A Systematic Review on Predicting the Performance of Students in Higher Education in Offline Mode Using Machine Learning Techniques
For scholarly organizations, students’ academic performance (AP) computes student achievements in different academic subjects. Therefore, a systematic literature review based on machine learning approaches to improve student performance is proposed. This field creates a way to discover hidden exampl...
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Published in | Wireless personal communications Vol. 133; no. 3; pp. 1643 - 1674 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
New York
Springer US
01.12.2023
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
ISSN | 0929-6212 1572-834X |
DOI | 10.1007/s11277-023-10838-x |
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Abstract | For scholarly organizations, students’ academic performance (AP) computes student achievements in different academic subjects. Therefore, a systematic literature review based on machine learning approaches to improve student performance is proposed. This field creates a way to discover hidden examples from instructive information. Machine learning (ML) techniques are used for performance prediction. It is become a challenge due to its imbalanced dataset. This review aims to identify the best proposals that focus on various ML methods used for the performance analysis of students. This review will become helpful to the teachers in identifying the weak students, and it helps to improve their performance through proper guidance. Thus, it reflects in the student’s background and boosts their growth. It also brings benefits to students, teachers, and institutions. This study focused on applying machine learning techniques to predict students’ performance in recent times. With a systematic approach, the research identified the existing prediction methods and tools used to predict students’ performance and observed the researchers’ type of variables in this research area. In this study, almost more than 100 papers were analyzed to reveal various modern techniques that are commonly used to predict student performance and the goals they need to achieve in this field. The results from the various research will help improve students’ academics and monitor the student’s performance, which would also improve their literacy rate. |
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AbstractList | For scholarly organizations, students’ academic performance (AP) computes student achievements in different academic subjects. Therefore, a systematic literature review based on machine learning approaches to improve student performance is proposed. This field creates a way to discover hidden examples from instructive information. Machine learning (ML) techniques are used for performance prediction. It is become a challenge due to its imbalanced dataset. This review aims to identify the best proposals that focus on various ML methods used for the performance analysis of students. This review will become helpful to the teachers in identifying the weak students, and it helps to improve their performance through proper guidance. Thus, it reflects in the student’s background and boosts their growth. It also brings benefits to students, teachers, and institutions. This study focused on applying machine learning techniques to predict students’ performance in recent times. With a systematic approach, the research identified the existing prediction methods and tools used to predict students’ performance and observed the researchers’ type of variables in this research area. In this study, almost more than 100 papers were analyzed to reveal various modern techniques that are commonly used to predict student performance and the goals they need to achieve in this field. The results from the various research will help improve students’ academics and monitor the student’s performance, which would also improve their literacy rate. |
Author | Rahul Katarya, Rahul |
Author_xml | – sequence: 1 surname: Rahul fullname: Rahul email: rahulzhere023@gmail.com organization: Big Data Analytics and Web Intelligence Laboratory, Department of Computer Science and Engineering, Delhi Technological University – sequence: 2 givenname: Rahul surname: Katarya fullname: Katarya, Rahul organization: Big Data Analytics and Web Intelligence Laboratory, Department of Computer Science and Engineering, Delhi Technological University |
