machine learning based predictive analytics of student academic performance in stem education

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The document presents research on using machine learning algorithms to predict student performance in courses. It tested eight algorithms on data from Bradley University and evaluated their predictive accuracy. Based on the results, it makes recommendations on selecting and using ML algorithms for predictive analytics in STEM education. It also summarizes student feedback from surveys on using ML-based predictive analytics. The proposed system aims to address the lack of a system in Malaysia to analyze student data and monitor progress. It reviews literature on predicting student performance with machine learning techniques and identifies the most important attributes to improve student achievement and success. The system architecture requires a computer with at least 4GB RAM, 100GB disk, and the ability to run Python programs in an IDE like PyCharm.

Abstract:
Machine Learning (ML) is expected, in the near future, to provide various venues and effective
tools to improve education in general, and Science-Technology-Engineering- Mathematics
(STEM) education in particular. The Gartner Analytics Ascendancy Model requires the use of
four types of data analytics to be considered comprehensive: descriptive, diagnostic, predictive
and prescriptive data analytics. This paper presents the outcomes of a research and development
project at Bradley University (Peoria, IL, USA) aimed at the setup and benchmarking of eight
ML algorithms for predictive learning analytics, specifically, a prediction of student academic
performance in a course. The analyzed and tested ML algorithms include linear regression,
logistic regression, k- nearest neighbor classification, naïve Bayes classification, artificial
neural network regression and classification, decision tree classification, random forest
classification, and support vector machine classification. Based on the obtained accuracy of
the analyzed and tested ML algorithms, we have formulated a set of recommendations for faculty
and practitioners in terms of selection, setup and utilization of ML algorithms in predictive
analytics in STEM education. We also performed formative and summative surveys of
undergraduate and graduate students in Computer Science and Computer Information Systems
courses to understand their opinion about utilization of ML-based predictive analytics in
education; a summary of obtained student feedback is presented in this paper.
Existing System:
Predicting student’s performance becomes more challenging due to the large volume of data in
educational databases. Currently in Malaysia, the lack of existing system to analyze and monitor
the student progress and performance is not being addressed. There are two main reasons of why
this is happening. First, the study on existing prediction methods is still insufficient to identify
the most suitable methods for predicting the performance of students in Malaysian institutions.
Second is due to the lack of investigations on the factors affecting student’s achievements in
particular courses within Malaysian context. Therefore, a systematical literature review on
predicting student performance by using ML techniques is proposed to improve student’s
achievements.

Proposed System:
The main objective of this paper is to provide an overview on the Machine Learning techniques
that have been used to predict student’s performance. This paper also focuses on how the
prediction algorithm can be used to identify the most important attributes in a student’s data. We
could actually improve student’s achievement and success more effectively in an efficient way
using educational ML techniques. It could bring the benefits and impacts to students, educators
and academic institutions.
System Architecture:

SYSTEM CONFIGURATION:
Hardware requirements:
Processer : Any Update Processer
Ram : Min 4 GB
Hard Disk : Min 100 GB
Software requirements:
Operating System : Windows family
Technology : Python 3.6
IDE : PyCharm

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More from CloudTechnologies (20)

PublicEduChain A Framework for Sharing Student-Owned Educational Data on Publ...CloudTechnologies

PublicEduChain: A Framework for Sharing Student-Owned Educational Data on Public Blockchain Network | IEEE 2024 CSE Major Projects Description: Explore an innovative IEEE 2024 CSE Major Project—PublicEduChain! This project introduces a groundbreaking framework for securely sharing student-owned educational data on a public blockchain network, using Python. Ideal for BTech students in Hyderabad, this project combines the power of blockchain technology with the need for secure and decentralized data management in education. 🔍 Key Features: Student-owned educational data sharing Public blockchain network integration Python programming for blockchain Secure and decentralized data management IEEE 2024 standards and compliance 🎓 Why This Project? Perfect for final-year CSE students, this project not only meets IEEE 2024 guidelines but also offers a practical application of blockchain in education. It’s a must-have for students looking to make an impact in the tech industry. 📍 Location: Hyderabad, India #Blockchain #Python #IEEE2024 #CSEProjects #Hyderabad #EducationalData #PublicEduChain #BTechFinalYearProjects #DecentralizedData #StudentOwnedData

Blockchain Based Logging to Defeat Malicious Insiders The Case of Remote Heal...CloudTechnologies

