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Home > Books > Learning Disabilities - Neurological Bases, Clinical Features and Strategies of Intervention

The Child with Learning Difficulties and His Writing: A Study of Case

Submitted: 30 May 2019 Reviewed: 16 August 2019 Published: 20 November 2019

DOI: 10.5772/intechopen.89194

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The purpose of this paper is to present one child with learning difficulties writing process in multigrade rural elementary school in México. It presents Alejandro’s case. This boy lives in a rural area. He shows special educational needs about learning. He never had specialized attention because he lives in a marginalized rural area. He was integrated into regular school, but he faced some learning difficulties. He was always considered as a student who did not learn. He has coursed 2 years of preschool and 1 year of elementary school. Therefore, this text describes how child writes a list of words with and without image as support. Analysis consists to identify the child’s conceptualizations about writing, his ways of approaching, and difficulties or mistakes he makes. The results show that Alejandro identifies letters and number by using pseudo-letters and conventional letter. These letters are in an unconventional position. There is no relationship grapheme and phoneme yet, and he uses different writing rules. We consider his mistakes as indicators of the learning process.

• writing difficulties
• learning difficulties
• writing learning
• writing process
• special education

Author Information

Edgardo domitilo gerardo morales *.

• Faculty of Philosophy and Letters, National Autonomous University of Mexico, México City, México

*Address all correspondence to: [email protected]

1. Introduction

One of the purposes of Mexican education system is that students acquire conventional writing during first grades in elementary school [ 1 ]. This purpose consists of students to understand the alphabetical code, its meaning, and functionality. In this way, they can integrate into a discursive community.

The elementary school teacher teaches a heterogeneous group of children [ 1 ,  2 ]. Some students show different acquisition levels of the writing. This is due to literacy environment that the family and society provide. Thus, some children have had great opportunities to interact with reading and writing practices than others. Therefore, some students do not learn the alphabetical principle of writing at the end of the scholar year. They show characteristics of initial or intermediate acquisition level of the writing. In this way, it is difficult for children to acquire writing at the same time, at the term indicated by educational system or teachers.

In addition, there may be children with learning difficulties in the classroom. Department of Special Education teaches some children. Students with special educational needs show more difficulties to learn than their classmates [ 3 ]. They require more resources to achieve the educational objectives. These authors point out that special educational needs are relative. These needs arise between students’ personal characteristics and their environment. Therefore, any child may have special educational needs, even if he/she does not have any physical disability. However, some students with learning difficulties do not have a complete assessment about their special educational needs. On the one hand, their school is far from urban areas; on the other hand, there are not enough teachers of special education for every school. In consequence, school teachers do not know their students’ educational needs and teach in the same way. Thereby, students with learning difficulties do not have the necessary support in the classroom.

Learning difficulties of writing may be identified easily. Children with special educational needs do not learn the alphabetical principle of writing easily; that is, they do not relate phoneme with grapheme. Therefore, children show their conceptualizations about writing in different ways. Sometimes, teachers censor their students’ written productions because they do not write in a conventional way. Children with special educational needs are stigmatized in the classroom. They are considered as less favored. At the end of the scholar year, children do not pass.

Therefore, the purpose of this paper is to present one child with special educational needs writing process in a Mexican multigrade rural school. This text describes how the child writes a list of words with and without image as support. Analysis consists to identify the child’s conceptualizations about writing [ 4 ], his ways of approaching, and difficulties or mistakes he makes. These mistakes are the indicators of learning process [ 5 ].

This paper presents Alejandro’s case. This boy lives in a rural area. He shows special educational needs about learning. He never had specialized attention because he lives in a marginalized rural area. He was integrated into regular school, but he faced some learning difficulties. He was always considered as a student who does not learn. Therefore, this text describes Alejandro’s writing, what he does after 2 years of preschool and 1 year of elementary school.

2. Children with learning difficulties and their diagnosis

According to the National Institute for the Evaluation of Education [ 6 ], Mexican education system provides basic education (preschool, elementary, and secondary school) for students with special educational needs. There are two types of special attention: Center of Multiple Attention (CAM, in Spanish) and Units of Service and Support to Regular Education (USAER, in Spanish). In the first one, children with special educational needs go to this Center. These children receive attention according to basic education and their educational needs. In the second, specialized teachers on special education go to school and provide support to students. These teachers provide information to school teachers too. In this way, there is educational equity and inclusion in Mexican school [ 7 ].

Physical appearance : Teacher describes the child’s physical characteristics. These features indicate the type of food the student eats, care his or her person, the parents’ attention, among other elements.

Behavior observed during the assessment : In this section, the teacher should record the conditions in which the assessment was carried out: child’s attitude, behavior, and interest.

Child’s development history : This section presents conditions in which pregnancy developed, physical development (ages in which child held his/her head, sat, crawled, walked, etc.), language development (verbal response to sounds and voices, age in which said his/her first words and phrases, etc.), family (characteristics of their family and social environment, frequent activities, etc.), hetero-family history (vision, hearing, etc.), medical history (health conditions, diseases, etc.), and scholar history (age at which he/she started school, type of school, difficulties, etc.).

Present condition : In this, there are four aspects:

It refers to student’s general aspects: intellectual area (information processing, attention, memory, understanding, etc.), motor development area (functional skills to move, take objects, position of his/her body, etc.), communicative-linguistic area (phonological, semantic, syntactic and pragmatic levels), adaptation and social interaction area (the child’s skills to initiate or maintain relationships with others), and emotional area (the way of perceiving the world and people). In each one, it mentions the instruments he suggests, although there is not enough information about them [ 3 ].

The second aspect is the curricular competence level. Teacher identifies what the student is capable of doing in relation to established purposes and contents by official curriculum.

The third aspect is about the learning style and motivation to learn. It presents physical-environmental conditions where the child works, their interests, level attention, strategies to solve a task, and the incentives he receives.

The fourth aspect is information about the student’s environment: factors of the school, family, and social context that influence the child’s learning.

Psycho-pedagogical assessment allows to identify children’s general educational needs. In this way, the school teacher could have information about the students’ difficulties. However, it is a general assessment. It contains several aspects and does not go deeper into one.

Therefore, this paper does not propose a new assessment. It consists of presenting one child’s writing difficulties, his ways of conceptualizing writing, and some mistakes he gets to make.

3. Students with learning difficulties and their scholar integration

Since 1993, Mexican system education has tried to offer special education services to students with special educational needs in basic education [ 8 ]. The first step was to promote the integration of these children in regular education classrooms. However, only insertion of the student in the school was achieved. Therefore, the system of education searched for mechanisms to provide advice to teacher. In this way, student with learning difficulties can be attended at the same time in the classroom [ 8 ].

Educational integration has been directly associated with attention of students with learning difficulties, with or without physical disabilities [ 8 ]. However, this process implies a change in the school. For this, it is necessary to provide information and to create awareness to the educational community, permanent updating of teachers, joint work between teacher, family, and specialized teachers.

At present, Mexican education system looks at educational integration as process in which every student with learning difficulties is supported individually [ 9 ]. Adapting the curriculum to the child is the purpose of educational integration.

Curricular adequacy is one of the actions to support students with learning difficulties [ 10 , 11 ]. This is an individualized curriculum proposal. Its purpose is to attend the students’ special educational needs [ 3 ]. At present, Mexican education system indicates that there should be a curricular flexibility to promote learning processes. However, it is important to consider what the child knows about particular knowledge.

Regarding the subject of the acquisition of written language, it is necessary to know how the children build their knowledge about written. It is not possible to make a curricular adequacy if teachers do not have enough information about their students. However, children are considered as knowledge builders. Therefore, there are learning difficulties at the process.

4. Alejandro’s case

This section presents Alejandro’s personal information. We met him when we visited to his school for other research purposes. We focused on him because the boy was silent in class. He was always in a corner of the work table and did not do the activities. For this, we talked with his teacher and his mother to know more about him.

Alejandro is a student of an elementary multigrade rural school. He was 7 years old at the time of the study. He was in the second grade of the elementary school. His school is located in the region of the “Great Mountains” of the state of Veracruz, Mexico. It is a rural area, marginalized. To get to this town from the municipal head, it is necessary to take a rural taxi for half an hour. Then, you have to walk on a dirt road for approximately 50 min.

Alejandro’s family is integrated by six people. He is the third of the four sons. He lives with his parents. His house is made of wood. His father works in the field: farming of corn, beans, and raising of sheep. His mother is a housewife and also works in the field. They have a low economic income. Therefore, they receive a scholarship. One of his older brothers also showed learning difficulties at school. His mother says both children have a learning problem. But, they do not have any money for attending their sons’ learning difficulties. In addition, there are no special institutes near their house.

The boy has always shown learning difficulties. He went to preschool for 2 years. However, he did not develop the necessary skills at this level. At classes, this child was silent, without speaking. Preschool teachers believed that he was mute. Nevertheless, at scholar recess, he talked with his classmates. Alejandro was slow to communicate with words in the classroom.

When he started elementary school, Alejandro continued to show learning difficulties. At classes, he was silent too. He just watched what his classmates did. He did not do anything in the class. He took his notebook out of his backpack and just made some lines. Occasionally, he talked with his classmates. When the teacher asked him something, Alejandro did not answer. He looked down and did not answer. He just ducked his head and stayed for several minutes.

When Alejandro was in second grade, he did different activities than his classmates. His teacher drew some drawings for him and he painted these drawings. Other occasions, the teacher wrote some letters for him to paint. The child did every exercise during several hours. He did not finish his exercises quickly. Sometimes he painted some drawings during 2 h.

Although Alejandro requires specialized attention, he has not received it. He has not had a full psycho-pedagogical assessment at school by specialized teachers. His school does not have these teachers. Also, the child was not submitted to neurological structural examination or neurophysiological studies to exclude an organic origin of his learning difficulties. His parents do not have enough financial resources to do this type of study for him. In addition, one specialized institution that can do this type of study for free is in Mexico City. It is so far from child’s house. It would be expensive for the child’s parents. Therefore, he is only attended as a regular school student.

For this reason, this paper is interested in the boy’s writing process. This is because Alejandro coursed 2 years of preschool and 1 year of elementary school; however, he does not show a conventional writing yet. In this way, it is interesting to analyze his conceptualizations about writing and difficulties he experiences.

5. Methodology

The purpose of this paper is to know the child’s ways to approach writing spontaneously and his knowledge about the writing system. For this, the author used a clinical interview. He took into account the research interview guide “Analysis of Disturbances in the Learning Process of Reading and Writing” [ 12 ].

The clinical interview was conducted individually. We explored four points, but we only present two in this text: to write words and to write for image.

Interviewer took the child to the library room at school. There were no other students. First, the interviewer gave the child a sheet and asked to write his name. Alejandro wrote his name during long time. Interviewer only asked what it says there. He answered his name: “Alejandro.” Next, the interviewer asked the child to write some letters and numbers he knew. Alejandro wrote them. The interviewer asked about every letter and number. The child answered “letter” or “number,” and its name.

To write words : The interviewer asked the child to write a group of words from the same semantic field in Spanish (because Alejandro is from Mexico) and one sentence. Order of words was from highest to lowest number of syllables. In this case, interviewer used semantic field of animals. Therefore, he used following words: GATO (cat), MARIPOSA (butterfly), CABALLO (horse), PERRO (dog), and PEZ (fish). The sentence was: EL GATO BEBE LECHE (The cat drinks milk). The interviewer questioned every written word. He asked the child to show with his finger how he says in every written production.

To write for image : This task was divided into two parts. The first analyzed the size and second analyzed the number.

Interviewer used the following image cards: horse-bird and giraffe-worm ( Figure 1 ). Every pair of cards represents a large animal and a small animal.

Cards with large and small animals.

The purpose of this first task was to explore how the child writes when he looks at two images of animals with different size. The animal names have three syllables in Spanish: CA-BA-LLO (horse), PA-JA-RO (bird), etc. In this way, we can see how the child writes.

The interviewer used the following pair of cards for second task ( Figure 2 ).

Cards for singular and plural.

First card shows one animal (singular) and the second shows some animals (plural). In this way, we search to explore how the child produces his writings when he observes one or more objects, if there are similarities or differences to write.

The interviewer asked what was in every card. Next, he asked the child to write something. Alejandro wrote something in every picture. Afterward, the interviewer asked the child to read every word that he wrote. Child pointed with his finger what he wrote.

After, the interview was transcribed for analysis. We read the transcription. The author analyzed every written production. He identified the child’s conceptualizations about writing. He compared the written production and what the child said. In this way, the analysis did not only consist to identify the level of writing development. This text describes the child’s writing, the ways in which he conceptualizes the writing, the difficulties he experienced to write, and his interpretations about writing.

6. Alejandro’s writing

This section describes Alejandro’s writing process. As we already mentioned, Alejandro is 7 years old and he studies in the second grade of the elementary school. His teacher says the child should have a conventional writing, because he has already coursed 1 year of elementary school, but it is not like that. Most of his classmates write a conventional way, but he does not.

We organized this section in three parts. The first part presents how Alejandro wrote his name and how he identifies letters and numbers; the second part refers to the writing of words; and the third part is writing for picture.

6.1 Alejandro writes his name and some letters and numbers

The first part of the task consisted of Alejandro writing his name and some letters and numbers he knows. His name was requested for two reasons. The first reason is to identify the sheet, because the interviewer interviewed other children in the same school. Also, it was necessary to identify every written productions of the group of students. The second reason was to observe the way he wrote his name and how he identified letters and numbers.

The interviewer asked the child to write his name at the top of the sheet. When the interviewer said the instructions, Alejandro was thoughtful during a long time. He was not pressed or interrupted. He did not do anything for several seconds. The child looked at the sheet and looked at everywhere. After time, he took the pencil and wrote the following on the sheet ( Figure 3 ).

Alejandro’s name.

