Analysis and Prediction of Student Data Using Data Science: A Review

Ganorkar, Sandeep S.; Tiwari, Nandita; Namdeo, Varsha

doi:10.1007/978-981-15-5224-3_44

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…Many scholarly works have been carried out by some scholars in this research area. Ganorkar et al (2021) reviewed the work done by various researchers on analysis and prediction of students' data for identifying important attributes.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Efficacy of Machine Learning Techniques for Predicting Students’ Academic Performances

Agwi,

Akpojaro

2024

AIMS Research Journal

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Rainfall as an environmental feat can change fast and yield significant influence in downstream hydrology known as runoff with a variety of implications such as erosion, water quality, and infrastructures. These, in turn, impact the quality of life, sewage systems, agriculture, and tourism of a nation, to mention a few. It chaotic, complex and dynamic nature has necessitated studies in the quest for future direction of such runoff via prediction models. With little successes in use of knowledge driven models – many studies have now turned to data-driven models. Dataset is retrieved from Metrological Center in Lagos, Nigeria for the period 1999–2019 for the Benin-Owena River Basin. Data is split: 70% for train, and 30% for test. Our study adapts a spatial-temporal profile hidden Markov trained deep neural network. Result yields a sensitivity of 0.9, specificity 0.19, accuracy of 0.74, and improvement rate of classification of 0.12. Other ensembles underperformed when compared to proposed model. The study reveals annual rainfall is an effect of variation cycle. Models will help simulate future floods and provide, lead time warnings in flood management. Keywords: Evidence, Rainfall Runoff, Southern Nigeria, Hybrid Ensemble Machine Learning Approach Nwanze Ashioba, Frances Emordi, Patrick Ejeh, Christopher Odiakaose, Christopher Odeh, Obiageli Attoh, & Maduabuchuku Azaka. (2024): Empirical Evidence for Rainfall Runoff in Southern Nigeria Using a Hybrid Ensemble Machine Learning Approach. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 1. Pp 73-86. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N1P6

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Section: Literature Reviewmentioning

confidence: 99%

Exploring the Efficacy of Machine Learning Techniques for Predicting Students’ Academic Performances

Agwi,

Akpojaro

2024

AIMS Research Journal

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“…As per Ganorkar et al (2020), the dataset was collected from the student database of RKDF Institute of Science and Technology. Prime data mining methodologies which were applied were association, classification, clustering and DT.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Academic Result Prediction System

Bhushan,

Verma,

Garg

et al. 2023

International Journal of Software Innovation

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Students' academic performance is a critical issue as it decides his/her career. It is pivotal for the educational institutes to track the performance record because it can help to enhance the standard of their quality education. Thus, the role of the academic result prediction system comes into existence which uses semester grade point average (SGPA) as a metric. The proposed work aims to create a model that can forecast the SGPA of students based on certain traits. It predicts the result in the form of SGPA of computer science students considering their past academic performance, study, and personal habits during their academic semester using different machine learning models, and to compare them based on different accuracy parameters. Some models that are widely used and are found effective in this field are regression algorithms, classification algorithms, and deep learning techniques. The results conclude that deep learning techniques are the most effective in the proposed work because of their high accuracy and performance, depending upon the attributes used in the prediction.

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Big Data Methods in Learning Analytics System by Using Dask Cluster Computer Framework

Gafarov

Khairullina

2022

Advances in Computer Science for Engineering and Education

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Analysis and Prediction of Student Data Using Data Science: A Review

Cited by 5 publications

References 2 publications

Exploring the Efficacy of Machine Learning Techniques for Predicting Students’ Academic Performances

Exploring the Efficacy of Machine Learning Techniques for Predicting Students’ Academic Performances

Machine Learning-Based Academic Result Prediction System

Big Data Methods in Learning Analytics System by Using Dask Cluster Computer Framework

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