# Implementing the Hybrid Neuro-Fuzzy System to Model Specific Learning Disability in Special University Education Programs

Authors: Lin Wending
Journal: Journal of Mathematics
 (2022)

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## Abstract

Learning difficulty is a significant issue that comprehensively needs to be taken care of by university instructors with a focus on altering their ways of educating students since various degrees of learning difficulty could be observed among students. Hence, proper approaches should be adapted and utilized that provide chances for each student, securing his/her educational right. By doing so, it guarantees that each student has a right of learning with a different pace of conducting it. For example, the same educational and teaching materials cannot be adapted to each student. Hence, the training of students with learning difficulty should follow a model that is characterized as “from bottom to top,” namely, “normal to learning with difficulties.” The characterization of this approach is very similar to that of the treatment of human beings: biological malfunctions by medicine and mechanical malfunctions by psycho-sociology. This paper proposes a diagnostic model to determine the cognitive ability of students who deal with learning disabilities. The computerized model is proposed based on a combination of fuzzy logic and machine learning utilizing association rules and cellular automata. The advantage of the proposed method is that it helps find the needs of these types of students, so the widely implemented practices that shape his/her learning behavior are avoided. Therefore, practical recognition of each student with a different pace of learning should be realized.


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## Body

## 1. Introduction

The ability of every human being to learn is a phenomenon associated with the maintenance and evolution of life, education, progress, and the development of culture [1]. Nevertheless, the learning process does not finalize thoroughly and satisfactorily. Some observable hinders negatively impact the learning process due to personal issues. Therefore, the learning process ends up with incomplete, fragmented, or even failed outcomes [2].In the contemporary world, acquiring more knowledge at a faster pace is expected. On the other hand, individuals encountering problems pertinent to learning and acquisition of knowledge naturally are observed. Besides, mass educational implementations, relatively higher living standards, and awareness of educational rights of disabled people are the core issues in modern societies. Hence, people with learning disabilities become an entity that needs to be taken care of regarding educational, psychological, and social dimensional perspectives [3]. Therefore, it is one of the focused research areas by researchers and professionals of education, so the scientific and educational aspects of this issue can be characterized by the following:(1)
Gradual paradigm changes from the “medical-biological model” to the “psycho-pedagogical model” are needed.(2)
The psycho-pedagogical research provides a new way of understanding to deal with cognitive functions taking part in the learning processes and difficulties observed in the learning processes, for example, in reading, spelling (e.g., dyslexia), and mathematics.(3)
Students who have learning difficulties can be promptly identified, diagnosed in detail, and helped systematically.(4)
Dealing with those issues requires professors to get informed about areas of cognitive functions, such as learning difficulties, dyslexia, dysgraphia, and difficulty with mathematics.Given that each subject has specific cognitive requirements, a more functional approach to categorizing learning disabilities would be to examine whether a student’s abilities and weaknesses meet the cognitive requirements of the subjects (i.e., courses) that he or she deals with. Based on this reasoning, we can assume that if a person has a weakness in one or more of the areas involved and is required for the cognitive processing of a learning object, he/she will certainly face difficulties during the above process [5].Also, universities and colleges are increasingly aware of the needs of students with disabilities and learning difficulties. They can offer support in some ways, and students may be able to benefit from extra financial help.In this spirit, this work aims to construct a realistic diagnostic model of a student’s cognitive ability based on his/her learning disabilities. This modeling is based on a hybrid neuro-fuzzy system [6], which combines the use of association rules [7] and cellular automata [8] for its application when the investigation of special learning difficulties in special education programs is a concern [9, 10].Although this application is an innovative approach that is presented for the first time in the research community, various attempts have been made to use innovative technologies in various types of special education in the literature. What the proposed method brings as a novelty can be summarized as follows. (1) Ambiguity and uncertainty are common features in the assessment of educational output since different evaluators have different evaluation measures. (2) The proposed method provides a great amount of flexibility to reach a fuzzy inferential diagnostic assessment in managing and updating data.


