A neural network regression model for predicting student learning success based on prior achievements

The paper describes a project utilizing data analysis tools to predict student performance based on their prior achievements. The task was addressed using historical educational data from over 35,000 students over a span of seven years, containing information on 1.24 million grades. Neural network r...

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Main Author: Dorrer Mikhail
Format: Article
Language:English
Published: EDP Sciences 2025-01-01
Series:ITM Web of Conferences
Online Access:https://www.itm-conferences.org/articles/itmconf/pdf/2025/03/itmconf_hmmocs-III2024_05007.pdf
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author Dorrer Mikhail
author_facet Dorrer Mikhail
author_sort Dorrer Mikhail
collection DOAJ
description The paper describes a project utilizing data analysis tools to predict student performance based on their prior achievements. The task was addressed using historical educational data from over 35,000 students over a span of seven years, containing information on 1.24 million grades. Neural network regression tools were employed to build models that predict future grades, thereby enhancing educational processes. The predictive capability of the model was assessed using the coefficient of determination and the root mean square error (RMSE) through 10-fold cross-validation of the dataset into training and testing sets. More than 70% of the developed grade prediction models demonstrated a coefficient of determination greater than 0.7, with the RMSE of predicted grades from actual values being less than one point on a five-point scale. This indicates a satisfactory solution to the prediction problem.
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spelling doaj-art-5368e2cf50004fefbf8e209da77c5bfe2025-08-20T03:12:46ZengEDP SciencesITM Web of Conferences2271-20972025-01-01720500710.1051/itmconf/20257205007itmconf_hmmocs-III2024_05007A neural network regression model for predicting student learning success based on prior achievementsDorrer Mikhail0Siberian State University of Science and Technology named after M.F. ReshetnevThe paper describes a project utilizing data analysis tools to predict student performance based on their prior achievements. The task was addressed using historical educational data from over 35,000 students over a span of seven years, containing information on 1.24 million grades. Neural network regression tools were employed to build models that predict future grades, thereby enhancing educational processes. The predictive capability of the model was assessed using the coefficient of determination and the root mean square error (RMSE) through 10-fold cross-validation of the dataset into training and testing sets. More than 70% of the developed grade prediction models demonstrated a coefficient of determination greater than 0.7, with the RMSE of predicted grades from actual values being less than one point on a five-point scale. This indicates a satisfactory solution to the prediction problem.https://www.itm-conferences.org/articles/itmconf/pdf/2025/03/itmconf_hmmocs-III2024_05007.pdf
spellingShingle Dorrer Mikhail
A neural network regression model for predicting student learning success based on prior achievements
ITM Web of Conferences
title A neural network regression model for predicting student learning success based on prior achievements
title_full A neural network regression model for predicting student learning success based on prior achievements
title_fullStr A neural network regression model for predicting student learning success based on prior achievements
title_full_unstemmed A neural network regression model for predicting student learning success based on prior achievements
title_short A neural network regression model for predicting student learning success based on prior achievements
title_sort neural network regression model for predicting student learning success based on prior achievements
url https://www.itm-conferences.org/articles/itmconf/pdf/2025/03/itmconf_hmmocs-III2024_05007.pdf
work_keys_str_mv AT dorrermikhail aneuralnetworkregressionmodelforpredictingstudentlearningsuccessbasedonpriorachievements
AT dorrermikhail neuralnetworkregressionmodelforpredictingstudentlearningsuccessbasedonpriorachievements