Authors: Akshay Bodkhe, Niranjan Zingade, Bhavesh Mundada
DOI Link: https://doi.org/10.22214/ijraset.2023.52851
Certificate: View Certificate
Failure at any phase of education happens frequently. The rise in drop - out rates is a result of numerous reasons. Poor grades are one of the biggest causes of school abandonment. This has an influence on performance because so many students find it difficult to adjust to the institution\'s learning environment once they get there. Other factors include student participation in extracurricular tasks and politics. Learners\' performances frequently tend to be unsatisfactory for these different predictable and unpredictable reasons, which have an impact on development. As a result, it\'s important to examine undergraduate results to identify the real reasons for students\' varied level of performance. The primary goal of our research work is to identify the numerous variables that affect achievement at the under-graduation level. Therefore, the main motivation behind this effort is to help students identify the factors that lead to their performance so that they can take action to change their results. The learners, course teachers, and others will have the opportunity to improve the environment once the major elements have been recognized and assessed. This paper highlights the importance of using student data to drive improvement in education planning. It then presents techniques of how to obtain knowledge from databases such as large arrays of student data from academic Institution databases. To early predict the student’s academic performance, we have proposed deep learning model of Recurrent Neural (RNN) classifier. This proposed methodology is compared with various traditional machines learning classification models and deep learning classifier.
B. Scope and Objectives
The scope of machine learning-based result analysis for predicting student's academic performance can include the following:
II. DESIGN FLOW/PROCESS
a. Data Cleaning: It addresses noisy data, missing information, etc. Different strategies have been adopted when some data in the information is incomplete, such as filling in the gaps or disregarding the tuples. Data may contain null values that are incomprehensible to machines. This noisy data may result from poor data collecting, incorrect data input, etc. Regression, clustering, and the binning approach are used to address it.
b. Data Transformation: This technique is used to change the data into the form that is suited for the mining procedure. Normalization, attribute choice, and discretization are involved in this technique.
c. Stop word Removal and Stemming (Porter’s algorithm): Then we will apply various pre-processing steps such as lexical analysis, stop word removal, stemming (Porter’s algorithm), index term selection and data cleaning in order to make our dataset proper.
2. Feature Extraction and Selection: From the data input, this procedure retrieves a variety of features. The extracted features are then standardized using a feature selection threshold, which eliminates redundant and unnecessary features for training. The normalized data with relational characteristics is used to extract a variety of hybrid attributes, and training is carried out by selecting an optimization strategy. The hybrid method has been used for feature selection from fully extracted features— selecting the best quality increases classification accuracy. Many irrelevant features appear during the feature extraction, which need to eliminate when we choose the parts. The benefit of this method is that it provides a respective feature selection for the individual feature set.
3. Classification: After the module has been successfully executed, the selected features are given as input to the training module, which produces comprehensive Background Knowledge for the overall system. After we get the training model, we can feed the testing data into it and get the prediction of classification. The testing stage includes pre-processing of testing text, vectorization, and classification of the testing text. The module testing evaluates the system's predictive performance using deep learning (RNN) methods. This step assessed the system's performance using different datasets.
III. LITERATURE SURVEY
IV. SYSTEM REQUIREMENTS
A. Functional Requirements
B. Non-Functional Requirements
C. Software Requirements
a. Operating System: -Windows XP/7/8
b. Programming Language: JAVA/J2EE/
c. Tools: Eclipse or Higher, Heidi SQL, JDK 1.7 or Higher
d. Database: MySQL 5.1
a. MySQL 5.1
D. Hardware Requirements
The satisfaction that accompanies the successful completion and it asks would be incomplete without the mention of the people who made it possible and whose constant encouragement and guidance have been a source of inspiration throughout the course of this project. We take this opportunity to express my sincere thanks to my guide respected Dr. M.V.Munot ma’am for their support and encouragement throughout the completion of this project. Finally, I would like to thank all the teaching and non-teaching faculty members and lab staff of the department of electronics and telecommunications engineering for their encouragement. I also extend our thanks to all those who helped us directly or indirectly in the completion of this project.
In conclusion, machine learning has proven to be a powerful tool in analysing and predicting student performance. By utilizing various data sources and algorithms, machine learning models can identify patterns and relationships between factors such as attendance, study habits, and assessment scores to make accurate predictions about a student\'s academic performance. This information can be used by educators and institutions to develop targeted interventions and support systems to help students succeed. However, it is important to note that machine learning is not a silver bullet and should be used in conjunction with other forms of data analysis and human expertise to ensure the best outcomes for students. Overall, machine learning has the potential to revolutionize the way we approach education and student success.
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Copyright © 2023 Akshay Bodkhe, Niranjan Zingade, Bhavesh Mundada. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET52851
Publish Date : 2023-05-23
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here