## 2. Related Literature

The literature related to the educational approaches to differentiate the training of students with disabilities mainly focuses on methods dealing with overcoming the learning complexities.A model was proposed by Manghirmalani et al. [11] to diagnose and classify learning disorders. Fuzzy expert systems, called a soft computing technology, help learning disabilities be classified into several types. Besides, the requirements to implement this system and outcomes were provided in this research.Prentzas [12] presented a survey about artificial intelligence methods used in early educational technologies. Intelligent tutoring and adaptive educational hypermedia systems, as well as robots, were discussed in the article. The results showed that students were encouraged to participate in learning and social activities, and they remained interested in technology resources even after long periods of interaction.An intelligent agent-based classification model, proposed by Elsayed [4], provides a psycho-pedagogical assessment for professors, helping determine students with learning impairments in classrooms. Disabilities of pupils are classified based on the information of the agents. The model functions based on previously classified examples and stored data in the system. Besides, interactive problem-solving assistance and a new knowledge domain can be gained by deriving generalizations from studying the stored data.Two data mining methods, called fuzzy and neuro-fuzzy, were employed by David and Balakrishnan [6] to devise a new strategy predicting learning disabilities in school-aged kids. The objective of the strategy depends on constructing an algorithm to impute missing values and evaluate the performance of the classifier based on preprocessing methods. When the basic assessment approach was utilized in the prediction of learning difficulties, checklists were commonly employed. Thus, there exists a dependence between the mood of the students and data cases so redundancy and missing values could be contained.Garg and Sharma [13] attempted to investigate how artificial intelligence has impacted the schooling of students with exceptional needs. The data were gathered through a qualitative study involving targeted interviews with both professors and kids needing special attention. The information was also gathered from academic databases, newspapers, periodicals, and blogs. Content analysis was used to examine the responses received. Finally, the study attempted to suggest a paradigm for an inclusive future of special needs education based on targeted interviews [14–16].The literature suggests that various aspects of the research dealing with students having learning difficulties have been conducted. However, a gap still exists in the research regarding the differences between students when learning processes are under investigation.


