Neurodevelopmental framework for learning

Neurodevelopmental framework for learning, like all frameworks, is an organizing structure through which learners and learning can be understood. Intelligence theories and neuropsychology inform many of them. The framework described below is a neurodevelopmental framework for learning. The neurodevelopmental framework was developed by the All Kinds of Minds Institute in collaboration with Dr. Mel Levine and the University of North Carolina’s Clinical Center for the Study of Development and Learning. It is similar to other neuropsychological frameworks, including Alexander Luria’s cultural-historical psychology and psychological activity theory, but also draws from disciplines such as speech-language pathology, occupational therapy, and physical therapy. It also shares components with other frameworks, some of which are listed below. However, it does not include a general intelligence factor (abbreviated g), since the framework is used to describe learners in terms of profiles of strengths and weaknesses, as opposed to using labels, diagnoses, or broad ability levels. This framework was also developed to link with academic skills, such as reading and writing. Implications for education are discussed below as well as the connections to and compatibilities with several major educational policy issues.

This framework consists of 8 constructs, sometimes referred to as systems (Levine, 1998)^[1]:

Constructs

• attention- mental energy, processing incoming information, and regulating output (Posner & Rothbart, 2007)^[2]
• temporal-sequential ordering- processing and production of material that is serial (Bishoff-Grethe, Goedert., Willingham, & Grafton, 2004^[3]; Parmentier, Andres, Elfors, & Jones, 2006^[4]; Zorzi, Prifitis, Meneghello, Marenzi, & Umilt, 2006^[5])
• spatial-ordering- processing and production of material that is visual and/or spatial (van Garderen, 2006^[6]; Mammarella, Cornoldi, Pazzaglia, Toso, Grimoldi,& Vio, 2006^[7]; Kozhevnikov, Motes, & Hegarty, 2007^[8]).
• memory- storage and retrieval of information (after brief or long delays), or mentally suspending information while using it (Swanson & Jerman, 2007^[9]; Kail & Hall, 2001^[10]; Imbo & Vandierendonck, 2007^[11]).
• language- understanding and use of linguistic sounds, words, sentences, and discourse (Katzir, Youngsuk, Wolf, O’Brien, Kennedy, Lovett, & Morris, 2006^[12]; Nagy, Berninger, & Abbott, 2006^[13]; Altemeier, Jones, Abbott, & Berninger, 2006^[14]).
• neuromotor function- control over movement of large muscles, hands, and fingers (Williams, Thomas, Maruff, & Wilson, 2008^[15]; Bar-Haim & Bart, 2006^[16]; Contreras-Vidal, 2006^[17]).
• social cognition- navigation of interaction with others, including verbal and nonverbal tactics (Blake & Shiffrar, 2007^[18]; Blakemore, 2007^[19]; Brewer & Hewstone, 2004^[20]; Holtgraves & Kashima, 2008^[21])
• higher order cognition- complex and sophisticated thinking (Russ, Scherr, Hammer, & Mineska, 2008^[22]; Hertzog, 2007^[23]; Amsterlaw, 2006^[24]).

In addition to the 8 constructs, this framework includes several “cross-construct” phenomena: rate alignment (working at optimal speed), strategy use (working and thinking tactically), chunk size capacity- the amount of material that can be processed, stored or generated, and metacognition (degree of knowledge about learning and insight into one’s own neurodevelopmental strengths and weaknesses). (Benjamin & Bird, 2006^[25]; Broekkamp & Van Hout-Wolters, 2007^[26]; Flavell, 1979^[27]; Halford, Wilson, & Phillips, 1998^[28]; Hofer, 2004^[29]; Lungu, Liu, Waechter, Willingham, & Ashe, 2007^[30]).

