Review of the Literature by tyndale



                           Chapter 3 --Review of the Literature

       This literature review will present findings concerning the history of the study of the

brain with regard to learning; proposals for an appropriate learning environment for LD students,

together with the importance of that environment; and the benefits of using a brain-compatible

approach to teach mathematical concepts.

       Hart, (1983), points out that the human brain does not process information logically, but

instead reflects the illogical events and accidents of history, i.e., evolution. As it has evolved

over the past hundreds of millions of years, the brain has developed three parts--the so-called

“triune brain” (MacLean, 1978).

       The oldest part of the brain is the “reptilian brain” (or R-complex), which consists largely

of the brain stem. Its purpose is related to actual physical survival and overall upkeep of the

body. Because the reptilian brain is basically concerned with physical survival, the behaviors

under its control are similar to the survival behaviors of animals. The R-complex behaviors are

automatic, have a ritualistic quality, and resist change.

       The second brain to evolve was the limbic system, made up of the amygdala (which

associates events with emotion) and the hippocampus (the part of the brain dealing with

contextual memories). Our contextual memories are made up of internal information and

information received externally through our senses.

       The third brain is our “thinking brain,” the neocortex. This part of the brain is

responsible for language, including speech and writing. Most of the processing of sensory data

occurs in the neocortex. It handles logical and formal operational thinking, and allows us to plan

for the future. (Hart, 1983)

       Hart (1983) refers to the collection of patterns and programs in the brain as a program

structure, or “proster”. His proster definition of learning is that learning is the “extraction of

meaningful patterns from confusion”. (Hart, 1983, p. 95) A person needs a collection of

programs for almost any action. One’s store of patterns and programs reflects his/her

experiences, and is not the same as intelligence. The “biasing” which affects one’s proster is the

total of what is stored in the brain: past experiences, plans, goals, fears, and older brain

influences. Much of this biasing is well below the conscious level. Since a teacher cannot alter

biases already stored in her students’ brains from their experiences, it becomes important for her

to change the setting or the situation (the specific circumstances immediately surrounding the

students). Learning in the classroom depends greatly on previous learning and biases stored in

the brains of each individual. Giving all students the same instruction without regard to what

they bring to the learning situation will almost guarantee a high rate of failure. (Hart,1983)

       Caine & Caine (1991) continue Hart’s reasoning, namely, that the more meaningful and

challenging the events and activities in the classroom, the more opportunity exists for all children

to learn well. These authors make a distinction between rote memorization and “natural

memory”, which builds on students’ actual involvement and participation in activities around

them. They see the three brains operating together, giving them the capacity to re-program old,

outmoded ways of thinking or acting. In other words, the three layers of the brain interact.

Concepts and emotions are interconnected, and emotions energize our memories. Students’

enthusiasm prompts more solid learning and understanding. This is a way of looking at learning

as a “new survival principle”. (Caine et al., 1991, p. 61)

       Some types of learning are positively strengthened by “relaxed alertness” and challenge,

but inhibited by perceived threat, which narrows our perceptual field. Hart (1983) calls that

perceptual narrowing “downshifting”. When a person downshifts (feels threatened), his

responses become more automatic and limited. He is not as able to notice environmental and

internal cues. His ability to engage in open-ended thinking and questioning is reduced.

Downshifting appears to negatively affect many higher-order cognitive functions of the brain and

thus can prevent us from learning and discovering solutions for new problems. It also appears to

hinder our ability to see relationships and interconnectedness. Under stress, the ability of the

brain to find the proper programs and patterns is reduced. The brain’s short-term memory and

ability to form permanent new memories are inhibited. (Caine & Caine, 1991)

       Making maximum connections in the brain requires the state of “relaxed alertness”, a

combination of low threat and high challenge. If the teacher wants students to understand what

the information presented means, to question it, and to make connections with what they already

know, then the teacher needs to provide a richly-stimulating, low-threat environment. Therefore,

the teacher’s goal is to create low-threat conditions for learning. The key is for educators to

appreciate what must actually take place for students to learn effectively, and then create

procedures which allow that to happen. (Caine & Caine, 1991)

        Furner & Duffy (2002) relate how often a teacher’s covert behaviors (being hostile,

exhibiting gender bias, having an uncaring attitude, expressing anger, having unrealistic

expectations, or embarrassing students in front of peers) can cause anxiety in students and a

feeling of being threatened. Several teaching techniques cause math anxiety, such as assigning

the same work for everyone, teaching problem by problem out of the textbook, and accepting

only one way to solve a problem. Much of math anxiety could actually be test anxiety. It’s

important for teachers to incorporate strategies to prevent and reduce math anxiety, thereby

assisting students in becoming confident mathematical thinkers.

