouraging students from attributing
their successes and failures to things over which they have no control such as poor luck,
or how smart or “dumb” they are.
A major source of the effectiveness of process praise is that it is clearly commensurate
with the student’s effort and achievement. Excessive, non-credible praise has been found
to be counterproductive. By affecting students’ beliefs about why they succeed or fail in
academic settings, praise for effort and strategies can be expected to lead to increased
resilience when students encounter obstacles and setbacks, and to increased learning and
higher achievement. Specifically, students whose teachers use praise for effort and
strategies rather than praise for intelligence will:
• Apply more effort, not less, when material is difficult for them;
• Seek challenges;
• Set higher goals for themselves;
• Look at failures as opportunities to learn; and
• Learn more.
Despite the strong program of research that has been developed on this topic, there are
two areas in which more attention is needed.
• Special needs students. Although exceptionally strong research designs have been
used in reaching the conclusions reported here, research has not been conducted
with students with special needs such as learning disabilities. Special needs
students, including the gifted, are vulnerable to the deleterious effects of person
praise, but their learning needs and outcomes may differ from those of other
segments of the student population.
• Minority and gender pipeline issues in mathematics and science. Another
potential research direction for this work would be to conduct larger-scale trials
on the use of feedback for effort and strategies at the postsecondary level in

particularly difficult mathematics and science courses, with the aim of
discovering whether appropriate feedback results in lessened attrition from these
course, especially among minority and female students, who are more likely to
hold entity theories of intelligence.
References
Dweck, C. S. (1999). Caution—Praise can be dangerous. American Educator, 23(1), 1-
5.
Dweck, C. S. (2000). Self-theories: Their role in motivation, personality, and
development. Philadelphia: Taylor & Francis/Psychology Press.
Dweck, C. S. (2006). Mindset: The new psychology of success. New York: Random
House.
Dweck, C. S., & Molden, D. C. (2005). Self theories: Their impact on competence
motivation and acquisition. (pp. 122-140). In A. J. Elliot & C. S. Dweck (Eds.),
Handbook of competence and motivation. New York: Guilford Press.
Kamins, M. & Dweck, C.S. (1999). Person vs. process praise and criticism: Implications
for contingent self-worth and coping. Developmental Psychology, 35, 835-847.
Research in Brain Function and Learning
(prepared by Margaret Semrud-Clikeman, Michigan State University)
The way children learn depends on their age, level of development and brain maturity.
Before birth, nerve cells (neurons) travel to their eventual locations within the brain of
the fetus. It is not known why some neurons find a home and others do not, but after a
neuron settles down it continues to grow and develop within its region of the brain.
Some neurons do not find a place and die off. When pruning does not happen or is
incomplete, disorders in learning and/or behavior can be the result. Visual and auditory
systems are present at birth and continue to develop in the first few months of life as the
brain reacts to the environment (Hynd & Willis, 1988; Semrud-Clikeman, 2006). During
a healthy child’s preschool years, motor and sensory systems continue to develop, as do
auditory and visual skills. Since brain development after birth is influenced by inputs
from the environment, and because those inputs are unique to each child, every human
brain is unique.
During early elementary years, growing neural networks of connected neurons and fibers
are essential to the transmission of information throughout the brain. Skills such as
visual-motor coordination, reasoning, language, social understanding and memory,
perspective-taking and social interaction are closely tied to development of the tracts of
the right hemisphere, as well as to the areas of the brain that are tied to emotional
processing (also called the limbic system) (Semrud-Clikeman, 2007). As neural
networks form, the child learns both academically and socially. Skills in reading,
mathematics and writing become more specialized and developed. During late
elementary and middle school years, the child’s brain activity is mostly in the posterior
regions where the areas for auditory, visual, and tactile functioning intersect (Semrud-
Clikeman & Ellison, 2010). The frontal lobes begin to mature more fully in middle
school, and this maturation continues through high school and adulthood. Inferential
thinking becomes more emphasized in schools, while rote learning is de-emphasized.