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CitedBy_id | crossref_primary_10_1016_j_ijedro_2024_100433 |
Cites_doi | 10.3991/ijet.v10i1.4189 10.1007/978-3-030-22475-2_1 10.1007/978-3-319-71084-6_55 10.1109/IJCNN.1993.713989 10.1109/ECTI-NCON.2019.8692227 10.1109/FSKD.2016.7603268 10.1016/j.knosys.2013.03.012 10.1186/s40165-014-0010-2 10.1145/3170358.3170410 10.1145/3185089.3185101 10.1109/CNT.2014.7062736 10.1109/IACS.2019.8809106 10.1007/978-3-319-26690-9_5 10.3844/jcssp.2018.654.662 10.1109/ICSIMA.2013.6717966 10.1007/978-981-13-6459-4_18 10.1016/j.ejor.2018.02.031 10.1016/j.compedu.2012.08.015 10.1109/IIAI-AAI.2015.170 10.1109/ISCV49265.2020.9204013 10.1145/2365952.2366027 10.2495/DNE-V11-N3-239-249 10.14786/flr.v1i1.13 10.1016/j.eswa.2013.07.046 10.1016/j.compedu.2018.12.006 10.1109/CIDM.2014.7008697 10.1109/ETCM.2017.8247553 10.1109/ICACCI.2017.8125923 10.1109/INES.2018.8523888 10.1016/j.eswa.2012.02.112 10.1109/BigData.2015.7363847 10.1080/08839510490442058 10.1109/ICCOINS.2018.8510600 10.1007/s10639-017-9645-7 10.1016/j.childyouth.2018.11.030 10.19026/rjaset.9.1403 10.5897/IJSTER2017.0415 10.1145/1140123.1140194 10.1145/3325917.3325919 10.1007/s10758-019-09408-7 10.1109/RITA.2013.2244695 10.1145/2905055.2905150 10.1109/MINTC.2018.8363153 10.1016/j.chb.2017.01.047 10.1016/j.compedu.2019.103676 10.1007/978-3-319-19773-9_59 10.1109/IAC.2018.8780547 10.1109/ACCESS.2020.2986809 10.1109/ICAIIC.2019.8669085 10.3390/app10031042 10.5815/ijmecs.2018.06.01 10.1016/j.nedt.2007.07.012 10.1145/2959100.2959133 10.1109/ITSIM.2008.4631535 10.1186/s40561-022-00192-z 10.1016/j.procs.2015.07.372 10.1145/3230977.3231012 10.1109/ACCESS.2022.3151652 10.1007/978-3-319-91192-2_21 10.1007/978-3-319-74781-1_3 10.1016/j.eswa.2011.05.048 10.1109/ISDA.2009.15 10.1007/s10462-018-9620-8 10.1109/ICACCI.2016.7732181 10.1145/3318396.3318419 10.1109/AICAI.2019.8701260 10.1109/ICETECH.2015.7275025 10.1177/0020720916688484 10.1007/978-3-030-03493-1_14 10.1080/0142159X.2017.1309376 10.1109/ICCED.2018.00055 10.1109/IIAI-AAI.2017.73 10.3233/IFS-141229 10.1007/978-3-319-25159-2_58 10.1109/PDGC.2014.7030728 10.1109/ICIEV.2016.7760058 10.1109/HSI.2017.8005026 10.1109/ISET.2015.33 10.1145/3241815.3241875 10.1109/WEEF.2017.8467150 10.1016/j.eswa.2013.08.042 10.1109/INAPR.2018.8626856 10.1016/j.procs.2016.09.380 10.1007/978-3-319-95168-3_2 10.1016/j.protcy.2016.08.114 10.1007/978-3-319-11200-8_5 10.1016/j.procs.2015.12.157 10.1587/transinf.2016DAP0026 10.1007/978-3-319-44159-7_15 10.1145/3027385.3029479 10.1007/978-3-319-06773-5_16 10.1016/j.iheduc.2018.02.001 10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.315 10.1007/978-981-10-2750-5_35 10.1007/978-3-319-23485-4_42 10.1002/9781118548387 10.1007/s11135-017-0644-y 10.1016/j.lindif.2017.05.003 10.1145/3099023.3099034 10.1109/ICICI.2017.8365371 10.3390/electronics11071005 10.1109/MECON53876.2022.9751956 10.1109/ICCSE.2016.7581557 10.1109/HICSS.2016.16 10.9734/cjast/2021/v40i631320 |
ContentType | Journal Article |
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Keywords | Regression Educational data mining Student performance prediction Clustering Classification |
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References | CR39 Jishan, Rashu, Haque, Rahman (CR71) 2015; 2 CR36 CR35 Pallathadka, Wenda, Ramirez-Asís, Asís-López, Flores-Albornoz, Phasinam (CR5) 2023; 80 Chung, Lee (CR25) 2019; 96 CR34 Xu, Liu, Sun, Zou, Huang, Zhang (CR51) 2017; 100 CR32 Saa, Al-Emran, Shaalan (CR112) 2019; 24 CR31 CR30 Nandeshwar, Menzies, Nelson (CR15) 2011; 38 Navamani, Kannammal (CR11) 2015; 9 Feng, Fan, Chen (CR12) 2022; 10 Tan, Shao (CR19) 2015 Kabakchieva (CR4) 2012; 1 CR49 CR48 CR47 CR46 Manne, Kantheti (CR7) 2021; 40 CR44 CR43 CR42 CR40 Gray, Perkins (CR24) 2019; 131 Masci, Johnes, Agasisti (CR87) 2018; 269 Şen, Uçar, Delen (CR105) 2012; 39 Rochman, Rachmad, Damayanti (CR66) 2018; 7 CR59 CR58 CR57 CR56 Poudyal, Mohammadi-Aragh, Ball (CR111) 2022; 11 CR55 CR54 CR53 CR52 CR50 Hamsa, Indiradevi, Kizhakkethottam (CR73) 2016; 25 Do, Chen (CR78) 2013; 10 Bobadilla, Ortega, Hernando, Gutiérrez (CR45) 2013; 46 Fonteyne, Duyck, De Fruyt (CR20) 2017; 56 Rastrollo-Guerrero, Gomez-Pulido, Duran-Dominguez (CR14) 2020; 10 CR68 CR67 CR64 CR62 CR61 Moseley, Mead (CR82) 2008; 28 Dimililer (CR115) 