The interviewer looked at Alejandro’s writing. He asked if something was lacking. The interviewer was sure that Alejandro knew his name and his writing was not complete. However, Alejandro was thoughtful, and looked at the sheet for a long time. The interviewer asked if his name was already complete. The child answered “no.” The interviewer asked the child if he remembered his name. Alejandro denied with his head. So, they continued with another task.

Alejandro has built the notion of his name. We believe that he has had some opportunities to write his name. Perhaps, his teacher has asked him to write his name on his notebooks, as part of scholar work in the classroom. We observed that Alejandro used letters with conventional sound value. This is because he wrote three initial letters of his name: ALJ (Alejandro). The first two letters correspond to the beginning of his name. Then, he omits “E” (ALE-), and writes “J” (ALJ). However, Alejandro mentions that he does not remember the others. This may show that he has memorized his name, but at that moment he failed to remember the others, or, these letters are what he remembers.

Subsequently, the interviewer asked Alejandro to write some letters and numbers he knew. The sequence was: a letter, a number, a letter, another letter, and number. In every Alejandro’ writing, the interviewer asked the child what he wrote. In this way, Alejandro wrote the following ( Figure 4 ).

Letters and numbers written by Alejandro.

For this task, Alejandro wrote for a long time. He did not hurry to write. He looked at sheet and wrote. The child looked at the interviewer, looked at the sheet again and after a few seconds he wrote. The interviewer asked about every letter or number.

We can observe that Alejandro differentiates between letter and number. He wrote correctly in every indication. That is, when the interviewer asked him to write a letter or number, he did so, respectively. In this way, Alejandro knows what he needs to write a word and what is not, what is for reading and what is not.

Also, we can observe that the child shows a limited repertoire of letters. He did not write consonants. He used only vowels: A (capital and lower) and E (lower). It shows us that he differentiates between capital and lower letter. Also, he identifies what vowels and letters are because the child answered which they were when the interviewer asked about them.

6.2 Writing words from the same semantic field

Asking the child to write words spontaneously is a way to know what he knows or has built about the writing system [ 12 ]. Although we know Alejandro presents learning difficulties and has not consolidated a conventional writing, it is necessary to ask him to write some words. This is for observing and analyzing what he is capable of writing, what knowledge he has built, as well as the difficulties he experiences.

The next picture presents what Alejandro wrote ( Figure 5 ). We wrote the conventional form in Spanish next to every word. We wrote these words in English in the parentheses too.

List of words written by Alejandro.

At the beginning of the interview, Alejandro did not want to do the task. He was silent for several seconds. He did not write anything. He looked at the sheet and the window. The interviewer insisted several times and suspended the recording to encourage the child to write. Alejandro mentioned he could not write, because he did not know the letters and so he would not do it. However, the interviewer insisted him. After several minutes, Alejandro took the pencil and started to write.

Alejandro wrote every word for 1 or 2 min. He required more seconds or minutes sometimes. He looked at the sheet and his around. He was in silence and looking at the sheet other times. We identified that he needs time to write. This shows that he feels insecure and does not know something for writing. He feels insecure because he was afraid of being wrong and that he was punished by the interviewer for it. It may be that in class he is penalized when he makes a mistake. There is ignorance because he does not know some letters, and he has a low repertoire of the writing system. Thus, Alejandro needs to think about writing and look for representing it. Therefore, this is why the child needs more time to write.

We identified that the child does not establish a phoneme-grapheme relationship. He only shows with his finger from left to right when he read every word. He does not establish a relationship with the letters he used. In each word, there is no correspondence with the number of letters. The child also does not establish a constant because there is variation in number and variety of letters sometimes.

Alejandro used letters unrelated to the conventional writing of the words. For example, when he wrote GATO (cat), Alejandro used the following letters: inpnAS. It is possible to identify that no letter corresponds to the word. Perhaps, Alejandro wrote those letters because they are recognized or remembered by him.

Alejandro shows a limited repertoire of conventional letters. This is observed when he uses four vowels: A, E, I, O. The child used these vowels less frequently. There is one vowel in every word at least. When Alejandro wrote PEZ (fish), he used two vowels. We observed that he writes these vowels at the beginning or end of the word. However, we do not know why he places them that way. Maybe this is a differentiating principle by him.

There is qualitative and quantitative differentiation in Alejandro’s writing. That is, he did not write any words in the same way. All the words written by him are different. Every word has different number and variety of letters. When two words contain the same number of letter, they contain different letters.

When Alejandro wrote MARIPOSA (butterfly), he used five letters. The number of letters is less than what he used for GATO (cat). Maybe he wrote that because the interviewer said “butterfly is a small animal.” This is because the cat is bigger than the butterfly. Therefore, it may be possible that he used lesser letters for butterfly.

In Spanish, PERRO (dog) contains five letters. Alejandro wrote five letters. In this case, Alejandro’s writing corresponds to the necessary number of letters. However, it seems that there is no writing rules for him. This is for two reasons: first, because there is no correspondence with the animal size. Horse is larger than dog and Alejandro required lesser letters for horse than for dog. Second, CABALLO (horse) is composed by three syllables and PERRO (dog) by two. Alejandro used more letters to represent two syllables. In addition, it is observed that there is a pseudo-letter. It looks like an inverted F, as well as D and B, horizontally.

When Alejandro wrote PEZ (fish), the interviewer first asked how many letters he needed to write that word. The child did not answer. Interviewer asked for this again and student said that he did not know. Then, interviewer said to write PEZ (fish). For several minutes, Alejandro just looked the sheet and did not say anything. The interviewer questioned several times, but he did not answer. After several minutes, Alejandro wrote: E. The interviewer asked the child if he has finished. He denied with his head. After 1 min, he started to write. We observed that his writing contains six letters. Capital letters are predominated.

Alejandro used inverted letters in three words. They may be considered as pseudo-letters. However, if we observe carefully they are similar to conventional letters. The child has written them in different positions: inverted.

May be there is a writing rule by Alejandro. His words have a minimum of four letters and a maximum of six letters. This rule has been established by him. There is no relation to the length of orality or the object it represents.

We can identify that Alejandro shows a primitive writing [ 4 ]. He is still in writing system learning process. The phoneticization process is not present yet. The child has not achieved this level yet. He only uses letters without a conventional sound value. There is no correspondence to phoneme-grapheme, and he uses pseudo-letters sometimes.

6.3 To write for image

Write for image allows us to know what happens when the child writes something in front of an image [ 12 ]. It is identified if there is the same rules used by the child, number of letters, or if there is any change when he writes a new word. It may happen that the length of the words is related to the size of the image or the number of objects presented. In this way, we can identify the child’s knowledge and difficulties when he writes some words.

6.3.1 The image size variable

The first task is about observing how the child writes when he is in front of two different sized images. That is, we want to identify if the image size influences on his writings. Therefore, two pairs of cards were presented to Alejandro. Every pair of cards contained two animals, one small and one large. The interviewer asked Alejandro to write the name on each one ( Figure 6 ).

Horse and bird writing.

Based on the writing produced by Alejandro, we mentioned the following:

Alejandro delimits his space to write. When he wrote for first pair of words, the child drew a wide rectangle and he made an oval and several squares for the second pair of words. The child wrote some letters to fill those drawn spaces. It seems that Alejandro’s rule is to fill the space and not only represent the word.

When Alejandro writes the words, we identified that he presents difficulty in the conventional directionality of writing. He wrote most of words from left to right (conventional directionality), but he wrote some words from right to left (no conventional). For example, the child started to write the second word on the left. He wrote seven letters. He looked at the sheet for some seconds. After, he continued to write other letters on the right. He wrote and completed the space he had left, from right to left.

Alejandro shows two ways to write: left–right (conventional) and right–left (no conventional). When he wrote the last word, the child wrote one letter under another. There was no limited space on the sheet. Alejandro wrote it there. The child has not learned the writing directionality.

When we compared Alejandro’s writings, we identified that the number of letters used by him does not correspond to the image size. Although the images were present and he looked them when he wrote, the child took into account other rules to write. The six names of animals had three syllables in Spanish and Alejandro used nine letters for CABALLO (horse) and eleven for PÁJARO (bird). The letters used by him are similar to the conventional ones. However, these are in different positions. There are no phonetic correspondences with the words. The child shows a primitive writing. Alejandro has not started the level of relation between phoneme and grapheme yet. We can believe that the boy wrote some letters to cover the space on the sheet. Alejandro takes into account the card size instead of the image size.

After writing a list of words, the interviewer asked Alejandro to read and point out every word he wrote. The purpose of this task is to observe how the child relates his writing to the sound length of the word. When Alejandro read CABALLO (horse), he pointed out as follows ( Figure 7 ).

Alejandro reads “caballo” (horse).

Alejandro reads every word and points out what he reads. In this way, he justifies what he has written. In the previous example, Alejandro reads the first syllable and points out the first letter, second syllable with the second letter. At this moment, he gets in conflict when he tries to read the third syllable. It would correspond to the third letter. However, “there are more letters than he needs.” When he reads the word, he shows the beginning of phoneticization: relation between one syllable with one letter. This is the syllabic writing principle [ 4 ]. Nevertheless, he has written more letters. Therefore, Alejandro says “o” in the other letters. In this way, we can point out that Alejandro justifies every letters and there is a correspondence between what he reads and what he writes.

When Alejandro reads the second word, the child pointed out as follows ( Figure 8 ).

Alejandro reads “pájaro” (bird).

Alejandro makes a different correspondence syllable-letter than the first word. Although his writing was in two ways, his reading is only one direction: from left to right. The first syllable is related to first three letters he wrote. The second syllable is related to the fourth letter. But, he faces the same problem as in the previous word: “there are many letters.” So he justifies the other letters as follows. He reads the third syllable in relation to the sixth and seventh letter. And, reads “o” for the other letters.

When interviewer showed the next pair of cards, Alejandro wrote as following ( Figure 9 ).

Giraffe and worm writing by Alejandro.

When the interviewer shows the pair of cards to Alejandro, the child said “It’s a zebra.” So, the interviewer said “It’s a giraffe and it’s a worm” and pointed out every card. The interviewer asked Alejandro to write the name of every animal. First, the child draws a rectangle across the width of the sheet. Next, he started to write on the left side inside the rectangle. He said the first syllable “JI” while writing the first letter. After, he said “ra,” he wrote a hyphen. Then, he said “e” and wrote another letter. At that moment, he looked at the sheet and filled the space he left with some letters ( Figure 10 ).

Giraffe writing.

Alejandro shows different rules of writing. These rules are not the same as previous. He delimited the space to write and filled the space with some letters. The child tries to relate the syllable with one letter, but he writes others. There is a limited repertoire of letters too. In this case, it seems that he used the same letters: C capital and lower letter, A capital and lower letter, and O. We believe that he uses hyphens to separate every letter. However, when he wrote the first hyphen, it reads the second syllable. We do not know why he reads there. Alejandro had tried to use conventional letters. He uses signs without sound value. In addition, there is no relation phoneme and grapheme.

When Alejandro wrote GUSANO (worm), he drew a rectangle and divided it into three small squares. Then, he drew other squares below the previous ones. After, he began to write some letters inside the squares, as seen in the following picture ( Figure 11 ).

Worm writing.

Alejandro used other rules to write. They are different than the previous. Alejandro has written one or two letters into every box. At the end, he writes some letters under the last box. There is no correspondence between what he reads and writes. There are also no fixed rules of writing for him. Rather, it is intuited that he draws the boxes to delimit his space to write.

6.3.2 Singular and plural writing

The next task consists to write singular and plural. For this, the interviewer showed Alejandro the following images ( Figure 12 ).

Cards with one cat and four cats.

Alejandro drew an oval for first card. This oval is on the left half of the sheet. He wrote the following ( Figure 13 ).

Alejandro writes GATO (cat).

Next, the interviewer asked Alejandro to write for the second card, in plural. For this, Alejandro draws another oval from the middle of the sheet, on the right side. The child did not do anything for 1 h 30 min. After this time, he wrote some different letters inside the oval ( Figure 14 ). He wrote from right to left (unconventional direction).

Alejandro writes GATOS (cats).

Alejandro wrote in the opposite conventional direction: from right to left. He tried to cover the delimited space by him. His letters are similar to the conventional ones. Also, there are differences between the first and the second word. He used lesser letters for first word than the second. That is, there are lesser letters for singular and more letters for plural. Perhaps, the child took into account the number of objects in the card.

The writing directionality may have been influenced by the image of the animals: cats look at the left side. Alejandro could have thought he was going to write from right to left, as well as images of the cards. Therefore, it is necessary to research how he writes when objects look at the right side. In this way, we can know if this influences the directionality of Alejandro’s writing.

With the next pair of images ( Figure 15 ), the interviewer asked Alejandro to write CONEJO (rabbit) and CONEJOS (rabbits).

Cards with one rabbit and three rabbits.

Alejandro draws a rectangle in the middle of the sheet for the first card (rabbit). He said “cone” (rab-) and wrote the first letter on the left of the sheet. Then, he said “jo” (bit) and wrote the second letter. He said “jo” again and wrote the third letter. He was thoughtful for some seconds. He started to write other letters. His writing is as follows ( Figure 16 ).

Alejandro writes CONEJO (rabbit).

At the beginning, Alejandro tries to relate the syllables of the word with first two letters. However, he justifies the other letters when he read the word. There is no exact correspondence between the syllable and the letter. As well as his writing is to fill the space he delimited.

Alejandro takes into account other rules for plural writing. He drew a rectangle across the width of the sheet. Starting on the left, he said “CO” and wrote one letter. Then, he said “NE” and drew a vertical line. After, he said “JO” and wrote other letters. His writing is as follows ( Figure 17 ).

Alejandro writes CONEJOS (rabbits).

Alejandro writes both words differently. He reads CONEJO (rabbit) for first word and CONEJOS (rabbits) for the second. Both words are different from each other. But, he wrote them with different rules. This is confusing for us because there are vertical lines between every two letters in the second word. We believe that the child tried to represent every object, although he did not explain it.