This shift in focus is supported by the increased connectivity in the brain and by chemical
changes in the neuronal pathways that support both short and long term memory. These
chemical changes can continue for hours, days and even weeks after the initial learning
takes place (Gazzaniga, Ivry, & Magnun, 2002). Learning becomes more consolidated, as
it is stored in long-term memory.
The refined development of the frontal white matter tracts begins around age 12 and
continues into a person’s twenties. This region of the brain is crucial for higher cognitive
functions, appropriate social behaviors, and the development of formal operations. For
some adolescents, brain development matches our educational expectations. For others,
the two do not coincide and there is a mismatch between biology and education. When
the transition to more adult behavior is problematic, the difficulty may be due to brain
maturation. Some adolescents need more structure while others need more freedom.
Teachers and schools are beginning to recognize that smaller groupings and more contact
with adults help adolescents learn. These changes are very appropriate and in tune with
the social and emotional needs of adolescents - as well as brain maturation - that is
occurring at this crucial time.
It would be helpful to have more research in brain function and research in the following
areas:
• There are gaps in evaluating brain structure and function in typically developing
children as well as those experiencing difficulties.
• The success of interventions is just beginning to be studied to evaluate whether
brain changes accompany various learning experiences. Studies that evaluate
brain activation prior to intervention and then after intervention would be helpful
to determine how neural networks are established and what can be done to
encourage such networks.
• In addition, if markers can be identified in children with learning and/or
attentional problems that are consistent over development, earlier identification
may be possible.
• The technology is just being developed to be able to picture activity in the brain
and to also measure the white matter tracts associated with specific activities.
Although this is an exciting time, it can also be frustrating as we do not fully
understand the development of a typical brain let alone that of a child with delays
or with special abilities. It is expected that this literature will increase in the next
decade and provide much needed answers to educators and parents.
References
Fletcher, J. M., & Taylor, H. (1984). Neuropsychological approaches to children:
Toward a developmental neuropsychology. Journal of Clinical Neuropsychology,
6, 39- 56.

Gazzaniga, M. S., Ivry, R. B., & Mangun, G. R. (2002). Cognitive neuroscience: The
biology of the mind (2nd ed.). New York: W.W. Norton & Company.
Hynd, G. W., & Willis, W. G. (1988). Pediatric neuropsychology. Orlando, FL: Grune
& Stratton.
Semrud-Clikeman, M. (2007). Social competence in children. New York: Springer.
Semrud-Clikeman, M., & Ellison, P.A.T. (2010). Child Neuropsychology. New York:
Springer.
Semrud-Clikeman, M., Pliszka, S. R., Lancaster, J., & Liotti, M. (2006). Volumetric
MRI differences in treatment-naïve vs chornically treated children with ADHD.
Neurology, 67, 1023-1027.
Primarily Behavioral Topics
Classroom Management
(prepared by Thomas R. Kratochwill, University of Wisconsin-
Madison)
Although there is no agreed upon definition of classroom management, the framework
offered by Evertson and Weinstein (2006) represents a contemporary and widely
accepted view. Specifically, they note that classroom management has two distinct
purposes: “It not only seeks to establish and sustain an orderly environment so students
can engage in meaningful academic learning, it also aims to enhance student social and
moral growth” (p. 4).
The Institute of Education Sciences (IES) through the What Works Clearinghouse has
offered suggestions on reducing behavior problems in elementary schools (Epstein,
Atkins, Cullinan, Kutash, & Weaver, 2008) and is available on the IES website at
http://ies.ed.gov/ncee and http://ies.ed.gov/ncee/wwc/publications/practice guides. The
practice guide offers five major recommendations for addressing behavior problems
along with a corresponding level of evidence. These recommendations are:
1. Identify the specifics of the problem behavior and the conditions that prompt and
reinforce it (Moderate evidence).
2. Modify the classroom learning environment to decrease problem behavior (Strong
evidence).
3. Teach and reinforce new skills to increase appropriate behavior and preserve a
positive classroom climate (Strong evidence).
4. Draw on relationships with professional colleagues and students’ families for
continued guidance and support (Moderate evidence).
5. Assess whether school-wide behavior problems warrant adopting school-wide
strategies or programs and, if so, implement ones shown to reduce negative and
foster positive interactions (Moderate evidence) (Epstein et al., 2008, pp 6-7).
In recent years, classroom management has turned to two issues: preventing discipline
problems and intervening with current discipline problems. To address these concerns
researchers have established and evaluated systems of services and one such model is

