Supervised Learning Algorithms in Educational Data Mining: A Systematic Review

Alaa Khalaf, Jasim Mohammed Dahr, Ihab Ahmed Najim, Mohammed B. M. Kamel, Ali Salah Hashim, Wid Aqeel Awadh, Aqeel Majeed Humadi


The academic institutions always looking for tools that improve their performance and enhance individuals outcomes. Due to the huge ability of data mining to explore hidden patterns and trends in the data, many researchers paid attention to Educational Data Mining (EDM) in the last decade. This field explores different types of data using different algorithms to extract knowledge that supports decision-making and academic sector development. The researchers in the field of EDM have proposed and adopted different algorithms in various directions. In this review, we have explored the published papers between 2010-2020 in the libraries (IEEE, ACM, Science Direct, and Springer) in the field of EDM are to answer review questions. We aimed to find the most used algorithm by researchers in the field of supervised machine learning in the period of 2010-2020. Additionally, we explored the most direction in the EDM and the interest of the researchers. During our research and analysis, many limitations have been examined and in addition to answering the review questions, some future works have been presented.


Educational Data Mining; Supervised Learning Algorithms; Supervised Methods; Systematic Literature Review

Full Text:



R. S. Baker and K. Yacef, "The state of educational data mining in 2009: A review and future visions," JEDM| Journal of Educational Data Mining, vol. 1, pp. 3-17, 2009.

C. Romero and S. Ventura, "Data mining in education," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 3, pp. 12-27, 2013.

M. C. Mihaescu and P. S. Popescu, "Review on publicly available datasets for educational data mining," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, p. e1403, 2021.

C. Romero and S. Ventura, "Educational data mining: a review of the state of the art," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 40, pp. 601-618, 2010.

J. Ranjan and K. Malik, "Effective educational process: a data‐mining approach," Vine, 2007.

C. Romero and S. Ventura, "Educational data mining and learning analytics: An updated survey," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 10, p. e1355, 2020.

P. Ihantola, A. Vihavainen, A. Ahadi, M. Butler, J. Börstler, S. H. Edwards, et al., "Educational data mining and learning analytics in programming: Literature review and case studies," Proceedings of the 2015 ITiCSE on Working Group Reports, pp. 41-63, 2015.

A. Charitopoulos, M. Rangoussi, and D. Koulouriotis, "On the use of soft computing methods in educational data mining and learning analytics research: A review of years 2010–2018," International Journal of Artificial Intelligence in Education, vol. 30, pp. 371-430, 2020.

S. A. Salloum, M. Alshurideh, A. Elnagar, and K. Shaalan, "Mining in educational data: review and future directions," in Joint European-US Workshop on Applications of Invariance in Computer Vision, 2020, pp. 92-102.

A. Hamoud, H. Adday, T. Obaid, and R. Hameed, "Design and Implementing Cancer Data Warehouse to Support Clinical Decisions," International Journal of Scientific & Engineering Research, vol. 7, pp. 1271-1285, 2016.

A. K. Hamoud, A. S. Hashim, and W. A. Awadh, "CLINICAL DATA WAREHOUSE: A REVIEW," Iraqi Journal for Computers and Informatics, vol. 44, 2018.

A. Hamoud and T. Obaid, "Building Data Warehouse for Diseases Registry: First step for Clinical Data Warehouse," International Journal of Scientific & Engineering Research, vol. 4, pp. 636-640, 2013.

A. Hamoud and T. A. S. Obaid, "Design and Implementation Data Warehouse to Support Clinical Decisions Using OLAP and KPI," Department of Computer Science, University of Basrah, 2013.

A. Hamoud and T. Obaid, "Using OLAP with Diseases Registry Warehouse for Clinical Decision Support," International Journal of Computer Science and Mobile Computing, vol. 3, pp. 39-49, 2014.