In summary, Alejandro shows different writings. He used pseudo-letters and conventional letter. These letters are in unconventional positions. There is no relationship between grapheme and phoneme yet; and, he uses different writing rules.

7. Conclusions

We described Alejandro’s writing process. According to this description, we can note the following:

Alejandro is a student of an elementary regular school. He presents learning difficulties. He could not write “correctly.” However, he did not have a full assessment by specialized teachers. His school is so far from urban areas and his parents could not take him to a special institution. Therefore, he has not received special support. Also, there is not a favorable literacy environment in his home. His teacher teaches him like his classmates. Usually, he has been marginalized and stigmatized because “he does not know or work in class.”

We focused on Alejandro because he was a child who was always distracted in class. We did not want to show his writing mistakes as negative aspects, but as part of his learning process. Errors are indicators of a process [ 5 ]. They inform the person’s skills. They allow to identify the knowledge that is being used [ 13 ]. In this way, errors can be considered as elements with a didactic value.

Alejandro showed some knowledge and also some difficulties to write. The child identifies and distinguishes letters and numbers. We do not know if he conceptualizes their use in every one. When he wrote, he shows his knowledge: letters are for reading, because he did not use any number in the words.

The writing directionality is a difficulty for Alejandro. He writes from left to right and also from right to left. We do not know why he did that. We did not research his reasons. But, it is important to know if there are any factors that influence the child to write like this.

The student does not establish a phoneme-grapheme relationship yet. He is still in an initial level to writing acquisition. He uses conventional letters and pseudo-letters to write. There are no fixed rules to write: number and variety of letters. However, we observed student’s thought about writing. He justifies his writings when he reads them and invents letters to represent some words.

There is still a limited repertoire of letters. He used a few letters of the alphabet. Therefore, Alejandro needs to interact with different texts, rather than teaching him letter by letter. Even if “he does not know those letters.” In this way, he is going to appropriate other elements and resources of the writing system.

Time he takes to write is an important element for us. He refused to write for several minutes at the beginning. After, he wrote during 1 or 2 min every word. As we mentioned previously, we believe that Alejandro did not feel sure to do the task. Perhaps, he thought that the interviewer is going to penalize for his writing “incorrectly.” He felt unable to write. Therefore, it is important that children’s mistakes are not censored in the classroom. Mistakes let us to know the child’s knowledge and their learning needs.

We considered that class work was not favorable for Alejandro. He painted letters, drawings, among others. These were to keep him busy. Therefore, it is important for the child to participate in reading and writing practices. In this way, he can be integrated into the scholar activities and is not segregated by his classmates.

About children with learning difficulties, it is important that these children write as they believe. Do not censor their writings. They are not considered as people incapable. It is necessary to consider that learning is a slow process. Those children will require more time than their classmates.

Special education plays an important role in Mexico. However, rather than attending to the student with learning difficulties in isolation, it is necessary that the teacher should be provided with information and the presence of specialized teachers in the classroom. In this way, the student with learning difficulties can be integrated into class, scholar activities, and reading and writing practices.

We presented Alejandro’s writing process in this paper. Although he was considered as a child with learning difficulties, we identified he shows some difficulties, but he knows some elements of the writing system too.

Acknowledgments

I thank Alejandro, his parents, and his teacher for the information they provided to me about him.

Conflict of interest

The authors declare no conflict of interest.

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© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Slow learners- A universal probem and providing educatioanl problems to them to be a successful learner

2020, International Journal of Social Sciences

Education plays an important role in a country's development. Parents strongly feel that learning should be cultivated among their children. A teacher can make this become true with his teaching efficiency. But sometimes they may fail to do such due to different reasons. There are different types of learners such as fast learners, average learners, and slow learners. This learning difficulty may arise from poor memory, unawareness about the importance of education and lack of fundamental knowledge and psychological factors. If the teacher can bring out the children's inner talents through the use of different conditions, slow learners will be happier at learning. This article tries to solve this universal problem by applying an inspiring quote from the universal scientist Albert Einstein saying "I never teach my pupils. I only attempt to provide the conditions in which they can learn". Data was collected through the Case study method. Counseling was used as a tool for their enhancement. The major finding was that slow learners were more successful by providing suitable conditions and educational opportunities to them

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• Dev Cogn Neurosci
• v.40; 2019 Dec

Faster learners transfer their knowledge better: Behavioral, mnemonic, and neural mechanisms of individual differences in children’s learning

Hyesang chang.

a Department of Psychiatry & Behavioral Sciences, Stanford, CA 94305, United States

Miriam Rosenberg-Lee

d Department of Psychology, Rutgers University, Newark, NJ 07102, United States

Shaozheng Qin

e State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Faculty of Psychology at Beijing Normal University, Beijing, China

Vinod Menon

b Department of Neurology & Neurological Sciences, Stanford, CA 94305, United States

c Stanford Neurosciences Institute, Stanford University School of Medicine, Stanford, CA 94305, United States

Associated Data

• • We examined neurocognitive mechanisms underlying learning and near transfer in elementary school children.
• • A five-day tutoring led to marked differentiation of behavioral and brain responses between trained and novel problems.
• • Children who learned trained problems faster transferred their learnt knowledge to novel problems better.
• • Faster learners showed greater overlap in local neural representations between trained and novel problems.
• • More efficient learning led to greater differentiation of functional brain circuits engaged by trained and novel problems.

Why some children learn, and transfer their knowledge to novel problems, better than others remains an important unresolved question in the science of learning. Here we developed an innovative tutoring program and data analysis approach to investigate individual differences in neurocognitive mechanisms that support math learning and “near” transfer to novel, but structurally related, problems in elementary school children. Following just five days of training, children performed recently trained math problems more efficiently, with greater use of memory-retrieval-based strategies. Crucially, children who learned faster during training performed better not only on trained problems but also on novel problems, and better discriminated trained and novel problems in a subsequent recognition memory task. Faster learners exhibited increased similarity of neural representations between trained and novel problems, and greater differentiation of functional brain circuits engaged by trained and novel problems. These results suggest that learning and near transfer are characterized by parallel learning-rate dependent local integration and large-scale segregation of functional brain circuits. Our findings demonstrate that speed of learning and near transfer are interrelated and identify the neural mechanisms by which faster learners transfer their knowledge better. Our study provides new insights into the behavioral, mnemonic, and neural mechanisms underlying children’s learning.

1. Introduction

Efficient learning involves the ability to acquire knowledge quickly while also being able to transfer the newly acquired knowledge beyond specific contexts and instances. A fundamental unresolved question in the science of learning is why some individuals learn and transfer their knowledge better than others, a question first posed by Thorndike in his seminal studies of learning ( Thorndike, 1906 ). This issue is particularly relevant in childhood when transfer of knowledge is critical for successful learning and cognitive development ( Spelke, 2000 ). Here we employ an individual differences approach to address critical gaps in our understanding of brain and cognitive mechanisms that support learning and near transfer in elementary school children.

Cognitive training in adults has been shown to be effective in improving performance on specific tasks and problem sets that individuals are trained on. Whether training also improves performance on novel problems or contexts remains controversial, even for near transfer involving “transfer tasks that share many elements with the practiced tasks” ( Simons et al., 2016 ). Possible reasons are that extant studies have focused on difficult-to-achieve “far” transfer effects across disparate cognitive domains and contexts ( Melby-Lervåg and Hulme, 2013 ) and few have considered individual differences in learning that may influence transfer abilities to novel problems ( Jaeggi et al., 2011 ). Whether cognitive training facilitates “near” transfer of knowledge between trained and novel, but structurally similar, problems remains unresolved ( Jaeggi et al., 2014 ). Understanding the mechanisms underlying individual differences in learning and near transfer is thus crucial for addressing this question and, specifically, for determining why some children learn and transfer their knowledge better than others.

Investigations of the brain basis of learning and transfer are faced with a central challenge. On the one hand, learning of specific instances should lead to formation of more distinct or segregated circuits that allow trained problems to be processed differently from novel ones ( Bassett et al., 2015 ; Iuculano et al., 2015 ; Jolles et al., 2013 ). On the other hand, generalizable knowledge should depend on formation of shared neural representations and functional integration of circuits involved in processing trained and novel problems ( Dahlin et al., 2008 ; Schlichting et al., 2015 ). Thus, learning and transfer may rely on competing neural mechanisms; consequently, a major unaddressed question in the field is whether segregation and integration processes occur in parallel to support different aspects of learning. In the present study, we use multiple brain imaging analysis techniques to investigate this question by examining overlap of neural representations within specific brain areas as well as segregation of large-scale functional brain circuits during a critical stage of cognitive skill acquisition in elementary school children.

We developed a novel interactive five-day math tutoring program ( Fig. 1 ) and data analysis approach ( Fig. 2 ), which allowed us to characterize individual differences in learning profiles, its relation to near transfer, and the neural mechanisms underlying successful learning and near transfer in children. We focus on arithmetic problem solving in which learning-related brain and behavioral changes, as well as changes in problem-solving strategies, can be systematically assessed in a quantitatively rigorous manner ( Anderson et al., 2014 ; Cho et al., 2011 ; Delazer et al., 2003 ; Geary, 2011 ; Rosenberg-Lee et al., 2018 ). Understanding mechanisms underlying successful learning and near transfer in this domain is particularly relevant in today’s technologically-driven world as arithmetic knowledge provides foundational knowledge and skills gained in early childhood are strongly predictive of later academic achievement and professional success ( Butterworth and Walsh, 2011 ; Faulkner, 2008 ; Geary, 2011 ; Geary et al., 2017 ; Jordan et al., 2009 ).

Training protocol and sample tutoring materials. (a) Before training, children completed a WJ-III math fluency test, a strategy assessment, and received an introductory lesson for addition problem solving. Training sessions were spread across five days within a week and consisted of a variety of activities for solving 14 problems in the training set. The number of exposures to each problem in each activity is reported in parenthesis. Each problem was presented 14 times per day, and 70 times over the training period. At the end of training, children’s problem-solving strategies for trained and novel problems were reassessed. After training, children completed a fMRI task that involved solving trained and novel problems in the scanner. This was followed by a recognition memory task, outside of the scanner, in which children were asked to discriminate between problems they have practiced during tutoring and those they saw in the fMRI task. The type of problems presented at each stage are indicated below the training protocol. (b) Sample materials used in the tutoring . In flash cards , children verbally produced an answer to trained problems presented on a physical flash card. The tutor proceeded to the next problem once a correct answer was provided. Children completed untimed flash cards without a timer. In timed flash cards , the tutor marked the time children spent in each of three rounds (each round: full deck of trained problems) on a sailboat. Children were instructed to try to beat their previous time(s). In computerized flash cards , children typed in their answer to trained problems and received feedback on their response. In treasure hunt , children placed physical cards with trained problems on numbers corresponding to the answers in a ‘treasure map.’ Upon completion of all tutoring activities, children filled a treasure board and received a prize. (c) Sample lesson presented during tutoring . At pre-training and on training days 1–3, children reviewed a break-apart strategy with the problems in the training set and were encouraged to solve them using a method of their choice. On training days 4–5, children were asked to retrieve answers directly from memory whenever possible.

Schematic overview of analysis approach. Behavioral and neural correlates of learning outcomes were examined (i) at the group level and (ii) in terms of individual differences in learning and near transfer across trained and novel problems. Trained problems consisted of a set of 14 addition problems learned across five days; Novel problems consisted of a set of 14 novel addition problems that were matched in difficulty to Trained problems. Behavioral performance was assessed using an efficiency score (ES) in each child that was based on accuracy and reaction times. Learning rates were computed in each child using an exponential regression fit to daily ES of trained problems (see Methods for details).

Behavioral studies have shown that repeated practice is associated with improved performance and a shift from effortful procedural to memory-based retrieval strategies during arithmetic problem solving ( Barrouillet and Fayol, 1998 ; Imbo & Vandierendonck, 2008; LeFevre et al., 1996; Logan, 1988; Siegler and Shipley, 1995 ; Siegler & Shrager, 1984). Neuroimaging studies in adults have demonstrated that arithmetic training is associated with decreased activation in the fronto-parietal cortex and relative increases in the left angular gyrus ( Delazer et al., 2003 , 2005 ; Grabner et al., 2009 ; Ischebeck et al., 2006 ) and the medial temporal lobe (MTL) ( Bloechle et al., 2016 ; Klein et al., 2018 ). In children, the hippocampus has been implicated in training-related performance gain and increase in retrieval rates as well as age-related changes in brain activation and neural representational stability ( Qin et al., 2014 ; Rivera et al., 2005 ; Rosenberg-Lee et al., 2018 ; Supekar et al., 2013 ). However, there have been no systematic investigations of how children’s learning and near transfer of problem-solving knowledge and skills varies across individuals. Here we employ an individual differences approach to address critical gaps in our understanding of brain and cognitive mechanisms that support learning and near transfer of arithmetic problem-solving skills in elementary school children.

Prior to tutoring, children aged 8–10 years completed a basic arithmetic fluency test, used to control for baseline math ability, and a strategy assessment task, which assessed the relative use of retrieval and elaborate verbal and finger counting procedures during problem solving ( Wu et al., 2008 ). During tutoring, children practiced a set of double-digit plus single-digit addition problems – problems not typically solved by memory retrieval without deliberate practice and appropriate for training 8–10 years old children – across five sessions within a one-week period. The tutoring was designed to facilitate arithmetic problem-solving skills as well as rapid retrieval of math facts through interactive sessions with a tutor ( Supekar et al., 2013 ). The present study used a similar one-on-one tutoring protocol as in Supekar et al., but here we used more complex addition problems and employed a more intensive, short-term training lasting 5 days. Similar to experimental designs used in arithmetic training studies of adults ( Delazer et al., 2003 ), children’s performance on both trained and novel problems were examined post-training. Crucially, performance on trained problems was assessed by a computerized task on each of the 5 days of tutoring and individual learning profiles were determined in each child. To our knowledge, no other studies used a similar experimental design and individual differences approach in 8–10 years old children.

