International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 01 | Jan 2024
p-ISSN: 2395-0072
www.irjet.net
A COMPREHENSIVE STUDY FOR IDENTIFICATION OF FAST AND SLOW LEARNERS USING MACHINE LEARNING APPROACH Shrawani Rayalkar1, Chirag Nere2, Dr. Hrushikesh Kulkarni2* 1School of Data science,2 School of Mechatronics Engineering, Symbiosis Skills and Professional University, Pune
---------------------------------------------------------------------***--------------------------------------------------------------------schools are focusing more on improving various aspects Abstract - This research paper presents a detailed and one important factor among them is quality learning.
investigation into the identification of fast and slow learners in an academic setting through the implementation of a machine learning-based ensemble model. The proposed ensemble model utilizes a training dataset to generate rules, subsequently applied to a testing dataset for predicting academic performance.
A fast learner is someone who gains the skills of being a strategic thinker and a good listener and applies them to learning quickly. A fast learner not only learns things at small interval of time but also gains enough knowledge than a normal person.A slow learner work on tasks more slowly, have poor memory and difficulties understanding concepts and subject taught to them.
The goal is to have a better understanding of how students learn and identify the settings in which they learn to improve educational outcomes and to gain insights into and explain educational phenomena. The biggest challenge is to improve the quality of the educational processes so as to enhance student’s performance. Thus, it is crucial to set new strategies and plans for a better management of the current processes. It is concerned with developing methods for exploring the unique types of data that come from educational environments. The paper uses trains various machine learning model using real time data to find the prediction and accuracy of the models and hence finds out which works accurately and hence helping in academic actions can be taken for each student accordingly
Prediction of student’s performance is challenging task as it depends on many factors such as grades, class performance, demographic data and emotional features. It is important for the teachers to forecast the future performance of a student based on his past performances, identifying weak students at an early stage so that additional material and special attention can be facilitated to avoid the risk of failure [1]. Besides this, various statutory and regulatory bodies such as National Assessment and Accreditation Council (NAAC) and the National Accreditation Board (NBA) which are accrediting the higher education institutions also stress the identification of the learning levels of the students and accordingly steer the teaching– learning process for them. If the weak learners are identified at the start of the semester, the respective subject teachers can plan their academic activity for a better understanding of the subject matter by such students, and their results improve as they go to higher semesters. To improve the students’ performance by tailored teaching–learning activities for the slow and fast learners is the key outcome of this research [3].
Experimental results, conducted using a dataset from a computer science department, demonstrate the model's efficacy with an accuracy of 90.83%. The ensemble model not only provides valuable insights for instructors to scrutinize and enhance student performance but also aids learners in self-assessment and corrective actions. The study concludes with potential extensions, suggesting the development of a recommender system for course selection based on performance predictions and incorporating indirect factors affecting academic performance. The integration of predictive analytics in the evolving landscape of educational technology is also discussed, highlighting its potential in assisting students through personalized recommendations based on their predicted performance.
2. Materials and Methods The methodology for this research involved the identification of fast and slow learners in a college environment to facilitate the adaptation of academic teaching methods for personalized learning. The study utilized a dataset comprising records of approximately 1000 students, encompassing their performance marks in three subjects and the corresponding number of study hours per day. Five distinct machine learning models, namely Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), were employed to classify students into
Key Words: Slow learners, fast learners, Education performance, machine learning
1. INTRODUCTION Educational data has become an important resource in this modern time, contributing much to the welfare and growth of the society. Educational system is becoming more competitive because of the number of schools and institutions which are growing rapidly. The educational
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