## 3. The Proposed Method

### 3.1. Hybrid Neuro-Fuzzy System

ANFIS, the adaptive neuro-fuzzy inference system, is one of the adaptive networks that consists of both adaptive and non-adaptive nodes [17]. The adaptive network consists of a multilayer feed-forward network where each node is assigned to a specific function based on the incoming signals. Each specific node is denoted by a set of parameters. All nodes are connected via directional links. Customizable nodes are the ones depending on the parameters associated with that node. To minimize the predefined error, the parameters should be changed based on the learning rules [18].The type of operation of each node can vary. The choice of the operation of a node depends on the total processing of the inputs and the output on which the adaptive network must perform. The parameter set of an adaptive network is composed of the union of the parameters of each adaptive node. Then, these parameters are updated based on the provided training dataset. Thus, the construction of the desired relationship between input and output is realized by a gradient-based learning process [19].ANFIS is a systematic effort to generate “if-then” rules between input-output pairs. The model acquires a set of data (pairs of incoming-outgoing data) and divides them into datasets for training and control. Training data form a set of data, input, and output vectors.This information is normalized to be appropriate in form for the training process. This is achieved by assigning to each term a value between 0 and 1 using the max-min method. The normalized data are then used as input and output values to train the ANFIS model. This creates two vectors for ANFIS training such as an input vector and an output vector. Training data are used to find the introductory initial parameters for the participation functions.The least squares method is employed to find the resulting parameters. For each data pair, if an error computed becomes greater than a specific value set, the gradient descent method is employed to update the initial parameters of the input.The process ends when the error becomes less than the specified value [20]. The proposed hybrid system then applies association rules with a learning method to discover correlations and patterns between variables [7]. The problem of correlation rule mining is defined as follows:(1)
LetI=i1,i2,…,in be a set consisting of n binary properties called objects.(2)
LetD=t1,t2,…,tm denote a set consisting of transactions.(3)
LetD denote a unique transaction ID and contain a subset of I.(4)
Let the correlation rules be defined by the following [21]:(1)assume thatX∩Y=∅andX⇒Y,where XandY∈I.The following three rules are defined based on this logic [22, 23]:(2)Rule:X⇒Y=Support=frq qX,YN,Confidence=frqX,YfrqX,Lift=SupportSuppX×SuppY.Association rules are embedded in cellular automata to automate their implementation process [8]. Cellular automata are computing system models with emerging complexity that model the behavior of dynamic systems [24]. They are called cellular because they are inspired by cellular devices where each cell interacts with its neighbors in space and is automatic. Then, the behavior of each cell is completely determined by the corresponding model [8, 25].It is a “society” of digital systems that move, mate, give birth, or die as time passes in a two-dimensional grid based on predetermined simple rules [7, 26]. They are implemented in a grid of cells where each cell can be in a predefined number of distinct states with specific neighborhood relationships, predetermining and indicating its neighbors in the process of updating their states in each cell.Each cell renews its state based on a predefined transition rule that takes the states of adjacent cells as inputs since the renewal of all cells takes place synchronously. Thus, there is a deterministic evolution of the system over time [26]. Therefore, time is called discrete.The proposed embodiment consists ofC, Σ, Νf, where C denotes a d-dimensional grid of cells identified by vectors of ZN, Σ denotes the possible states that a cell can take, N denotes the neighborhood with N⊂Zd, and f is a transition function form ΣN⟶Σ. The configuration consists of states of the cells, and the initial state has a vital role and needs to be determined when the initial design is set.LetC be the set of cells in a grid, and the neighborhood function, denoted by n, can be defined as [25, 27](3)nx,y:C×C⟶B=True,False.This function will return True if the pair of cells that constitute the input inC are neighbors. For example, in a two-dimensional grid, a particular neighborhood function nx,y will return True if x and y are neighbors within the neighborhood of co where the relationship is defined by [24–27](4)Nco=c∈C|nco,c=True.The size of the neighborhoodNco is independent of co. If the set of possible cell states is determined, the transition function applied to all cells simultaneously can be defined as follows [8, 25]:(5)u:SN⟶S.Hence, the transition function takes the states of all cells in the neighborhood of a cell at timet and sets the state of that cell at the next time. The proposed method helps simplify the diagnostic procedure whose outcomes are not governed by a random state of disorders [28, 29].