Following tutoring, children’s problem-solving strategies were reassessed. Children were asked to solve recently trained problems intermixed with a set of novel addition problems during a functional magnetic resonance imaging (fMRI) scanning session. Novel problems, matched to trained problems in structure and difficulty, were presented only at post-training fMRI session in order to minimize exposure to these problems, similar to the experimental design used in arithmetic training fMRI studies in adults ( Delazer et al., 2003 , 2005 ; Grabner et al., 2009 ; Ischebeck et al., 2006 ). After the fMRI scan, a recognition memory task was administered outside of the scanner to determine children’s ability to discriminate between trained and novel problems. By carefully designing matched two types of problems, trained and novel, only varying in whether or not they were practiced, the current study examined training-induced differences between these problems while minimizing practice effects on novel problems.

We first examined behavioral performance and strategies used to solve problems before and after training. We hypothesized that training would result in more efficient performance and increased reliance on memory-retrieval-based strategies for trained, compared to novel, problems, similar to past developmental and training studies ( Barrouillet and Fayol, 1998 ; Delazer et al., 2003 ; Siegler and Shipley, 1995 ). We next examined brain activation levels for trained versus novel problems. Based on previous developmental findings ( Skeide et al., 2018 ; Qin et al., 2014 ; Rosenberg-Lee et al., 2018 ) and training studies ( Bloechle et al., 2016 ; Klein et al., 2018 ), we predicted that for trained, compared to novel, problems after training, children would exhibit greater responses in the MTL, reflecting greater engagement of learning and memory systems for specific problems they had practiced, and a relative decrease in fronto-parietal network activation, due to reduced working memory demands (Chein & Schneider, 2012; Salmi et al., 2018 ) for practiced problems ( Delazer et al., 2003 ).

We then examined individual differences in learning and tested the hypothesis that children who were faster learners, with steeper learning curves across training, would, after training, solve novel problems more efficiently, reflecting near transfer. We investigated whether faster learning rate would be associated with greater ability to discriminate between trained and novel problems. To elucidate the neural mechanisms by which individuals transfer their learnt knowledge to novel problems, we then used representational similarity analysis (RSA). RSA provides a unique window into common neural patterns of information processing across task conditions and problem types within specific brain areas ( Haxby et al., 2014 ; Kriegeskorte et al., 2008 ). Here, we used RSA to assess the degree of overlap in the spatial patterns of brain activity between trained and novel problems and predicted that, after training, faster learners would exhibit greater similarity in neural representations between trained and novel problems, reflecting integration of local neural representations across structurally similar problems. To determine how learning rate influences responses to individual problems, we examined stability of neural representational patterns, across individual trained or novel problems. Building on a previous study where we found that neural representations of individual problems become more stabilized across cognitive skill development ( Qin et al., 2014 ), we hypothesized that children who learn faster would reach later stages of learning more quickly and demonstrate greater neural representational pattern stability across individual problems.

Finally, we examined whether individual differences in learning rates are associated with greater segregation of functional circuits associated with trained and novel problems. We reasoned that as a child acquires greater expertise, specific functional brain pathways would be preferentially strengthened ( Jolles et al., 2016 ). Thus, we hypothesized that faster learners would show greater differentiation between functional circuits and reconfiguration of large-scale brain networks engaged by trained and novel problems including the MTL and fronto-parietal cortical regions that support numerical problem solving in children ( Arsalidou et al., 2018 ; Cho et al., 2012 ; Menon, 2016 ). Together, this approach allowed us to uncover the neurocognitive mechanisms by which rapid learning facilitates enhanced near transfer in children.

2.1. Participants

A total of 29 children in 3rd and 4th grades (age: M  = 9.49, SD  = .46, 14 females) were recruited with flyers sent to schools and posted at libraries and community centers in the San Francisco Bay Area. All participants were right-handed and did not report any current neurological or psychiatric illness. All study protocols were approved by the Stanford University School of Medicine Institutional Review broad and informed consent was obtained from the parents of the children. Children received $50 for participating in the training program and $50 for completing the fMRI scanning session. Children had average to above average math abilities as reported by their parents and confirmed using the Mathematical Fluency subtest of the Woodcock Johnson Third Edition (standardized score: M  = 107.50, SD  = 12.96; percentile rank: M  = 65.45, SD  = 26.86; WJ-III, Woodcock et al., 2001 ).

fMRI data from 22 children (age: M  = 9.46, SD  = .48, 10 females) were investigated in the current study; data from 7 children were excluded due to (i) missing behavioral data recorded from the fMRI task (n = 3) or (ii) inadequate whole brain coverage or excessive head movement (see fMRI preprocessing section below) in the scanner (n = 4). Analysis of the influence of learning rate on brain activity and connectivity was based on data from 17 children (age: M  = 9.42, SD  = .53, 8 females) who had complete behavioral data on a computerized flash card task administered in each training session across the five training sessions as well as high-quality fMRI and in-scanner behavioral data. These sample sizes were determined to be adequate based on previous fMRI studies of learning in adults ( Delazer et al., 2003 , 2005 ). Critically, each child performed four runs of an event-related fMRI task (as described below) which significantly enhanced power to detect effects of interest within each individual, similar to fMRI designs used in visual neuroscience research ( Nee, 2018 ).

2.2. Training sets

There were two training sets, Group A and Group B (Supplementary Figure S5 shows all problems), counterbalanced across participants. Problems used as training set (presented across training sessions) in Group A (or Group B) was used as novel set (presented in fMRI task) in Group B (or Group A). The training (or novel) sets consisted of 14 single-digit plus double-digit problems. The stimuli were created by taking all the possible single-digit problems with operands from 2 to 9, excluding ties. This set contained 56 problems of which 28 had the smallest number first and 28 were the reverse problems with the largest digit first. We divided the 28 smallest digit first problems into two well-balanced sets of 14 such that the sum of each set equaled exactly 154. Specifically, each set had 6 ‘small’ problems with sums of 10 or less and 8 ‘large’ problems with sums of 11 or more. 1 In both sets, the sum of all the small problems was 49 and the sum of all the large problems was 105. The problems were randomly assigned to be involved in seven double-digit first or seven double-digit second problems, such that the total sum of either type of problems was 77. For each training set, the numbers 2–9 appear at least once in the single-digit spot. Decade values in the double-digit operands ranged from 20 – 80. These seven values were added to the first operand in the seven double-digit first problems and to the second operand in the seven double-digit second problems. The assignment of decades to single-digit problems had several constraints: (1) double-digit multiples of 11 were excluded; (2) amongst the set of 14 problems, at least one problem summed to a value in each of the decades from 20 to 90; (3) within the training sets, sums were separated by at least 3 units; (4) between the training sets all sums were unique.

We also created alternate novel sets that were used as novel problems in strategy assessments and unseen problems in recognition memory task (see below for task descriptions). These stimuli were created using the same single-digit problems as the A and B sets, but reversing the presentation order. Thus all 56 single-digit problems were used once. The same procedure was used to produce the double-digit operands, except that a few criteria were relaxed because there were no problems which met all of the criteria. Specifically, we allowed double-digit numbers to be multiples of 11, divisions of the stimuli into two groups had sums from 76 to 78 instead of 77 exactly and not all sums were unique between the alternate sets. Between the training sets and the alternate sets, all problems were unique (including reverses), but sums did repeat.

2.3. Training protocol

The overall training protocol is summarized in Fig. 1 . Children participated in five days of training with a tutor, after completing WJ-III Math Fluency sub-test, strategy assessment, and introductory lesson for addition problem solving.

Strategy assessment. Children completed a 28-item pre-training strategy assessment in which they were asked to solve the problems from the training set (either A or B, counterbalanced across participants) and an alternate novel set. After providing their answers, children were asked which method they used to solve the problem, including counting, decomposing the problem, or retrieving the answer from memory. Children’s responses and the category of the strategy used were recorded by trained assessors. Despite the inherent noise in this approach, this method has been shown to have good fidelity to expected response pattern across problem types ( Wu et al., 2008 ). Children solved the same problems in a different order before and after training. Half of the problems for strategy assessment consisted of problems that were later practiced during training (trained problems), and the other half were novel problems. Trained and novel problems were designed to be matched in structure and difficulty.

Introductory lesson. After the strategy assessment, all problems were drawn from the training set for that participant (A or B). To facilitate learning of complex arithmetic, children were introduced to a break-apart strategy that involved breaking down a double-digit number to a multiple of 10 plus single-digit numbers (e.g., 65 + 7 => 60 + 5 + 7 = 60 + 12 = 72). On a 14-problem worksheet, the tutor demonstrated this method on a problem not involving a carry and then asked the child to use it for the next non-carry problem. Then the tutor demonstrated the method on a carry problem and asked the child to solve two more problems using this method. Children then completed 8 problems using the method of their choice. For the final problem, they were instructed to use the break-apart rule.

2.4. Five-day training

Training consisted of the multiple activities across five days (see Supplementary Materials for details). Fig. 1 a lists the activities seen each day and the number of exposures to each problem in each activity. Tutoring gradually strengthened associations between problems and answers for trained problems through repeated practice (14 times per problem each day). For the first three days of tutoring, children practiced solving the addition problems using the break-apart method; in the last two days, children were asked to retrieve answers directly from memory whenever possible. Children accumulated stickers for completing each activity. Each tutoring session took less than one hour. Upon completion of all activities, children accumulated 20 stickers on a “treasure board” and selected a small plastic prize from a “treasure box.”

2.5. Learning rate

Each individual’s learning rate was computed by first regressing their efficiency score (ES: accuracy divided by mean reaction time for correct trials) on days of training in an exponential regression model ( y = a e b x ). Participants’ initial performance on computerized flash card task (intercept of learning curve) and their standardized score of WJ-III math fluency test were then regressed out from the slope of the learning curve. Higher scores indicated faster learning rates. Ranging from -1.72 (slowest learner) and 1.16 (fastest learner), this measure of learning rate controlled for basic arithmetic fluency and initial task performance.

2.6. fMRI task

Participants completed a delayed verification task in the scanner (Supplementary Fig. 1). Participants held a custom-made MR-compatible button box, in their right hand. Each trial began with a 500 ms fixation cross, followed by a double-digit plus single digit problem presented for 6 s. During the response phase, a possible sum was presented for up to 3 s. Participants were instructed to press the left button with their index finger if the potential solution was the correct answer or the right button with their middle finger if it was incorrect. Upon indicating their choice, a blank screen was presented until the full 10-second trial length was reached. Trials were followed by a jitter period ranging from 8 to 12 seconds.

In each run, 7 trained and 7 novel problems were drawn from the set of 14 trained problems that participants practice during training, and 14 novel problems (different from the set of novel problems used in the strategy assessment). There were 4 runs total, and participants saw each problem twice, once in the first two runs, and once in the second two runs. The problems were randomly assigned to appear in the first or second run. For the third and fourth runs, a new random distribution of the items was used. Each run lasted 4 min and 50 s, including 10 s at the beginning of each run to allow for scanner equilibration.

In each run, half the problems were presented with their correct sums and the other half were presented with incorrect sums. Incorrect sums were created by adding or subtracting 1, 2, or 10 from the correct sum 2 . In each run all possible incorrect sums were used once. One extra problem with either +1 or -1 was included to have seven incorrect problems. If the first run had an extra +1 trial, then the second run would have the -1 trial and vice versa. Problems with presented sums that differed by +10 and -10 from the correct answer were used to prevent participants from using only the ones digit to solve the problems. Within each run correct and incorrect problems were presented in a pseudo-random order where no more than 3 correct or incorrect problems appeared in a row.

2.7. Recognition memory task

After the fMRI task, participants performed a source memory test aimed at assessing their explicit knowledge of problems in the training set and problem seen in the scanner. Participants saw 42 problems in total, presented in a random order: 14 problems they practiced in tutoring (trained problems), 14 novel problems they saw in the scanner (novel problems) and 14 problems they had not seen before (unseen problems). For each problem participants were first asked: Did you see this problem in the scanner? ( seen in the scanner question ) and then asked: Did you practice this problem in tutoring? ( practiced in tutoring question ). For each problem type, children’s accuracy in identifying whether it was seen in the scanner and whether it was seen in tutoring was computed. Children’s discrimination sensitivity ( d’ : z (hit rate) – z (false alarm rate)) was measured using responses to problems recently seen and unseen for seen in the scanner question and those to trained and novel problems for practiced in tutoring question . For individuals with false alarm rates of 0, false alarm rate of 1 2   ×   n u m b e r   o f   l u r e s was used to reduce biased estimates.

2.8. fMRI data acquisition

Images were acquired on a 3 T GE Signa scanner (General Electric, Milwaukee, WI) using a custom-built head coil at the Stanford University Lucas Center. Head movement was minimized during the scan by cushions placed around the participant’s head. A total of 31 axial slices (4.0 mm thickness, 0.5 mm skip) parallel to the anterior commissure (AC)-posterior commissure (PC) line and covering the whole brain were imaged with a temporal resolution of 2 s using a T2* weighted gradient echo spiral in-out pulse sequence ( Glover and Lai, 1998 ) with the following parameters: TR =2 s, TE = 30 msec, flip angle = 80°, 1 interleave. The field of view was 22 cm, and the matrix size was 64 × 64, providing an in-plane spatial resolution of 3.4375 mm. To reduce blurring and signal loss from field inhomogeneity, an automated high-order shimming method based on spiral acquisitions was used before acquiring functional MRI scans ( Kim et al., 2002 )

2.9. fMRI preprocessing

Functional MRI data were analyzed using SPM8 ( http://www.fil.ion.ucl.ac.uk/spm/ ). The first 5 volumes were not analyzed to allow for T1 equilibration. A linear shim correction was applied separately for each slice during reconstruction ( Glover and Lai, 1998 ). Images were realigned to the first scan to correct for motion and slice acquisition timing. Following procedures similar to those used in AFNI 3dDespike ( Cox, 1996 ), deviant volumes resulting from spikes in movement greater than 0.5 voxels or spikes in the global signal greater than 5% were then interpolated using the two adjacent scans. All participants included in the analysis had less than 10% of volumes interpolated from spikes due to movement and less than 15% total volumes interpolated. Images were then spatially normalized to standard MNI space using the echo-planar imaging template provided with SPM8, resampled every 2 mm using trilinear sinc interpolation, and smoothed with a 6 mm full-width half-maximum Gaussian kernel to decrease spatial noise prior to statistical analysis.