Implications for Educational Practice

Students come to school with unique neurodevelopmental profiles; the strengths and weaknesses of each learner’s brain are different. Schools place numerous demands on brain functions. A mismatch between the strengths and weaknesses a learner brings to school and the demands of the classroom can cause barriers to academic success. Identifying the nature of the mismatch and where the learning breakdown is taking place can help teachers, parents and students to develop effective strategies and remedy the problem. Identification is based on observable phenomena: what is seen in the classroom and at home, what evidence can be gathered and analyzed, and what hypothesis can be generated based on observations. Labeling a student as learning disabled, ADD, or ADHD is not as descriptive and or useful as applying the neurodevelopmental framework. A neurodevelopmental framework can be used to categorize phenomena into profiles, which can be used to describe students rather than label them with diagnostic categories. The framework can be used in three broad areas:

In Schools

Neurodevelopmental profiles can be used for students identified as being in need of academic or behavioral support. Mismatches between a student’s profile and the classroom environment can lead to student frustration which may be expressed as behavior issues. Schools implementing a neurodevelopmental framework may see a reduction in disciplinary referrals as the mismatch is understood and modifications are incorporated. Profiles may be used for students as part of a special education referral or when an Individualized Education Plan or 504 is developed. The reauthorization of IDEA has given rise to Response to Intervention, a tiered approach to addressing the specific needs of each child, which is wholly compatible with describing students’ neurodevelopmental profiles.

In Classrooms

Within a classroom, each subject area and each instructional strategy places different neurodevelopmental demands on students. Awareness of these demands can lead teachers to an instructional analysis of what and how they are teaching. Differentiated Instruction, or differentiation, is the practice of using a variety of instructional tools, resources or strategies to meet the needs of all learners. It is enhanced through a more specific understanding of the neurodevelopmental demands on targeted groups of students, resulting in a closer alignment of cognitive strengths to academic content. In addition to differentiation, a neurodevelopmental framework supports several curriculum design tools or principles used in many classrooms such as Understanding by Design and Universal Design for Learning.

Individual Students

Teachers who use a neurodevelopmental framework observe student behavior and work, looking for recurrent themes or patterns. Detailed analysis of strengths, weaknesses and affinities allows teachers to develop a neurodevelopmental profile of a student. (Teachers who utilize a Multiple Intelligences or learning styles framework will be familiar with the idea that each student’s brain works differently. A neurodevelopmental approach is more specifically descriptive of learning breakdowns.) A process of demystification occurs with the student, allowing the student to develop insight into his or her own learning. In partnership with the student, the teacher develops targeted action plans strategies chosen to utilize and develop neurodevelopmental strengths while bolstering areas of weakness. Teachers who practice differentiation will be familiar with the idea of modifying instruction to meet the needs of diverse learners

Professional Development

To use any learning framework, teachers and other school-based individuals need to invest in professional development. Professional development can take the form of face-to-face trainings, on-line training and college or graduate school courses. The neurodevelopmental framework described above is the basis for professional development from All Kinds of Minds, an educational non-profit institute, through the Schools Attuned program.