2018; 52 Iyanda, Ninan, Ajayi, Anyabolu (CR63) 2018; 10 Costa, Fonseca, Santana, de Araújo, Rego (CR86) 2017; 73 Arora, Saini (CR81) 2013; 2 Hosmer, Lemeshow, Sturdivant (CR17) 2013 Hussain, Zhu, Zhang, Abidi, Ali (CR26) 2019; 52 CR79 Shanthini, Vinodhini, Chandrasekaran (CR37) 2018; 14 Bharara, Sabitha, Bansal (CR100) 2018; 23 CR77 CR76 Sultana, Khan, Abbas (CR90) 2017; 54 Huang, Fang (CR27) 2013; 61 CR75 CR113 CR114 CR72 Tomasevic, Gvozdenovic, Vranes (CR70) 2020; 143 CR110 CR6 CR8 CR89 CR88 Yağcı (CR1) 2022; 9 CR85 CR83 Howard, Meehan, Parnell (CR38) 2018; 37 CR80 Ahmed, Rizaner, Ulusoy (CR106) 2016; 102 Kaur, Singh, Josan (CR101) 2015; 57 Kotsiantis, Pierrakeas, Pintelas (CR9) 2004; 18 Saqr, Fors, Tedre (CR84) 2017; 39 CR18 Márquez-Vera, Morales, Soto (CR103) 2013; 8 Chamillard (CR74) 2006; 38 CR13 Abidin, Dom (CR3) 2012; 41 CR99 CR10 CR98 CR97 CR96 Alloghani, Al-Jumeily, Mustafina, Hussain, Aljaaf (CR41) 2020 CR95 Shahiri, Husain (CR69) 2015; 72 CR94 CR93 CR92 CR91 Chen, Do (CR116) 2014; 27 CR29 CR28 Ghorbani, Ghousi (CR33) 2020; 8 Thammasiri, Delen, Meesad, Kasap (CR16) 2014; 41 CR23 CR22 CR104 CR21 CR102 Peña-Ayala (CR2) 2014; 41 Musso, Kyndt, Cascallar, Dochy (CR65) 2013; 1 Adewale, Bamidele, Lateef (CR60) 2018; 9 CR108 CR109 CR107 10838_CR62 10838_CR64 10838_CR61 N Arora (10838_CR81) 2013; 2 AM Shahiri (10838_CR69) 2015; 72 B Şen (10838_CR105) 2012; 39 E Howard (10838_CR38) 2018; 37 P Kaur (10838_CR101) 2015; 57 H Hamsa (10838_CR73) 2016; 25 JY Chung (10838_CR25) 2019; 96 M Alloghani (10838_CR41) 2020 C Masci (10838_CR87) 2018; 269 EMS Rochman (10838_CR66) 2018; 7 10838_CR67 10838_CR68 JMA Navamani (10838_CR11) 2015; 9 AT Chamillard (10838_CR74) 2006; 38 10838_CR52 10838_CR53 10838_CR54 10838_CR50 AM Adewale (10838_CR60) 2018; 9 A Peña-Ayala (10838_CR2) 2014; 41 A Nandeshwar (10838_CR15) 2011; 38 AA Saa (10838_CR112) 2019; 24 10838_CR59 CC Gray (10838_CR24) 2019; 131 10838_CR55 10838_CR56 10838_CR57 10838_CR58 10838_CR40 10838_CR42 10838_CR43 D Thammasiri (10838_CR16) 2014; 41 10838_CR114 S Poudyal (10838_CR111) 2022; 11 10838_CR113 K Dimililer (10838_CR115) 2018; 52 10838_CR110 M Yağcı (10838_CR1) 2022; 9 10838_CR48 10838_CR49 M Tan (10838_CR19) 2015 10838_CR44 10838_CR46 R Manne (10838_CR7) 2021; 40 10838_CR47 10838_CR30 10838_CR31 R Ghorbani (10838_CR33) 2020; 8 S Bharara (10838_CR100) 2018; 23 10838_CR32 A Shanthini (10838_CR37) 2018; 14 10838_CR109 10838_CR108 10838_CR107 N Tomasevic (10838_CR70) 2020; 143 10838_CR104 ST Jishan (10838_CR71) 2015; 2 10838_CR102 M Hussain (10838_CR26) 2019; 52 LG Moseley (10838_CR82) 2008; 28 S Kotsiantis (10838_CR9) 2004; 18 10838_CR39 10838_CR34 10838_CR35 EB Costa (10838_CR86) 2017; 73 10838_CR36 K Xu (10838_CR51) 2017; 100 10838_CR21 G Feng (10838_CR12) 2022; 10 H Pallathadka (10838_CR5) 2023; 80 MF Musso (10838_CR65) 2013; 1 10838_CR28 10838_CR29 10838_CR22 S Sultana (10838_CR90) 2017; 54 10838_CR23 10838_CR95 10838_CR96 10838_CR97 10838_CR10 10838_CR98 10838_CR91 10838_CR92 10838_CR93 10838_CR94 M Saqr (10838_CR84) 2017; 39 J Bobadilla (10838_CR45) 2013; 46 JL Rastrollo-Guerrero (10838_CR14) 2020; 10 10838_CR18 10838_CR99 10838_CR13 10838_CR85 10838_CR80 JF Chen (10838_CR116) 2014; 27 QH Do (10838_CR78) 2013; 10 10838_CR83 L Fonteyne (10838_CR20) 2017; 56 10838_CR88 AR Iyanda (10838_CR63) 2018; 10 10838_CR89 10838_CR75 10838_CR76 C Márquez-Vera (10838_CR103) 2013; 8 10838_CR72 AM Ahmed (10838_CR106) 2016; 102 D Kabakchieva (10838_CR4) 2012; 1 DW Hosmer (10838_CR17) 2013 10838_CR6 B Abidin (10838_CR3) 2012; 41 10838_CR77 10838_CR79 10838_CR8 S Huang (10838_CR27) 2013; 61 |
References_xml | – volume: 61 start-page: 133 year: 2013 end-page: 145 ident: CR27 article-title: Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models publication-title: Computers & Education – ident: CR22 – ident: CR97 – volume: 9 start-page: 262 issue: 4 year: 2015 end-page: 271 ident: CR11 article-title: Predicting performance of schools by applying data mining techniques on public examination results publication-title: Research Journal of Applied Sciences, Engineering and Technology – ident: CR68 – volume: 18 start-page: 411 issue: 5 year: 2004 end-page: 426 ident: CR9 article-title: Predicting students’ performance in distance learning using machine learning techniques publication-title: Applied Artificial Intelligence – ident: CR39 – volume: 56 start-page: 34 year: 