       The National Council of Teachers of Mathematics identified “equity” as their first

principle for school mathematics. All students have the right to learn math and feel confident in

their math abilities, and teachers must strive to see that “mathematics can and will be learned by

all students”.(NCTM, 2000, p.13) Their recommendations for reducing math anxiety include:

   acccommodate for different learning styles;

   teach the students to use “self-talk” to guide themselves positively to a solution

   create a variety of testing environments;

   design positive experiences in math classes;

   remove the importance of ego from classroom practice;

   emphasize that everyone makes mistakes in mathematics;

   make math relevant;

   let students have some input into their own evaluations;

   allow for different social approaches to learning mathematics;

   emphasize the importance of original, quality thinking rather than rote maniplation of


   characterize math as a human endeavor;

   use writing in a math journal for thinking, expressing feelings, and solving problems;

   use of discussion and cooperative group work;

   use of manipulatives, calculators, computers, and all forms of technology      (NCTM, 2000)

       Resnick, Greeno, and Collins (1996) discuss three different perspectives for

understanding the learning process. These perspectives are the behaviorist perspective, the

cognitive/rationalist perspective; and the situative/pragmatist perspective.

       The behaviorist perspective looks at learning as a process by which associations and

skills are acquired. Transfer occurs to the extent that behaviors learned in one situation are

carried over to another situation. Motivation is extrinsic, provided in the form of incentives for

attending to the important aspects of the learning situation. This form of knowledge is often

expressed as behavioral objectives. Programmed instruction was such an approach.

       The cognitive/rationalist view incorporates the information-processing models of

reasoning and problem-solving. Piaget’s theory of logical structures impacts this cognitive

theory. Another important theme in the cognitive view of knowing is the concept of

metacognition, the capacity to reflect upon one’s own thinking, and thereby monitor and manage

it. Motivation is intrinsic. Developmental psychologists had originally thought that a reflective,

self-monitoring capacity discriminated more advanced children from their less-advanced peers.

       The situative/pragmatist perspective focuses on the way knowledge is distributed in the

world among individuals, the books they use, and the communities and practices in which they

participate. From this point of view, one form of knowing stems from groups that carry out

cooperative activities. The practices of the community provide patterns which organize the

group’s activities and the participation of the individuals who are alert to those patterns.

       Cognitive research has moved toward a concern with more naturalistic learning

environments. In this new, emerging theory, success in cognitive functions such as reasoning,

remembering, and perceiving is regarded as an achievement of a system, with contributions of all

the individuals who participate. Cooperative learning is one example of how this perspective can

be utilized in the classroom. Transfer of learning within this perspective often depends on the

manner in which solutions to problems are presented. If students understand the solution to a

problem as an example of a general method, and if they understand the general features of the

learning situation that are relevant to use of the method, the abilities they learn are more likely to

be transferred to new situations. The situative/pragmatist perspective involves students as

central to the functioning of the community, enhancing their sense of identity. The motivation

to learn the values and practices of the community establishes their identity as community

members. Mathematics classrooms are communities of practice in which students participate by

thinking about mathematical topics and discussing their ideas. (Resnick et al., 1996)

       Bryant, Bryant, and Hammill (2000) have attempted to identify specific behavioral

characteristics in the areas of arithmetic and word problem-solving. For instance, math

difficulties may be evidenced in problems with a) math fact automaticity; b) arithmetic

strategies; c) interpretation of word problem sentence construction; and d) word problem-solving

skills. Teachers need to task-analyze skills carefully and to provide students with strategies for

remembering and executing multiple steps to solve math problems. It appears that students may

require sequenced, explicit, systematic teaching with practice and corrective feedback, coupled

with activities that promote meaningful understanding of the steps inherent in solving math

problems. Instructional implications for the behavior “has difficulty with the language of math”

suggest that vocabulary (e.g., numerator, difference, sum, minuend) and abstract symbols (e.g., <,

>, +) specific to each math lesson should be identified and taught according to what we know

about effective instructional routines (e.g., explicit instruction, examples, and guided practice).

The variable, “has difficulty with multi-step problems”, is the single most important behavior for

predicting math weaknesses. Another variable is “makes re-grouping (re-naming) errors”.