called Positive Behavior Support (PBS) (Crone & Horner, 2003; Crone, Horner, &
Hawken, 2004). PBS programs typically involve a school-wide system of evidence-
based support for teachers (as noted in point 5 above; see Freiberg & Lapointe, 2006), as
well as small group and individualized programs for more serious student discipline
concerns (see Robinson & Griesemer, 2006).
PBS is typically set up as a multi-level model of intervention beginning with school-wide
systems of support (called universal or primary prevention), small groups or more
focused interventions for students who share problems such as aggression (called selected
or secondary intervention). It also includes individualized interventions for students who
need very focused and more intense services for problematic and disruptive behavior
(called indicated or tertiary intervention). Tertiary interventions are typically established
for students with a wide range of disruptive behavior and are developed by using a
functional analysis of the problematic behavior. Detailed information on establishing
systems of positive behavior support can be obtained from Lewis, Newcomer, Trussell,
and Richter (2006) and a national assistance center (i.e., the Office of Special Education
Programs Technical Assistance Center on Positive Behavioral Interventions & Supports
at http://www.pbis.org).
Despite advances in the area of classroom management, gaps in the research literature
still exist. Some of the major themes emerging from the Handbook of Classroom
Management (Evertson & Weinstein, 2006) portend future research agendas in the field
of classroom management. In addition, some of the recent focus on PBS elicits potential
research priorities:
• More research is needed on why and how PBS is effective in classroom
management, both at a system outcomes and student outcomes level.
• More research attention needs to be devoted to PBS applied in middle and high
school settings (Emmer & Gerwels, 2006). Much of the research literature is
focused on early elementary school children. Yet, we know that teachers face
serious challenges with older students as they progress through school. The need
for effective management strategies with the older group is a priority for
researchers.
• More research is needed on student background characteristics and how these
characteristics influence learning and behavior in inclusive classroom settings
(Soodak & McCarthy, 2006). In some of the research on classroom management,
information on student characteristics is absent. Traditional information includes
only age and grade. Information on students such as minority status, SES, etc.
would help shed light on how these characteristics predict responsiveness to
school-wide, classroom, and individual interventions in inclusive classroom
environments.

• More research is needed on the role of intrinsic rewards in supporting student
behavior in instructional settings (Reeve, 2006). Most classroom management
programs involve external reward systems to manage student behavior. Other
options such as intrinsic reward systems have not been researched as often and
deserve attention in the future. Another agenda for researchers is to use both
types of reward systems in combination.
• More research is needed on the effects of teacher-pupil relationships on
classroom management versus the exclusive focus on behavior management
(Pianta, 2006). Similar to the need for more research on intrinsic reward systems,
there is great need for research on the role that teacher-pupil relationships have
on positive student outcomes in classroom management research.
• Future research needs to focus on identifying the types of supports that could help
teachers implement day-to-day classroom management practices that have
already been identified in research (Gettinger & Kohler, 2006).
• More research attention should focus on classroom management issues
associated with transitioning students with special needs into regular classroom
systems (Brophy, 2006). Research that elaborates interventions and management
for these transitions is a priority. In this work, it will be important to emphasize
the adaptations in classroom management systems that will be necessary for
special need students.
References
Brophy, J. (2006). History of research on classroom management. In C. M. Evertson &
C. S. Weinstein (Eds.), Handbook of classroom management: Research, practice,
and contemporary issues (pp. 17-43). Mahwah, NJ: Lawrence Erlbaum
Associates.
Crone, D. H., & Horner, R. H. (2003). Building positive behavior support systems in
schools: Functional behavioral assessment. New York: Guilford.
Crone, D. H., Horner, R. H., & Hawken, L. S. (2004). Responding to behavior problems
in schools: The behavior education program. New York: Guilford.
Emmer, E. T., & Gerwels, M. C. (2006). Classroom management in middle and high
school classrooms. In C. M. Evertson & C. S. Weinstein (Eds.), Handbook of
classroom management: Research, practice, and contemporary issues (pp. 407-
438. Mahwah, NJ: Lawrence Erlbaum Associates.
Epstein, M., Atkins, M., Cullinan, D., Kutash, K., & Weaver, R. (2008). Reducing
behavior problems in the elementary school classroom: A practice guide (NCEE
#2008-012). Washington, DC: National Center for Education Evaluation and
Regional Assistance, Institute of Education Sciences, U.S. Department of
Education. Retrieved from
http://ies.ed.gov/ncee/wwc/pdf/practiceguides/behavior_pg_092308.pdf
Evertson, C. M., & Weinstein, C. S. (2006). Classroom management as a field of
inquiry. In C. M. Evertson & C. S. Weinstein (Eds.), Handbook of classroom