2.10. fMRI statistical analyses

Task-related brain activation was identified using the general linear model (GLM) implemented in SPM8. In the individual subject analyses, interpolated volumes flagged at the preprocessing stage were de-weighted and did not contribute to calculating the model fit. Brain activity representing correct and incorrect trials for each trained and novel condition – a total of four conditions – was modeled using boxcar functions corresponding the 6 s where the problem was presented and convolved with a canonical hemodynamic response function and a temporal dispersion derivative to account for voxel-wise latency differences in hemodynamic response. Additionally, six movement parameters generated from the realignment procedure were included as regressors of no interest. Low-frequency drifts at each voxel were removed using a high-pass filter (.5 cycles/min). Serial correlations were accounted for by modeling the fMRI time series as a first-degree autoregressive process. Voxel-wise t-statistics maps contrasting correct trials for trained and novel problems were generated for each participant. Significant activation clusters, were identified using a height threshold of p  <  .01, with whole-brain family-wise error rate correction at p  <  .01 (cluster extent of 128 voxels) based on Monte Carlo simulations using custom Matlab scripts.

2.11. Multivoxel representational similarity analysis

To assess the neural representation shared across trained and novel problems, indicative of near transfer of knowledge, multivariate spatial correlation of patterns of brain activity between trained and novel problem solving was computed across the whole brain in each individual. Specifically, representational similarity across two conditions in the neighborhood surrounding each voxel of each individual’s brain was obtained using a searchlight mapping method ( Kriegeskorte et al., 2006 , 2008 ). First, a 6-mm spherical region centered on each voxel was selected. Next, similarity between trained and novel problems (we only included correct trials) was computed within this region using the spatial correlation between voxel-wise brain activation (beta-weights). Searchlight maps were then created for each individual by going through every voxel across the whole brain. Parametric correlation analyses were performed between similarity scores and the behavioral index of learning rate to examine whether the extent of similarity in the patterns of neural activity between trained and novel items relate to individual differences in learning. Significant clusters were determined using the same threshold criterion noted above.

2.12. Multivoxel pattern stability analysis

A trial-by-trial pattern stability analysis was implemented to obtain a measure of inter-problem pattern stability ( Qin et al., 2014 ), separately for trained and novel problems, in the neighborhood surrounding each voxel in each participant's brain. Inter-problem neural pattern stability scores only for correctly solved problems were computed using a pairwise correlation method in a 6-mm spherical region centered on each voxel. Pairwise correlation maps of two consecutive neighbor trials were excluded in order to mitigate potential collinearity because of close proximity in time. The averaged similarity scores were assigned to the central voxel and repeated across all brain voxels to create participant-specific similarity maps. These searchlight maps were subsequently entered into a second-level random effects analysis to determine variations in pattern stability in each participant. Significant clusters were determined using the same threshold criterion noted above.

2.13. Functional connectivity analysis

A seed-based generalized psychophysiological interaction (gPPI) analysis ( McLaren et al., 2012 ) was performed to examine functional connectivity of the right MTL seed with the rest of the brain during trained, compared to novel, problem solving. The seed ROI was defined as a 5-mm sphere centered at the peak of the right MTL cluster where a significant association between similarity across trained and novel items and learning was observed. Functional connectivity was modeled using a standard GLM at the individual subject level. To control for task-related activation, physiological noise, and other confounding factors, (i) the four task conditions (correct and incorrect trained and novel problems; psychological variable), (ii) deconvolved average time series from the seed ROI (physiological variable), and (iii) 6 motion parameters were included as regressors of no interest. Psychophysiological regressors of interest were defined as a product of the first two regressors (psychological and physiological variables) convolved with a canonical HRF. Contrast images corresponding to trained, compared to novel, condition were entered into a group level analysis. Learning rate was included as a regressor of interest to determine whether the functional connectivity of the seed ROI with the rest of the brain is associated with individual differences in learning. Significant clusters were determined using the same threshold criterion noted above.

2.14. Network analysis

Network analysis was performed to investigate discriminability of large-scale brain network connectivity patterns between trained and novel problems, and its relation to individual learning rates. Network nodes were defined using 5-mm spheres centered at regional peaks in brain activation for correctly solved trained and novel problems. For each participant and for each task condition (trained and novel), a 23 × 22 connectivity matrix, excluding the diagonal elements, was created using gPPI. To ensure normality, connectivity values across the two task conditions were first z-transformed. We then used a linear SVM classifier (LIBSVM: https://www.csie.ntu.edu.tw/∼cjlin/libsvm/) and leave-one-out cross validation to determine discriminability of functional connectivity patterns between trained and novel problems. For each participant we then computed the sum of the distance from the hyperplane to trained and novel problems to obtain network connectivity pattern distance between trained and novel problems and examined its relation to individual differences in learning rate, using a similar procedure to build the hyperplane separating two conditions as Uddin et al. (2011) .

3.1. Training improves behavioral performance and increases children’s use of retrieval strategy, with some children learning faster than others

We examined training-induced improvements in behavioral performance using multiple quantitative assays.

Assessment of learning over 5 days of training . Computerized flash cards were used to examine learning. To control for variations in speed-accuracy tradeoff and to lower the probability of type I errors by reducing the number of statistical tests required, we computed a composite efficiency score (ES; Townsend and Ashby, 1978 ), obtained by dividing accuracy by mean reaction time, for each child on each day. Over the course of training, participants improved significantly on trained problems from day 1 (ES: M  = 0.14, SD  = 0.05) to day 5 (ES: M  = 0.23, SD  = 0.08; F (4, 64) = 39.69, p  <  .001). Next, we fitted daily measures of the ES in an exponential regression model ( y = a e b x ) to determine each child’s learning rate b . Learning curves were well fit by the exponential regression ( y  = 0.125 e 0.133 x , adjusted R 2  = 0.99, p  <  0.001), with large individual differences in learning rates (coefficient of variation = 0.34; Fig. 3 a).

Behavioral changes with training. (a) Individual learning profiles assessed by efficiency score (ES) for trained problems in the computerized flash card task across five days of training. (b) ES for trained and novel problems after training in the fMRI task. (c) Increase in use of memory-based retrieval strategies for trained and novel problems, measured from strategy assessments before and after training. Error bars represent standard error of the mean.

fMRI task performance on trained and novel problems. After training, participants performed significantly better on trained (ES: M  = 0.72, SD  = 0.17), compared to novel (ES: M  = 0.65, SD  = 0.14), problems ( t (21) = 4.2, p  <  0.001; Fig. 3 b).

Strategies used for solving trained and novel problems . We examined children’s strategies for problem solving before and after training, including direct memory-based retrieval, counting, and other procedural strategies ( Wu et al., 2008 ). Training increased children’s use of direct memory-based retrieval strategies for both trained (22%–71%) and novel (19%–43%) problems. Increases in memory-based retrieval strategies were greater for trained ( M  = 0.50, SD  = 0.36), compared to novel ( M  = 0.24, SD  = 0.30) problems ( t (20) = 3.50, p  =  0.002; Fig. 3 c).

These results demonstrate that five days of training results in significant performance gains and increased use of memory-based retrieval strategies for trained problems.

3.2. Faster learners show greater transfer to novel problems

We examined how individual differences in learning rate during training influences performance on trained and novel problems. Learning rates predicted performance on both trained ( r  = 0.57, p  =  0.018; Fig. 4 a) and novel ( r  = 0.62, p  =  0.008; Fig. 4 b) problems performed during fMRI scanning. The correlation between learning rate and performance did not differ between trained and novel problems ( z = -0.53, p  =  0.60). Because we controlled for arithmetic problem-solving abilities prior to training, these effects are driven by differences in training-induced learning rather than pre-existing differences. Learning rate was not significantly associated with pre-training math fluency or retrieval strategy use ( r s ≤ 0.25, p s ≥ 0.33), which further rules out the possibility that faster learners were better at arithmetic or used more retrieval strategy before training. These results suggest that faster learners perform better not only on problems they learned during training but were also able to transfer their skills to novel but structurally similar problems.

Faster learners show enhanced performance on trained and novel problems and better discrimination between trained and novel problems. (a–b) Learning rates across a 5-day training predicted greater efficiency in solving trained and novel problems in the fMRI task. (c) Learning rate predicted discrimination sensitivity ( d’ ) between trained and novel problems during a recognition memory task in which children were asked to determine whether they had practiced specific problems during training. Individual learning rates were computed as described in Fig. 2 and Methods. d’ : z (hit rate) – z (false alarm rate). Dashed lines represent 95% confidence interval.

3.3. Faster learners show better discrimination between trained and novel problems

We then investigated whether individual learning rates during training are associated with children’s ability to discriminate between trained and novel problems. Following the fMRI scan, we presented children with the same trained and novel problems that they had just solved during the fMRI scan, along with novel lures they had not seen in either the training sessions or during fMRI scanning. Children were asked to determine whether they practiced the presented problem during the 5 days of tutoring. We found that learning rates were positively correlated with discrimination sensitivity ( d’ ) between trained and novel problems ( r = 0.60, p  =  0.011; Fig. 4 c). Children were also asked to determine whether the problem had been presented during fMRI scanning itself. In this case, learning rates were not significantly correlated with d’ between recently seen (trained and novel) and unseen problems ( r = 0.18, p  =  0.50). These results indicate that faster learners do not simply have better recognition memory overall, as they were not better than slower learners at distinguishing between trials seen and unseen during fMRI scanning. Rather, faster learners had better recollection of which specific problems they had practiced during the five days of training.

3.4. Training-induced differences between trained and novel problems in the MTL and neocortical systems in children

To identify overall training-related differences between problems in neural activity, we contrasted brain responses between correctly solved trained and novel problems. We found that trained problems elicited significantly greater activity in the anterior and medial hippocampus (HIP), parahippocampal gyrus (PHG), the middle temporal gyrus, and the angular gyrus bilaterally, as well as the left medial prefrontal cortex and the right posterior cingulate cortex. We also observed significantly reduced activity for trained problems in the inferior frontal gyrus, intraparietal sulcus, supramarginal gyrus, supplementary motor area, middle occipital gyrus, cerebellum, and the right caudate ( Fig. 5 ; Supplementary Table S1). These results indicate that five days of training decreases engagement of fronto-parietal systems and increases involvement of the MTL and angular gyrus on trained, compared to novel, problems.

Brain areas showing differences in activation for trained versus novel problems. (a) Trained problems elicited greater activity than novel problems (shown in red-yellow) in the hippocampus (HIP), parahippocampal gyrus (PHG), angular gyrus (AG), middle temporal gyrus (MTG), medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC). Novel problems elicited greater activity (shown in blue-green) in the inferior frontal gyrus (IFG), insula, middle and superior frontal gyri, intraparietal sulcus (IPS), supramarginal gyrus (SMG), supplementary motor area (SMA), middle occipital gyrus (MOG), cerebellum (CBL), and the caudate (CAU). (b) Medial temporal lobe (MTL) activation clusters (shown in orange) overlaid on anterior (a) and medial (m) HIP (cyan) and PHG (green) anatomical boundaries from the AAL atlas. L: left; R: right.

3.5. Faster learners show greater representational similarity between trained and novel problems in the MTL and neocortex

To investigate how learning influences shared neural representations across trained and novel problems, we conducted an RSA between the two types of problems across the whole brain and then examined its relation to individual learning rates. We found that learning rates predicted representational similarity in the right MTL (both HIP and PHG) with local maximum at MNI coordinates x  = 38, y = -10, z = -28 ( r = 0.59, p  =  0.012; Fig. 6 ), and in multiple temporal and frontal cortical regions (Supplementary Figure S2; Supplementary Table S2). These relations were specific to multivoxel representations, as activation levels in these regions were not correlated with learning rate (Supplementary Figure S3). These results indicate that faster learners show a greater degree of shared neural representations between trained and novel problems, reflecting a key mechanism underlying near transfer.

Faster learners show increased representational similarity between trained and novel problems in the MTL. (a) Learning rate predicts multivoxel representational similarity between trained and novel problems in the R MTL region of interest (ROI, black circle). The ROI was defined using a 5-mm sphere centered at MTL cluster peak with a significant association between learning rate and representational similarity across trained and novel problems [MNI coordinates: 38 -10 -28]. (b) Learning rate predicted representational similarity across trained and novel problems in R MTL region encompassing R HIP (white) and R PHG (orange). (c) R MTL ROI overlaid on R HIP activation for trained, compared to novel, problems (purple). (d–f) Scatter plots of the relationship between learning rate and representational similarity between trained and novel problems in R MTL ROI (d) as well as R MTL region that overlapped with R HIP (e) and R PHG (f). Anatomical boundaries from the AAL atlas are shown for visualization of brain structures. Gray shaded area represents 95% confidence interval. Abbreviations as in Fig. 5 .

3.6. Faster learners show greater stability of representational patterns between individual novel problems in the MTL and IPS

To investigate how learning rate influences neural responses to individual problems, we conducted analysis on a trial-by-trial stability of multivariate representational patterns, known to be associated with cognitive skill development ( Qin et al., 2014 ), separately for trained and novel problems. We found that multivoxel representational similarity across individual novel trials in the right MTL (HIP) and the right intraparietal sulcus was positively correlated with learning rate (Supplementary Figure S4, Supplementary Table S4). This effect was specific to novel problems, as no brain region showed significant relationship between learning rate and trial-by-trial pattern stability for trained problems. These results suggest that faster learners show enhanced neural pattern stability when solving novel problems, highlighting another mechanism underlying stable near transfer.