Errors in re-naming are often indicative of a conceptual misunderstanding of place value and its

application to subtraction problems. These variables in students indicate potentially serious

difficulties in mathematics. If behaviors persist despite intensive, individualized remedial efforts

in the general education classroom or with remedial specialists, and if other behaviors cited

earlier are present, it is quite possible that the student has a mathematics learning disability and

should be evaluated for that possibility. (Bryant et al., 2000)

       Davis and Parr (1997) examined the characteristics of students with specific learning

disabilities in either reading and spelling, or arithmetic.   As a criterion for learning disability

eligibility, these students’ under-achievement was deemed to be not due to sensory handicap,

mental retardation, or cultural or environmental disadvantage.

       Two main groups of learning disabilities were included: disabilities in 1) linguistic-

phonological processing (associated with difficulties in reading or spelling; and 2) visual-spatial

analysis (associated with difficulties in arithmetic). (In the research done by Davis et al., 1997,

students in the first group were referred to as Group R-S; students in the second group were

referred to as Group A.) Students with underlying deficits in visual-spatial-organizational and

psychomotor skills have been noted to have weaknesses on tasks of mechanical arithmetic and

handwriting. Myklebust (1975) associated this latter group with lower scores on performance

tasks as compared to verbal tasks, poor math skills, poor spatial orientation, clumsiness, and poor

social perception and interpretation of social situtations.

       Using scores from the Woodcock-Johnson (Revised), Davis et al. (1997) felt that students

who were identified as having specific academic deficiencies in arithmetic may be at relatively

high risk for emotional-behavioral problems at school. A cognitive weakness in mathematics

would be associated with a poor ability to process information involving non-verbal cues. Scores

on the WJ-R were compared with scores on the WISC-III.

       On the WISC-III, the Perceptual Organization (PO) factor and the Verbal Comprehension

(VC) factor were used. The four subtests making up the PO composite were: Picture

Completion, Picture Arrangement, Block Design, and Object Assembly. The four subtests which

made up the VC composite were: Information, Similarities, Vocabulary, and Comprehension.

Group A scored significantly higher on the VC factor in relation to their PO score, as predicted.

Comparisons using measures from the Wechsler Intelligence Scale for Children-Third Edition

(WISC-III) indicated that Group A was weaker in nonverbal skills than Group R-S, despite

equivalent average IQ scores between the two groups. Group A students were more likely than

Group R-S students to have counseling provided as part of their Individualized Educational Plan,

suggesting greater socio-emotional difficulty among Group A students. Group A students had

more well-developed auditory-perceptual skills with deficits in visual-perceptual-organizational

skills. Their performance IQs were significantly lower than their verbal IQs. Group A’s

strengths stemmed from underlying assets in phonemic discrimination, segmentation, and

blending which leads to a relatively strong ability to match phonemes and graphemes within a

system of codified rules (words). (Davis et al., 1997)

       The neuropsychological deficiencies hypothesized to underlie the poor academic

achievement shown by Groups R-S and A would be expected to have far-reaching effects,

affecting not only the complex process of academic learning, but also other childhood tasks that

are non-academic in nature, such as social interactions. Group A students had greater difficulty

with nonverbal perception, interpretation, and expression. The difficulties with social interaction

that group A children had are believed to be due to their inadequate nonverbal reasoning

abilities, particularly in new situations where flexibility is needed. Group A children, identified

as the Nonverbal Learning Disabilities (NLD) group, tend to encounter increasing levels of

difficulty as the task demands become newer and more complex. In contrast, these children

exhibit well-developed auditory-perceptual skills.

       Rivera and Pedrotty (1997) discuss trends in the fields of mathematics special education.

Studies seem to indicate that individuals with mathematics learning disabilities tend not to

perform at a level commensurate with their peers on the basic functional skills (e.g., telling time,

counting change) that are necessary for successful adult living. Investigations have shown that

these students may exhibit difficulties using metacognitive problem-solving strategies, memory

and retrieval processes and generalization skills. They also may have limited proficiency with

speed of processing, problem conceptualization, and use of effective calculation strategies.

