The role of demographic and academic features in a student performance prediction
Educational Data Mining is widely used for predicting student's performance. It’s a challenging task because a plethora of features related to demographics, personality traits, socio-economic, and environmental may affect students' performance. Such varying features may depend on the level...
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| Published in | Scientific reports Vol. 12; no. 1; pp. 12508 - 9 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
22.07.2022
Nature Publishing Group Nature Portfolio |
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| Online Access | Get full text |
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| Abstract | Educational Data Mining is widely used for predicting student's performance. It’s a challenging task because a plethora of features related to demographics, personality traits, socio-economic, and environmental may affect students' performance. Such varying features may depend on the level of study, program offered, nature of subject, and geographical location. This study attempted to predict the final semester’s results of students studying Doctor of Veterinary Medicine (DVM) based on their pre-admission academic achievements, demographics, and first semester performance. The imbalanced data led to non-generic prediction models, so it was addressed through synthetic minority oversampling technique. Among five prediction models, the Support Vector Machine led the best with 92% accuracy. The decision tree model identified key features affecting students’ performance. The analysis led to the conclusion that marks obtained in Biology, Islamiat, and Urdu at Matric and English at Intermediate level affected the students’ performance in their final semester. The findings provide useful information to predict students’ performance and guidelines for academic institutes’ management regarding improving students’ achievement. It is speculated that adoption of digital transformation may help reduce difficulty faced in data collection and analysis. |
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| AbstractList | Educational Data Mining is widely used for predicting student's performance. It's a challenging task because a plethora of features related to demographics, personality traits, socio-economic, and environmental may affect students' performance. Such varying features may depend on the level of study, program offered, nature of subject, and geographical location. This study attempted to predict the final semester's results of students studying Doctor of Veterinary Medicine (DVM) based on their pre-admission academic achievements, demographics, and first semester performance. The imbalanced data led to non-generic prediction models, so it was addressed through synthetic minority oversampling technique. Among five prediction models, the Support Vector Machine led the best with 92% accuracy. The decision tree model identified key features affecting students' performance. The analysis led to the conclusion that marks obtained in Biology, Islamiat, and Urdu at Matric and English at Intermediate level affected the students' performance in their final semester. The findings provide useful information to predict students' performance and guidelines for academic institutes' management regarding improving students' achievement. It is speculated that adoption of digital transformation may help reduce difficulty faced in data collection and analysis.Educational Data Mining is widely used for predicting student's performance. It's a challenging task because a plethora of features related to demographics, personality traits, socio-economic, and environmental may affect students' performance. Such varying features may depend on the level of study, program offered, nature of subject, and geographical location. This study attempted to predict the final semester's results of students