3.7. Faster learning is associated with segregation of MTL circuitry between trained and novel problems

We next investigated the relation between learning rate and training-related differences between trained and novel problems in functional circuits, focusing on the right MTL region (MNI coordinates: x  = 38, y = -10, z = -28) where faster learners showed greater neural representation similarity ( Fig. 6 , Supplementary Table S2). Seed-based generalized psychophysiological interaction (gPPI; McLaren et al., 2012 ) analysis revealed that faster learners demonstrate reduced functional connectivity between the right MTL and multiple brain regions including the HIP, bilateral supramarginal gyrus, middle temporal gyrus, left angular gyrus, and right supplementary area for trained, compared to novel, problems ( Fig. 7 , Supplementary Table S3). These results suggest that faster learners demonstrate greater differentiation of MTL circuits engaged by trained and novel problems.

Faster learners show greater differentiation of MTL functional connectivity between trained and novel problems. (a) Generalized psychophysiological interaction (gPPI) analysis revealed reduced functional connectivity between the right MTL and multiple fronto-parietal and temporal lobe regions for trained, compared to novel, problems in faster learners. MTL seed region was defined as described in Fig. 6 . (b) Scatter plots show relationship between learning rate and MTL connectivity for trained, compared to novel, problems. Dashed lines represent 95% confidence interval. Abbreviations as in Fig. 5 .

3.8. Faster learning is associated with greater network distance between trained and novel problems

Finally, we examined how short-term training alters functional network configuration to support learning gains. We used 23 regions of interest (ROIs) that showed task-related activity ( Fig. 8 a) as nodes in gPPI analysis to determine connectivity between 23 × 22 links of this network for trained and novel problems ( Fig. 8 b-c). To determine how learning induces different multivariate connectivity patterns, a linear support vector machine (SVM) classifier was performed, using links that showed significant differences in connectivity between trained and novel problems as input features ( Fig. 8 d). A network connectivity pattern distance between trained and novel problems was then computed using the sum of the distance from the hyperplane to trained and novel problems. We found that this multivariate measure of distance in connectivity patterns between trained and novel problems was significantly correlated with learning rate ( r = 0.52, p  =  0.032; Fig. 8 e). These results indicate that faster learners demonstrate greater segregation of network configuration between trained and novel problems.

Faster learners show greater differentiation in large-scale brain network configuration between trained and novel problems. (a) Task-related brain activation was used to identify 23 network nodes. (b–c) Task-related effective connectivity was used to compute a network connectivity matrix for trained (b) and novel (c) problems. (d) 23 links showed significant differences in connectivity between trained and novel problems ( p < .05, in yellow). These links were used as input features in a linear SVM classifier. Leave one out cross validation and permutation tests (1000 samples) revealed a classification accuracy of 70% ( p  =  0.01). (e) Learning rate predicted greater differentiation of brain networks between trained and novel problems. Connectivity pattern distance was computed using sum of absolute distance from the SVM hyperplane (separating connectivity between trained and novel problems) to each condition. Dashed lines represent 95% confidence interval. Abbreviations as in Fig. 5 .

4. Discussion

The current study investigated neurocognitive mechanisms underlying learning and near transfer of problem-solving skills in children at ages 8–10, a developmental stage crucial for knowledge acquisition. A five-day math tutoring protocol was sufficient to elicit significant learning of arithmetic facts, characterized by marked differentiation of behavioral and brain responses between trained and novel problems. Crucially, individual differences in learning rate obtained during the five-day training predicted near transfer of learning, with faster learners showing greater performance gains on novel, but similarly structured, problems. Faster learners showed greater overlap in neural representations within MTL regions implicated in memory formation, as well as greater segregation of large-scale brain circuits between trained and novel problems, indicating emergence of more distinct specialized functional circuitry with learning. Taken together, our study provides new insights into the neurocognitive mechanisms underlying learning and near transfer in a domain critical for academic and professional success ( Geary et al., 2017 ; Menon, 2016 ).

4.1. Faster learning during short-term training predicts near transfer to novel problems

Following five days of training, all children exhibited significant improvements on trained problems. Compared to novel problems, children completed trained problems more efficiently with an overall 11% greater performance efficiency. Children also showed 26% greater increase in use of sophisticated memory-based retrieval strategies for trained, relative to novel, problems. These findings demonstrate that one-week training was effective in improving task efficiency, with greater use of memory-based strategies for trained, compared to novel, problems. The finding that children also showed an increase in retrieval strategy for novel problems after tutoring indicates that children were able to transfer their strategy use to structurally similar problems that were not explicitly trained on.

Crucially, children who were faster learners showed greater transfer to novel problems. Previous behavioral research has shown that children learn single-digit math facts by progressing from frequent use of counting, through intermediate strategies, until they are eventually able to directly retrieve the answer from long-term memory ( Barrouillet and Fayol, 1998 ). However, there is a large variation between individuals in this progression as children use a mix of different strategies across different problems and contexts ( Geary et al., 2004 ; Imbo and Vandierendonck, 2007 ; Siegler and Shipley, 1995 ). In line with these developmental studies, we uncovered an important source of individual variability with respect to both learning of trained problems and near transfer of learning. Specifically, faster learning rates predicted better performance on both trained and novel problems. Learning rates also predicted better discrimination between trained and novel problems as assessed by a subsequent recognition memory task. Notably, these effects were not dependent on problem solving abilities prior to training, ruling out the possibility that faster learners are better at problem solving or mathematics in general.

Together, these behavioral results demonstrate that five days of training are sufficient to achieve significant learning of trained problems and that transfer of learning to similar but novel problems is dependent on how well a child learns. More broadly, these findings suggest that training can induce learning and near transfer – children who learn more quickly are better able to apply such knowledge and skills to novel but structurally similar problems.

4.2. Faster learners show greater similarity in neural responses between trained and novel problems

To identify neural mechanisms underlying learning and near transfer to novel problems in children, we used RSA and determined the degree of overlap in the spatial patterns of brain activity between trained and novel problems. We found that faster learners showed greater similarity of neural representations between trained and novel problems in the MTL including the right anterior hippocampus and parahippocampal gyrus, suggesting that the MTL facilitates near transfer of learning in children. The role of the MTL in learning and near transfer to structurally similar problems is consistent with a large body of evidence from the memory literature suggesting that the hippocampus, and in particular its anterior aspects, is involved in generalizable representations of recently learnt items ( Bowman and Zeithamova, 2018 ; Collin et al., 2015 ; Gerraty et al., 2014 ; Komorowski et al., 2013 ; Schlichting et al., 2015 ; Shohamy and Wagner, 2008 ; Tompary and Davachi, 2017 ). In addition to the MTL, faster learners showed greater representational similarity in the superior frontal gyrus, supplementary motor area, precentral gyrus, superior, middle, and inferior temporal gyri, middle occipital gyrus, and thalamus/brainstem, indicating that they also rely on multiple cortical systems to transfer their learning.

Next, to examine whether faster learners also show more stable representations across individual trained or novel problems, we used trial-by-trial similarity analysis, which assesses similarity of neural representations across individual problems ( Qin et al., 2014 ). This approach previously revealed that inter-problem pattern stability in the MTL increases from childhood to adulthood as individuals’ problem solving strategies and underlying neurocognitive processes become more stable with cognitive skill development ( Qin et al., 2014 ). We discovered that faster learners showed greater pattern stability across individual problems, specifically for novel problems, in the right hippocampus and right intraparietal sulcus. This finding suggests that children who learn trained problems more quickly may be able to scaffold knowledge from training sessions and apply it more consistently to novel problems, compared to those who learn more slowly. In contrast, for trained problems, all children show similar inter-trial pattern stability possibly due to item-specific representations of well-practiced instances. Interestingly, within parietal cortex, the enhanced pattern stability for novel problems were observed not in the angular gyrus, a region involved in integrating general contextual information ( Ramanan et al., 2017 ), but in the right intraparietal sulcus, a region important for representing and manipulating numerical quantity ( Cantlon, 2012 ; Menon, 2016 ; Nieder, 2016 ). As previous arithmetic training studies have not examined neural representations and their relation to individual differences in learning rate, changes in learning-rate-dependent neural representations across typical and atypical development ( Kucian, 2016 ) remains an important topic for future research. Our findings demonstrate for the first time that children who are faster learners rely on both MTL for associative learning and IPS for quantity processing to build generalizable problem-solving skills.

Although the hippocampus and parahippocampal regions of the MTL showed greater task-related activation for trained, relative to novel, problems, activation levels in these regions were not correlated with learning rate, suggesting that common neural representations underlying near transfer of learning are independent of overall engagement of the MTL. This finding, along with behavioral evidence of enhanced performance on novel problems, supports the hypothesis that children, who learn quickly during tutoring, are more likely to transfer their knowledge to novel problems - through the engagement of overlapping neural representations in the MTL and other cortical regions. Faster learning is also linked to pattern stability in the MTL across similar problems, creating a substrate that facilitates near transfer, a process that may promote stability in the MTL over development, even as task-related activation in MTL wanes ( Qin et al., 2014 ). These findings suggest that the MTL plays a crucial role in building common representations that facilitate near transfer of newly learnt skills in children.

4.3. Faster learners show more segregated brain circuits and network configuration between trained and novel problems

Beyond training-related differences between problems in isolated brain regions, we found that faster learners show more segregated brain circuits and network configuration between trained and novel problems. Network analysis revealed that faster learning children exhibit more segregated task-related brain network configurations between trained and novel problems. Specifically, faster learners showed more differentiated connectivity patterns within a large-scale brain network comprised of 23 core regions involved in arithmetic problem solving, including the MTL and fronto-parietal cortical regions. To link these findings to the MTL region that plays a key role in learning and near transfer, as identified by representational similarity analysis, we examined its task-related functional connectivity. Analysis of functional brain circuits revealed that faster learners showed lower MTL connectivity, for trained, compared to novel, problems in multiple brain regions, including bilateral hippocampus, supramarginal gyrus, middle temporal gyrus, left angular gyrus, and right supplementary motor area. Thus, MTL regions involved in learning and near transfer at the local circuit level also show functional segregation at the large-scale circuit level.

Our findings provide new evidence for greater segregation of hippocampal functional circuits and large-scale network configuration in faster learners. We suggest that increased neural efficiency for trained problems among faster learners may free up neural resources and facilitate near transfer and that children who are slower to learn, although they are able to learn specific problems that they are trained on, may struggle with near transfer. Furthermore, in an advance over previous studies on learning, our findings provide new evidence linking two parallel processes – integration of shared neural representations and segregation of large-scale brain systems anchored in the MTL – with efficient learning and near transfer in children. While these two aspects of learning are distinguishable, they may not be mutually exclusive, as observed in the current study. Item-specific learning can be represented in a larger scale in distributed brain regions, whereas more general, near transfer process may occur at the local neural representational level.

4.4. Broader implications, limitations, and future directions

Previous behavioral studies have shown that young children’s basic arithmetic skills and their memory-based strategy use predict their later mathematics achievement ( Butterworth and Walsh, 2011 ; Faulkner, 2008 ; Geary, 2011 ; Geary et al., 2017 ; Jordan et al., 2009 ). Our study advances understanding of how children learn arithmetic problems and provides insights into developing effective arithmetic training paradigms by enhancing retrieval strategy use in young children. Moreover, unlike other cognitive training studies that typically examine group-level differences between trained and novel problems, we demonstrate that individual differences in learning is a critical factor mediating near transfer. Our study provides a template for future studies of learning and transfer in children with learning disabilities. As the current work is specific to near transfer in the mathematics domain, future studies may also benefit from examining a broader range of learning and transfer across various domains and skills, using a similar individual differences approach used in the current study.

One limitation of the current study is that the sample size included in the analysis was modest. Limitations in sample size are common in neuroimaging studies of children and represent a major challenge in cognitive training studies where children’s learning profiles and fMRI data are considerably more difficult to acquire compared to studies that do not involve learning. Future studies using a larger sample are needed to address generalizability of our findings by extending the study to children at the low end of math abilities, including children with learning disabilities. Another limitation is that we acquired fMRI data after, but not before, training. This experimental design, similar to previous studies in adults ( Delazer et al., 2003 , 2005 ; Grabner et al., 2009 ; Ischebeck et al., 2006 ), allowed us to examine training-related differences between different types of problems that were carefully matched in structure and difficulty. In the current study, there were no significant associations between pre-training math fluency and learning rate or pre-training frequency of retrieval strategy use and learning rate, ruling out the possibility that faster learners were better at arithmetic or used more retrieval strategy before training. A pre-test post-test fMRI task design with a control group, using an individual differences approach, as developed here, is needed for further clarifying the specificity of training-induced brain plasticity. Such designs will also help resolve whether neural responses differ significantly in amplitude and localization of activation between before and after training, and allow disambiguation of activation and deactivation profiles in the MTL and angular gyrus ( Bloechle et al., 2016 ; Klein et al., 2019).

5. Conclusion

Our study demonstrates that two key components of efficient learning in children are related: (1) the speed of learning and (2) the “depth” of learning reflected in near transfer. We show for the first time that rapid learning facilitates near transfer and that cognitive training in young children induces two distinct brain processes that support learning and near transfer: overlap in neural representations at the level of regional circuitry that facilitates near transfer and segregation of large-scale brain networks that promotes more efficient processing of trained problems. Notably, these patterns of results are learning-rate dependent: faster learners draw on common representations in multiple brain areas to allow better performance of similar problems, while efficiently recruiting specialized brain networks for problems they have learned. These findings advance our understanding of how learning and near transfer are represented in the developing brain in a cognitive domain critical for academic success.

Declaration of Competing Interest

Acknowledgments.

This research was supported by the United States National Institutes of Health to V.M. (HD059205, MH084164, HD094623), M.R.-L. (MH101394), and S.Q. (MH105601), and by the Stanford Maternal & Child Health Research Institute Postdoctoral Support Award to H.C. We thank Emily Escovar, Amirah Khouzam, and Caitlin Tenison for assistance with the study and Drs. Daniel Abrams, Aarthi Padmanabhan, Flora Schwartz, and Kaustubh Supekar for helpful feedback on the manuscript.