management: Research, practice, and contemporary issues (pp. 3-16). Mahwah,
NJ: Lawrence Erlbaum Associates.
Freiberg, H. J., & Lapointe, J. M. (2006). Research-based programs for preventing and
solving discipline problems. In C. Evertson and C. S. Weinstein (Eds.). Handbook
of classroom management: Research, practice, and contemporary issues (pp. 735-
786). Mayhwah, NJ: Erlbaum.
Gettinger, M., & Kohler, K. (2006). Process-outcome approaches to classroom
management and effective teaching. In C. M. Evertson & C. S. Weinstein (Eds.),
Handbook of classroom management: Research, practice, and contemporary
issues (pp. 73-96). Mahwah, NJ: Lawrence Erlbaum Associates.
Lewis, T. J., Newcomer, L. L., Trussell, R., & Richter, M. (2006) Schooolwide positive
behavior support: Building systems to develop and maintain appropriate social
behavior. In C. M. Evertson & C. S. Weinstein (Eds.), Handbook of classroom
management: Research, practice, and contemporary issues (pp. 833-854).
Mahwah, NJ: Lawrence Erlbaum Associates.
Pianta, R. C. (2006). Classroom management and relationships between children and
teachers: Implications for research and practice. In C. M. Evertson & C. S.
Weinstein (Eds.), Handbook of classroom management: Research, practice, and
contemporary issues (pp. 685-710). Mahwah, NJ: Lawrence Erlbaum Associates.
Reeve, J. (2006) Extrinsic rewards and inner motivation. In C. M. Evertson & C. S.
Weinstein (Eds.), Handbook of classroom management: Research, practice, and
contemporary issues (pp. 645-664). Mahwah, NJ: Lawrence Erlbaum
Associates.
Robinson, S. L., & Griesemer, S. M. R. (2006) Helping individual students with problem
behavior. In C. M. Evertson & C. S. Weinstein (Eds.), Handbook of classroom
Soodak, L. C, & McCarthy, M. R. (2006). Classroom management in inclusive settings.
In C. M. Evertson & C. S. Weinstein (Eds.), Handbook of classroom
Bullying in School
(prepared by Sandra Graham, University of California, Los Angeles)
School bullying - also commonly labeled peer victimization or peer harassment - is
defined as repeated physical, verbal or psychological abuse of victims by perpetrators
who intend to cause them harm (Olweus, 1993). Hitting, kicking, shoving, name-calling,
spreading rumors, excluding others and intimidating gestures (e.g., eye rolling) by
powerful peers are all examples of behaviors that constitute abuse that is physical, verbal,
or psychological in nature. It is estimated that 40% to 80% of school age children
experience bullying at some point during their school careers and 10-15% may be
chronic victims or bullies (Nansel et al., 2001).
School bullying is associated with a host of adjustment difficulties (see Juvonen &
Graham, 2001; Sanders & Phye, 2004). Students who are chronic victims of bullying
often are rejected by their peers, have low self-esteem, and feel depressed, anxious and

lonely. In addition to psychological challenges, some victimized youth also have
elevated levels of physical symptoms, leading to frequent visits to the nurse, and
absenteeism (Nishina, Juvonen, & Witkow, 2005). It is not difficult to imagine the
chronic victim who becomes so anxious about going to school that she or he tries to avoid
it at all costs. Other research suggests victimization by peers is associated with negative
attitudes toward school and with poor school performance, even as early as kindergarten
with extension into the adolescent years (e.g., Kochenderfer & Ladd, 1996; Schwartz,
Gorman, Nakamoto, & Toblin, 2005).
Although there is now a large literature on the consequences of school bullying, there are
at least four major gaps in the literature that require further research.
• There is a critical need for more longitudinal research. Studies of school bullying
have not adequately examined the severity and chronicity of experiences with
victimization across time (Kochenderfer-Ladd & Waldrop, 2001). We need to know
more about what factors place children at risk for being the long term targets of
bullying and about the long term consequences of chronic experiences with bullying.
Some research suggests that the experience of peer victimization fluctuates from time
point to time point; however, not enough is known about continuities and
discontinuities in the experience of bullying and how situational factors, like school
transitions where there is an opportunity to redefine one’s identity, can alter the
course of victimization. We need longitudinal studies that span critical school
transitions to address these questions.
• The field needs more intervention research. Although research on school bullying
has increased dramatically in the last decade, intervention studies to reduce the
prevalence of bullying remain relatively rare. Some intervention strategies are
targeted to individuals – either perpetrator or victim – while others take a whole-
school approach under the assumption that eliminating victimization is the collective
responsibility of students, teachers and administrators. We need more research on
both types of intervention that utilize the best principles of intervention research. By
best principles, we mean experimental studies with treatment and control groups
using random assignment and state of the art statistical techniques. This would help
us better determine what works for which students, and under what conditions.
• There is a need for more research that takes a developmental perspective. We know
that peer bullying begins as early as preschool (Alsaker & Valkanover, 2001) and
that it occurs throughout elementary and secondary school. But the types of bullying
that occur at particular developmental periods are quite distinct. For example,
physical bullying appears to be much more common in younger children, whereas the
more covert and psychological tactics such as social ostracism and rumors become
more normative among older children and adolescents (e.g., Archer & Coyne, 2005;
Nansel et al., 2001). Until we know more about the type and course of bullying at
different ages, we will not be sure about the developmental appropriateness of
particular intervention approaches.

