International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 01 | Jan 2026
p-ISSN: 2395-0072
www.irjet.net
Predictive Analytics and Insights for Personalizing Special Education of Students with Dyscalculia Sohini Banerjee1, Dr. Shweta M. Barhate2 1Research Scholar, Department of Electronics and Computer Science, RTM Nagpur University, Nagpur,
Maharashtra, India
2Associate Professor, Department of Electronics and Computer Science, RTM Nagpur University, Nagpur,
Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Learning disability encompasses the unique
suffering from dyscalculia by analysing the different features related to dyscalculia.
challenges faced by a student to acquire process and demonstrate any knowledge or skill. Identification of learning disability students helps the educators to create an inclusive environment enabling the students to reach their full potential. The paper delves deep into the machine learning based personalized learning pathways, prediction of student performances, intervention with assistive technologies, math related educational tools and games along with behavior analysis using machine learning. Classification algorithms of machine learning analyses and learns patterns from the dataset. This study analyses the statistics of all the features of the dataset and creates models with different classification algorithms like Logistic Regression, Decision Tree, Random Forest, K-nearest neighbor, Support Vector Machine and XG Boost for predicting quarterly goal for these students. Finally the results are compared by creating the confusion matrix and computing the accuracy, precision, recall and F1 score to predict the optimum models for the purpose.
2. LITERATURE REVIEW 2.1 Early Detection and Diagnosis Several studies have been proposed to study data from various cognitive tests by machine learning and identifying the students with dyscalculia with great accuracy. [1] A study suggests that adaptive tests can be implemented to adjust the difficulty based on the individual's response and machine learning algorithms can be used to identify patterns in large datasets for identifying dyscalculia. [2] Another research highlights the need of machine learning to enhance the learning experience by applying adaptive algorithms to detect dyscalculia. It also summarizes the various AI advancements especially in the field of screening and intervention tools to provide tailored interventions. [3] Application of Convolutional Neural Networks (CNNs) to model the factors associated with dyscalculia y simulating impaired neural connections through dropout is suggested in another research. [4] Various algorithms like Support Vector Machines (SVM), Simple Logistic Regression, Naive Bayes, and Random Forest are explored to identify dyscalculia so that the challenges in its diagnosis can be addressed. [5] Further research has been done in exploring the abilities of complex algorithms for the detection of dyscalculia by analyzing medical data. [6] Substantial work has been done for the development of a system to enhance the detection of Dyscalculia through machine learning and hence reducing the efforts required behind the traditional detection process. [7] Another study shows proposes a knowledge-based analytical tool utilizing a machine learning decision tree algorithm to identify mathematical difficulties associated with dyscalculia in students. [8] Machine learning can also be applied to create adaptive assessment tools for promotion of inclusive educational environment. [9]
Key Words: Dyscalculia, Learning Disability, Logistic Regression, Decision Tree, Random Forest, K-nearest neighbour, Support Vector Machine, XG Boost, Accuracy, Precision, Recall, F1 Score.
1. INTRODUCTION Learning disability, a neurological disorder can range from specific academic areas to broader cognitive functions. Specific learning disability affecting a student’s ability related to understanding, learning and performing math related task is called dyscalculia. Dyscalculia affects academic performance as students struggle with number sense, basic arithmetic, and mathematical reasoning. Students with dyscalculia experience difficulties with number identification, patterns, geometrical shapes and basic mathematical operations. Machine Learning has proved to be a powerful tool used for the detection and diagnosis of Dyscalculia. Classification algorithm in machine learning plays a vital role in categorizing data into a number of predefined labels and thus helps in automatic decision making by analysing large datasets and improving accuracy. This paper discusses the comparison of a few classification algorithms for predicting quarterly goals for students
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2.2 Personalized Learning Research shows machine learning-based personalized learning path decision-making method that can be adapted for children with Dyscalculia. By analysing learners' behavioural data and mastery of knowledge points, the
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