The Learning Brain

Learning from Research

Posted by on Dec 7, 2013 in Blog, The Learning Brain

This entry is part 1 of 3 in the series Digitalized Brain Training Programs

A synthesis of best practices for assisting Canadian adults to enhance their literacy level suggested that technology-based instruction is beneficial for adults leaners because it allows for “individualization, immediate feedback and privacy” (Canadian Council on Learning, 2006, 4). However, there is still a considerable amount of uncertainty regarding learners’ ability to transfer their learning skills to a technology-based learning environment. Karjanmaa (2001) has noted that “studying in a virtual environment sets new kind of demands on learning skills” (Karjanmaa, 2001). The author has also argued that although there are differences among learners regarding their ability to learn with technology-based learning, everyone should be given equal opportunity to learn successfully in this learning environment and proposed four key postulates of meaningful learning in a virtual environment:

  • Fearless interaction: The learning environment should not stiffened learners with fear. It should instead encourage risk-taking.
  • Experience of mastering: The learning environment should be competency-based and support self-respect.
  • Sense of sharing: The learning environment should make learners feel that they are making a valuable contribution to something important.
  • Positive tension: The learning environment should encourage learners to invest effort and energy to overcome learning difficulties (Karjanmaa, 2001, citing Neimi, 2002)
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Cognitive Augmentation Training

Posted by on Dec 7, 2013 in Blog, The Learning Brain

This entry is part 1 of 2 in the series The learning brtain

Cognitive Augmentation Training

Chinien, Boutin & Letteri conducted a study designed to assess the effectiveness of a Cognitive-Based Instructional System (CBIS) as a dropout prevention strategy for students who were experiencing difficulties in coping with the information processing demands imposed upon them by school learning. These students were empowered to succeed in school learning through cognitive skills augmentation and transfer training. Cognitive-based research over the last 15 years has demonstrated that one of the most important factors contributing to achievement differences is the profile of cognitive skills that a student brings to academic tasks (Letteri, 1992). Letteri further argued that in order to succeed, a student “must possess a repertoire of thinking skills that meet the cognitive demands of learning and performance tasks. Without appropriate cognitive skills, students can never be self-directed and independent in academic tasks” (p. 59). Chinien, Boutin and Letteri (1997) have identified seven cognitive skills that can contribute to effective learning:  Analytical, Focus, Reflective, Narrow, Complex, Sharpener  and Tolerant. These seven cognitive skills facilitate the acquisition and deployment of essential skills. Following is a brief description of these cognitive skills:

  •  Analytical: Overcoming the influence of an embedded context and viewing items as separate from the background (analytic).
  •  Focus: Maintaining attention to the specific and important part in the problem and disregarding all irrelevant data.
  •  Reflective: Taking sufficient amount of time to make a complete and accurate comparison between the given problem and prior problems for correct identification.
  •  Narrow: Selecting from alternative solution strategies the one which most accurately satisfies the problem task.
  •  Complex: Defining the problem accurately by specific category for the purpose of selecting appropriate solutions.
  •  Sharpener: Comparing a problem with all other problems in a similar category and applying solution procedures, which have been successful in the past.
  •  Tolerant: Having the ability and willingness to deal with information that may not be consistent with what they know, to explore novel areas of learning.

These seven cognitive skill dimensions have been found to determine and predict with high accuracy learners’ levels of success in academic learning and performance tasks. The aim of Chinien, Boutin and Letteri’s project was to provide cognitive augmentation and transfer training to at risk students.

The target population for this study consisted of 175 junior-high school students who had been identified as potential dropouts. Four junior-high school sites were included in this demonstration project. All participating teachers (n=7) received seven full days of training in the CBIS. The CBIS teachers also received on-site individualized training and coaching while they were working on the augmentation strategies with their students. These project activities spanned the entire school year.