• School contextual factors that affect bullying merit greater attention. Features of the
school context such as size, teacher-student ratio, location and distance from home,
racial/ethnic composition, and organizational structure change from childhood to
adolescence, but very little is known about the effects of these changes on bullying or
its prevention. For example, one might hypothesize that bullying will be more
extensive in larger schools where there are more “unowned spaces” with minimal
adult supervision; or that students are more likely to be victimized going to and from
school when they travel longer distances. It would also be important to know (1)
whether small learning communities (e.g., schools within schools) decrease the
amount and seriousness of bullying; or (2) whether academic tracking, which limits
the mixing opportunities of students, exacerbates bullying behavior during non-
tracked classes. Contextual variables that increase students’ sense of belonging are
presumed to result in more positive overall climate, including less bullying, (Payne &
Gottfredson, 2004), but we still do not know enough about the psychological
mechanisms that explain school context effects.
References
Alsaker, F. D. & Valkanover, S. (2001). Early diagnosis and prevention of victimization
in kindergarten. In J. Juvonen & S. Graham (Eds.), Peer harassment in school:
The plight of the vulnerable and victimized (pp.175–195). New York: Guilford
Press.
Archer, J., & Coyne, S. (2005). An integrated review of indirect, relational, and social
aggression. Personality and Social Psychology Review, 9, 212-230.
Card, N. A., & Hodges, E. V. E. (2008). Peer victimization among schoolchildren:
Correlations, causes, consequences, and considerations in assessment and
intervention. School Psychology Quarterly, 23(4), 451-461.
Juvonen, J. & Graham, S. (Eds.), (2001). Peer harassment in school: The plight of the
vulnerable and victimized. New York: Guilford Press.
Kochenderfer, B., & Ladd, G. (1996). Peer victimization: Causes or consequences of
school maladjustment? Child Development, 67, 1305-1317.
Kochenderfer-Ladd, B., & Waldrop, J. (2001). Chronicity and instability of children’s
peer victimization experiences as predictors of loneliness and social satisfaction
trajectories. Child Development, 72, 134-151.
Merrell, K. W., Gueldner, B. A., Ross, S. W., & Isava, D. M. (2008). How effective are
school bullying intervention programs? A meta-analysis of intervention research.
School Psychology Quarterly, 23(1), 26-42.
Nansel, T., Overpeck, M., Pilla, R. , Ruan, W. Simons-Morton, B., & Scheidt, P. (2001).
Bullying behaviors among US youth: Prevalence and association with
psychosocial adjustment. The Journal of the American Medical Association, 28,
2094-2100.
Nishina, A., Juvonen, J., & Witkow M. (2005). Sticks and stones may break my bones,
but names will make me sick: The consequences of peer harassment. Journal of
Clinical Child and Adolescent Psychology, 34, 37-48.
Olweus, D. (1993). Bullying at school. Malden, MA: Blackwell Publishers, Inc.
Payne, A., & Gottfredson, D. (2004). Schools and bullying: Factors related to bullying
and school-based bullying interventions. In C. Sanders & G. Phye (Eds.),

Bullying: Implications for the classroom (pp. 159-176). San Diego, CA:
Academic Press.
Sanders, C., & Phye, G. (2004). Bullying: Implications for the classroom. San Diego,
CA: Academic Press.
Schwartz, D., Gorman, D., Nakamoto, J., & Tobin, R. (2005). Victimization in the peer
group and children’s academic functioning. Journal of Educational Psychology,
97, 425-435.
Primarily Affective Topics
Improving Students’ Relationships with Teachers to Provide Essential
Supports for Learning
(prepared by Sara Rimm-Kaufman, University of Virginia)
Improving students’ relationships with teachers has important, positive and long-lasting
implications for students’ academic and social development. If a student feels a personal
connection to a teacher, experiences frequent communication with a teacher, and receives
more guidance and praise than criticism from the teacher; the student is likely to become
more trustful of that teacher, show more engagement in the academic content presented,
display better classroom behavior, achieve at higher levels academically, and be less
likely to drop-out of school (Birch & Ladd, 1997; Croninger & Lee, 2001; Gregory &
Ripski, 2008; Hamre & Pianta, 2001; McCombs, 2004; Murray & Malmgren, 2005;
Wentzel, 1997, 2002).
Research on teacher-child relationships is typically rooted in either attachment theory or
self-system theory. In relation to attachment theory; children use their relationship with
adults to organize their experiences (Bowlby 1969). Central to this model, children
develop “internal working models” based on their family experiences of how adult-child
relationships ought to work. These “working models” become applied to other
relationships, beyond those in the home or the classroom, and perpetuate themselves over
time. Thus, if a child experiences a close, warm relationship with a teacher, he or she is
likely to behave towards this teacher (as well as future teachers) in a way that is
consistent with this model. With self-system theory, students come to the classroom with
basic psychological needs—competence, autonomy, and relatedness—all of which can be
met in a classroom through children’s interactions with teachers and the learning
environment (Deci & Ryan, 2002). Competence refers to a student’s need to feel capable
of academic work; autonomy suggests a feeling of connectedness between the student’s
behavior and his/her person values and goals; and relatedness implies that a student feels
socially connected to teachers or peers. It follows that classroom practices that foster
feelings of competence, autonomy, and relatedness are likely to produce the engagement
and motivation required for academic learning and success. Positive teacher-student
relationships help children meet these needs.
Existing research suggests that improving students’ relationships with their teacher alone
will not produce children’s gains in achievement. However, in classrooms with high
quality academic instruction; those students who have close, positive, and supportive