Other Learning Frameworks

Numerous frameworks are available that describe development and help to organize observations of learning behavior. Intelligence theories date back to the 19th century and the early 20th century, such as Charles Spearman’s concept of general intelligence factor, or g. Though there were exceptions (e.g., Thorndike), most theories of intelligence included g, a general index of cognitive ability (Bolles, 1993^[31] Francher, 1990^[32]). An intelligence theory that has drawn considerable attention is Cattell-Horn-Carroll (CHC), which is grounded in extensive factor analytic research from cognitive ability test databases, as well as studies of development and heritability. CHC is actually an amalgam of Cattell-Horn Gf-Gc theory and Carroll’s three-tier model. Carroll (1993)^[33] proposed a framework with the broadest level a general intelligence factor conceptually similar to Spearman’s g. This general factor was divided into eight narrower abilities, each consisting of narrow factors. Cattell-Horn’s model was similar on several fronts, including its hierarchical structure. In the 1990’s, Carroll’s model was combined with Cattell-Horn’s work by Flanagan, McGrew, and Ortiz (2000)^[34]. CHC contains three strata: stratum III is g, stratum II consists of broad cognitive abilities, and stratum I consists of narrow cognitive abilities. The broad cognitive abilities (stratum II) include fluid reasoning (or Gf, forming and recognizing logical relationships among patterns, inferencing, and transforming novel stimuli) and comprehension-knowledge (or Gc, using language and acquired knowledge). There is on-going discussion by proponents of CHC about g’s importance in the framework. The Structure of Intellect (SOI) model includes three axes (with 5-6 components per axis) that form a 3-dimensional cube; because each dimension is independent, there are 150 different potential aspects of intelligence (Guilford, 1982)^[35]. Howard Gardner has written about several categories of intelligence, as opposed to a hierarchical model (Gardner, 1999)^[36]. Neuropsychologists have sought to map various mental abilities onto brain structures. In so doing they have created frameworks that include factors and sub-components. Luria (1973)^[37] organized brain functions into now-familiar categories, such as speech and memory. Luria’s conception of attention included three units: Unit 1 (brainstem and related areas) regulates cortical activity and levels of alertness, Unit 2 (lateral and posterior regions of neocortex) analyzes and stores newly received information, and Unit 3 (frontal lobes) programs and regulates activity (Luria, 1973)^[38]. More recently, the PASS (Planning, Attention, Successive, and Simultaneous) model (Das, Naglieri, & Kirby 1994)^[39] yields both a global index of ability while emphasizing specific cognitive processes. For example, “successive” refers to information that is perceived, interpreted, and/or remembered in a serial order (e.g., language), whereas “simultaneous” refers to material that is perceived, interpreted, and/or remembered as a whole (e.g. visual-spatial).

Policies and Initiatives Compatible with a Neurodevelopmental Framework

The neurodevelopmental framework can be found within a number of policy contexts throughout the American educational system. As educators grapple with the challenge of meeting the needs of all learners as well as educational policy requirements, they are utilizing a neurodevelopmental lens to meet these demands. While many districts and states are exploring a variety of ways to utilize the framework in both professional development offerings as well as systematic teacher development processes, No Child Left Behind, IDEA, and RtI are all federal policies that support instructional application of the framework.

No Child Left Behind (NCLB)

The No Child Left Behind Act (NCLB) creates a context that is very compatible with the neurodevelopmental framework. In a broad sense, NCLB puts an increased emphasis on “science based research,” which would include the vast amount of scientific evidence supporting the framework. NCLB proposes the idea that education should be supported by science rather than guesswork, and that teachers should have some sort of empirical evidence to support interventions. The neurodevelopmental framework provides the opportunity to bring the science of learning to the art of teaching. Under NCLB’s assessment requirements, states are required to disaggregate data by subgroups. In this way, NCLB sheds light on the specific groups of students who are struggling rather than averaging all of the data together. The neurodevelopmental framework provides recognition that all students’ minds have different strengths and weaknesses, and one possible explanation for the achievement gap is a mismatch between instructional and student strengths and weaknesses in learning.

Individuals with Disabilities Education Act (IDEA)

In the years since the passage of Public Law 94-142 (now referred to as IDEA), significant progress has been made toward meeting major national goals for developing and implementing effective programs and services for early intervention, special education, and related services. Still, many students with disabilities have their strengths overlooked. The neurodevelopmental framework provides concepts and vocabulary to understand both students’ strengths and weaknesses. The 2004 reauthorization of IDEA opened the door further for a connection to the neurodevelopmental framework. IDEA 2004 permits the use of federal program dollars for students who are not specifically identified as having a learning disability. Of the money a school district receives under Part B of IDEA, a maximum of 15 percent may be used for “early intervention services” for unidentified students. This means schools are encouraged to understand the way students learn before they are ever referred to special education. Several concepts have become part of the special education vocabulary because of this law, including Free Appropriate Public Education (FAPE), Individualized Education Program (IEP) and Least Restrictive Environment (LRE). These acronyms have become foundational to U.S. efforts to create a high quality system of special education and ensure equal access to education for all students.