### 3.2. Experiments

Utilizing the proposed approach that combines the findings of studies concerning both the processed information and the categorized students having learning difficulties helps identify the groups of weaknesses that characterize most students. The framework presented herein suggests that three main groups or categories of cognitive impairments typically occur in students with learning difficulties such as reading, spelling, and mathematics. These weaknesses are expressed in [4, 15, 16] as follows:(1)
Coding symbols.(2)
Processing the language.(3)
Perceiving and processing space and numbers.If students have learning difficulties in reading, spelling, and mathematics, one or more of the mentioned problems above may be observed. The experimental process for the implementation of the model was based exclusively on the feedback of the system with data extracted after interaction with students.When a student is assessed concerning the reception and comprehension of oral speech, two different and related functions or skills, namely, the distinction and perception of the phonological elements of the language, as well as the understanding of the semantic content of the language are tested. While the former is associated with the development of the phonological system of language and the function of hearing, the second is pertinent to the development of the semantic and syntactic system of language [4]. The way of evaluating these functions was implemented by asking the student to repeat words or phrases or to present the meaning of some words used in daily life with his/her sentences. Besides, some words with more than one meaning and sentences with the complex syntactic structure are used to assess him/her. During the evaluation, we pay attention to the completeness of the semantic definition of the words, the ability to choose the correct meaning of the word, the ability to identify the deep structure (semantic content) of the sentence, etc. Also, by providing instructions to the student, we assess whether he/she can perform them without the need to repeat or explain the instructions.Also, the evaluation of learning difficulties in mathematics starts from the general to the specific element. This practically means that by evaluating a student’s mathematical level, we start from the general mathematical elements and abilities to the more specific ones. In general, the mathematical skills of the specific students are tested to relate weaknesses and abilities to the level of problem solving, so the student’s ability to apply mathematical concepts and mathematical manipulations to problems in real-life situations is determined.Also, the recording of the student’s attitude towards mathematics as well as the emotions that he/she experiences through his/her involvement is evaluated. Besides, interviewing students and observing the process is helpful to assessment. When the process of active construction of knowledge is realized, the assessment based on the data must be continuous and comparisons should be conducted since individuals use different ways and means of representation of reality, which are called practical, pictorial, and symbolic representations.The student in the practical way of representing knowledge uses specific objects (fingers, sticks, numerator, and so on) to perform various operations. Then, pictures, drawings, graphs, and lines are used to calculate some quantities such as sum or difference. Finally, the symbolic way of representing the various operations is realized that is considered the most difficult part [30, 31].To assess the learning abilities and to identify the respective learning difficulties of the students, indicative skills tests were performed. For example, to better understand the analysis of each number as a sum of two additives, an activity was performed for the analysis of 10.The process can be described as follows: we have two groups with 10 green and 10 red pegs. We want to make a team with 10 pegs by taking a few pegs from the green and a few from the red team. The students work in groups with their objects and then each group announces one of the many ways of composing 10 pegs, which is conducted by a makeshift abacus with pegs (in a tightrope that we have to fasten at both ends of the board, we have hung 10 green and 10 red pegs). Thus, students are given the opportunity, after forming the 10 in a way, to write on the board and the symbolic representation, e.g., 6 + 4 = 10. In the end, after all the ways are suggested by the students, the specific objects (pegs) are removed, and the symbolic representations remain for mnemonic retention in the long-term memory.The results of each process with intermediate assessment steps are placed as input to the intelligent system to identify its learning abilities. Some indicative diagrams of the display of the results after the various tests performed are shown in Figures1 and 2.Figure 1
Dyslexia plot.Figure 2
Dyslexia test values.It is important to say that to decide if the cause of reading problems is dyslexia, the process should be further evaluated with a set of brain functions. Each of the sensory areas must be checked for any learning disabilities [4, 31]. The areas to be explored are the visual, the auditory, the motion, the mnemonic, and the successor abilities.(1)
Decoding, reading, and optical detection tests are conducted by the optical tests.(2)
Repetition of words and sentences, spelling tests, and acoustic perception are conducted by acoustic tests.(3)
Drawing copies, writing, tactile detection, and quick naming of objects (e.g., language expression) are conducted by kinetic tests.(4)
Multiplication tables, months of the year, alphabet, repetition of polysyllabic words, and determination of rhythmic succession are conducted by succession tests.


### 3.3. The Implementation of the Proposed Method

The desired result of a test array is to be able to assess which brain functions are involved in learning to read, spell, and learn in general. It is important for the computer assessment tool to include multiple tests and preferably more than one measurement of each sensory area to be examined.It should be noted that the tests mentioned herein are an attempt to make this possible. It is not a definitive and unique solution, but a proposal as to which areas should be considered. A prerequisite, to carry out such an assessment, is that the examiner is fully acquainted with how the specific areas can be assessed. Therefore, the test array is a tool in which each assessor can bring his/her assessment material. Accordingly, in addition to cognitive tests, the assessment should include a more general psychological assessment, e.g., a WISC (Wechsler’s Intelligence Scale for Children) or a WAIS (Wechsler’s Adult Intelligence Scale) test or equivalent [32–34].