relationships with their teachers will attain higher levels of achievement (Lee & Smith,
1999).
The literature on teacher-child relationships is growing, particularly in early childhood
research. Currently, three gaps in the literature require mention.
• There is surprisingly little in the literature that examines the district-level, school-
level and teacher characteristics that contribute to positive teacher-student
relationships. The few studies that exist on teacher characteristics find that the
personal characteristics of teachers are very important in predicting the quality
of their relationships with students. (For example, some teachers have a great
deal of conflict with almost all of the children in their classroom.) In fact, little is
known about what factors predict whether a teacher is likely to develop a good or
bad relationship with students. There is a need for both observational research
as well as experimental studies that examine district-level and school-level
ecologies and their implications for teacher-child relationships (Rimm-Kaufman
& Ponitz, 2009).
• There are relatively few longitudinal studies of teacher-child relationships. A few
exceptions include (1) Hamre and Pianta’s (2001) investigation showing that
early positive teacher-child relationships contributed to later achievement and
positive school behaviors and (2) O’Connor and McCartney’s (2007) research
suggesting that when predicting third grade achievement, high quality teacher-
child relationships in the early years of school were moderately protective in
situations where children had poor attachment relationships with their mothers.
Yet, there is enough research on teacher-child interactions to know that they are
consequential for children. Understanding these from a longitudinal perspective
will give us a richer picture of how a single good teacher does (or does not) have
the potential to shift a child’s developmental trajectory as well as the way in
which sustained high quality interactions between teachers and children may
contribute to children’s growth and development. Further, such longitudinal
studies have the potential to identify links between high quality teacher-child
relationships and children’s achievement, relationships with peers, and even,
relationships with family members.
• Too little research has been conducted on the cultural specificity of behaviors that
accompany teacher-student relationship styles. Not all teachers show that they
care for their students in the same way and their actual behavior toward their
students may be culturally specific and therefore misunderstood.
• There is a need for research focusing on training teachers to reflect and consider
the extent to which they have created warm and supportive relationships with
students. Some promising tools have been developed, such as the Classroom
Assessment Scoring System (Pianta, Hamre, & LaParo, 2007). This resource
offers a framework for teachers to think through the nature of the climate they
create in the classroom. The Consortium on Chicago School Research surveys