The Cognitive Profile Assessment Instrument (CPAI) was used to assess the cognitive profiles of all 175 students at the beginning of the project. The CPAI consists of seven different sections, each designed to measure one of the seven cognitive skills: analytical/global, focus/non-focus, reflective/impulsive, narrow/broad, complex/simple, sharpener/leveller and tolerant/intolerant. The CPAI sorts the student population into three large categories (Types I, II, and III), which are called Cognitive Profile Types as described below:

Type I Profile: These students show evidence of strength in a majority (four or more) of the seven cognitive skills. They are typically in the top 15-18% of the population in academic achievement;

Type II Profile: These students do not demonstrate particular strengths or weaknesses in the controls included in the cognitive profile. They tend to be highly inconsistent and are usually of average (mediocre) academic achievement. Type II students comprise 60-70% of the population.

Type III Profile: These students demonstrate a major deficit (4 or more) in terms of the cognitive skills as indicated by their cognitive profiles. These students typically present severe learning problems and are several grade levels below their placement grade in all areas of standardized testing. These students usually have long histories of failure and, as research indicates, no amount of assistance has been able to rectify the situation. Type III students represent 15-18% of the school population.

The CPAI was administered at the beginning and at the end of the school year as pretest and posttest to the treatment (n = 45) and control (n = 45) groups. Students were classified as Type I, II or III according to their performance on the CPAI. All Type III and extreme Type II students (n = 45) were selected for the CBIS cognitive augmentation and transfer training. The 45 students selected for the CBIS training were assigned to the classroom teachers who were involved in the project. The augmentation training treatment was administered to students on a one-on-one basis. Each student received an average of 20 hours of CBIS training during the school year. The teachers used CBIS basic augmentation strategies to modify the cognitive profile of their Type II and III students. The objective was to enable Type II and III students to perform as Type I on cognitive tasks. Students were given training and practice in the skills of monitoring, directing and controlling their information processing system sequentially for each of the seven cognitive skills. Once the students had acquired a degree of comfort with specific cognitive skills, these guiding principles were used to assist them in transferring the newly acquired cognitive skills to various subject matter that they had to learn at school. This was achieved by showing the students the relationship between the augmentation exercises and the cognitive skill requirement for academic tasks. Students were also coached to apply the augmented cognitive skill to complete academic tasks. Authentic materials such as homework and other assignments were used to increase the meaningfulness and effectiveness of the transfer process.

In general, a large percentage of the treatment and control groups did not experience any change in cognitive profile, a moderate number demonstrated a positive change, and fewer showed a negative change. The greatest gain for both treatment and control groups was on sharpener. The treatment group showed substantial gains on four of the cognitive skills (complex, analytical, reflective, and narrow), while very little gain was observed on tolerance. The positive and negative changes among the control group were unexpected. Finally, analysis of the nature of change by overall cognitive profile type for the treatment group revealed that 46% experienced no change. Data indicated that 38% of the treatment group experienced a positive change in overall cognitive profile type, 20% moved from Type II to I; 2% moved from Type III to I; and 16% moved from Type III to II. Seven percent experienced a negative change, moving from Type II to III. Results also indicated that 44% of the control group students moved from Type II to I and 11% experienced a negative change moving from Type I to II.

A follow-up study was conducted two and one half years after the initial project implementation. The purpose was to provide additional evidence regarding the effectiveness of the CBIS as a dropout prevention strategy. All four schools involved in the CBIS project were surveyed in order to determine the status of the CBIS students. Three out of the four schools responded to the follow-up survey. Therefore, data were available for 30 out of the 45 CBIS students. Only 3 out of these 30 (9.9%) at-risk students had dropped out of school. One of these students had been required to withdraw because of irregular attendance, although he was performing well academically and socially. The other two students had behavioral problems and one of them had encountered problems with the law. Further analysis of the school survey indicates that nearly all of the other 27

CBIS students were doing well academically and only four were experiencing some difficulties with their schoolwork. The majority of these students was demonstrating a positive attitude toward school and were not experiencing significant behavioral problems. These results support the contention that cognitive skills are modifiable through augmentation and transfer training. A somewhat significant number of students did not register an overall change in profile designation. The lack of significant difference may suggest that the instruments used in this study were not sufficiently sensitive to detect small but important changes in cognitive profile. Furthermore, these results cannot be attributed exclusively to a lack of effectiveness of the augmentation and transfer strategies. Post-debriefing interviews indicated that many teachers believed that they needed more training and practice in cognitive-based learning in order to be able to fully implement the program. Many teachers also reported that they did not have sufficient time to cover the augmentation and transfer training for all seven cognitive skills.