A. M. Humadi and A. K. Hamoud, "Liver Hepatitis Diagnosing based on Fuzzy Inference System," Journal of Southwest Jiaotong University, vol. 54, 2019.

A. Humadi and A. Hamoud, "Online Real Time Fuzzy Inference System Based Human Health Monitoring and Medical Decision Making," International Journal of Computer Science and Information Security, vol. 15, pp. 197-204, 2017.

H. N. H. Al-Hashimy, A. K. Hamoud, and F. A. Hussain, "The Effect of Not Using Internet of Things in Critical life Situations in the Health Field and the Effect on Iraqi Profitability: Empirical Study in Basra," Journal of Southwest Jiaotong University, vol. 54, 2019.

W. A. Awadh, A. S. Hashim, and A. K. Hamoud, "A REVIEW ON INTERNET OF THINGS ARCHITECTURE FOR BIG DATA PROCESSING," Iraqi Journal for Computers and Informatics, vol. 46, pp. 11-19, 2020.

W. A. Awadh, A. S. Hashim, and A. Hamoud, "A Review of Various Steganography Techniques in Cloud Computing," University of Thi-Qar Journal of Science, vol. 7, pp. 113-119, 2019.

R. Baker, "Data mining for education," International encyclopedia of education, vol. 7, pp. 112-118, 2010.

R. Asif, A. Merceron, S. A. Ali, and N. G. Haider, "Analyzing undergraduate students' performance using educational data mining," Computers & Education, vol. 113, pp. 177-194, 2017.

E. Fernandes, M. Holanda, M. Victorino, V. Borges, R. Carvalho, and G. Van Erven, "Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil," Journal of Business Research, vol. 94, pp. 335-343, 2019.

E. B. Costa, B. Fonseca, M. A. Santana, F. F. de Araújo, and J. Rego, "Evaluating the effectiveness of educational data mining techniques for early prediction of students' academic failure in introductory programming courses," Computers in Human Behavior, vol. 73, pp. 247-256, 2017.

P. Hemingway and N. Brereton, "What is a systematic review', What is Series," Bandolier, April, 2009.

J. H. Littell, J. Corcoran, and V. Pillai, Systematic reviews and meta-analysis: Oxford University Press, 2008.

H. Cooper, A. B. Allen, E. A. Patall, and A. L. Dent, "Effects of full-day kindergarten on academic achievement and social development," Review of educational research, vol. 80, pp. 34-70, 2010.

A. O'Connor, K. Anderson, C. Goodell, and J. Sargeant, "Conducting systematic reviews of intervention questions I: writing the review protocol, formulating the question and searching the literature," Zoonoses and public health, vol. 61, pp. 28-38, 2014.

B. Kitchenham and S. Charters, "Guidelines for performing systematic literature reviews in software engineering," 2007.

D. Gough, S. Oliver, and J. Thomas, An introduction to systematic reviews: Sage, 2017.

C. Jalota and R. Agrawal, "Analysis of Educational Data Mining using Classification," in 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 2019, pp. 243-247.

K. I. M. Ramaphosa, T. Zuva, and R. Kwuimi, "Educational data mining to improve learner performance in Gauteng primary schools," in 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD), 2018, pp. 1-6.

O. Sukhbaatar, K. Ogata, and T. Usagawa, "Mining Educational Data to Predict Academic Dropouts: a Case Study in Blended Learning Course," in TENCON 2018-2018 IEEE Region 10 Conference, 2018, pp. 2205-2208.

R. Ahuja and Y. Kankane, "Predicting the probability of student's degree completion by using different data mining techniques," in 2017 Fourth International Conference on Image Information Processing (ICIIP), 2017, pp. 1-4.

K. Bunkar, U. K. Singh, B. Pandya, and R. Bunkar, "Data mining: Prediction for performance improvement of graduate students using classification," in 2012 Ninth International Conference on Wireless and Optical Communications Networks (WOCN), 2012, pp. 1-5.

