Student Performance Prediction with Optimum Multilabel Ensemble Model

Yekun, Ephrem Admasu; Haile, Abrahaley Teklay

doi:10.1515/jisys-2021-0016

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…In addition, since the values in each column attribute are on different scales, the data need to be normalized to the range of [0, 1]. The formula for normalization is as follows: where max represents the maximum value and min represents the minimum value [12].…”

Section: Data Preprocessingmentioning

confidence: 99%

Multi-Output Based Hybrid Integrated Models for Student Performance Prediction

Xue

Niu

2023

Applied Sciences

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In higher education, student learning relies increasingly on autonomy. With the rise in blended learning, both online and offline, students need to further improve their online learning effectiveness. Therefore, predicting students’ performance and identifying students who are struggling in real time to intervene is an important way to improve learning outcomes. However, currently, machine learning in grade prediction applications typically only employs a single-output prediction method and has lagging issues. To advance the prediction of time and enhance the predictive attributes, as well as address the aforementioned issues, this study proposes a multi-output hybrid ensemble model that utilizes data from the Superstar Learning Communication Platform (SLCP) to predict grades. Experimental results show that using the first six weeks of SLCP data and the Xgboost model to predict mid-term and final grades meant that accuracy reached 78.37%, which was 3–8% higher than the comparison models. Using the Gdbt model to predict homework and experiment grades, the average mean squared error was 16.76, which is better than the comparison models. This study uses a multi-output hybrid ensemble model to predict how grades can help improve student learning quality and teacher teaching effectiveness.

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Section: Data Preprocessingmentioning

confidence: 99%

Multi-Output Based Hybrid Integrated Models for Student Performance Prediction

Xue

Niu

2023

Applied Sciences

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“…This method leverages the collective intelligence of diverse models, harnessing their individual strengths to achieve superior predictive accuracy and robustness [17]. Ensemble techniques have gained widespread popularity in the realm of machine learning, particularly in regression analysis [18]. Regression analysis algorithms are some of the most popular ensemble learning used in machine learning models [19].…”

Section: Introductionmentioning

confidence: 99%

Students Final Academic Score Prediction Using Boosting Regression Algorithms

Muhammady,

Nugraha,

Nastiti

et al. 2024

J. Ilm. Tek. Elektro Komput. Dan Inform

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Academic grades are crucial in education because they assist students in acquiring the knowledge and skills necessary to succeed in school and their future. Accurately predicting students' final academic performance grade score is important for educational decision-makers. However, creating precise prediction models based on students' historical data can be challenging due to the complex nature of academic data. This research analyzes student academic data totaling 649 Portuguese language course student data that has been processed according to data requirements which are then predicted using XGBoost Regressor, Light Gradient Boosting Machine (LGBM), and CatBoost. This research aims to develop a robust prediction model that can effectively predict students' final academic performance. This research offers valuable insights into the factors that influence academic success and provides practical implications for educational institutions looking to improve their decision-making processes. The prediction requires identifying key predictors of academic performance, such as previous grades, attendance records, and socio-economic background. The research makes a contribution by improving the matrix MAE in this research is less than the previous research from 2.2 average each algorithm to 0.22 average, this less MAE means the better model. The research achieved MAE score of 0.22 average. In conclusion, this research is expected to address the challenge of predicting student academic performance through the application of advanced machine learning techniques. The results provide valuable insights for decision-makers in education and highlight the importance of a data-driven approach to improving academic performance. By utilizing machine learning algorithms, educational institutions can effectively support student learning and success.

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Analytical Review and Study on Student Performance Prediction: A Challenging Overview

Baruah¹,

Baruah²

2022

Lecture Notes in Networks and Systems

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Student Performance Prediction with Optimum Multilabel Ensemble Model

Cited by 5 publications

References 24 publications

Multi-Output Based Hybrid Integrated Models for Student Performance Prediction

Multi-Output Based Hybrid Integrated Models for Student Performance Prediction

Students Final Academic Score Prediction Using Boosting Regression Algorithms

Analytical Review and Study on Student Performance Prediction: A Challenging Overview

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