2017 end-page: 48 ident: CR20 article-title: Program-specific prediction of academic achievement on the basis of cognitive and non-cognitive factors publication-title: Learning and Individual Differences – volume: 1 start-page: 686 issue: 4 year: 2012 end-page: 690 ident: CR4 article-title: Student performance prediction by using data mining classification algorithms publication-title: International Journal of Computer Science and Management Research – volume: 96 start-page: 346 year: 2019 end-page: 353 ident: CR25 article-title: Drop-out early warning systems for high school students using machine learning publication-title: Children and Youth Services Review – volume: 10 start-page: 396 issue: 12 year: 2013 end-page: 405 ident: CR78 article-title: A comparative study of hierarchical ANFIS and ANN in predicting student academic performance publication-title: WSEAS Transactions on Information Science and Applications – volume: 2 start-page: 1 issue: 1 year: 2015 end-page: 25 ident: CR71 article-title: Improving accuracy of students’ final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique publication-title: Decision Analytics – ident: CR54 – volume: 25 start-page: 326 year: 2016 end-page: 332 ident: CR73 article-title: Student academic performance prediction model using decision tree and fuzzy genetic algorithm publication-title: Procedia Technology – ident: CR80 – ident: CR77 – ident: CR8 – ident: CR42 – volume: 11 start-page: 1005 issue: 7 year: 2022 ident: CR111 article-title: Prediction of student academic performance using a hybrid 2D CNN model publication-title: Electronics – ident: CR92 – ident: CR88 – year: 2015 ident: CR19 article-title: Prediction of student drop-out in e-Learning program through the use of machine learning method publication-title: International Journal of Emerging Technologies in Learning doi: 10.3991/ijet.v10i1.4189 – year: 2013 ident: CR17 publication-title: Applied logistic regression – ident: CR57 – volume: 41 start-page: 105 year: 2012 end-page: 109 ident: CR3 article-title: Prediction of preclinical academic performance using ANFIS model publication-title: Int Proc Econ Dev Res – volume: 131 start-page: 22 year: 2019 end-page: 32 ident: CR24 article-title: Utilizing early engagement and machine learning to predict student outcomes publication-title: Computers & Education – volume: 2 start-page: 4425 issue: 9 year: 2013 end-page: 4432 ident: CR81 article-title: A fuzzy probabilistic neural network for student’s academic performance prediction publication-title: International Journal of Innovative Research in Science, Engineering and Technology – ident: CR36 – ident: CR85 – volume: 9 start-page: 11 issue: 1 year: 2022 ident: CR1 article-title: Educational data mining: Prediction of students’ academic performance using machine learning algorithms publication-title: Smart Learning Environments – ident: CR109 – year: 2020 ident: CR41 article-title: A systematic review on supervised and unsupervised machine learning algorithms for data science publication-title: Supervised and Unsupervised Learning for Data Science doi: 10.1007/978-3-030-22475-2_1 – volume: 40 start-page: 78 issue: 6 year: 2021 end-page: 89 ident: CR7 article-title: Application of artificial intelligence in healthcare: Chances and challenges publication-title: Current Journal of Applied Science and Technology – ident: CR18 – ident: CR91 – ident: CR47 – ident: CR72 – ident: CR89 – ident: CR30 – ident: CR10 – ident: CR6 – ident: CR108 – volume: 14 start-page: 654 issue: 5 year: 2018 end-page: 662 ident: CR37 article-title: Predicting students’ academic performance in the university using meta decision tree classifiers publication-title: Journal of Computer Science – ident: CR94 – ident: CR44 – volume: 38 start-page: 14984 issue: 12 year: 2011 end-page: 14996 ident: CR15 article-title: Learning patterns of university student retention publication-title: Expert Systems with Applications – volume: 23 start-page: 957 issue: 2 year: 2018 end-page: 984 ident: CR100 article-title: Application of learning analytics using clustering data Mining for Students’ disposition analysis publication-title: Education and Information Technologies – ident: CR52 – ident: CR13 – ident: CR114 – volume: 10 start-page: 19558 year: 2022 end-page: 19571 ident: CR12 article-title: Analysis and prediction of students’ academic