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Monika Puttaramaiah

Bio: Monika Puttaramaiah is an academic researcher. The author has contributed to research in topics: Machine learning & Artificial intelligence. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Papers
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Journal ArticleDOI
TL;DR: It is suggested that machine learning remains one of the promising forecasting technologies with the power to enhance effective academic forecasting that would assist the education industry in planning and making better decisions to enrich the quality of education.
Abstract: The study examines the prospects and challenges of machine learning (ML) applications in academic forecasting. Predicting academic activities through machine learning algorithms presents an enhanced means to accurately forecast academic events, including the academic performances and the learning style of students. The use of machine learning algorithms such as K-nearest neighbor (KNN), random forest, bagging, artificial neural network (ANN), and Bayesian neural network (BNN) has potentials that are currently being applied in the education sector to predict future events. Many gaps in the traditional forecasting techniques have greatly been bridged by the use of artificial intelligence-based machine learning algorithms thereby aiding timely decision-making by education stakeholders. ML algorithms are deployed by educational institutions to predict students' learning behaviours and academic achievements, thereby giving them the opportunity to detect at-risk students early and then develop strategies to help them overcome their weaknesses. However, despite the benefits associated with the ML approach, there exist some limitations that could affect its correctness or deployment in forecasting academic events, e.g., proneness to errors, data acquisition, and time-consuming issues. Nonetheless, we suggest that machine learning remains one of the promising forecasting technologies with the power to enhance effective academic forecasting that would assist the education industry in planning and making better decisions to enrich the quality of education.

3 citations


Cited by
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Proceedings ArticleDOI
13 Oct 2022
TL;DR: In this paper , a focus is given on additional external factors such as geographical location, parent education, health status etc. that can affect a students' performances apart from the grades in any course.
Abstract: India's Education system is very old and due to a large population of students in India, there are some serious issues in analyzing and predicting students' performance. In the Indian Context, every institution has its own set of standards for evaluating student success, there is no proper procedure for monitoring and analyzing a student's performance and progress. One of the major factors is lack of research in existing prediction approaches, making it difficult to determine the optimal prediction methodology for visualizing student academic growth and performance. Another reason could be the lack of research into the areas that can affect students' academic performance and achievement. In this paper, focus is given on additional external factors like geographical location, parent education, health status etc. that can affect a students' performances apart from the grades in any course. That will be more effective in visualizing and analyzing student's performance. For experimental work, data has been collected from UCI repository and results are obtained from two different machine learning algorithms (KNN and Logistic Regression). Performance analysis is also done for these two algorithms based on accuracy level of results as well as with some existing work.

1 citations

Proceedings ArticleDOI
13 Oct 2022
TL;DR: In this article , a focus is given on additional external factors such as geographical location, parent education, health status etc. that can affect a students' performances apart from the grades in any course.
Abstract: India's Education system is very old and due to a large population of students in India, there are some serious issues in analyzing and predicting students' performance. In the Indian Context, every institution has its own set of standards for evaluating student success, there is no proper procedure for monitoring and analyzing a student's performance and progress. One of the major factors is lack of research in existing prediction approaches, making it difficult to determine the optimal prediction methodology for visualizing student academic growth and performance. Another reason could be the lack of research into the areas that can affect students' academic performance and achievement. In this paper, focus is given on additional external factors like geographical location, parent education, health status etc. that can affect a students' performances apart from the grades in any course. That will be more effective in visualizing and analyzing student's performance. For experimental work, data has been collected from UCI repository and results are obtained from two different machine learning algorithms (KNN and Logistic Regression). Performance analysis is also done for these two algorithms based on accuracy level of results as well as with some existing work.