## 3.1. Hybrid Neuro-Fuzzy System

ANFIS, the adaptive neuro-fuzzy inference system, is one of the adaptive networks that consists of both adaptive and non-adaptive nodes [17]. The adaptive network consists of a multilayer feed-forward network where each node is assigned to a specific function based on the incoming signals. Each specific node is denoted by a set of parameters. All nodes are connected via directional links. Customizable nodes are the ones depending on the parameters associated with that node. To minimize the predefined error, the parameters should be changed based on the learning rules [18].The type of operation of each node can vary. The choice of the operation of a node depends on the total processing of the inputs and the output on which the adaptive network must perform. The parameter set of an adaptive network is composed of the union of the parameters of each adaptive node. Then, these parameters are updated based on the provided training dataset. Thus, the construction of the desired relationship between input and output is realized by a gradient-based learning process [19].ANFIS is a systematic effort to generate “if-then” rules between input-output pairs. The model acquires a set of data (pairs of incoming-outgoing data) and divides them into datasets for training and control. Training data form a set of data, input, and output vectors.This information is normalized to be appropriate in form for the training process. This is achieved by assigning to each term a value between 0 and 1 using the max-min method. The normalized data are then used as input and output values to train the ANFIS model. This creates two vectors for ANFIS training such as an input vector and an output vector. Training data are used to find the introductory initial parameters for the participation functions.The least squares method is employed to find the resulting parameters. For each data pair, if an error computed becomes greater than a specific value set, the gradient descent method is employed to update the initial parameters of the input.The process ends when the error becomes less than the specified value [20]. The proposed hybrid system then applies association rules with a learning method to discover correlations and patterns between variables [7]. The problem of correlation rule mining is defined as follows:(1)
LetI=i1,i2,…,in be a set consisting of n binary properties called objects.(2)
LetD=t1,t2,…,tm denote a set consisting of transactions.(3)
LetD denote a unique transaction ID and contain a subset of I.(4)
Let the correlation rules be defined by the following [21]:(1)assume thatX∩Y=∅andX⇒Y,where XandY∈I.The following three rules are defined based on this logic [22, 23]:(2)Rule:X⇒Y=Support=frq qX,YN,Confidence=frqX,YfrqX,Lift=SupportSuppX×SuppY.Association rules are embedded in cellular automata to automate their implementation process [8]. Cellular automata are computing system models with emerging complexity that model the behavior of dynamic systems [24]. They are called cellular because they are inspired by cellular devices where each cell interacts with its neighbors in space and is automatic. Then, the behavior of each cell is completely determined by the corresponding model [8, 25].It is a “society” of digital systems that move, mate, give birth, or die as time passes in a two-dimensional grid based on predetermined simple rules [7, 26]. They are implemented in a grid of cells where each cell can be in a predefined number of distinct states with specific neighborhood relationships, predetermining and indicating its neighbors in the process of updating their states in each cell.Each cell renews its state based on a predefined transition rule that takes the states of adjacent cells as inputs since the renewal of all cells takes place synchronously. Thus, there is a deterministic evolution of the system over time [26]. Therefore, time is called discrete.The proposed embodiment consists ofC, Σ, Νf, where C denotes a d-dimensional grid of cells identified by vectors of ZN, Σ denotes the possible states that a cell can take, N denotes the neighborhood with N⊂Zd, and f is a transition function form ΣN⟶Σ. The configuration consists of states of the cells, and the initial state has a vital role and needs to be determined when the initial design is set.LetC be the set of cells in a grid, and the neighborhood function, denoted by n, can be defined as [25, 27](3)nx,y:C×C⟶B=True,False.This function will return True if the pair of cells that constitute the input inC are neighbors. For example, in a two-dimensional grid, a particular neighborhood function nx,y will return True if x and y are neighbors within the neighborhood of co where the relationship is defined by [24–27](4)Nco=c∈C|nco,c=True.The size of the neighborhoodNco is independent of co. If the set of possible cell states is determined, the transition function applied to all cells simultaneously can be defined as follows [8, 25]:(5)u:SN⟶S.Hence, the transition function takes the states of all cells in the neighborhood of a cell at timet and sets the state of that cell at the next time. The proposed method helps simplify the diagnostic procedure whose outcomes are not governed by a random state of disorders [28, 29].