Response to Intervention (RtI)

Response to Intervention (RtI) is sometimes referred to as Response to Instruction as decisions are constantly made about the unique individual responses to instructional delivery. This approach works to the benefit of all students within the classroom, because teachers are actively engaged in discovering the source of learning breakdowns. In the past, special education was seen as the only way students could receive help. If they do not meet special education requirements, some students still fail in the classroom. RtI empowers teachers to troubleshoot problems that students are encountering, many times eliminating the need for special education referrals. The neurodevelopmental framework equips educators with the concepts and vocabulary to make sound judgments about instruction, making it a natural complement to the spirit of IDEA in general, and the RtI framework in particular. Identifying learning breakdowns and how to intervene with appropriate accommodations and interventions is an essential skill for successful RtI implementation.

Footnotes

1. ^ Levine, M.D. (1998). Developmental Variation and Learning Disorders, Second Edition. Cambridge, MA: Educators Publishing Service.
2. ^ Posner, M.I., & Rothbart, M.K. (2007). Educating the Human Brain. Washington, DC: American Psychological Association.
3. ^ Bishoff-Grethe, A., Goedert, K.M., Willingham, D.T., & Grafton, S.T. (2004). Neural substrates of response-based sequence learning using fMRI. Journal of Cognitive Neuroscience, 16 (1), 127-138.
4. ^ Parmentier, F.B.R., Andres, P., Elford, G., & Jones, D.M. (2006). Organization of visuo-spatial serial memory: interaction of temporal order with spatial and temporal grouping. Psychological Research, 70, 200-217.
5. ^ Zorzi, M., Priftis, K., Meneghello, F., Marenzi, R. & Umilt, C. (2006). The spatial representation of numerical and non-numerical sequences: Evidence from neglect. Neruopsychologia, 44, 1061-1067.
6. ^ Garderen, D. (2006). Spatial visualization, visual imagery, and mathematical problem solving of students with varying abilities. Journal of Learning Disabilities, 39 (6), 496-506.
7. ^ Mammarella, I., Cornoldi, C., Pazzaglia, F., Toso, C., Grimoldi, M., & Vio, C. (2006). Evidence for a double dissociation between spatial-simultaneous and spatial- sequential working memory in visuospatial (nonverbal) learning disabled children. Brain and Cognition, 62, 58-67. doi: 10.1016/j.bandc.2006.03.007.
8. ^ Kozhevnikov, M., Motes, M., & Hegarty, M. (2007). Spatial Visualization in Physics Problem Solving. Cognitive Sciences, 31, 549-579.
9. ^ Swanson, H., Jerman, O. (2007). The influence of working memory on reading growth in subgroups of children with reading disabilities. Journal of Experimental Child Psychology, 96, 249–283. doi:10.1016/j.jecp.2006.12.004
10. ^ Kail, R., & Hall, L. K. (2001). Distinguishing short-term memory from working memory. Memory and Cognition, 29 (1), 1-9.
11. ^ Imbo, I., Vandierendonck, A. (2007). The development of strategy use in elementary school children: Working memory and individual differences. Journal of Experimental Child Psychology, 96 (4), 284-309. doi:10.1016/j.jecp.2006.09.001
12. ^ Katzir, T., Youngsuk K., Wolf, M., O’Brien, B., Kennedy, B., Lovett, M., & Morris, R. (2006). Reading fluency: The whole is more than the parts. Annals of Dyslexia, 56 (1).
13. ^ Nagy, W., Berninger, V., & Abbott, R. (2006). Contributions of morphology beyond phonology to literacy outcomes of upper elementary and middle-school students. Journal of Educational Psychology, 98 (1), 134–147. doi: 10.1037/0022-0663.98.1.134
14. ^ Altemeier, L., Jones, J., Abbott, R., & Berninger, V. (2006) Executive functions in becoming writing readers and reading writers: Note taking and report writing in third and fifth graders. Developmental Neuropsychology, 29 (1), 161-173.