studying Doctor of Veterinary Medicine (DVM) based on their pre-admission academic achievements, demographics, and first semester performance. The imbalanced data led to non-generic prediction models, so it was addressed through synthetic minority oversampling technique. Among five prediction models, the Support Vector Machine led the best with 92% accuracy. The decision tree model identified key features affecting students' performance. The analysis led to the conclusion that marks obtained in Biology, Islamiat, and Urdu at Matric and English at Intermediate level affected the students' performance in their final semester. The findings provide useful information to predict students' performance and guidelines for academic institutes' management regarding improving students' achievement. It is speculated that adoption of digital transformation may help reduce difficulty faced in data collection and analysis. Educational Data Mining is widely used for predicting student's performance. It’s a challenging task because a plethora of features related to demographics, personality traits, socio-economic, and environmental may affect students' performance. Such varying features may depend on the level of study, program offered, nature of subject, and geographical location. This study attempted to predict the final semester’s results of students studying Doctor of Veterinary Medicine (DVM) based on their pre-admission academic achievements, demographics, and first semester performance. The imbalanced data led to non-generic prediction models, so it was addressed through synthetic minority oversampling technique. Among five prediction models, the Support Vector Machine led the best with 92% accuracy. The decision tree model identified key features affecting students’ performance. The analysis led to the conclusion that marks obtained in Biology, Islamiat, and Urdu at Matric and English at Intermediate level affected the students’ performance in their final semester. The findings provide useful information to predict students’ performance and guidelines for academic institutes’ management regarding improving students’ achievement. It is speculated that adoption of digital transformation may help reduce difficulty faced in data collection and analysis. Abstract Educational Data Mining is widely used for predicting student's performance. It’s a challenging task because a plethora of features related to demographics, personality traits, socio-economic, and environmental may affect students' performance. Such varying features may depend on the level of study, program offered, nature of subject, and geographical location. This study attempted to predict the final semester’s results of students studying Doctor of Veterinary Medicine (DVM) based on their pre-admission academic achievements, demographics, and first semester performance. The imbalanced data led to non-generic prediction models, so it was addressed through synthetic minority oversampling technique. Among five prediction models, the Support Vector Machine led the best with 92% accuracy. The decision tree model identified key features affecting students’ performance. The analysis led to the conclusion that marks obtained in Biology, Islamiat, and Urdu at Matric and English at Intermediate level affected the students’ performance in their final semester. The findings provide useful information to predict students’ performance and guidelines for academic institutes’ management regarding improving students’ achievement. It is speculated that adoption of digital transformation may help reduce difficulty faced in data collection and analysis. |
| ArticleNumber | 12508 |
| Author | Choi, Gyu Sang Omar, Muhammad Bokhari, Rahat H. Anwar, Waheed Bilal, Muhammad |
