References
[1]. Camacho-Morles J, Slemp G R, Pekrun R, Loderer K, Hou H and Oades L G 2021 Activity achievement emotions and academic performance: a meta-analysis Educ. Psychol. Rev. 33 1051–95
[2]. Baker R S J d and Yacef K 2009 The state of educational data mining in 2009: a review and future visions J. Educ. Data Min. 1 3–17
[3]. Rahman S R, Islam M A, Akash P P, Parvin M, Moon N N and Nur F N 2021 Effects of co-curricular activities on student’s academic performance by machine learning Curr. Res. Behav. Sci. 2 100057
[4]. Asif R, Merceron A, Ali S A and Haider N G 2017 Analyzing undergraduate students’ performance using educational data mining Comput. Educ. 113 177–94
[5]. Ahmad Z and Shahzadi E 2018 Prediction of students’ academic performance using artificial neural network Bull. Educ. Res. 40 157–64
[6]. Dabhade P, Agarwal R, Alameen K P, Fathima A T, Sridharan R and Gopakumar G 2021 Educational data mining for predicting students’ academic performance using machine learning algorithms Mater. Today Proc. 47 5260–7
[7]. Olabanjo O A, Wusu A S and Manuel M 2022 A machine learning prediction of academic performance of secondary school students using radial basis function neural network Trends Neurosci. Educ. 29 100190
[8]. Hoffait A S and Schyns M 2017 Early detection of university students with potential difficulties Decis. Support Syste. 101 1–11
[9]. Fernandes E, Holanda M, Victorino M, Borges V, Carvalho R and Van Erven G 2019 Educational data mining: predictive analysis of academic performance of public school students in the capital of Brazil J. Bus. Res. 94 335–43
[10]. Cruz-Jesus F, Castelli M, Oliveira T, Mendes R, Nunes C, Sa-Velho M and Rosa-Louro A 2020 Using artificial intelligence methods to assess academic achievement in public high schools of a European Union country Heliyon 6 e04081
[11]. Musso M F, Hernández C F R and Cascallar E C 2020 Predicting key educational outcomes in academic trajectories: a machine-learning approach High. Educ. 80 875–94
[12]. Xu X, Wang J, Peng H and Wu R 2019 Prediction of academic performance associated with internet usage behaviors using machine learning algorithms Comput. Hum. Behav. 98 166–73
[13]. Liao C H and Wu J Y 2022 Deploying multimodal learning analytics models to explore the impact of digital distraction and peer learning on student performance Comput. Educ. 190 104599Sarkar S, Agrawal S, Baker T, Maddikunta P K and Gadekallu T R 2022 Catalysis of neural activation functions: adaptive feed-forward training for big data applications Applied Intelligence vol 52.12 p 13364-83
[14]. Han J and Kamber M 2006 Data Mining Concepts and Techniques 2nd edn (San Francisco, CA: Morgan Kaufmann)
[15]. Arias Ortiz E and Dehon C 2013 Roads to success in the Belgian French community’s higher education system: predictors of dropout and degree completion at the Université Libre de Bruxelles Res. High. Educ. 54 693–723
[16]. Huang S and Fang N 2013 Predicting student academic performance in an engineering dynamics course: a comparison of four types of predictive mathematical models Comput. Educ. 61 133–45
[17]. Cristianini N and Shawe-Taylor J 2000 An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods (Cambridge: Cambridge University Press)
[18]. Vapnik V N 1998 Statistical Learning Theory (Hoboken, NJ: John Wiley & Sons)
[19]. Breiman L 2001 Random forests Mach. Learn. 45 5–32
[20]. Breiman L, Friedman J H, Olshen R A and Stone C J 1984 Classification and Regression Trees (New York, NY: CRC Press) pp 246–280
[21]. Domingos P 2012 A few useful things to know about machine learning Commun. ACM 55 78-87
Cite this article
