Predict student’s performance based on machine learning algorithms

Research Article
Open access

Predict student’s performance based on machine learning algorithms

Yuqi Han 1*
  • 1 Macau University of Science and Technology    
  • *corresponding author hanyuqi49777@163.com
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/17/20230948
ACE Vol.17
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-025-7
ISBN (Online): 978-1-83558-026-4

Abstract

Several studies have used models for students’ academic performance prediction to improve teaching quality. The aim of this study is to use machine learning algorithms to forecast students’ performances using their daily study behaviors and the extent of parents’ concerns about their children’s studies to generate synchronous predictions with daily teaching activities. The data includes study attitudes, behaviors, demographic features, and parents’ concerns about the students. The preprocessed dataset after feature engineering was used to train the models (i.e Support Vector Machine, Decision Tree, Random Forest, And K Nearest Neighbor). Random Forest has the best performance among the algorithms applied. The impact of students’ daily study behavior is highly related to academic achievement, and parents’ impact is also an influencing factor on children’s performances. This study could encourage and motivate parents to care more about their children’s studies with their favorable actions and behaviors. Besides, this study would help students realize the importance of their daily performance and realize it is essential to their final exam grades.

Keywords:

student’s performance, education data mining, machine learning

Han,Y. (2023). Predict student’s performance based on machine learning algorithms. Applied and Computational Engineering,17,233-240.
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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


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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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN:978-1-83558-025-7(Print) / 978-1-83558-026-4(Online)
Editor:Roman Bauer, Marwan Omar, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.17
ISSN:2755-2721(Print) / 2755-273X(Online)

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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