S. S. Athani, S. A. Kodli, M. N. Banavasi, and P. S. Hiremath, "Student academic performance and social behavior predictor using data mining techniques," in 2017 International Conference on Computing, Communication and Automation (ICCCA), 2017, pp. 170-174.

P. Rojanavasu, "Educational data analytics using association rule mining and classification," in 2019 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON), 2019, pp. 142-145.

F. J. Kaunang and R. Rotikan, "Students' Academic Performance Prediction using Data Mining," in 2018 Third International Conference on Informatics and Computing (ICIC), 2018, pp. 1-5.

C.-C. Kiu, "Data Mining Analysis on Student’s Academic Performance through Exploration of Student’s Background and Social Activities," in 2018 Fourth International Conference on Advances in Computing, Communication & Automation (ICACCA), 2018, pp. 1-5.

J. d. O. Kelly, A. G. Menezes, A. B. de Carvalho, and C. A. Montesco, "Supervised learning in the context of educational data mining to avoid university students dropout," in 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), 2019, pp. 207-208.

V. Mhetre and M. Nagar, "Classification based data mining algorithms to predict slow, average and fast learners in educational system using WEKA," in 2017 International Conference on Computing Methodologies and Communication (ICCMC), 2017, pp. 475-479.

K. Parmar, D. Vaghela, and P. Sharma, "Performance prediction of students using distributed data mining," in 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015, pp. 1-5.

H. Göker, H. I. Bülbül, and E. Irmak, "The estimation of students' academic success by data mining methods," in 2013 12th International Conference on Machine Learning and Applications, 2013, pp. 535-539.

B. Guo, R. Zhang, G. Xu, C. Shi, and L. Yang, "Predicting students performance in educational data mining," in 2015 International Symposium on Educational Technology (ISET), 2015, pp. 125-128.

T. Devasia, T. Vinushree, and V. Hegde, "Prediction of students performance using Educational Data Mining," in 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), 2016, pp. 91-95.

N. Ketui, W. Wisomka, and K. Homjun, "Using classification data mining techniques for students performance prediction," in 2019 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON), 2019, pp. 359-363.

M. P. Martins, V. L. Migueis, and D. Fonseca, "A data mining approach to predict undergraduate students' performance," in 2018 13th Iberian Conference on Information Systems and Technologies (CISTI), 2018, pp. 1-7.

R. Patil, S. Salunke, M. Kalbhor, and R. Lomte, "Prediction system for student performance using data mining classification," in 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 2018, pp. 1-4.

B. Al Breiki, N. Zaki, and E. A. Mohamed, "Using Educational Data Mining Techniques to Predict Student Performance," in 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA), 2019, pp. 1-5.

C. E. L. Guarín, E. L. Guzmán, and F. A. González, "A model to predict low academic performance at a specific enrollment using data mining," IEEE Revista Iberoamericana de tecnologias del Aprendizaje, vol. 10, pp. 119-125, 2015.

N. Buniyamin, U. bin Mat, and P. M. Arshad, "Educational data mining for prediction and classification of engineering students achievement," in 2015 IEEE 7th International Conference on Engineering Education (ICEED), 2015, pp. 49-53.

K. Shaukat, I. Nawaz, S. Aslam, S. Zaheer, and U. Shaukat, "Student's performance in the context of data mining," in 2016 19th International Multi-Topic Conference (INMIC), 2016, pp. 1-8.

H. Goker and H. I. Bulbul, "Improving an early warning system to prediction of student examination achievement," in 2014 13th International Conference on Machine Learning and Applications, 2014, pp. 568-573.

M. Wati, W. Indrawan, J. A. Widians, and N. Puspitasari, "Data mining for predicting students' learning result," in 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT), 2017, pp. 1-4.

M. H. Rahman and M. R. Islam, "Predict Student's Academic Performance and Evaluate the Impact of Different Attributes on the Performance Using Data Mining Techniques," in 2017 2nd International Conference on Electrical & Electronic Engineering (ICEEE), 2017, pp. 1-4.