Han,Y. (2023). Predict student’s performance based on machine learning algorithms. Applied and Computational Engineering,17,233-240.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Camacho-Morles J, Slemp G R, Pekrun R, Loderer K, Hou H and Oades L G 2021 Activity achievement emotions and academic performance: a meta-analysis Educ. Psychol. Rev. 33 1051–95
[2]. Baker R S J d and Yacef K 2009 The state of educational data mining in 2009: a review and future visions J. Educ. Data Min. 1 3–17
[3]. Rahman S R, Islam M A, Akash P P, Parvin M, Moon N N and Nur F N 2021 Effects of co-curricular activities on student’s academic performance by machine learning Curr. Res. Behav. Sci. 2 100057
[4]. Asif R, Merceron A, Ali S A and Haider N G 2017 Analyzing undergraduate students’ performance using educational data mining Comput. Educ. 113 177–94
[5]. Ahmad Z and Shahzadi E 2018 Prediction of students’ academic performance using artificial neural network Bull. Educ. Res. 40 157–64
[6]. Dabhade P, Agarwal R, Alameen K P, Fathima A T, Sridharan R and Gopakumar G 2021 Educational data mining for predicting students’ academic performance using machine learning algorithms Mater. Today Proc. 47 5260–7
[7]. Olabanjo O A, Wusu A S and Manuel M 2022 A machine learning prediction of academic performance of secondary school students using radial basis function neural network Trends Neurosci. Educ. 29 100190
[8]. Hoffait A S and Schyns M 2017 Early detection of university students with potential difficulties Decis. Support Syste. 101 1–11
[9]. Fernandes E, Holanda M, Victorino M, Borges V, Carvalho R and Van Erven G 2019 Educational data mining: predictive analysis of academic performance of public school students in the capital of Brazil J. Bus. Res. 94 335–43
[10]. Cruz-Jesus F, Castelli M, Oliveira T, Mendes R, Nunes C, Sa-Velho M and Rosa-Louro A 2020 Using artificial intelligence methods to assess academic achievement in public high schools of a European Union country Heliyon 6 e04081
[11]. Musso M F, Hernández C F R and Cascallar E C 2020 Predicting key educational outcomes in academic trajectories: a machine-learning approach High. Educ. 80 875–94
[12]. Xu X, Wang J, Peng H and Wu R 2019 Prediction of academic performance associated with internet usage behaviors using machine learning algorithms Comput. Hum. Behav. 98 166–73
[13]. Liao C H and Wu J Y 2022 Deploying multimodal learning analytics models to explore the impact of digital distraction and peer learning on student performance Comput. Educ. 190 104599Sarkar S, Agrawal S, Baker T, Maddikunta P K and Gadekallu T R 2022 Catalysis of neural activation functions: adaptive feed-forward training for big data applications Applied Intelligence vol 52.12 p 13364-83
[14]. Han J and Kamber M 2006 Data Mining Concepts and Techniques 2nd edn (San Francisco, CA: Morgan Kaufmann)
[15]. Arias Ortiz E and Dehon C 2013 Roads to success in the Belgian French community’s higher education system: predictors of dropout and degree completion at the Université Libre de Bruxelles Res. High. Educ. 54 693–723
[16]. Huang S and Fang N 2013 Predicting student academic performance in an engineering dynamics course: a comparison of four types of predictive mathematical models Comput. Educ. 61 133–45
[17]. Cristianini N and Shawe-Taylor J 2000 An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods (Cambridge: Cambridge University Press)
[18]. Vapnik V N 1998 Statistical Learning Theory (Hoboken, NJ: John Wiley & Sons)
[19]. Breiman L 2001 Random forests Mach. Learn. 45 5–32
[20]. Breiman L, Friedman J H, Olshen R A and Stone C J 1984 Classification and Regression Trees (New York, NY: CRC Press) pp 246–280
[21]. Domingos P 2012 A few useful things to know about machine learning Commun. ACM 55 78-87