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Structural Cognitive Modifiability

Posted by on Dec 7, 2013 in Blog, The Learning Brain

This entry is part 2 of 2 in the series The learning brtain

Structural Cognitive Modifiability

Reuven Feuerstein, from the International Center for the Enhancement of Learning Potential, has been a pioneer in research and development focused on Structural Cognitive Modifiability (SCM).SCM is based on the fundamental assumption that every human being is capable of modifying his or her cognitive structure, no matter the severity of the challenge (mental, physical or emotional), through adequate mediated learning experience (MLE) (Feuerstein, Rand, Hoffman, & Miller, 1979).

 Mediated Learning Experience

Feuerstein’s theory of MLE is built on the earlier work by the Russian researcher Lev Vygotsky, who developed mediation as a strategy to help people to develop their cognitive skills. The MLE is designed to assist people to overcome their learning difficulties by providing a mediator between the person and the stimulus and between the learner and the response. The MLE process is believed to produces the plasticity and flexibility of adaptation required to enhance intelligence (Gibson, 2001). MLE is designed to assist the learner to develop internal learning mechanisms that can later be applied independently to solve problems in other contexts through self-mediation and learning-how-to-learn (Slabbert, 2001).

Successful MLE is based on three critical criteria: (1) mediation of intentionality and reciprocity; (2) mediation of meaning; and (3) transcendence mediation (Slabbert, 2001). The purpose of the mediation of intentionality and reciprocity is to focus the attention of the learner towards a stimulus and get the learner to generate a response. The aim is to promote efficient registration and processing of information. Mediation of meaning constitutes of the labeling of information and the deployment of the necessary efforts by the learner who actually experiences the stimuli. The objective of transcendence mediation is to assist the learner to generalize the learning experience (Slabbert, 2001).

Research indicated that mediation during free play can elicit 5 times higher mediation for meaning than regulation of behavior. To illustrate the importance of context in successful mediation, Slabbert noted: “our research shows that, of all the places that teenagers prefer, the school is the one place where they least wish to be. Moreover, when they are in school, the classroom is the one place they strongly wish to avoid. They far prefer the cafeteria, the library, or the hallways” (Slabbert, 2001, p. 11).

The learning tasks used during MLE need to challenge the learner both in term of novelty and complexity in order to ensure that the learner exceeds his or her capability and capacity. The watering down of learning tasks would make them uninteresting and meaningless. The mediator must bring the learner closer to the learning task, rather than the learning task closer to the learner (Slabbert, 2001). The learning tasks must also be interesting in order to capture and maintain the motivation of learners. The mediator must also take into consideration that a considerable amount of effort is necessary to change the learner’s cognitive structure and to maintain that change in a “self-regulating and self-perpetuating way” (Slabbert, 2001, p. 15). The mediator promotes this effort by: enticing, eliciting, evoking and even provoking learners and wooing them to come nearer and get engaged with the stimulus. Without revealing any secrets that lie within the stimulus, the facilitator of learning causes the ignition of the three power generators of learning (Slabbert, 2001 citing Claxton, 1999):

  • Resilience is the first power generator of learning. Resilience helps to generate the energy necessary to cope with novel problems of increased complexity that can trigger feelings of uncertainty, doubts, confusion, frustration, surprise, disappointment, apprehension, failure and setback.
  • Resourcefulness is the second power generator of learning. Resourcefulness helps the learner to enable the necessary resources such as language, intellect and intuition, in order to cope with challenging problems.
  • Reflection is the third generator of learning. Reflection is ignited when the learner needs to develop an appropriate strategy to solve a problem without external assistance. The strategy involves planning, executing, monitoring and assessing (Slabbert, 2001).

The Feuerstein MLE strategy includes two key intervention components: The Learning Potential Assessment Device (LPAD) and Instrumental Enrichment (IE). These two interventions are briefly described below.