## 3.2. Experiments

Utilizing the proposed approach that combines the findings of studies concerning both the processed information and the categorized students having learning difficulties helps identify the groups of weaknesses that characterize most students. The framework presented herein suggests that three main groups or categories of cognitive impairments typically occur in students with learning difficulties such as reading, spelling, and mathematics. These weaknesses are expressed in [4, 15, 16] as follows:(1)
Coding symbols.(2)
Processing the language.(3)
Perceiving and processing space and numbers.If students have learning difficulties in reading, spelling, and mathematics, one or more of the mentioned problems above may be observed. The experimental process for the implementation of the model was based exclusively on the feedback of the system with data extracted after interaction with students.When a student is assessed concerning the reception and comprehension of oral speech, two different and related functions or skills, namely, the distinction and perception of the phonological elements of the language, as well as the understanding of the semantic content of the language are tested. While the former is associated with the development of the phonological system of language and the function of hearing, the second is pertinent to the development of the semantic and syntactic system of language [4]. The way of evaluating these functions was implemented by asking the student to repeat words or phrases or to present the meaning of some words used in daily life with his/her sentences. Besides, some words with more than one meaning and sentences with the complex syntactic structure are used to assess him/her. During the evaluation, we pay attention to the completeness of the semantic definition of the words, the ability to choose the correct meaning of the word, the ability to identify the deep structure (semantic content) of the sentence, etc. Also, by providing instructions to the student, we assess whether he/she can perform them without the need to repeat or explain the instructions.Also, the evaluation of learning difficulties in mathematics starts from the general to the specific element. This practically means that by evaluating a student’s mathematical level, we start from the general mathematical elements and abilities to the more specific ones. In general, the mathematical skills of the specific students are tested to relate weaknesses and abilities to the level of problem solving, so the student’s ability to apply mathematical concepts and mathematical manipulations to problems in real-life situations is determined.Also, the recording of the student’s attitude towards mathematics as well as the emotions that he/she experiences through his/her involvement is evaluated. Besides, interviewing students and observing the process is helpful to assessment. When the process of active construction of knowledge is realized, the assessment based on the data must be continuous and comparisons should be conducted since individuals use different ways and means of representation of reality, which are called practical, pictorial, and symbolic representations.The student in the practical way of representing knowledge uses specific objects (fingers, sticks, numerator, and so on) to perform various operations. Then, pictures, drawings, graphs, and lines are used to calculate some quantities such as sum or difference. Finally, the symbolic way of representing the various operations is realized that is considered the most difficult part [30, 31].To assess the learning abilities and to identify the respective learning difficulties of the students, indicative skills tests were performed. For example, to better understand the analysis of each number as a sum of two additives, an activity was performed for the analysis of 10.The process can be described as follows: we have two groups with 10 green and 10 red pegs. We want to make a team with 10 pegs by taking a few pegs from the green and a few from the red team. The students work in groups with their objects and then each group announces one of the many ways of composing 10 pegs, which is conducted by a makeshift abacus with pegs (in a tightrope that we have to fasten at both ends of the board, we have hung 10 green and 10 red pegs). Thus, students are given the opportunity, after forming the 10 in a way, to write on the board and the symbolic representation, e.g., 6 + 4 = 10. In the end, after all the ways are suggested by the students, the specific objects (pegs) are removed, and the symbolic representations remain for mnemonic retention in the long-term memory.The results of each process with intermediate assessment steps are placed as input to the intelligent system to identify its learning abilities. Some indicative diagrams of the display of the results after the various tests performed are shown in Figures1 and 2.Figure 1
Dyslexia plot.Figure 2
Dyslexia test values.It is important to say that to decide if the cause of reading problems is dyslexia, the process should be further evaluated with a set of brain functions. Each of the sensory areas must be checked for any learning disabilities [4, 31]. The areas to be explored are the visual, the auditory, the motion, the mnemonic, and the successor abilities.(1)
Decoding, reading, and optical detection tests are conducted by the optical tests.(2)
Repetition of words and sentences, spelling tests, and acoustic perception are conducted by acoustic tests.(3)
Drawing copies, writing, tactile detection, and quick naming of objects (e.g., language expression) are conducted by kinetic tests.(4)
Multiplication tables, months of the year, alphabet, repetition of polysyllabic words, and determination of rhythmic succession are conducted by succession tests.