1 It should be noted that carry operation was not manipulated within each problem size category: a ‘small’ problem that sums to 10 and all ‘large’ problems involve carry operations, while ‘small’ problems that sum to less than 10 do not require carry operations. Nonetheless, these problems were carefully distributed across two problem sets (trained and novel) to be matched in difficulty and counterbalanced across participants to allow comparisons between trained and novel problems. Future studies may benefit from an experimental design that manipulates both problem size and carry operations to understand whether individuals learn these problems differently.

2 Considering the possibility that the difficulty of arithmetic problems may vary depending on the distance between the correct answer and presented solution, we have carefully matched the distribution of different distances between trained and novel problems in order to minimize interaction with such distance effect. In fact, we found a significant main effect of problem type (trained, novel), F (1, 21) = 9.91, p = 0.0049, and a significant main effect of absolute distance between correct answer and presented solution (0,1,2,10), F (3, 63) = 31.27, p < .001, but did not observe a significant interaction between problem type and distance, F (3, 63) = 0.95, p = 0.42. Thus, while participants’ performance varied across problems with different distances between correct answer and presented solution, these variations were not significantly different between trained and novel problems.

Appendix A Supplementary material related to this article can be found, in the online version, at doi: https://doi.org/10.1016/j.dcn.2019.100719 .

Appendix A. Supplementary data

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• Vishal's account

Slow Learner Child: Characteristics and How to Deal?

Who Is a Slow Learning Child?

What are the different types of slow learners, what are the characteristics of a slow learning child, what causes slow learning in kids, what are the symptoms of slow learning, challenges of a slow learning child, how to help and handle a slow learner, what parents of slow learners should not do.

Many people hold the misconception that if their child is a slow learner, this automatically signifies incompetence or a lack of effort on the child’s part. However, neither assumption is accurate. One of the most challenging realities for parents to come to terms with is the recognition that their child is a slow learner. Acknowledging and understanding that your child is a slow learner doesn’t equate to accepting defeat but rather opens a pathway to tailored support and guidance. When it comes to assisting a slow learning child who finds learning more challenging than his or her peers, there are numerous proactive steps parents can take to significantly ease the child’s educational journey.

When it comes to slow learner meaning, a slow learning child is a child that hits his developmental markers at a much slower rate than compared to his peers. It is commonly misunderstood that these children fail at learning or are merely ‘dumb’. The truth is that every child has his own pace to learn and develop. Some children naturally learn much faster, and others are known to take their time to learn the same concepts and lessons.

Slow learners can present a range of challenges in their learning journey, often requiring specific strategies and supports tailored to their unique needs. Here are some common types of learning challenges slow learners may face:

• Difficulty with Reading (Dyslexia): Challenges with reading, understanding, and interpreting text. Children may struggle with phonemic awareness, decoding words, reading fluency, and comprehension.
• Challenges with Math (Dyscalculia): Difficulties in understanding numbers, learning how to manipulate numbers, and learning math facts and concepts. This can include problems with counting, calculation, and math reasoning.
• Writing Difficulties (Dysgraphia): Issues with handwriting, spelling, organizing ideas, and composing written content. Children may struggle with the physical act of writing as well as with expressing their thoughts coherently on paper.
• Problems with Attention and Concentration (Attention Deficit Hyperactivity Disorder – ADHD): While not exclusively a learning disorder, ADHD can significantly impact learning. Children may have difficulty staying focused, following directions, and completing tasks, which affects their learning pace.
• Auditory and Visual Processing Disorders: These disorders affect how the brain processes auditory and visual information. Children may have difficulty understanding language or visual cues, which can impact reading, writing, and math learning.
• Language Disorders (Including Dysphasia/Aphasia): Challenges with understanding and producing spoken language, which can affect reading comprehension and written expression, as well as social interaction.
• Memory Disorders: Problems with short-term or working memory can affect a child’s ability to retain and use information learned in school, hindering their ability to keep up with peers.
• Executive Functioning Issues: Difficulties with planning, organizing, strategizing, paying attention to and remembering details, and managing time and space. This can impact a wide range of learning activities and tasks.
• Non-Verbal Learning Disabilities: Challenges with nonverbal cues, such as facial expressions and body language, which can impact social skills. Additionally, there may be difficulties with spatial relationships and fine motor skills .

A slow learning child is one that struggles to hit basic developmental milestones that can broadly be categorised into four groups – developmental, social, personal or educational. Here are a few symptoms of slow learning child. Understanding these characteristics of slow learners will help immensely

• Developmental: The characteristics of a child with developmental learning disorders  include poor memory and a delay in speech and language developmental patterns . This means your child could take longer to start speaking than others or may need more prolonged and repetitive lessons to learn most concepts.
• Social: A child who has social learning disabilities is usually known to relate to children younger to him more often and avoid interacting with peers. Such children are also prone to behaviour that is associated with children younger than their age. These children are often just labelled as introverts due to the inability to connect with their peers leading them to be quieter or more reserved.
• Personal: Children with personal learning problems seem to have less control over their emotions. They tend to fall back to anger quickly, get frustrated faster than most, express emotions like anxiety for seemingly minor issues or get depressed over setbacks. These children are most likely to have significant problems with self-esteem, confidence and are prone to acts of aggression or emotional instability.
• Educational: Children with learning or educational disabilities take longer to process and understand the information provided to them. These children may be proficient in intellectual knowledge, but it takes them longer to understand & grasp the concepts.

Most parents may wonder why some children are slow learners. The truth is that there is no clear answer. Yet, there are a few underlying reasons that can be the main causes but these causes of slow learners may not be specific to your child.

• Trauma – Your child could have gone through a trauma in the past which is causing a delay in his development. It is now widely accepted that trauma of any nature – be it physical, psychological or emotional – can have the same impact on children.
• Premature Birth – One of the reasons for problems while learning is the premature birth. This can also be the reason behind your child having a slower rate of brain development.
• Medical – Another common cause for learning issues could be medical – diseases of the brain or nervous system can cause problems for children to hit learning milestones. These are often treatable, but when not treatable, they are manageable.
• Pampering – The most common cause for learning delays is the over-pampering nature of parents. At times, the learning process requires action and failure. A lot of children who are pampered are known to have the problems solved for them, which is why they never learn or adapt.

Recognizing the symptoms of slow learning in children is crucial for early intervention and support. Slow learners may show a variety of signs that indicate they process information at a different pace than their peers. Here are key slow learner symptoms to look out for:

1. Limited Vocabulary

Slow learners often have a smaller range of vocabulary than their peers, which can impact their communication and comprehension skills.

2. Difficulty Following Instructions

They may struggle to understand and follow multi-step instructions or frequently need reminders.

3. Poor Memory Skills

This includes challenges with short-term memory, such as difficulty remembering what they’ve just learned or been told.

4. Challenges in Reading and Writing

Slow learners might find it hard to learn to read, display reading below grade level, and have trouble with writing tasks, including forming letters and spelling.

5. Struggles With Math Concepts

They often have difficulties understanding and applying math concepts, procedures, facts, and problem-solving strategies .

6. Problems Paying Attention

A short attention span and easy distractibility can hinder their learning process.

7. Lower Social Skills

They may display less understanding of social cues and have fewer social skills compared to peers, which can affect their interactions and relationships.

8. Trouble With Problem-Solving and Critical Thinking

Slow learners can find it challenging to engage in critical thinking or solve problems that require applying knowledge in new ways.

9. Poor Coordination

This can include both fine motor skills, like handwriting , and gross motor skills, like catching a ball.

10. Slow Work Pace

They often take longer to complete tasks and assignments, needing more time to process information.

A child who is diagnosed as a slow learner can face many challenges in his lifetime. He may struggle to keep up with his peers, find it difficult to stay motivated while learning, go through many bouts of depression or anxiety or struggle to communicate and build connections with people. To understand the possible challenges that your child may face as a slow learner, talk to a child learning and developmental specialist.

There are numerous ways to help a child who is a slow learner. Here are a few methods  on how to motivate slow learners:

Motivation is one of the most essential requirements for children who are slow learners. To help them continue learning and to keep them motivated, it is important to praise them when they get a concept or technique correctly. Even the smallest victory should be acknowleged and praised.

As with any child, a slow learning child will be motivated to stay the course and learn as much as possible if there is a reward at the end of it. Try setting rewards for milestones to keep your child motivated and to help him focus on the task at hand.

3. Smaller Targets

When working with a child who is a slow learner, it is important to set small targets that are achievable and within reach. As a parent, it is your responsibilty to understand what is achievable for your child.

4. Failure Isn’t Bad

Make sure you reinforce the notion that failure isn’t a bad thing. Be realistic with teachers and other caregivers as well as with yourself that your child will fail more often than other children. When he does, do not berate him. Instead, encourage him to try again.

5. Be Open With Caregivers

Be it a teacher, your partner, parents, the babysitter or any other caregiver, be open about your child’s struggle. Let them know the situation and educate them about how to handle the situation with your child as a slow learner.

6. Be Patient

Do not compare your child to other children. This will only de-motivate you as well as your child. Stay patient when working with him and be sure that he will hit the milestones in front of him, even if it is at a much slower rate. Make sure you do not lose you patience and scream at him, as this will only demotivate him.

7. Keep Space for Aids

Whether it be post-it notes, reminders on calendars or calculators, keep room for visual and auditory aids when working with your child. These aids can be beneficial at a subconscious level. Find aids that can be used passively so your child continues to learn.

8. Be Supportive

Being vocally supportive is essential when dealing with slow learners. Encourage your child to keep at it until he succeeds. Just knowing you believe in your child will keep him motivated to keep learning and trying.

Supporting a slow learner requires patience, understanding, and a positive approach from parents. It’s equally important to be aware of actions and attitudes that can hinder their progress or affect their self-esteem. Here are critical things parents of slow learners should avoid doing:

1. Overloading With Information

Avoid overwhelming your child with too much information at once. This can lead to confusion and frustration, making the learning process more difficult.

2. Expressing Frustration or Disappointment

Refrain from showing frustration or disappointment in your child’s learning pace or comparing them to siblings or peers. This can harm their self-esteem and motivation.

3. Ignoring Their Efforts

Do not overlook or undervalue the effort your child is putting into their work, even if the outcomes aren’t immediately impressive. Recognition and encouragement are key.

4. Setting Unrealistic Expectations

Avoid setting expectations that are not aligned with your child’s current abilities. Unrealistic goals can lead to feelings of failure and discourage further effort.

5. Neglecting Their Emotional Needs

Do not ignore the emotional support your child needs. Slow learners may experience anxiety, depression, or low self-esteem due to their challenges.

6. Focusing Solely on Academic Performance

Avoid concentrating exclusively on academic achievements. Encourage and celebrate their strengths, talents, and non-academic successes as well.

7. Skipping Breaks and Leisure Time

Do not minimize the importance of breaks and leisure activities in your child’s schedule. Overloading them with continuous learning can lead to burnout and decreased productivity.

8. Using Negative Labels

Refrain from using negative labels like “lazy” or “incapable.” Such labels can become internalized, affecting their self-image and motivation.

9. Forgoing Professional Help When Needed

Avoid the reluctance to seek professional help or educational resources that can support your child’s learning journey. Early intervention can make a significant difference.

10. Ignoring Their Learning Style

Do not overlook the importance of understanding and catering to your child’s preferred learning style. Tailoring your approach can enhance their learning efficiency.

Here are some frequently asked questions about slow learning children.

1. How are slow learners different from those who have learning disability?

A learning disability is a physiological condition that prevents the child from learning in the same way as others. They can still learn at the same pace as their peers if they are taught in a different manner that works around the condition. For instance, Dyslexic children can learn as fast as any other child if they are read to instead of being made to read.

Slow learners have problems keeping up with their peers due to the inability to grasp a concept or understand what is taught to them. Slow learners have developmental issues whereas, children with learning disabilities do not.

2. Is slow learning and ADHD the same?

ADHD is an issue that is focused on attention retention. Slow learners struggle to hit learning milestones due to the inability to grasp information quickly.

3. Does slow learning mean my child has autism?

No. Autism is a condition where the child does not identify with social interactions and norms. Slow learning may be a symptom of it, but not all slow learners have autism.

4. What strategies can teachers use to help slow learners in the classroom?

Teachers can adopt several strategies to support slow learning disability effectively. Differentiating instruction to cater to diverse learning styles and speeds is crucial; this might involve breaking down tasks into smaller, manageable steps, using multi-sensory teaching methods, and providing extra time for certain tasks. Establishing a positive relationship with each student, setting realistic and achievable goals, and providing regular, constructive feedback can also significantly impact their learning journey. Moreover, integrating technology and educational software can make learning more engaging and accessible for slow learners.

Dealing with a slow learning child requires patience. It is essential to remember that a child should never be self-diagnosed as a slow learner. If you think your child may have trouble learning, contact your doctors and have him tested to be sure of his condition.

References/Resources:

1. What Causes a Child to Be a Slow Learner?; The Vision Development Team; https://www.sensoryfocus.com/what-causes-a-child-to-be-a-slow-learner/

2. Learning strategies for slow learners using the project-based learning model in primary school; ResearchGate; https://www.researchgate.net/publication/329014557_LEARNING_STRATEGIES_FOR_SLOW_LEARNERS_USING_THE_PROJECT_BASED_LEARNING_MODEL_IN_PRIMARY_SCHOOL

3. Hafidah Hafidah. H, Rukli. R; Treating Slow Learners in Learning Repeated Addition using Realistic Mathematics Education Approach; Education Resources Information Center; https://files.eric.ed.gov/fulltext/EJ1363732.pdf

4. Mushtag. R, Khan. M, Roohi. T, Ghori. U; Improving The Academic Performance Of Slow Learners Through Effective Teaching Strategies; International Journal Documentation and Research Institute; http://ijdri.com/me/wp-content/uploads/2022/01/23.pdf

5. Daga. P, Jain. E; The Psychosocial Factors of Slow Learners: A Comparative Study Between Government and Private Schools Students; The International Journal of Indian Psychology; https://ijip.in/wp-content/uploads/2022/03/18.01.079.20221001.pdf

How to Deal with a Stubborn Child?   How to Deal With a Highly Sensitive Child? Tips for Parents to Handle Naughty Kids Effective Tips to Deal With a Defiant Child

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Understanding 'Slow Learners' And Giving Them The Right Support

author parentcircle author parentcircle author parentcircle author parentcircle author parentcircle author parentcircle author parentcircle author parentcircle author parentcircle

Slow learners need special care when they are taught something. Here are some useful tips that will come to your aid when teaching slow learners.