The Learning Potential Assessment Device

The Learning Potential Assessment Device (LPAD) is a dynamic cognitive assessment designed to evaluate the modifiability of the student. The LPAD assesses the student’s capacity to change his or her cognitive structures by means of learning. The LPAD does not measure individual performance by comparing it to accepted norms, but rather assesses the person’s learning potential. The results of LPAD assessment provide information about the person’s learning capacities and possible achievements in the future and can lead to recommendations on how to realize them. The LPAD battery includes a series of 15 tasks aimed at assessing students’ ability to modify their perception, memory, attention, logical reasoning and problem solving.

The LPAD has also been used to help students who were labeled as learning disabled or mentally retarded. The LPAD revealed such students’ true learning potential and provided information that could lead to their successful integration into regular classrooms.

Instrumental Enrichment

Feuerstein’s Instrumental Enrichment offers a curriculum to develop the cognitive functions that are necessary for learning. It also directly addresses the disposition for learning, and indirectly addresses study skills. This makes Instrumental Enrichment a powerful tool for developing learning readiness in those who have significant deficiencies in cognitive functioning and for strengthening learning readiness in those whose cognitive functioning is reasonably well-developed. Instrumental Enrichment (IE) is a cognitive intervention method designed to turn individuals into independent learners. It can help generate the cognitive prerequisites for effective learning. The IE program has been applied to various populations of learners, from children with learning difficulties to gifted students to adult workers employed by industrial companies.

The IE program has been translated into all major European and some Asian languages and is applied in more than 40 countries worldwide. The theoretical and applied aspects of Dr. Feuerstein’s work have resulted in a large number of experimental, educational, clinical and industry-based studies. Approximately 1,500 articles and 40 books dealing with these studies have been published. According to Bradley the evidence to the effectiveness of the Feuerstein approach is weak however, and is of questionable validity because of lack of rigor in research design (Bradley, 1983).

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Neuroplasticity and neurogenesis

Posted by on Dec 7, 2013 in Blog, The Learning Brain

This entry is part 1 of 2 in the series The architecture of the human brain

Neuroplasticity and neurogenesis

Only a few decades ago, we were unable to help people improve their cognitive information processing skills because of a constraining belief that the brain was hard-wired to function in predetermined ways and could not be changed. However, recent brain research focused on neuroplasticity has demonstrated that the brain is “soft-wired” and can be modified: “with the right kind of stimulation and activity, the brain can dramatically change and remodel itself to become more efficient and effective in processing information” (Hardy, J.; Scanlon, 2009,p. 3). Additionally, a recent development in cognitive modifiability has demonstrated that the brain can be modified in predictable ways with proper training. These two revolutionary developments of our century have paved the way for innovative approaches in developing and enhancing cognitive skills.

The first recorded inference to neuroplasticity dates back to the 1700s, when the Swiss philosopher Jean-Jacques Rousseau suggested that the brain was continually being reorganized by experiences. Chopra & Tanzi noted: “this may have been the first declaration that our brains are flexible and plastic, capable of adapting to changes in our environment”(Chopra & Tanzi, 2012, p. 29). Johansson provided an extended definition of plasticity: “the concept of brain plasticity implies that the brain is adaptable, and includes all the mechanisms responsible for the brain capacity to change in response to incoming stimulations, our activities and thoughts” (Johansson, 2006, p. 50).

William James, who is also known as the father of experimental psychology, was the first to coin the word plasticity in the context of brain research as early as 1890. He argued that: “organic matter, especially nervous tissue, seemed endowed with very extraordinary degree of plasticity” (Begley, 2007, p. 5). Sherrington and Brown studying monkey cortex suggested that repeated, habitual movements “leave a physical trace in the motor cortex of the animal,…and these…were as individual as fingerprints” (Begley, 2007, p. 28). These researchers provided the first empirical evidence that “habits both produce and are reflections of changes in the brain” (Begley, 2007, p. 29). In 1915, Ivory Franz conducted the first research into neuroplasticity. His research provided evidence that each animal’s cortex was different. This finding led him to hypothesize that these differences probably reflected the unique motor habits and skills of each monkey (Begley, 2007, p. 29).

