Class result prediction using machine learning

Pushpa, S.; Manjunath, T N; Mrunal, T V; Singh, Amartya; Suhas, C

doi:10.1109/smarttechcon.2017.8358559

Cited by 21 publications

(10 citation statements)

References 3 publications

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“…Predicting graduation grade point averages was tackled by ANN, SVM, and Extreme Learning Machine (ELM) [28], where SVM gave the highest accurate prediction (97.98%). Student performance in the previous semester along with test grades from the current semester were used as input attributes for a series of algorithms (SVM, NB, RF and Gradient Boosting) that predict student grades [29].…”

Section: Supervised Learningmentioning

confidence: 99%

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

2020

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Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many processes involved in usual students’ activities which handle massive volumes of data collected from software tools for technology-enhanced learning. Thus, analyzing and processing these data carefully can give us useful information about the students’ knowledge and the relationship between them and the academic tasks. This information is the source that feeds promising algorithms and methods able to predict students’ performance. In this study, almost 70 papers were analyzed to show different modern techniques widely applied for predicting students’ performance, together with the objectives they must reach in this field. These techniques and methods, which pertain to the area of Artificial Intelligence, are mainly Machine Learning, Collaborative Filtering, Recommender Systems, and Artificial Neural Networks, among others.

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Section: Supervised Learningmentioning

confidence: 99%

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

2020

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“…Kandi and Sharan (2022) [6] present a review, "Placement Prediction and Analysis Using Machine Learning," which is possible among the many zeroed in on the use of prescient models to foresee the result of understudy positions. Additionally, Thangavel et al [7] (2017) and Rao et al [2](2018) talk about suggestion frameworks and instructive information mining procedures to foresee understudy arrangement.…”

Section: Literature Reviewmentioning

confidence: 99%

Career Path Insights

Jadhav,

Pandey,

Zine

et al. 2024

Preprint

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“Career Path Insights” is a digital platform that helps students navigate their path to success through a blend of technology, user engagement, and educational tools. It provides a user-friendly website with various features to support students in making well-informed career choices. Some of its key features include user registration, data input forms for academic and skill details, prediction of eligibility using machine learning, personalized career suggestions, user dashboards, interactive data display, a resume creator, and additional features to improve the overall user experience.

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“…Clustering facts mining method is used for studying the huge set of scholar database. This method will accelerate the looking technique and the additionally yield the type end result extra accurately [1]. M.Ramaswami and R.Bhaskaran have used CHAID prediction version to investigate the interrelation among variables which can be used to are expecting the final results of the overall performance at better secondary faculty schooling.…”

Section: Literature Reviewmentioning

confidence: 99%

Student Performance Prediction using AI and ML

Karale¹,

Narlawar²,

Bhujba³

et al. 2022

IJRASET

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As we all know that due to this pandemic situation many problems had been faced in the education stream. As we saw that the most of the students got good marks and on the other hand most students got lesser or average marks. In Critical case the good students got average marks or lesser then there expectations. As a result, all students got the admission but if the person is not that capable but taking admission to that college due to good result .So it seems unfair so to resolve this loophole, we decided to build a software for student performance prediction. It is an important desire in most of the educational departments and institutes to predict the student’s performance. Machine Learning algorithm and descriptive datasets include school, Sex, Age, Address, Family size, P status, M education, F education, M job, F job, Reason, Guardians, Travel time, Study time, Failures, School sup, Family sup, Activities, Free time, Go out, Health, attendance. We have been considered that the prediction and classification of student performance respectively using three type of machine learning algorithms such as Random Forest, ANN, XG Boost are implemented to predict the student’s academic performance. The prediction based models are created to predict academic outcomes of student performance at the end of the year each. The analysis of demographical attributes release that they are also potential indicators of a student's academic success or failure. The results of these case study gives techniques for accurately predicting student performance, and compare the accuracy with MI algorithms Index Terms- ML and AI, ANN, Random Forest, XG Boost prediction model. Index Terms- ML and AI, ANN, Random Forest, XG Boost prediction model. Keywords: Machine Learning, XG-BOOST, Random ForestAlgorithm , Artificial Neural Network, Social Media Post.

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Class result prediction using machine learning

Cited by 21 publications

References 3 publications

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

Career Path Insights

Student Performance Prediction using AI and ML

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