performance based on educational data mining publication-title: IEEE Access – ident: CR55 – ident: CR83 – volume: 39 start-page: 757 issue: 7 year: 2017 end-page: 767 ident: CR84 article-title: How learning analytics can early predict under-achieving students in a blended medical education course publication-title: Medical teacher – volume: 143 start-page: 103676 year: 2020 ident: CR70 article-title: An overview and comparison of supervised data mining techniques for student exam performance prediction publication-title: Computers & Education – volume: 28 start-page: 469 issue: 4 year: 2008 end-page: 475 ident: CR82 article-title: Predicting who will drop out of nursing courses: A machine learning exercise publication-title: Nurse education today – ident: CR102 – ident: CR49 – ident: CR93 – volume: 38 start-page: 260 issue: 3 year: 2006 end-page: 264 ident: CR74 article-title: Using student performance predictions in a computer science curriculum publication-title: ACM SIGCSE Bulletin – ident: CR35 – ident: CR29 – ident: CR61 – volume: 52 start-page: 651 issue: 1 year: 2018 end-page: 662 ident: CR115 article-title: Use of Intelligent Student Mood Classification System (ISMCS) to achieve high quality in education publication-title: Quality & quantity – ident: CR58 – ident: CR21 – volume: 9 start-page: 1 issue: 1 year: 2018 end-page: 8 ident: CR60 article-title: Predictive modelling and analysis of academic performance of secondary school students: Artificial Neural Network approach publication-title: International Journal of Science and Technology Education Research – ident: CR46 – volume: 8 start-page: 67899 year: 2020 end-page: 67911 ident: CR33 article-title: Comparing different resampling methods in predicting Students’ performance using machine learning techniques publication-title: IEEE Access – volume: 10 start-page: 1 issue: 6 year: 2018 end-page: 9 ident: CR63 article-title: Predicting student academic performance in computer science courses: A comparison of neural network models publication-title: International Journal of Modern Education & Computer Science – ident: CR96 – ident: CR67 – ident: CR75 – ident: CR50 – volume: 39 start-page: 9468 issue: 10 year: 2012 end-page: 9476 ident: CR105 article-title: Predicting and analyzing secondary education placement-test scores: A data mining approach publication-title: Expert Systems with Applications – volume: 269 start-page: 1072 issue: 3 year: 2018 end-page: 1085 ident: CR87 article-title: Student and school performance across countries: A machine learning approach publication-title: European Journal of Operational Research – ident: CR32 – volume: 24 start-page: 567 issue: 4 year: 2019 end-page: 598 ident: CR112 article-title: Factors affecting students’ performance in higher education: A systematic review of predictive data mining techniques publication-title: Technology, Knowledge and Learning – volume: 52 start-page: 381 issue: 1 year: 2019 end-page: 407 ident: CR26 article-title: Using machine learning to predict student difficulties from learning session data publication-title: Artificial Intelligence Review – volume: 10 start-page: 1042 issue: 3 year: 2020 ident: CR14 article-title: Analyzing and predicting students’ performance by means of machine learning: A review publication-title: Applied sciences – volume: 37 start-page: 66 year: 2018 end-page: 75 ident: CR38 article-title: Contrasting prediction methods for early warning systems at undergraduate level publication-title: The Internet and Higher Education – ident: CR64 – volume: 41 start-page: 321 issue: 2 year: 2014 end-page: 330 ident: CR16 article-title: A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition publication-title: Expert Systems with Applications – volume: 1 start-page: 42 issue: 1 year: 2013 end-page: 71 ident: CR65 article-title: Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks publication-title: Frontline Learning Research – ident: CR99 – ident: CR95 – ident: CR43 – volume: 72 start-page: 414 year: 2015 end-page: 422 ident: CR69 article-title: A review on predicting student’s performance using data mining techniques publication-title: Procedia Computer Science – ident: CR53 – volume: 27 start-page: 2551 issue: 5 year: 2014 end-page: 2561 ident: CR116 article-title: A cooperative cuckoo search–hierarchical adaptive neuro-fuzzy inference system approach for