The first evidence demonstrating brain plasticity came from a series of experiments conducted by Karl Lashley in 1923. The researcher trained rats to seek food rewards in a maze and then progressively removed part of their cortex to assess the point at which learned behaviors will be forgotten. Results showed that the rats could successfully navigate through the maze even after 90 percent of their cortex was removed. Chopra and Tanzi explained this phenomenon as follows: “in learning the maze, the rats create many different types of redundant synapses based on all their senses. Many different parts of their brains interact to form a variety of overlapping sensory associations. In other words the rats were not just seeing their way to the food in the maze; they were smelling and feeling their way as well” (Chopra & Tanzi, 2012, p. 26).

These findings failed to catch the attention of researchers and practitioners until the Canadian psychologist Donald O. Hebb advanced the concept of use-dependent plasticity of the nervous system in his book  (Hebb, 1949). After conducting an experiment in which he exposed laboratory rats to an enriched experience, Hebb concluded that: “the richer experience of the pet group during development made them better able to profit by new experience at maturity—one of the characteristics of the ‘intelligent’ human being” (Rosenzweig & Bennett, 1996). For all intents and purposes, Donald Hebb can be considered the father of neuroplasticity, since he provided the first hypothesis explaining how the brain remodeled itself continually in response to experience. According to his hypothesis, “when neurons fire simultaneously, their synaptic connections become stronger, raising the chances that the firing of one will trigger the firing of the other” (Begley, 2007, p. 30). It was only years later that researchers started testing Hebb’s hypothesis that “cells that fire together, wire together”. Hebb’s work inspired many scientists and acted as a catalyst for further research in brain plasticity (Johansson, 2006) . Neuroscientists from the University of California were able to demonstrate through a series of experiments with monkeys that the experience involved in learning a skill can promote the rewiring of the brain regions to create new circuits (Chopra & Tanzi, 2012).

There are three possible scenarios that can take place with the synaptic connection during the information processing process: (1) new synapses may be generated; (2) some synapses may be pruned, and (3) some may be weakened. Information processed and integrated by the brain enhances the effectiveness of the synapses. Research indicates that: “when neurons fire simultaneously, their synaptic connections become stronger…much as traveling the same dirt road over and over leaves ruts that make it easier to stay in the tracks on subsequent trips, so stimulating the same chain of neurons over and over” (Begley, 2007, p. 30). It has been suggested that traces left by people processing the same information will be different and specific to each individual (OECD, 2007). There is now compiling evidence that “the brain is the child of experience, undergoing physical changes in response to the life its owner leads” (Begley, 2007, p. 31). The brain’s capacity to process information does not depend only on the density of neurons, but also on the “richness of the connectivity between them” (OECD, 2007, p. 37).

There is a significant amount of evidence today which indicates that the brain is capable of learning because of its flexibility. Since the human brain has already attained 90 % of adult size by the age of 6, there was a common assumption among scientists that these changes in the brain can only occur during childhood. Research evidence however indicates that changes in brain structure are triggered by experience and environmental stimulations (OECD, 2007), and that thoughts can also alter the brain. OECD has indicated that: “the concept of plasticity and its implications are vital features of the brain. Educators, policy makers and all learners will all gain from understanding why it is possible to learn over a whole lifetime and indeed brain plasticity provides a strong neuroscientific argument for “lifelong learning” (OECD, 2007, p. 30).

Although neuroplasticity is hailed as the greatest discovery of the century, Canadian psychiatrist Norman Doidge argued that neuroplasticity is a double-edge sword. While plasticity can promote the development of a more resourceful brain, it also makes the brain vulnerable to negative outside influences. He noted that disorders and poor habits are also the results of brain plasticity. Debriefing comments made by a student who participated in cognitive enhancement training illustrate how some students may have been victimized by the negative effects of environmental influence on brain plasticity in learning: “I was always told I am stupid. Now I feel smarter” (Chinien, C., Boutin, F., Letteri, C., Cap, O., Porozny, 1995).

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