15. ^ Williams, J., Thomas, P., Maruff, P., Wilson, P. (2008). The link between motor impairment level and motor imagery ability in children with developmental coordination disorder. Human Movement Science, 27, 270-285.
16. ^ Bar-Haim, Y. & Bart, O. (2006). Motor function and social participation in kindergarten children. Social Development, 15 (2), 296-310.
17. ^ Contreras-Vidal, J. (2006). Development of forward models for hand localization and movement control in 6 to 10-year-old children. Human Movement Science, 25, 634-645. doi: 10.1016/j.humov.2006.07.006
18. ^ Blake, R. & Shiffrar, M. (2007). Perception of Human Motion. Annual Review of Psychology, 58 (47), 47-73. doi: 10.1146/annurev.psych.57.102904.190152
19. ^ Blakemore, S.J. (2007). Brain development during adolescence. Education Review, 20 (1), 82-90.
20. ^ Brewer, M.B. & Hewstone, M. (2004). Social Cognition, Malden, MA: Blackwell Publishing.
21. ^ Holtgraves, T.M. & Kashima, Y. (2008). Language, meaning, and social cognition. Personality and Social Psychology Review, 12 (1), 73-94. doi: 10.1177/1088868307309605
22. ^ Russ, R., Scherr, R., Hammer, D., & Mineska, J. (2008). Recognizing mechanistic reasoning in student scientific inquiry: A framework for discourse analysis developed from philosophy of science. Science Studies and Science Education, 1-28. doi: 10.1002/sce.20264
23. ^ Hertzog, N. (2007). Transporting Pedagogy: Implementing the project approach in two first grade classrooms. Journal of Advanced Academics, 18 (4), 530-564.
24. ^ Amsterlaw, J. (2006). Children’s beliefs about everyday reasoning. Child Development, 77 (2), 443-464. doi: 0009-3920/2006/7702-0014
25. ^ Benjamin, A. S., & Bird, R. D. (2006). Metacognitive control of the spacing of study repetitions. Journal of Memory and Language, 55, 126-137.
26. ^ Broekkamp, H. & Van Hout-Wolter, B.H.A.M. (2007). Students’ adaptation of study strategies when preparing for classroom tests. Educational Psychology, 19 , 401-428. doi: 10.1007/s10648-006-9025-0
27. ^ Flavell, J. (1979). Metacognition and cognitive monitoring: A new era of cognitive-developmental inquiry. American Psychologist, 34 (1), 906-911.
28. ^ Halford, G.S., Wilson, W.H., & Phillips, S. (1998). Processing capacity defined by relational complexity: Implications for comparative, developmental, and cognitive psychology. Behavioral and Brain Sciences, 21 (6).
29. ^ Hofer, B.K. (2004). Epistemological understanding as a metacognitive process: Thinking aloud during online searching. Educational Psychologist, 39 (1), 43-55.
30. ^ Lungu, O. V., Liu, T., Waechter, T., Willingham, D. T., & Ashe, J. (2007). Strategic modulation of cognitive control. Journal of Cognitive Neuroscience, 19(8), 1302-1315
31. ^ Bolles, R.C. (1993). The Story of Psychology: A Thematic History. Pacific Grove, CA: Brooks/Cole.
32. ^ Fancher, R.E. (1990). Pioneers of Psychology, Second Edition. New York: Norton & Company.
33. ^ Carroll, J. B. (1993). Human Cognitive Abilities: A Survey of Factor Analytic Studies. New York: Cambridge University.
34. ^ Flanagan, D.P., McGrew, K.S., & Ortiz, S. (2000). The Wechsler Intelligence Scales and Gf- Gc Theory: A contemporary approach to interpretation. Needham Heights, MA: Allyn & Bacon.
35. ^ Guilford, J.P. (1982). Cognitive psychology's ambiguities: Some suggested remedies. Psychological Review, 89, 48-59.
36. ^ Gardner, H. (1999). Intelligence Reframed. Multiple intelligences for the 21st century, New York: Basic Books.
37. ^ Luria, A.R. (1973). The Working Brain: An Introduction to Neuropsychology (B. Haigh, Trans.). New York: Basic Books.
38. ^ Luria, A.R. (1973). The Working Brain: An Introduction to Neuropsychology (B. Haigh, Trans.). New York: Basic Books/
39. ^ Das, J.P., Naglieri, J.A., & Kirby, J.R. (1994). Assessment of Cognitive Processes: The PASS Theory of Intelligence. Boston: Allyn and Bacon.