| Author_xml | – sequence: 1 givenname: Muhammad surname: Bilal fullname: Bilal, Muhammad organization: Department of Computer Science & IT, The Islamia University of Bahawalpur – sequence: 2 givenname: Muhammad surname: Omar fullname: Omar, Muhammad email: m.omar.nazeer@gmail.com organization: Department of Data Science, Faculty of Computing, The Islamia University of Bahawalpur, Department of Information and Communication Engineering, Yeungnam University – sequence: 3 givenname: Waheed surname: Anwar fullname: Anwar, Waheed organization: Department of Computer Science, Faculty of Computing, The Islamia University of Bahawalpur – sequence: 4 givenname: Rahat H. surname: Bokhari fullname: Bokhari, Rahat H. organization: Department of Computer Science, University of South Asia – sequence: 5 givenname: Gyu Sang surname: Choi fullname: Choi, Gyu Sang email: castchoi@ynu.ac.kr organization: Department of Information and Communication Engineering, Yeungnam University |
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| Cites_doi | 10.1155/2014/836895 10.1007/s10639-020-10346-6 10.1109/ACCESS.2020.2981905 10.1038/s41598-021-03867-8 10.1111/exsy.12135 10.1016/j.compedu.2019.103676 10.1016/j.compedu.2017.05.007 10.1016/j.compedu.2012.08.015 10.3390/app10113894 10.5688/ajpe81346 10.1109/TLT.2019.2913358 10.1016/j.procs.2015.12.157 10.5815/ijisa.2015.01.05 10.1007/s10462-011-9234-x 10.1016/j.compedu.2019.04.001 10.47678/cjhe.v23i2.183161 10.1613/jair.953 10.1023/A:1010933404324 10.20473/jisebi.7.1.1-10 10.1109/JSTSP.2017.2692560 10.1007/978-3-642-34041-3_27 10.1177/0020720916688484 10.1109/ICETAS.2017.8277884 10.1109/ACCT.2014.105 10.1007/978-3-540-39964-3_62 10.1109/ICSIMA.2013.6717966 10.1109/ICCOINS.2018.8510600 10.1007/BF00116251 |
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| References | Sultana, Khan, Abbas (CR38) 2017; 54 Polyzou, Karypis (CR24) 2019; 12 Asif, Merceron (CR34) 2015 Asif (CR39) 2016; 14 CR17 Yassein, Helali, Mohomad (CR1) 2017; 7 Yin, Gao, Karimi, Zhu (CR30) 2014 Qiu (CR23) 2022 Asif, Hina, Haque (CR15) 2017; 17 CR11 Breiman (CR28) 2001; 45 CR31 Singh, Kaur (CR12) 2016; 7 Hussain, Dahan, Ba-Alwib, Ribata (CR18) 2018; 9 Chawla, Bowyer, Hall, Kegelmeyer (CR26) 2002; 16 Muratov, Lewis, Fourches, Tropsha, Cox (CR25) 2002; 81 Mengash (CR3) 2020; 8 Mishra, Chaudhary (CR13) 2021; 12 Tomasevic, Gvozdenovic, Vranes (CR19) 2020; 143 Kotsiantis (CR6) 2012 Asif, Merceron, Ali, Haider (CR14) 2017 Shaziya, Zaheer, Kavitha (CR20) 2015; 4 Gil, da Cruz Martins, Moro, Costa (CR36) 2021; 26 Shahiri, Husain, Rashid (CR5) 2015; 72 Pavya, Srinivasan (CR33) 2017; 2 Zhang (CR29) 2001 Aggarwal, Mittal, Bali (CR37) 2021; 10 CR9 CR27 Hasan (CR10) 2020; 10 Rizvi, Rienties, Khoja (CR35) 2019; 137 Baker, Yacef (CR2) 2009; 1 Xu, Moon, Member, Van Der (CR8) 2017; 11 Santosa, Lukito (CR4) 2021; 7 CR22 Makhtar, Nawang, Nor, Shamsuddin (CR21) 2017; 95 Pyke, Sheridan (CR32) 1993; 23 Márquez-Vera (CR40) 2016; 33 Asif, Haider, Ali (CR16) 2016; 14 Huang, Fang (CR7) 2013; 61 A Polyzou (15880_CR24) 2019; 12 K Pavya (15880_CR33) 2017; 2 S Sultana (15880_CR38) 2017; 54 15880_CR9 J Xu (15880_CR8) 2017; 11 W Singh (15880_CR12) 2016; 7 L Breiman (15880_CR28) 2001; 45 E Muratov (15880_CR25) 2002; 81 N Tomasevic (15880_CR19) 2020; 143 R Asif (15880_CR14) 2017 S Rizvi (15880_CR35) 2019; 137 R Asif (15880_CR34) 2015 AM Shahiri (15880_CR5) 2015; 72 Y Zhang (15880_CR29) 2001 S Yin (15880_CR30) 2014 15880_CR22 15880_CR27 D Aggarwal (15880_CR37) 2021; 10 R Asif (15880_CR39) 2016; 14 SB Kotsiantis (15880_CR6) 2012 H Shaziya (15880_CR20) 2015; 4 RSJD Baker (15880_CR2) 2009; 1 R Hasan (15880_CR10) 2020; 10 NV Chawla (15880_CR26) 2002; 16 PD Gil (15880_CR36) 2021; 26 S Hussain (15880_CR18) 2018; 9 C Márquez-Vera (15880_CR40) 2016; 33 HA Mengash (15880_CR3) 2020; 8 SW Pyke (15880_CR32) 1993; 23 R Asif (15880_CR16) 2016; 14 15880_CR17 A Mishra (15880_CR13) 2021; 12 RG Santosa (15880_CR4) 2021; 7 F Qiu (15880_CR23) 2022 NA Yassein (15880_CR1) 2017; 7 S Huang (15880_CR7) 2013; 61 15880_CR11 R Asif (15880_CR15) 2017; 17 15880_CR31 M Makhtar (15880_CR21) 2017; 95 |