Ravi is in Class 1. He wears thick glasses and has unclear speech. He is social and enjoys interacting with other children, loves his music lessons, and can draw as well. But, in academics, he requires the help of the school counselor.

Ravi has been diagnosed as a 'slow learner' and needs extra help in school. But, a 'slow learner' is not a diagnostic category; it is a term that is used to describe a student who learns necessary academic skills at the rate and depth which is below average when compared to same-age peers. This is because, on a standardized IQ test, the score of the child is in the 70-80 range, which is below average intelligence. Thus, as in Ravi's case, a child who is a slow learner needs more time, more repetition, and extra help to grasp new concepts.

Pinky is another 10-year-old 'slow learner' studying in class 3. Although she tries to interact with other children, most of the time she sits alone in the class and struggles to complete her work during the extracurricular activities period. She gets easily distracted and tends to move around in the class, disturbing other children. The class teacher finds it difficult to handle Pinky since instead of completing her work, Pinky puts her head down and pretends to go to sleep. Sometimes, she lies down on the floor or runs out of the classroom.

Social skills deficits

Most slow learners enjoy making friends but have difficulty in maintaining social interaction and lack social skills. Most slow learners face difficulties because of their lower IQ levels, because of which they are not able to understand the rules of social engagement. This immaturity may be due not only to limited mental abilities but also because of lack of experience, poor health, or poor speech habits that further retard growth. These children like talking to people but are not able to take the initiative—their low self-esteem makes them withdraw from social interactions.

They sometimes appear immature in interpersonal relationships. They may find it difficult to keep friends, as they do not understand some simple skills like taking turns. There may be very few children who are willing to play with them. Many normal children are not patient enough to explain the rules and help the special children when they do not understand. In Ravi's case, he can manage with the help of the school counselor. However, Pinky's teacher is all alone and being without any guidance, finds it very difficult to manage Pinky in a class of 35 children.

Learning problems

Ravi and Pinky both struggle to keep up with the class syllabus. They both require assistance and continuous support from the school counselor and the class teacher. Many slow learners have difficulty planning for long-term goals. Completing a task within the given time frame is a major issue. This happens because the children get easily distracted and do not have internal strategies which can equip them to work on the task at hand. Due to this, they sometimes work very slowly and have difficulty taking multiple instructions. Reasoning skills are typically delayed, which makes it difficult for them to understand new concepts, and they require a lot of support.

Take the case of Mary who is also a slow learner. She faces criticism from her class teacher, who once told her parents - "Mary is not interested, no matter how hard I try to involve her in the classroom, I have 39 more children in the class. I think it will be better if she is sent to a special school."

Such negative feedback has led to poor self-esteem and low confidence in Mary. To hide these problems, Mary throws many tantrums in class, thus confusing her teacher. The teacher, who never understood Mary's fundamental problem as a slow learner and what it implies, becomes less and less inclined to do so with each progressive tantrum. She finally gives up altogether.

Helping the 'slow learner'

We have been working with slow learners in our organization, and it is a long process. There is no cure for the difficulty and intervention is a continual process. The children can make progress and can cope with their studies through the support provided. The experience is very rewarding, especially when a child has been written off by the teachers and is then able to perform well. Then the happiness on the child's face is the biggest reward that the parents and the therapists can get.

Creating awareness among teachers as well as parents about this problem is a major step in the intervention program designed to deal with the problem. The teachers and the parents must modify their expectations according to the capabilities of the child. They must motivate the child so that he also develops the confidence to try hard to do the best he is capable of. Teachers and caregivers must encourage other children to interact with the child so that the child gets positive role models and also gains through peer learning.

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achieving academic success

16 study tips for slow learners that actually work.

• December 24, 2021

Being a slow learner can be quite frustrating. But don’t worry – there are ways to improve your learning ability and speed up the process!

It can be frustrating to feel like you’re always struggling to keep up academically. Unfortunately,   it’s even worse when you feel as if you’re the only one having trouble with the material. Let’s face it, being a slow learner is not your fault-but it is your problem.

But don’t worry – there are some practical study tips for slow learners that can help. Read on for more information about how these tips can make a real difference in your life.

According to research, slow learning has a significant social and financial burden on families and communities. Additionally, since many slow learners are diagnosed late, at a mean age of 14.5 years, they miss out on beneficial educational rehabilitation. Notably, individual attention and remedial actions are necessary at a much younger age. parakh, 2019

"Never discourage anyone who makes continual progress, no matter how slow." - Sabatha Cokotho

Who is a slow learner?

A slow learner is someone who learns at a slower pace than the average person.  Several factors may contribute to slow learning including poverty, parental problems, cultural issues and unfavorable school conditions. 

For a long time, society has been geared towards fast-paced learning. This is because our world is constantly changing, and those who can keep up with the changes are often more successful. 

However, it’s important to note that many children experience school related issues at some point.

Despite what many people believe, being a slow learner does not mean that someone is unintelligent .

Unfortunately, this is a myth that many parents, teachers and even the persons themselves often believe to be true.

As we learn more about different learning styles, it is becoming increasingly clear that one size does not fit all.

In fact, it has been shown that students who are taught in a way that caters to their specific learning style often achieve better results.

Fortunately, research shows that a slow learner is still capable of achieving academic success in the regular classroom, even though at a slower rate. However, changes to fit the slower learning ability are necessary to prevent them from failing and dropping out . vasudevan, 2017

Consider watching my video on YouTube to learn more about slow learners

What is the difference between slow learning and learning disability?

While having a learning disability also affects a person’s ability to learn, it is not the same as being a slow learner. 

In fact, there are many different forms of learning disabilities, such as Dyslexia and Attention Deficit Disorder (ADD), Attention Deficit Hyperactivity Disorder (ADHD) and Non Verbal Learning Disability (NVLD). 

However, the diagnosis of a learning disability can be made only after a number of tests have been carried out by an expert. 

On the other hand, various tools and techniques can be used to identify a slow learner, including observation of behavior in the classroom, parental opinions, IQ tests etc. 

Read also:  7 Study techniques to increase your learning ability.

Slow doesn’t mean dumb

One of the biggest problems that slow learners face is a lack of self-confidence. This is often due to the misconception that they are unintelligent or dumb.

It is important for these students to understand that just because they learn at a slower pace than others, does not mean that they are not smart.

In fact, many famous people have struggled with being slow learners, including Albert Einstein and Steven Spielberg.

Read also:  19 Easy ways to increase your brain power. 

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Study tips for slow learners

Even though a slow learner has difficulties coping with what is normally expected, there are many different ways to help them improve their academic performance.

Some of the study tips include:

• Make learning fun
• Maximize strengths to learn faster
• Use examples
• Implement specific strategies for names and dates
• Underline and highlight
• Make mind maps
• Change your approach
• Use memory techniques
• Ask questions
• Use mnemonics
• Put knowledge into practice
• Use grouping
• Teach someone else
• Try individualized attention
• Stretch yourself

Read also: 11 Simple Tips For Slow Readers That Actually Work

"Some quit due to slow progress. Never grasping the fact that...Slow progress is progress." - Jeff Olson

1.Make it fun for slow learners

Many students learn better when they are having fun. As such, instead of learning biology in a dark lecture room, consider going to the park or an art museum. 

Not only will this be more enjoyable but you’ll be likely to retain much more information.

Read also: 16 Simple Tips To Appear Smarter Than You Actually Are

2.Maximize the strengths of slow learners

It’s important to maximize your strengths, particularly if you are a slow learner. Start by discovering your specific learning skills.

For instance, if you’re a visual learner, like 70 % of the population , you’d greatly benefit from learning materials that include images and diagrams .

Your spatial learning style might be well suited to mastering maps and 3D models.

Plus, pictures are worth a thousand words.  So, if you need to learn about something new, try finding images related to it on the internet or in textbooks, or draw your own. This will help you make sense of what you’re learning.

3.Using examples helps slow learners

If you’re a slow learner, solve a problem using an example before attempting the general case.

Show that you understand how it works and then, if possible, adapt it to some other examples.

4.Implement specific strategies for names and dates

If you find it difficult to remember names and dates, try to use them later on in the day.

In fact, it’s harder to recall information at a moment’s notice than it is to remind yourself of it. 

Also, try associating the names and dates with something you already know or using them in a song or rhyme. 

5.Underline and highlight if you’re a slow learner

Underlining and highlighting key sentences as you read can help you remember them later when you are trying to recall the text.

6.Make mind maps to improve slow learning

Making diagrams of how different parts relate to each other can help you understand complex systems and procedures better.

Actually, mind maps will force you to summarize the information, so that only the important points remain.  As such, it is also a good practice for revising at exam time.

Read also:  28 Proven tips to study effectively for exams. 

7.Change your approach if you’re a show learner

If you’re finding it difficult to grasp the material, try changing your approach.

Ask yourself, “What would I find easy to understand here?” Perhaps this will help you find an alternative way of looking at it.

"All students can learn and succeed, but not on the same day in the same way." - William G Spady

8.Use memory techniques to improve slow learning

If you struggle with slow learning, you can use memories of past experiences or events to help you remember information.

For example, attach a word to its relative position in the alphabet (A = 1, B = 2, etc.), as this is a method that can help you recall long lists of information.

9.Ask questions to improve slow learning

If you find yourself struggling to understand something, don’t be afraid to ask questions.

Asking questions may be difficult particularly if you’re timid, but it is the best way to clarify what you’ve been taught. Plus, it can do a world of good for your building your confidence. 

10.Use mnemonics to improve slow learning

Mnemonics are memory tricks to help you remember material by associating it with something else, for example acronyms or visual clues.

Use these whenever you can – they will make your life much easier.

11.Put your knowledge into practice to improve slow learning

It can be hard to remember what you’ve studied if it doesn’t sink in and make sense when you try using it in context.

So, start by changing the way you look at things – if you’re learning about different geometric shapes for example, play with some cardboard and cut out a few.

Drawing them will help you to visualize the shapes and see their differences.

Read also: 22 Main Reasons Why Students Fall Behind Academically

12.Use grouping to improve slow learning

Grouping information means organizing things together so they make sense as a whole, rather than as separate pieces of data.

For instance, if you’re learning about cells in biology, it might be easier to learn them by type (red blood cells, platelets etc.) first than by function (carrying oxygen, clotting etc.).

13.Teach someone else to improve slow learning

People learn better if they explain something to others rather than just reading about it in text.

This is because explaining to others improves your memory and helps you understand the subject in more depth . If you can’t find anyone, you could even teach your pet.

14.Individualized attention will boost slow learning

Individualized attention can be crucial for some students particularly those who struggle with distractions and need complete silence to focus. As such, they might require a tutor to get the most out of a study session.

15.Be patient with slow learners

Remembering new information takes time and effort, particularly if you are a slow learner.

Give yourself enough time to learn something properly because rushing things can mean you don’t remember as much as you could have, even if it feels like it’s taking forever.

That doesn’t mean you can’t reward yourself along the way though – a little celebration now and then can help your motivation.

16.Stretch yourself if you are a slow learner

Setting yourself goals can help you to be more productive and give you the motivation to push on until the end.

As such, you could set weekly or even daily goals for your revision timetable to improve slow learning.

This will encourage you to work steadily towards them every day, which means you anticipate learning and are less likely to develop bad habits of procrastination.

However, be careful not to get frustrated if you don’t meet your targets. Realize that you may need more time, guidance and attention than others. So, avoid competition, comparisons and labeling. 

"Remenmber, success is a journey not a destination. Have faith in your ability. You will do just fine." - Bruce Lee

Can being a slow learner change?

While some persons who are slow learners may remain delayed throughout life, with the right guidance and support,  slow learners can achieve moderate academic success .

In fact, with early detection and adaptation, slower learners lead normal professional, personal and social lives as adults.

It has even been shown that the human brain has the ability to rewire itself as we get older – so if your learning style was not suited to formal education, it may be time to take a new approach. 

If you are not sure what this might be, there is plenty of information online about alternative learning strategies and other options for adult learners.

Most importantly, remember that you can succeed if you try – and don’t let anybody tell you otherwise!

Final words on study tips for slow learners

The study tips for slow learners may not always be what you think they should be.

However, they are effective. If these tips sound like something that can be of benefit to you, then I encourage you to give them a try. It’s time to start learning at your full potential!

What would you add? Let me know in the comments below. 

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How to be smart without studying.

It’s no secret that studying hard is key to doing well in school.

But what if you’re one of those students who just doesn’t learn well from textbooks?

Don’t worry – there are plenty of other ways to get smart without hitting the books.

Here are a few tips to help you out.  Read more

How to study effectively for exams

Are you feeling anxious about your upcoming exams? Don’t worry, you’re not alone.

A lot of students feel stressed out before their tests.

However, there are some things you can do to make studying easier and less stressful.

In this blog post, we’ll share some tips on how to study effectively for exams. Keep reading to find out more!  Read more

Parakh M. (2019). The enormous economic burden of slow learners: A wake up call.  Journal of postgraduate medicine ,  65 (4), 199–200.

Vasudevan, A., (2017) Slow learners – Causes, problems and educational programmes , International journal of applied research , (3), pages (308-313)

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