Blockchain-Based Logging for Secure Remote Health Monitoring | BTech Major Project 2024 | IEEE 2024 CSE Projects BTech CSE Projects 2024 IEEE 2024 Projects Explore how blockchain technology can enhance security in remote health monitoring systems with this BTech major project, inspired by an IEEE 2024 paper. This project focuses on developing a blockchain-based logging system designed to prevent malicious insider activities, ensuring the integrity and security of sensitive health data. In this video, we guide you through the development process, from understanding the problem statement to implementing a secure logging mechanism using blockchain. This project is ideal for final-year Computer Science Engineering (CSE) students who are passionate about cybersecurity and healthcare technology. Key Highlights: Introduction to remote health monitoring systems and security challenges Overview of blockchain technology and its application in logging Designing a secure logging system to counteract malicious insiders Implementation using blockchain frameworks and tools Evaluation of the system’s security and performance Potential applications and future enhancements Who is this for? BTech CSE students seeking innovative IEEE 2024 project ideas Final-year engineering students focused on cybersecurity and blockchain Researchers and developers interested in secure healthcare systems Don’t forget to like, share, and subscribe for more project ideas and tutorials in the field of blockchain and cybersecurity! #Blockchain #Cybersecurity #BTechProjects2024 #IEEE2024 #CSEMajorProjects #FinalYearProjects #HealthTech #BlockchainInHealthcare #RemoteHealthMonitoring #EngineeringProjects Computer Science Engineering Projects CSE Major Projects 2024 BTech Final Year Projects IEEE Papers 2024 Engineering Projects 2024 BTech CSE IEEE Projects CSE Project Ideas 2024 Latest IEEE Projects 2024 Python Projects for BTech Deep Learning Projects 2024 Blockchain Projects 2024 IEEE 2024 Blockchain Projects BTech CSE Blockchain Projects Blockchain Major Projects IEEE Blockchain Papers 2024 CSE Final Year Projects 2024 Blockchain in Computer Science

Enhancing Personalized Learning Experiences by Leveraging Deep Learning for C...CloudTechnologies

Enhancing Personalized Learning with Deep Learning | BTech Major Project 2024 | IEEE 2024 CSE Projects Dive into the future of education with this cutting-edge BTech major project based on an IEEE 2024 paper, focusing on enhancing personalized learning experiences through deep learning. This project explores how deep learning algorithms can be utilized to understand and tailor educational content to individual learner needs, making it an essential project for final-year Computer Science Engineering (CSE) students. In this video, we walk you through the development of a system that leverages deep learning for content analysis and personalization. From conceptualization to implementation, we cover every aspect of this project, providing you with a clear understanding of how to build and execute it successfully. Key Highlights: Overview of personalized learning and its significance Introduction to deep learning models used for content understanding Techniques for content analysis and categorization Implementation details using Python and relevant libraries System design and architecture for personalization Testing and evaluation of personalized learning outcomes Future directions and potential improvements Who is this for? BTech CSE students looking for innovative IEEE 2024 project ideas Final-year engineering students interested in AI and education technology Educators and researchers exploring personalized learning solutions Make sure to like, comment, and subscribe for more project ideas, tutorials, and insights into the latest tech trends! #PersonalizedLearning #DeepLearning #BTechProjects2024 #IEEE2024 #CSEMajorProjects #FinalYearProjects #AIInEducation #ContentUnderstanding #PythonProjects #EdTech #EngineeringProjects

Machine Learning Classification to predict water purity based on Viruses and ...CloudTechnologies

Machine Learning Classification to predict water purity based on Viruses and Bacteria levels Water is a major resource in every day’s life for humans, animals and plants. The quality of water polluting due to the industrialization, mining, and some other factors. Drinking water and irrigation water are two different types, and the quality levels to be measured based on the usage. The world health organization released some threshold values based on some water parameters. The metrics are named as The Water Purity by Assessing and Eliminating Viruses and Bacteria (WPAEVB) and Irrigation WQI (IWQI) which can measure the water quality. This paper proposed a network architecture to analyze all the parameters by using machine learning tools (ML) tools which will determine the drinking water an irrigation water based on virus and bacteria values. The model is developed based on LoRa and land topology. Here we used three models SVM, logistic regression (LR), and random forest (RF) to know whether irrigation water is being used for drinking water by detecting the percentage and levels of bacteria and virus. The dataset was developed based on the ML models due to the lower availability of datasets related to irrigation and drinking water and bacteria and virus percentage is also calculated by using three models. After applying all the models, LR given the best performance for drinking water and SVM given the best results for irrigation water. The recursive feature elimination was done by applying all three mL models.

iot based safety and health monitoring for construction workersCloudTechnologies

Intelligent neonatal monitoring system based on android application using mul...CloudTechnologies

An iot based smart garden with weather station systemCloudTechnologies

A deep learning facial expression recognition based scoring system for restau...CloudTechnologies

Diabetes prediction using different machine learning approachesCloudTechnologies

Image based estimation of real food size for accurate food calorie estimationCloudTechnologies

Network intrusion detection using supervised machine learning technique with ...CloudTechnologies

Io t projectsCloudTechnologies

Cloud computing projects CloudTechnologies

Data mining projectsCloudTechnologies

This document provides information about projects and services offered by a company for academic projects. The company offers: 1) Complete projects including code, documentation, installation support, and video demonstrations. 2) Online support for projects until after final presentations. 3) Assistance with paper writing and publishing in journals and conferences. 4) A list of sample data mining and big data projects in areas like sentiment analysis, healthcare, recommendations, and security.

Python IEEE 2019 Projects List CloudTechnologies

This document provides information about various academic projects available in different domains like deep learning, image processing, artificial intelligence, natural language processing, etc. It lists project titles, publishing details, and technologies used. It also offers additional services like complete code, documentation, installation support, and assistance until project evaluation. Videos demonstrating some projects are embedded.

Machine learning projectsCloudTechnologies

Raspberry Pi based voice-operated personal assistant (Neobot)CloudTechnologies

Automation in Agriculture and IoTCloudTechnologies

Gas Leakage Detection Based on IOTCloudTechnologies

The Internet-of-Things based hand gestures using wearable sensors for human m...CloudTechnologies