| References_xml | – year: 2014 ident: CR30 article-title: Study on support vector machine-based fault detection in tennessee eastman process publication-title: Abst. Appl. Anal. vec. doi: 10.1155/2014/836895 – ident: CR22 – volume: 26 start-page: 2165 issue: 2 year: 2021 end-page: 2190 ident: CR36 article-title: A data-driven approach to predict first-year students’ academic success in higher education institutions publication-title: Educ. Inf. Technol. doi: 10.1007/s10639-020-10346-6 – volume: 8 start-page: 55462 year: 2020 end-page: 55470 ident: CR3 article-title: Using data mining techniques to predict student performance to support decision making in university admission systems publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2981905 – year: 2022 ident: CR23 article-title: Predicting students ’ performance in e - learning using learning process and behaviour data publication-title: Sci. Rep. doi: 10.1038/s41598-021-03867-8 – volume: 33 start-page: 107 year: 2016 end-page: 124 ident: CR40 article-title: Early dropout prediction using data mining: A case study with high school students publication-title: Expert Syst. doi: 10.1111/exsy.12135 – volume: 143 start-page: 103676 year: 2020 ident: CR19 article-title: Computers & Education An overview and comparison of supervised data mining techniques for student exam performance prediction publication-title: Comput. Educ. doi: 10.1016/j.compedu.2019.103676 – volume: 4 start-page: 9823 year: 2015 end-page: 9829 ident: CR20 article-title: Prediction of students performance in semester exams using a naïve bayes classifier publication-title: Int. J. Innov. Res. Sci. Eng. Technol. – volume: 2 start-page: 594 issue: 6 year: 2017 end-page: 598 ident: CR33 article-title: Feature selection techniques in data mining: a study publication-title: Int. Jour. Sci. Devel. Res.(IJSDR) – volume: 14 start-page: 374 year: 2016 end-page: 380 ident: CR39 article-title: Prediction of undergraduate student's performance using data mining methods publication-title: Int. J. Comp. Sci. Inf. Secur. (IJCSIS) – volume: 1 start-page: 3 year: 2009 end-page: 16 ident: CR2 article-title: The state of educational data mining in 2009: A review and future visions publication-title: J. Educ. Data Min. – year: 2017 ident: CR14 article-title: Analyzing undergraduate students’ performance using educational data mining publication-title: Comput. Educ. doi: 10.1016/j.compedu.2017.05.007 – volume: 7 start-page: 1 issue: 5 year: 2017 end-page: 5 ident: CR1 article-title: Predicting student academic performance in KSA using data mining techniques publication-title: J. Inf. Technol. Softw. Eng. – volume: 12 start-page: 4063 year: 2021 end-page: 4069 ident: CR13 article-title: Student performance measure by using different classification methods of data mining publication-title: Turk. J. Comput. Math. Educ. – volume: 61 start-page: 133 year: 2013 end-page: 145 ident: CR7 article-title: Computers & Education Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models publication-title: Comput. Educ. doi: 10.1016/j.compedu.2012.08.015 – volume: 10 start-page: 3894 issue: 11 year: 2020 ident: CR10 article-title: Predicting student performance in higher educational institutions using video learning analytics and data mining techniques publication-title: Appl. Sci. doi: 10.3390/app10113894 – ident: CR27 – volume: 17 start-page: 187 issue: 5 year: 2017 end-page: 191 ident: CR15 article-title: Predicting student academic performance using data mining methods publication-title: Int. J. Comput. Sci. Netw. Secur. – volume: 81 start-page: 46 issue: 3 year: 2002 ident: CR25 article-title: Computer-assisted decision support for student admissions based on their predicted academic performance publication-title: Am. J. Pharm. Educ. doi: 10.5688/ajpe81346 – volume: 12 start-page: 237 year: 2019 end-page: 248 ident: CR24 article-title: Feature extraction for next-term prediction of poor student performance publication-title: IEEE Trans. Learn. Technol. doi: 10.1109/TLT.2019.2913358 – volume: 72 start-page: 414 year: 