students to assess learning climate and student-teacher relationships
(http://ccsr.uchicago.edu/).
References
Birch, S. H. & Ladd, G. W. (1997). The teacher-child relationship and early school
adjustment. Journal of School Psychology, 55(1), 61-79.
Bowlby, J. (1969). Attachment and loss: Volume 1, Attachment. New York: Basic
Books.
Croninger, R. G. & Lee, V. E. (2001). Social capital and dropping out of high school:
Benefits to at-risk students of teachers' support and guidance. Teacher College
Record, 103(4), 548-581.
Deci, E. L., & Ryan, R. M. (2002). Overview of self-determination theory: An
organismic dialectical perspective. In E. L. Deci & R. M. Ryan (Eds.), Handbook
of self-determination research (pp. 3-33). Rochester, NY: University of
Rochester Press.
Gregory, A. & Ripski, M. (2008). Adolescent trust in teachers: Implications for behavior
in the high school classroom. School Psychology Review, 37(3), 337-353.
Hamre, B. K. & Pianta, R. C. (2001). Early teacher-child relationships and the trajectory
of children's school outcomes through eighth grade. Child Development, 72(2),
625-638.
Lee, V. & Smith, J. B. (1999). Social support and achievement for young adolescents in
Chicago: The role of school academic press. American Educational Research
Journal, 36(4), 907-945.
McCombs, B. L. (2004). The learner-centered psychological principles: A framework
for balancing a focus on academic achievement with a focus on social and
emotional learning needs (pp. 23-39). In J. E. Zins, R. P. Weissberg, M. C.
Wang, & H. J. Walberg (Eds.), Building academic success on social and
emotional learning: What does the research say? New York: Teachers College
Press.
Murray. C., & Malmgren, K. (2005). Implementing a teacher-student relationship
program in a high-poverty urban school: Effects on social, emotional and
academic adjustment and lessons learned. Journal of School Psychology, 43(2),
137-152.
O’Connor, E. E., & McCartney, K. (2007). Examining teacher-child relationships and
achievement as part of an ecological model of development. American
Educational Research Journal, 44, 340-369.
Pianta, R. C., La Paro, K. & Hamre, B. (2007). Classroom Assessment Scoring System
[CLASS] Manual, K-3. Baltimore, MD: Paul H. Brookes Publishing Company.
Rimm-Kaufman, S. E., & Ponitz, C. C. (2009). Introduction to the special issue on data-
based investigations of the quality of preschool and early child care
environments. Early Education and Development, 20, 201-210
Wentzel, K. (1997). Student motivation in middle school: The role of perceived
pedagogical caring. Journal of Educational Psychology, 89, 411-419.
Wentzel, K. (2002). Are effective teachers like good parents? Teaching styles and
student adjustment in early adolescence. Child Development, 73, 287-301.

Developing Responsible and Autonomous Learners: A Key to
Motivating Students
(prepared by Barbara McCombs, University of Denver)
Strategies to help motivate students to learn by providing autonomy-supportive
conditions and appropriate academic choices has been well-researched in recent decades,
along with research on how to promote self-regulated learning (e.g., Carver & Scheier,
1991; Ley & Young, 2001; Lin, 2001; McCombs, 1986, 1994; Paris & Paris, 2001).
Many learning strategies and motivational programs based on sound research are
available to teachers for students of various ages and school levels (e.g., Cornelius-White,
2007; Harter, 1999; McCombs, 2001, 2004, 2007; McCombs & Miller, 2007; Meece,
Herman, & McCombs, 2004; Schunk & Zimmerman, 1998; Weinstein & Hume, 1995;
Zimmerman, 1994, 1995; Zimmerman & Schunk, 2001).
European research in areas relevant to this topic help teachers understand that developing
student autonomy and motivation to learn is a complex phenomenon that involves the self
system – that is, students’ concepts of their abilities and competencies. For example,
Spielberger, (2006) has researched cross-cultural aspects of generalized or trait
characteristics that contribute to student learning and Walker, Pressick-Kilborn, Arnold,
and Sainsbury (2004) have studied evidence-based practices that help teachers address
sociocutural differences in student motivation and tendencies for self-regulation while
learning. These studies reveal how contextually-driven best practices are and remind
teachers to attend to not only differences in learning among students in different
discipline areas, but also differences in students’ perspectives based on their sociocultural
backgrounds. This point is also brought home in recent work by Ryan, Ryan, Arbuthnot,
and Samuels (2007) in studies of students differences in motivation to learn math
concepts and do well on standardized math exams.
The research on autonomous learning spans several areas, including self-regulated
learning, learning strategies, and motivation strategies. Research has recently been
more integrative as the field has recognized links between motivation, cognition, and
metacognition (McCombs, 2001; Zimmerman & Schunk, 2001). Although the research
has been growing, there are a number of areas in need of further research.
• Research has found significant relationships between interventions that help
students become more autonomous and self-regulated learners and a variety of
learning outcomes. Very little research exists, however, on the long-term or
longitudinal effects of many of these interventions for upper elementary through
high school students. It would be expected that effects would increase over time
for students exposed to quality intervention strategies.
• As with the previous area, there have been few studies that have systematically
examined the specific kinds of approaches to developing learners’ capacities for
self-regulated learning that work best with students from various cultural
backgrounds and/or genders. This research also needs to focus on the