predicting student academic performance publication-title: Journal of Intelligent & Fuzzy Systems – ident: CR113 – volume: 100 start-page: 768 issue: 4 year: 2017 end-page: 775 ident: CR51 article-title: Improve the prediction of student performance with hint’s assistance based on an efficient non-negative factorization publication-title: IEICE Transactions on Information and Systems – ident: CR79 – ident: CR56 – volume: 57 start-page: 500 year: 2015 end-page: 508 ident: CR101 article-title: Classification and prediction based data mining algorithms to predict slow learners in education sector publication-title: Procedia Computer Science – ident: CR40 – volume: 73 start-page: 247 year: 2017 end-page: 256 ident: CR86 article-title: Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses publication-title: Computers in Human Behavior – ident: CR98 – volume: 8 start-page: 7 issue: 1 year: 2013 end-page: 14 ident: CR103 article-title: Predicting school failure and drop-out by using data mining techniques publication-title: IEEE Revista Iberoamericana de Tecnologias del Aprendizaje – ident: CR104 – ident: CR23 – volume: 102 start-page: 137 year: 2016 end-page: 142 ident: CR106 article-title: Using data mining to predict instructor performance publication-title: Procedia Computer Science – volume: 54 start-page: 105 issue: 2 year: 2017 end-page: 118 ident: CR90 article-title: Predicting performance of electrical engineering students using cognitive and non-cognitive features for identification of potential drop-outs publication-title: International Journal of Electrical Engineering Education – volume: 46 start-page: 109 year: 2013 end-page: 132 ident: CR45 article-title: Recommender systems survey publication-title: Knowledge-Based Systems – ident: CR48 – volume: 80 start-page: 3782 year: 2023 end-page: 3785 ident: CR5 article-title: Classification and prediction of student performance data using various machine learning algorithms publication-title: Materials today: Proceedings – ident: CR31 – volume: 41 start-page: 1432 issue: 4 year: 2014 end-page: 1462 ident: CR2 article-title: Educational data mining: A survey and a data mining-based analysis of recent works publication-title: Expert systems with applications – ident: CR34 – ident: CR110 – volume: 7 start-page: 5 issue: 2 year: 2018 end-page: 10 ident: CR66 article-title: Predicting the final result of student national test with extreme learning machine publication-title: Pancaran Pendidikan – ident: CR59 – ident: CR76 – ident: CR107 – ident: CR28 – ident: CR62 – ident: 10838_CR35 doi: 10.1007/978-3-319-71084-6_55 – ident: 10838_CR61 doi: 10.1109/IJCNN.1993.713989 – ident: 10838_CR95 doi: 10.1109/ECTI-NCON.2019.8692227 – ident: 10838_CR64 doi: 10.1109/FSKD.2016.7603268 – volume: 46 start-page: 109 year: 2013 ident: 10838_CR45 publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2013.03.012 – volume: 2 start-page: 1 issue: 1 year: 2015 ident: 10838_CR71 publication-title: Decision Analytics doi: 10.1186/s40165-014-0010-2 – ident: 10838_CR21 doi: 10.1145/3170358.3170410 – ident: 10838_CR97 doi: 10.1145/3185089.3185101 – ident: 10838_CR68 doi: 10.1109/CNT.2014.7062736 – year: 2015 ident: 10838_CR19 publication-title: International Journal of Emerging Technologies in Learning doi: 10.3991/ijet.v10i1.4189 – ident: 10838_CR72 doi: 10.1109/IACS.2019.8809106 – ident: 10838_CR31 doi: 10.1007/978-3-319-26690-9_5 – ident: 10838_CR77 – volume: 1 start-page: 686 issue: 4 year: 2012 ident: 10838_CR4 publication-title: International Journal of Computer Science and Management Research – volume: 14 start-page: 654 issue: 5 year: 2018 ident: 10838_CR37 publication-title: Journal of Computer Science doi: 10.3844/jcssp.2018.654.662 – ident: 10838_CR62 doi: 10.1109/ICSIMA.2013.6717966 – ident: 10838_CR113 doi: 10.1007/978-981-13-6459-4_18 – volume: 269 start-page: 1072 issue: 3 year: 2018 ident: 10838_CR87 publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2018.02.031 – volume: 61 start-page: 133 year: 2013 ident: 10838_CR27 publication-title: Computers & Education doi: 10.1016/j.compedu.2012.08.015 – ident: 10838_CR40 doi: 10.1109/IIAI-AAI.2015.170 – ident: 10838_CR6 doi: 10.1109/ISCV49265.2020.9204013 – ident: 10838_CR52 doi: 10.1145/2365952.2366027 – ident: 10838_CR83 – ident: 10838_CR13 – ident: 10838_CR39 doi: 10.2495/DNE-V11-N3-239-249 – volume: 1 start-page: 