       Significant instructional strategies for these youngsters have been developed in recent

years. These strategies include explicit direct instruction, relevant practice, peer-mediated

instruction, and alternative algorithms that foster mathematical understanding and evaluative

thinking. Constructivist activities that support active student learning around problem-solving

situations can be facilitated by the teacher’s guidance and questioning. Social settings and

classroom communities are now recognized as important factors in helping to develop

mathematical cognitions. (Rivera et al., 1997)

       Rourke and Conway (1997) discuss the evidence that some brain systems are involved in

processes of calculation. They indicate that understanding brain-behavior relationships in

children who exhibit disabilities of arithmetic and mathematical reasoning requires a general

familiarity with some issues surrounding cerebral asymmetry. Cerebral asymmetry hypothesizes

that the left hemisphere specializes in the processing of routine behaviors, whereas the right

hemisphere is specialized for inter-modal integration, processing of new stimuli, and dealing

with informational complexity.

       A neuropsychological approach to LD is oriented toward the full range of brain-behavior

relationships that may interact with or affect the arithmetic learning situation. It appears that

neuropsychological assessment can reveal patterns of assets and deficits that are predictive of

later academic performance, including arithmetic.

       Desoete, Rowyers, and Buysse (2001) examined the relationship between metacognition

and mathematical problem solving in children with average intelligence, in order to help teachers

and therapists to gain a better understanding of contributors to successful math performance.

Flavell introduced the concept of metacognition in 1976, defining it as “one’s knowledge

concerning one’s own cognitive processes and products or anything related to them.”

Metacognition also refers to the active monitoring of these processes in relation to the cognitive

objects or data on which they bear. Executive control or metacognitive skills are the voluntary

control people have over their own cognitive processes.

       Keeler and Swanson (2001) investigated the relationship between working memory

(WM) and math achievement in children with and without math disabilities. WM is the limited

capacity system that allows simultaneous storage and processing of temporary information. WM

deficits underlie the difficulties of students with reading and mathematical disabilities. Students

with mathematical disabilities (MD) have shown memory difficulties related to higher order

skills (such as executive processing) and knowledge of algorithms. MD children who have

difficulty problem-solving use less efficient strategies for retrieving information than normally-

achieving peers. Keeler et al., (2001) concluded from the results of their study that since one of

the main functions of working memory is retrieval of stored long-term knowledge, storage rather

than processing efficiency may account for the poor WM performance of children with MD.; and

consequently, one way to improve math achievement is to understand the factors that relate to

working memory deficits and to develop methods of improving students’ awareness of effective

strategies. (Keeler et al., 2001)

      Multiple Intelligences theory gives a useful look at helping students retain what they learn.

Armstrong (2000) relates that Howard Gardner, a psychologist, views memory as “intelligence-

specific”. [Gardner (1999) has identified eight intelligences: Linguistic, Logical-mathematical,

Spatial, Musical, Bodily-kinesthetic, Interpersonal, Intrapersonal, and Naturalist.] This new

perspective on memory means that students with “poor memories” may have poor memories in

only one or two of the intelligences. Consequently, the solution to the memory problem involves

helping those students use their “good” memories in other intelligences (e.g., musical, spatial,

etc.). Work involving memorization of material in any subject should be taught in such a way

that all eight “memories” (Armstrong, 2000) are activated. The teacher’s job is to help students

associate what they need to learn with representatives of the different intelligences: Linguistic

with words; Logical-mathematical with numbers; Spatial with pictures; Musical with musical

phrases; Bodily-kinesthetic with physical movements; Interpersonal with social interactions;

Intrapersonal with examining personal feelings; and Naturalist with natural phenomena.

Armstrong’s interpretation is that once students have been introduced to memory strategies from

all eight intelligences, these students will be able to use those strategies that work best for them,

and then use them independently during their own study times. Kornhaber, Fierros, and

Veenema (2004) indicate that although a mathematician may require strong logical-mathematical

skills, he may also rely on spatial skills for visualizing relationships and tap into his interpersonal

intelligence to make mathematical ideas understood and interesting to others. These authors

believe that if schools were to engage a wider range of students’ strengths, more students would

succeed in school and on into their adult lives. Their book is based on a national investigation

(called SUMIT—Schools Using Multiple Intelligence Theory) of 41 diverse schools that

associate MI with improvements for students (Kornhaber, Fierros, & Veenema, 2004). It

identifies approaches that are successful across a wide variety of classrooms, schools, and

student groups. Their work illustrates a research-driven description of effective practices

involving MI.

     Silver, Strong, and Perini (2000) believe that multiple intelligences and learning styles can

be easily integrated to account for different processes of thought and feeling. Putting those two

models together in a way that will maximize achievement necessitates paying attention to four

key principles of learning: comfort, challenge, depth, and motivation. These four key principles

are guided by what current brain research (Caine & Caine, 1991) tells us about getting the most

out of the learning process.

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