Prediction of Student Performance Using Linear Regression

Sravani, B.; Bala, Myneni Madhu

doi:10.1109/incet49848.2020.9154067

Cited by 29 publications

(11 citation statements)

References 3 publications

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“…Algoritma Linear Regression ini menjelaskan relasi antara dua variabel dengan cara menyesuaikan garis regresi satu data dependent pada suatu data independent [12,13]. Pada penelitian ini, algoritma Linear Regression dibuat menggunakan konfigurasi default.…”

Section: Linear Regressionunclassified

Prediksi Harga Mobil Menggunakan Algoritma Regressi Dengan Hyper-Parameter Tuning

et al. 2022

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Seiring dengan bertumbuhnya tingkat aktivitas dan bisnis, mobil kini menjadi salah satu kebutuhan masyarakat. Dengan meningkatnya minat masyarakat terhadap mobil bekas, banyak orang berencana untuk memulai bisnis showroom mobil bekas. Masalah yang sering dihadapi pengusaha showroom adalah penetapan harga mobil bekas dengan tepat. Salah satu cara untuk melakukan prediksi harga adalah menggunakan metode Machine Learning. Untuk membuat prediksi harga yang lebih akurat dan memiliki nilai akurasi yang lebih tinggi, penelitian ini bertujuan untuk membuat sebuah model machine learning menggunakan algoritma regressi dengan bantuan hyper-parameter tuning untuk meningkatkan tingkat akurasi dari model yang dibuat dengan konfigurasi default. Model Machine Learning yang dibuat memiliki nilai yang berbeda, namun pada penelitian ini dipakai model Gradient Boost Regression yang memiliki nilai akurasi model sebesar 97% (setelah tuning) untuk melakukan prediksi. Dalam pencobaan prediksi, didapat nilai akurasi prediksi sebesar 80% dari 5 percobaan yang telah dilakukan.

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Section: Linear Regressionunclassified

Prediksi Harga Mobil Menggunakan Algoritma Regressi Dengan Hyper-Parameter Tuning

et al. 2022

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“…Artificial intelligence has also become a useful tool in this scenario to predict, using historical data, mid and final marks. For example, to predict academic grades and dropout [29,30], used classification [31][32][33], used regression in higher education and high school datasets, respectively. Moreover, students' marks may be predicted using clustering as [34] or applying supervised machine learning algorithms as [35,36].…”

Section: Introductionmentioning

confidence: 99%

Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence

et al. 2023

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This study provides the profiles and success predictions of students considering data before, during, and after the COVID-19 pandemic. Using a field experiment of 396 students and more than 7400 instances, we have analyzed students’ performance considering the temporal distribution of autonomous learning during courses from 2016/2017 to 2020/2021. After applying unsupervised learning, results show 3 main profiles from the clusters obtained in the simulations: students who work continuously, those who do it in the last-minute, and those with a low performance in the whole autonomous learning. We have found that the highest success ratio is related to students that work in a continuous basis. However, last-minute working is not necessarily linked to failure. We have also found that students’ marks can be predicted successfully taking into account the whole data sets. However, predictions are worse when removing data from the month before the final exam. These predictions are useful to prevent students’ wrong learning strategies, and to detect malpractices such as copying. We have done all these analyses taking into account the effect of the COVID-19 pandemic, founding that students worked in a more continuous basis in the confinement. This effect was still present one year after. Finally, We have also included an analysis of the techniques that could be more effective to keep in a future non-pandemic scenario the good habits that were detected in the confinement.

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“…e first is to establish statistical models, such as multivariate linear regression model and sparse factor analysis model. Sravani and Bala [1] predicted the students' performance based on a linear regression model.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Students’ Performance Based on the Hybrid IDA‐SVR Model

2022

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Students’ performance is an important factor for the evaluation of teaching quality in colleges. The aim of this study is to propose a novel intelligent approach to predict students’ performance using support vector regression (SVR) optimized by an improved duel algorithm (IDA). To the best of our knowledge, few research studies have been developed to predict students’ performance based on student behavior, and the novelty of this study is to develop a new hybrid intelligent approach in this field. According to the obtained results, the IDA-SVR model clearly outperformed the other models by achieving less mean square error (MSE). In other words, IDA-SVR with an MSE of 0.0089 has higher performance than DT with an MSE of 0.0326, SVR with an MSE of 0.0251, ANN with an MSE of 0.0241, and PSO-SVR with an MSE of 0.0117. To investigate the efficacy of IDA, other parameter optimization methods, that is, the direct determination method, grid search method, GA, FA, and PSO, are used for a comparative study. The results show that the IDA algorithm can effectively avoid the local optima and the blindness search and can definitely improve the speed of convergence to the optimal solution.

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Prediction of Student Performance Using Linear Regression

Cited by 29 publications

References 3 publications

Prediksi Harga Mobil Menggunakan Algoritma Regressi Dengan Hyper-Parameter Tuning

Prediksi Harga Mobil Menggunakan Algoritma Regressi Dengan Hyper-Parameter Tuning

Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence

Prediction of Students’ Performance Based on the Hybrid IDA‐SVR Model

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