42 issue: 1 year: 2013 ident: 10838_CR65 publication-title: Frontline Learning Research doi: 10.14786/flr.v1i1.13 – volume: 41 start-page: 321 issue: 2 year: 2014 ident: 10838_CR16 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2013.07.046 – volume: 131 start-page: 22 year: 2019 ident: 10838_CR24 publication-title: Computers & Education doi: 10.1016/j.compedu.2018.12.006 – volume: 10 start-page: 396 issue: 12 year: 2013 ident: 10838_CR78 publication-title: WSEAS Transactions on Information Science and Applications – ident: 10838_CR28 doi: 10.1109/CIDM.2014.7008697 – ident: 10838_CR85 doi: 10.1109/ETCM.2017.8247553 – ident: 10838_CR67 doi: 10.1109/ICACCI.2017.8125923 – ident: 10838_CR22 doi: 10.1109/INES.2018.8523888 – volume: 39 start-page: 9468 issue: 10 year: 2012 ident: 10838_CR105 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2012.02.112 – volume: 2 start-page: 4425 issue: 9 year: 2013 ident: 10838_CR81 publication-title: International Journal of Innovative Research in Science, Engineering and Technology – ident: 10838_CR58 doi: 10.1109/BigData.2015.7363847 – volume: 18 start-page: 411 issue: 5 year: 2004 ident: 10838_CR9 publication-title: Applied Artificial Intelligence doi: 10.1080/08839510490442058 – ident: 10838_CR98 doi: 10.1109/ICCOINS.2018.8510600 – volume: 23 start-page: 957 issue: 2 year: 2018 ident: 10838_CR100 publication-title: Education and Information Technologies doi: 10.1007/s10639-017-9645-7 – volume: 96 start-page: 346 year: 2019 ident: 10838_CR25 publication-title: Children and Youth Services Review doi: 10.1016/j.childyouth.2018.11.030 – volume: 9 start-page: 262 issue: 4 year: 2015 ident: 10838_CR11 publication-title: Research Journal of Applied Sciences, Engineering and Technology doi: 10.19026/rjaset.9.1403 – volume: 9 start-page: 1 issue: 1 year: 2018 ident: 10838_CR60 publication-title: International Journal of Science and Technology Education Research doi: 10.5897/IJSTER2017.0415 – volume: 38 start-page: 260 issue: 3 year: 2006 ident: 10838_CR74 publication-title: ACM SIGCSE Bulletin doi: 10.1145/1140123.1140194 – ident: 10838_CR80 doi: 10.1145/3325917.3325919 – volume: 24 start-page: 567 issue: 4 year: 2019 ident: 10838_CR112 publication-title: Technology, Knowledge and Learning doi: 10.1007/s10758-019-09408-7 – volume: 8 start-page: 7 issue: 1 year: 2013 ident: 10838_CR103 publication-title: IEEE Revista Iberoamericana de Tecnologias del Aprendizaje doi: 10.1109/RITA.2013.2244695 – ident: 10838_CR30 doi: 10.1145/2905055.2905150 – ident: 10838_CR44 doi: 10.1109/MINTC.2018.8363153 – volume: 73 start-page: 247 year: 2017 ident: 10838_CR86 publication-title: Computers in Human Behavior doi: 10.1016/j.chb.2017.01.047 – volume: 143 start-page: 103676 year: 2020 ident: 10838_CR70 publication-title: Computers & Education doi: 10.1016/j.compedu.2019.103676 – ident: 10838_CR53 – ident: 10838_CR46 doi: 10.1007/978-3-319-19773-9_59 – ident: 10838_CR110 doi: 10.1109/IAC.2018.8780547 – volume: 8 start-page: 67899 year: 2020 ident: 10838_CR33 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2986809 – ident: 10838_CR94 doi: 10.1109/ICAIIC.2019.8669085 – volume: 10 start-page: 1042 issue: 3 year: 2020 ident: 10838_CR14 publication-title: Applied sciences doi: 10.3390/app10031042 – volume: 10 start-page: 1 issue: 6 year: 2018 ident: 10838_CR63 publication-title: International Journal of Modern Education & Computer Science doi: 10.5815/ijmecs.2018.06.01 – volume: 28 start-page: 469 issue: 4 year: 2008 ident: 10838_CR82 publication-title: Nurse education today doi: 10.1016/j.nedt.2007.07.012 – ident: 10838_CR48 doi: 10.1145/2959100.2959133 – ident: 10838_CR75 doi: 10.1109/ITSIM.2008.4631535 – volume: 9 start-page: 11 issue: 1 year: 2022 ident: 10838_CR1 publication-title: Smart Learning Environments doi: 10.1186/s40561-022-00192-z – volume: 57 start-page: 500 year: 2015 ident: 10838_CR101 publication-title: Procedia Computer Science doi: 10.1016/j.procs.2015.07.372 – ident: 10838_CR88 doi: 10.1145/3230977.3231012 – volume: 10 start-page: 19558 year: 2022 ident: 10838_CR12 publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3151652 – ident: 10838_CR114 doi: 10.1007/978-3-319-91192-2_21 – ident: 10838_CR34 doi: 10.1007/978-3-319-74781-1_3 – volume: 38 start-page: 14984 issue: 12 year: 2011 ident: 10838_CR15 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2011.05.048 – ident: 10838_CR104 doi: 10.1109/ISDA.2009.15 – volume: 52 start-page: 381 issue: 1 year: 2019 ident: 10838_CR26 publication-title: Artificial Intelligence Review doi: 10.1007/s10462-018-9620-8 – ident: 10838_CR32 doi: 10.1109/ICACCI.2016.7732181 – ident: 10838_CR109 doi: 10.1145/3318396.3318419 – ident: 10838_CR93 doi: 10.1109/AICAI.2019.8701260 – ident: 10838_CR102 doi: 10.1109/ICETECH.2015.7275025 – volume: 54 start-page: 105 issue: 2 year: 2017 ident: 10838_CR90 publication-title: International Journal of Electrical Engineering Education doi: 10.1177/0020720916688484 – ident: 10838_CR55 doi: 10.1007/978-3-030-03493-1_14 – volume: 39 start-page: 757 issue: 7 year: 2017 ident: 10838_CR84 publication-title: Medical teacher doi: 10.1080/0142159X.2017.1309376 – ident: 10838_CR96 doi: 10.1109/ICCED.2018.00055 – ident: 10838_CR36 doi: 10.1109/IIAI-AAI.2017.73 – volume: 27 start-page: 2551 issue: 5 year: 2014 ident: 10838_CR116 publication-title: Journal of Intelligent & Fuzzy Systems doi: 10.3233/IFS-141229 – ident: 10838_CR50 doi: 10.1007/978-3-319-25159-2_58 – ident: 10838_CR92 doi: 10.1109/PDGC.2014.7030728 – ident: 10838_CR76 doi: 10.1109/ICIEV.2016.7760058 – ident: 10838_CR107 doi: 10.1109/HSI.2017.8005026 – ident: 10838_CR42 doi: 10.1109/ISET.2015.33 – volume: 7 start-page: 5 issue: 2 year: 2018 ident: 10838_CR66 publication-title: Pancaran Pendidikan – ident: 10838_CR47 doi: 10.1145/3241815.3241875 – ident: 10838_CR57 doi: 10.1109/WEEF.2017.8467150 – ident: 10838_CR8 – volume: 41 start-page: 1432 issue: 4 year: 2014 ident: 10838_CR2 publication-title: Expert systems with applications doi: 10.1016/j.eswa.2013.08.042 – ident: 10838_CR99 doi: 10.1109/INAPR.2018.8626856 – volume: 102 start-page: 137 year: 2016 ident: 10838_CR106 publication-title: Procedia Computer Science doi: 10.1016/j.procs.2016.09.380 – ident: 10838_CR23 doi: 10.1007/978-3-319-95168-3_2 – volume: 25 start-page: 326 year: 2016 ident: 10838_CR73 publication-title: Procedia Technology doi: 10.1016/j.protcy.2016.08.114 – ident: 10838_CR56 doi: 10.1007/978-3-319-11200-8_5 – volume: 72 start-page: 414 year: 2015 ident: 10838_CR69 publication-title: Procedia Computer Science doi: 10.1016/j.procs.2015.12.157 – volume: 100 start-page: 768 issue: 4 year: 2017 ident: 10838_CR51 publication-title: IEICE Transactions on Information and Systems doi: 10.1587/transinf.2016DAP0026 – ident: 10838_CR59 doi: 10.1007/978-3-319-44159-7_15 – ident: 10838_CR79 doi: 10.1145/3027385.3029479 – ident: 10838_CR49 doi: 10.1007/978-3-319-06773-5_16 – volume: 37 start-page: 66 year: 2018 ident: 10838_CR38 publication-title: The Internet and Higher Education doi: 10.1016/j.iheduc.2018.02.001 – ident: 10838_CR18 doi: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.315 – ident: 10838_CR43 doi: 10.1007/978-981-10-2750-5_35 – ident: 10838_CR89 doi: 10.1007/978-3-319-23485-4_42 – volume-title: Applied logistic regression year: 2013 ident: 10838_CR17 doi: 10.1002/9781118548387 – volume: 52 start-page: 651 issue: 1 year: 2018 ident: 10838_CR115 publication-title: Quality & quantity doi: 10.1007/s11135-017-0644-y – volume: 41 start-page: 105 year: 2012 ident: 10838_CR3 publication-title: Int Proc Econ Dev Res – volume: 56 start-page: 34 year: 2017 ident: 10838_CR20 publication-title: Learning and Individual Differences doi: 10.1016/j.lindif.2017.05.003 – ident: 10838_CR54 doi: 10.1145/3099023.3099034 – ident: 10838_CR108 doi: 10.1109/ICICI.2017.8365371 – volume: 11 start-page: 1005 issue: 7 year: 2022 ident: 10838_CR111 publication-title: Electronics doi: 10.3390/electronics11071005 – ident: 10838_CR10 doi: 10.1109/MECON53876.2022.9751956 – ident: 10838_CR29 doi: 10.1109/ICCSE.2016.7581557 – volume: 80 start-page: 3782 year: 2023 ident: 10838_CR5 publication-title: Materials today: Proceedings – ident: 10838_CR91 doi: 10.1109/HICSS.2016.16 – volume: 40 start-page: 78 issue: 6 year: 2021 ident: 10838_CR7 publication-title: Current Journal of Applied Science and Technology doi: 10.9734/cjast/2021/v40i631320 – year: 2020 ident: 10838_CR41 publication-title: Supervised and Unsupervised Learning for Data Science doi: 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Title | A Systematic Review on Predicting the Performance of Students in Higher Education in Offline Mode Using Machine Learning Techniques |
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