2015 end-page: 422 ident: CR5 article-title: A review on predicting student’s performance using data mining techniques publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2015.12.157 – volume: 95 start-page: 3993 year: 2017 end-page: 4000 ident: CR21 article-title: Analysis on students performance using naive bayes classifier publication-title: J. Theor. Appl. Inf. Technol. – year: 2015 ident: CR34 article-title: Predicting student academic performance at degree level: A case study publication-title: Int. J. Intell. Syst. Technol. Appl. doi: 10.5815/ijisa.2015.01.05 – year: 2012 ident: CR6 article-title: Use of machine learning techniques for educational proposes: a decision support system for forecasting students grades publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-011-9234-x – ident: CR17 – volume: 137 start-page: 32 year: 2019 end-page: 47 ident: CR35 article-title: The role of demographics in online learning; a decision tree based approach publication-title: Comput. Educ. doi: 10.1016/j.compedu.2019.04.001 – ident: CR31 – volume: 23 start-page: 44 issue: 2 year: 1993 end-page: 64 ident: CR32 article-title: Logistic regression analysis of graduate student retention publication-title: Can. J. Higher Educ. doi: 10.47678/cjhe.v23i2.183161 – ident: CR11 – ident: CR9 – volume: 16 start-page: 321 year: 2002 end-page: 357 ident: CR26 article-title: SMOTE: synthetic minority over-sampling technique publication-title: J. Artif. Intell. Res. doi: 10.1613/jair.953 – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: CR28 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 7 start-page: 1 year: 2021 end-page: 10 ident: CR4 article-title: Classification and prediction of students gpa using kmeans clustering algorithm to assist atudent admission process publication-title: J. Inf. Syst. Eng. Bus. Intell. doi: 10.20473/jisebi.7.1.1-10 – volume: 14 start-page: 374 year: 2016 end-page: 380 ident: CR16 article-title: Prediction of undergraduate student ’ s performance using data mining methods publication-title: Int. J. Comput. Sci. Inf. Secur. – volume: 7 start-page: 31 year: 2016 end-page: 36 ident: CR12 article-title: Comparative analysis of classification techniques for predicting computer engineering students’ academic performance publication-title: Int. J. Adv. Res. Comput. Sci. – volume: 10 start-page: 38 year: 2021 end-page: 49 ident: CR37 article-title: Significance of non-academic parameters for predicting student performance using ensemble learning techniques publication-title: Int. J. Syst. Dyn. Appl. – volume: 11 start-page: 742 year: 2017 end-page: 753 ident: CR8 article-title: A machine learning approach for tracking and predicting student performance in degree programs publication-title: IEEE J. Sel. Top. Signal Process. doi: 10.1109/JSTSP.2017.2692560 – volume: 9 start-page: 447 year: 2018 end-page: 459 ident: CR18 article-title: Educational data mining and analysis of students ’ academic performance using WEKA publication-title: Indones. J. Electr. Eng. Comput. Sci. – year: 2001 ident: CR29 article-title: Support vector machine classification algorithm and its application publication-title: Int. Conf. Inf. Comput. Appl. doi: 10.1007/978-3-642-34041-3_27 – volume: 54 start-page: 105 year: 2017 end-page: 118 ident: CR38 article-title: Predicting performance of electrical engineering students using cognitive and non-cognitive features for identification of potential dropouts publication-title: Int. J. Electr. Eng. Educ. doi: 10.1177/0020720916688484 – ident: 15880_CR11 doi: 10.1109/ICETAS.2017.8277884 – volume: 8 start-page: 55462 year: 2020 ident: 15880_CR3 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2981905 – year: 2022 ident: 15880_CR23 publication-title: Sci. Rep. doi: 10.1038/s41598-021-03867-8 – ident: 15880_CR17 doi: 10.1109/ACCT.2014.105 – volume: 16 start-page: 321 year: 2002 ident: 15880_CR26 publication-title: J. Artif. Intell. Res. doi: 10.1613/jair.953 – volume: 7 