effectiveness of programs or interventions that address the whole learner from a
cognitive, metacognitive, affective, motivational, and social perspective.
• With interest increasing in ways to reduce the achievement gap for various
economic and ethnic groups, further research on how programs that develop
student autonomy is needed. This research needs to focus on the effectiveness of
different types of programs for helping students become more autonomous and
self-regulated learners in reducing the achievement gap for different economic
and ethnic groups. It is reasonable to assume that the development of better self-
management and self-regulation skills in special populations can help reduce the
achievement gap.
• Finally, there is limited research on the effectiveness of autonomy development
programs for students with special learning needs. This research should
specifically focus on the types of strategies that are most effective for different
kinds of learning needs. It would be expected that this research would provide
useful information for tailoring self-regulation strategies to particular types of
learning needs.
References
Carver, C. S., & Scheier, M. F. (1991). Self-regulation and the self. In J. Strauss & G. R.
Goethals (Eds.), The self: Interdisciplinary approaches (pp. 168-207. New York:
Springer-Verlag.
Cornelius-White, J. (2007). Teachers who care are more effective: A meta-analysis of
learner-centered relationships. Review of Educational Research, 77(1), 113-143.
Harter, S. ( 1999). The construction of the self: A developmental perspective. New York:
Guilford Press.
Ley, K., & Young, D. B. (2001). Instructional principles for self-regulation. Educational
Technology Research and Development, 49(2), 93-103.
Lin, X. (2001). Designing metacognitive activities. Educational Technology Research
and Development, 49(1), 23-40.
McCombs, B.L. (1986). The role of the self-system in self-regulated learning.
Contemporary Educational Psychology, 11, 314-332.
McCombs, B.L. (1994). Strategies for assessing and enhancing motivation: Keys to
promoting self-regulated learning and performance. In H. F. O’Neil, Jr., & M.
Drillings (Eds.), Motivation: Research and theory (pp. 49-69). Hillsdale, NJ:
Lawrence Erlbaum.
McCombs, B. L. (2001). Self-regulated learning and academic achievement: A
phenomenological view. In B. J. Zimmerman & D. H. Schunk (Eds.), Self-
Regulated learning and academic achievement: Theory, Research, and Practice
(2nd
Ed., pp. 67-123). Mahwah, NJ: Lawrence Erlbaum Associates.
McCombs, B. L. (2004). Learner-centered principles and practices: Enhancing
motivation and achievement for children with learning challenges and disabilities.
International Review of Research in Mental Retardation, 28, 85-120.
McCombs, B. L. (2007). Balancing accountability demands with research-validated,
learner-centered teaching and learning practices. In C. E. Sleeter (Ed.), Educating

for democracy and equity in an era of accountability (pp. 41-60). New York:
Teachers College Press.
McCombs, B. L., & Miller, L. (2007). Learner-centered classroom practices and
assessments: Maximizing student motivation, learning, and achievement.
Thousand Oaks, CA: Corwin Press.
Paris, S. C., & Paris, A. H. (2001). Classroom applications of research on self-regulated
learning. Educational Psychologist, 36(2), 89-101.
Ryan, K. E., Ryan, A. M., Arbuthnot, K., & Samuels, M. (2007). Students’ motivation
for standardized math exams. Educational Researcher, 36(1), 5-13.
Schunk, D. H., & Zimmerman, B. J. (1998). Self-regulated learning: From teaching to
self-reflective practice. New York: Guilford Press.
Spielberger, C. D. (2006). Cross-cultural assessment of emotional states and personality
traits. European Psychologist, 11(4), 297-303.
Walker, R. A., Pressick-Kilborn, K., Arnold, L. S., & Sainsbury, E. J. (2004).
Investigating motivation in context: Developing sociocultural perspectives.
European Psychologist, 9(4), 245-256.
Weinstein, C. E., & Hume, L. M. (1995). Study strategies for lifelong learning.
Washington, DC: APA Books.
Zimmerman, B.J. (1994). Dimensions of academic self-regulation: A conceptual
framework for education. In D. H. Schunk & B.J. Zimmerman (Eds.), Self-
regulation of learning and performance: Issues and educational applications (pp.
3-21). Hillsdale, NJ: Erlbaum.
Zimmerman, B. J. (1995). Self-regulation involves more than metacognition: A social
cognitive perspective. Educational Psychologist, 30(4), 217-221.
Zimmerman, B. J. & Schunk, D. H. (Eds.) (2001). Self-regulated learning and academic
achievement: Theoretical perspectives (2nd
Ed). Mahwah, NJ: Lawrence
Erlbaum Associates.
Conclusion:
A great deal of psychological science exists that can guide teaching practices in ways that
are likely to enhance teaching and pupil learning. Nevertheless, much more focused
research is needed to provide evidence that the strategies described here effectively
impact learning with students at various developmental, ability/achievement and SES
levels, as well as children from culturally diverse backgrounds in US classrooms. This
document suggests potential lines of research to fill the gaps in our knowledge in 10 areas
identified by classroom teachers as being important to their practice.

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