References

• Altemeier, L., Jones, J., Abbott, R., & Berninger, V. (2006) Executive functions in becoming writing readers and reading writers: Note taking and report writing in third and fifth graders. Developmental Neuropsychology, 29 (1), 161-173.
• Amsterlaw, J. (2006). Children’s beliefs about everyday reasoning. Child Development, 77 (2), 443-464. doi: 0009-3920/2006/7702-0014
• Bar-Haim, Y. & Bart, O. (2006). Motor function and social participation in kindergarten children. Social Development, 15 (2), 296-310.
• Benjamin, A. S., & Bird, R. D. (2006). Metacognitive control of the spacing of study repetitions. Journal of Memory and Language, 55, 126-137.
• Bishoff-Grethe, A., Goedert, K.M., Willingham, D.T., & Grafton, S.T. (2004). Neural substrates of response-based sequence learning using fMRI. Journal of Cognitive Neuroscience, 16 (1), 127-138.
• Blake, R. & Shiffrar, M. (2007). Perception of Human Motion. Annual Review of Psychology, 58 (47), 47-73. doi: 10.1146/annurev.psych.57.102904.190152
• Blakemore, S.J. (2007). Brain development during adolescence. Education Review, 20 (1), 82-90.
• Brewer, M.B. & Hewstone, M. (2004). Social Cognition, Malden, MA: Blackwell Publishing.
• Broekkamp, H. & Van Hout-Wolter, B.H.A.M. (2007). Students’ adaptation of study strategies when preparing for classroom tests. Educational Psychology, 19 , 401-428. doi: 10.1007/s10648-006-9025-0
• Carroll, J. B. (1993). Human Cognitive Abilities: A Survey of Factor Analytic Studies. New York: Cambridge University.
• Contreras-Vidal, J. (2006). Development of forward models for hand localization and movement control in 6 to 10-year-old children. Human Movement Science, 25, 634-645. doi: 10.1016/j.humov.2006.07.006
• Das, J.P., Naglieri, J.A., & Kirby, J.R. (1994). Assessment of Cognitive Processes: The PASS Theory of Intelligence. Boston: Allyn and Bacon.
• Fancher, R.E. (1990). Pioneers of Psychology, Second Edition. New York: Norton & Company.
• Flanagan, D.P., McGrew, K.S., & Ortiz, S. (2000). The Wechsler Intelligence Scales and Gf- Gc Theory: A contemporary approach to interpretation. Needham Heights, MA: Allyn & Bacon.
• Flavell, J. (1979). Metacognition and cognitive monitoring: A new era of cognitive-developmental inquiry. American Psychologist, 34 (1), 906-911.
• Garderen, D. (2006). Spatial visualization, visual imagery, and mathematical problem solving of students with varying abilities. Journal of Learning Disabilities, 39 (6), 496-506.
• Gardner, H. (1999). Intelligence Reframed. Multiple intelligences for the 21st century, New York: Basic Books.
• Guilford, J.P. (1982). Cognitive psychology's ambiguities: Some suggested remedies. Psychological Review, 89, 48-59.
• Halford, G.S., Wilson, W.H., & Phillips, S. (1998). Processing capacity defined by relational complexity: Implications for comparative, developmental, and cognitive psychology. Behavioral and Brain Sciences, 21 (6).
• Hertzog, N. (2007). Transporting Pedagogy: Implementing the project approach in two first grade classrooms. Journal of Advanced Academics, 18 (4), 530-564.