start-page: 1 issue: 5 year: 2017 ident: 15880_CR1 publication-title: J. Inf. Technol. Softw. Eng. – volume: 10 start-page: 3894 issue: 11 year: 2020 ident: 15880_CR10 publication-title: Appl. Sci. doi: 10.3390/app10113894 – volume: 9 start-page: 447 year: 2018 ident: 15880_CR18 publication-title: Indones. J. Electr. Eng. Comput. Sci. – volume: 33 start-page: 107 year: 2016 ident: 15880_CR40 publication-title: Expert Syst. doi: 10.1111/exsy.12135 – ident: 15880_CR31 doi: 10.1007/978-3-540-39964-3_62 – ident: 15880_CR22 doi: 10.1109/ICSIMA.2013.6717966 – year: 2015 ident: 15880_CR34 publication-title: Int. J. Intell. Syst. Technol. Appl. doi: 10.5815/ijisa.2015.01.05 – volume: 61 start-page: 133 year: 2013 ident: 15880_CR7 publication-title: Comput. Educ. doi: 10.1016/j.compedu.2012.08.015 – year: 2012 ident: 15880_CR6 publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-011-9234-x – volume: 4 start-page: 9823 year: 2015 ident: 15880_CR20 publication-title: Int. J. Innov. Res. Sci. Eng. Technol. – volume: 11 start-page: 742 year: 2017 ident: 15880_CR8 publication-title: IEEE J. Sel. Top. Signal Process. doi: 10.1109/JSTSP.2017.2692560 – volume: 17 start-page: 187 issue: 5 year: 2017 ident: 15880_CR15 publication-title: Int. J. Comput. Sci. Netw. Secur. – volume: 81 start-page: 46 issue: 3 year: 2002 ident: 15880_CR25 publication-title: Am. J. Pharm. Educ. doi: 10.5688/ajpe81346 – ident: 15880_CR9 doi: 10.1109/ICCOINS.2018.8510600 – volume: 10 start-page: 38 year: 2021 ident: 15880_CR37 publication-title: Int. J. Syst. Dyn. Appl. – year: 2014 ident: 15880_CR30 publication-title: Abst. Appl. Anal. vec. doi: 10.1155/2014/836895 – year: 2001 ident: 15880_CR29 publication-title: Int. Conf. Inf. Comput. Appl. doi: 10.1007/978-3-642-34041-3_27 – volume: 2 start-page: 594 issue: 6 year: 2017 ident: 15880_CR33 publication-title: Int. Jour. Sci. Devel. Res.(IJSDR) – volume: 1 start-page: 3 year: 2009 ident: 15880_CR2 publication-title: J. Educ. Data Min. – year: 2017 ident: 15880_CR14 publication-title: Comput. Educ. doi: 10.1016/j.compedu.2017.05.007 – volume: 95 start-page: 3993 year: 2017 ident: 15880_CR21 publication-title: J. Theor. Appl. Inf. Technol. – volume: 7 start-page: 31 year: 2016 ident: 15880_CR12 publication-title: Int. J. Adv. Res. Comput. Sci. – volume: 12 start-page: 4063 year: 2021 ident: 15880_CR13 publication-title: Turk. J. Comput. Math. Educ. – volume: 7 start-page: 1 year: 2021 ident: 15880_CR4 publication-title: J. Inf. Syst. Eng. Bus. Intell. doi: 10.20473/jisebi.7.1.1-10 – volume: 72 start-page: 414 year: 2015 ident: 15880_CR5 publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2015.12.157 – ident: 15880_CR27 doi: 10.1007/BF00116251 – volume: 12 start-page: 237 year: 2019 ident: 15880_CR24 publication-title: IEEE Trans. Learn. Technol. doi: 10.1109/TLT.2019.2913358 – volume: 45 start-page: 5 year: 2001 ident: 15880_CR28 publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 26 start-page: 2165 issue: 2 year: 2021 ident: 15880_CR36 publication-title: Educ. Inf. Technol. doi: 10.1007/s10639-020-10346-6 – volume: 14 start-page: 374 year: 2016 ident: 15880_CR16 publication-title: Int. J. Comput. Sci. Inf. Secur. – volume: 137 start-page: 32 year: 2019 ident: 15880_CR35 publication-title: Comput. Educ. doi: 10.1016/j.compedu.2019.04.001 – volume: 143 start-page: 103676 year: 2020 ident: 15880_CR19 publication-title: Comput. Educ. doi: 10.1016/j.compedu.2019.103676 – volume: 14 start-page: 374 year: 2016 ident: 15880_CR39 publication-title: Int. J. Comp. Sci. Inf. Secur. (IJCSIS) – volume: 23 start-page: 44 issue: 2 year: 1993 ident: 15880_CR32 publication-title: Can. J. Higher Educ. doi: 10.47678/cjhe.v23i2.183161 – volume: 54 start-page: 105 year: 2017 ident: 15880_CR38 publication-title: Int. 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| Title | The role of demographic and academic features in a student performance prediction |
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