## 3.3. The Implementation of the Proposed Method

The desired result of a test array is to be able to assess which brain functions are involved in learning to read, spell, and learn in general. It is important for the computer assessment tool to include multiple tests and preferably more than one measurement of each sensory area to be examined.It should be noted that the tests mentioned herein are an attempt to make this possible. It is not a definitive and unique solution, but a proposal as to which areas should be considered. A prerequisite, to carry out such an assessment, is that the examiner is fully acquainted with how the specific areas can be assessed. Therefore, the test array is a tool in which each assessor can bring his/her assessment material. Accordingly, in addition to cognitive tests, the assessment should include a more general psychological assessment, e.g., a WISC (Wechsler’s Intelligence Scale for Children) or a WAIS (Wechsler’s Adult Intelligence Scale) test or equivalent [32–34].


## 4. Discussion and Conclusions

The educational treatment of learning disabilities is the goal of the whole process for both timely identification and correct diagnostic evaluation. It is the phase in which the professor demonstrates in practice how well he/she knows not only to interpret the results of the diagnostic evaluation but also to find ways to help the student learn [35].Fuzzy inferential models rely heavily on the uncertainty that has to do with information and especially with its lack in our attempt to model the problem. Information may, for example, be incomplete, inaccurate, fragmentary, unreliable, vague, or controversial. The use of fuzzy logic in assessment is considered necessary since each education system contains an amount of uncertainty. The reason for adopting this approach is that when it comes to educational evaluation, different evaluators have different evaluation measures. Three important factors that favor the vague approach to assessment are initially that the grades given by professors for student performance are not always accurate. Second, the exams consist of uncertain data, while finally many professors grade students in verbal terms.It, therefore, becomes clearer that fuzzy logic and fuzzy systems can deal with the ambiguity and uncertainty that exist in the process of educational evaluation. A fuzzy inferential model uses the process of approximate reasoning which mimics human reasoning. It utilizes the advantages of the natural language and the process of programming and building the system that become easier and more practical. It is obvious that a fuzzy inference system can be constructed quickly and has a higher utility value with minimal cost.Fuzzy inference systems make the best use of the experience and data derived from it. As we saw in the construction of the fuzzy diagnostic evaluation system presented herein, its foundation was based on data from human experience. The specialized knowledge of the developer was used in every step of the construction of the system.The proposed method provides a great amount of flexibility to reach a fuzzy inferential diagnostic assessment in managing and updating data. This system is based on the general principle of the evaluation process concerning the goals that we have set at the beginning. From these objectives, the system input variables are taken, and the language rules are set. Thus, the same procedure can be applied to other educational, medical, or evaluative procedures by changing variables and rules.Early diagnosis and educational intervention not only have great significance for the students having learning disabilities to avoid unnecessary negative consequences but also provide preparation to construct valuable special education programs. Learning problems such as dyslexia tend to weaken people’s self-confidence due to their daily familiarity with failure. Before diagnosis, a dyslexic person is often forced to deal with difficult and frustrating learning situations for several years.To build confidence and reshape one’s self-image, it is essential to avoid failure during training. In this spirit, the methodology presented herein is considered a first and perhaps an impressive step towards the ways of initial diagnosis and treatment of learning educational difficulties.



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*Source: 6540542-2022-02-15.xml*