• Hofer, B.K. (2004). Epistemological understanding as a metacognitive process: Thinking aloud during online searching. Educational Psychologist, 39 (1), 43-55.
• Holtgraves, T.M. & Kashima, Y. (2008). Language, meaning, and social cognition. Personality and Social Psychology Review, 12 (1), 73-94. doi: 10.1177/1088868307309605
• Imbo, I., Vandierendonck, A. (2007). The development of strategy use in elementary school children: Working memory and individual differences. Journal of Experimental Child Psychology, 96 (4), 284-309. doi:10.1016/j.jecp.2006.09.001
• Kail, R., & Hall, L. K. (2001). Distinguishing short-term memory from working memory. Memory and Cognition, 29 (1), 1-9.
• Katzir, T., Youngsuk K., Wolf, M., O’Brien, B., Kennedy, B., Lovett, M., & Morris, R. (2006). Reading fluency: The whole is more than the parts. Annals of Dyslexia, 56 (1).
• Kozhevnikov, M., Motes, M., & Hegarty, M. (2007). Spatial Visualization in Physics Problem Solving. Cognitive Sciences, 31, 549-579.
• Levine, M.D. (1998). Developmental Variation and Learning Disorders, Second Edition. Cambridge, MA: Educators Publishing Service.
• Lungu, O. V., Liu, T., Waechter, T., Willingham, D. T., & Ashe, J. (2007). Strategic modulation of cognitive control. Journal of Cognitive Neuroscience, 19(8), 1302-1315
• Luria, A.R. (1973). The Working Brain: An Introduction to Neuropsychology (B. Haigh, Trans.). New York: Basic Books.
• Mammarella, I., Cornoldi, C., Pazzaglia, F., Toso, C., Grimoldi, M., & Vio, C. (2006). Evidence for a double dissociation between spatial-simultaneous and spatial- sequential working memory in visuospatial (nonverbal) learning disabled children. Brain and Cognition, 62, 58-67. doi: 10.1016/j.bandc.2006.03.007.
• Nagy, W., Berninger, V., & Abbott, R. (2006). Contributions of morphology beyond phonology to literacy outcomes of upper elementary and middle-school students. Journal of Educational Psychology, 98 (1), 134–147. doi: 10.1037/0022-0663.98.1.134
• Parmentier, F.B.R., Andres, P., Elford, G., & Jones, D.M. (2006). Organization of visuo-spatial serial memory: interaction of temporal order with spatial and temporal grouping. Psychological Research, 70, 200-217.
• Posner, M.I., & Rothbart, M.K. (2007). Educating the Human Brain. Washington, DC: American Psychological Association.
• Russ, R., Scherr, R., Hammer, D., & Mineska, J. (2008). Recognizing mechanistic reasoning in student scientific inquiry: A framework for discourse analysis developed from philosophy of science. Science Studies and Science Education, 1-28. doi: 10.1002/sce.20264
• Swanson, H., Jerman, O. (2007). The influence of working memory on reading growth in subgroups of children with reading disabilities. Journal of Experimental Child Psychology, 96, 249–283. doi:10.1016/j.jecp.2006.12.004
• Williams, J., Thomas, P., Maruff, P., Wilson, P. (2008). The link between motor impairment level and motor imagery ability in children with developmental coordination disorder. Human Movement Science, 27, 270-285.
• Zorzi, M., Priftis, K., Meneghello, F., Marenzi, R. & Umilt, C. (2006). The spatial representation of numerical and non-numerical sequences: Evidence from neglect. Neruopsychologia, 44, 1061-1067.