B. Perez, C. Castellanos, and D. Correal, "Applying data mining techniques to predict student dropout: a case study," in 2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence (ColCACI), 2018, pp. 1-6.

V. Shanmugarajeshwari and R. Lawrance, "Analysis of students' performance evaluation using classification techniques," in 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE'16), 2016, pp. 1-7.

H. Almayan and W. Al Mayyan, "Improving accuracy of students' final grade prediction model using PSO," in 2016 6th International Conference on Information Communication and Management (ICICM), 2016, pp. 35-39.

M. S. Vyas and R. Gulwani, "Predictive analytics for E learning system," in 2017 International Conference on Inventive Systems and Control (ICISC), 2017, pp. 1-4.

M. Nicoletti, M. Marques, and M. Guimaraes, "A data mining approach for forecasting students' performance," in 2018 13th Iberian Conference on Information Systems and Technologies (CISTI), 2018, pp. 1-7.

E. C. Medina, C. B. Chunga, J. Armas-Aguirre, and E. E. Grandón, "Predictive model to reduce the dropout rate of university students in Perú: Bayesian Networks vs. Decision Trees," in 2020 15th Iberian Conference on Information Systems and Technologies (CISTI), 2020, pp. 1-7.

T. Chellatamilan, M. Ravichandran, R. Suresh, and G. Kulanthaivel, "Effect of mining educational data to improve adaptation of learning in e-learning system," 2011.

W. Quan and Q. Zhou, "Predicting the students with mental health risk by using Internet access logs," in 2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), 2018, pp. 140-140.

C. Ma, B. Yao, F. Ge, Y. Pan, and Y. Guo, "Improving prediction of student performance based on multiple feature selection approaches," in Proceedings of the 2017 International Conference on E-Education, E-Business and E-Technology, 2017, pp. 36-41.

A. Ahadi, V. Behbood, A. Vihavainen, J. Prior, and R. Lister, "Students' syntactic mistakes in writing seven different types of SQL queries and its application to predicting students' success," in Proceedings of the 47th ACM Technical Symposium on Computing Science Education, 2016, pp. 401-406.

T. Sinha and J. Cassell, "Connecting the dots: Predicting student grade sequences from bursty MOOC interactions over time," in Proceedings of the second (2015) ACM conference on learning@ scale, 2015, pp. 249-252.

W. Chango, R. Cerezo, and C. Romero, "Predicting academic performance of university students from multi-sources data in blended learning," in Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems, 2019, pp. 1-5.

R. A. Rustia, M. M. A. Cruz, M. A. P. Burac, and T. D. Palaoag, "Predicting Student's Board Examination Performance using Classification Algorithms," in Proceedings of the 2018 7th International Conference on Software and Computer Applications, 2018, pp. 233-237.

S. Altaf, W. Soomro, and M. I. M. Rawi, "Student Performance Prediction using Multi-Layers Artificial Neural Networks: A Case Study on Educational Data Mining," in Proceedings of the 2019 3rd International Conference on Information System and Data Mining, 2019, pp. 59-64.

L. M. Barbosa Manhães, S. M. S. da Cruz, and G. Zimbrão, "Towards automatic prediction of student performance in STEM undergraduate degree programs," in Proceedings of the 30th Annual ACM Symposium on Applied Computing, 2015, pp. 247-253.

W. Puarungroj, N. Boonsirisumpun, P. Pongpatrakant, and S. Phromkhot, "Application of data mining techniques for predicting student success in English exit exam," in Proceedings of the 12th International Conference on Ubiquitous Information Management and Communication, 2018, pp. 1-6.

P. Chaudhury, S. Mishra, H. K. Tripathy, and B. Kishore, "Enhancing the capabilities of student result prediction system," in Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies, 2016, pp. 1-6.

J. Figueiredo, N. Lopes, and F. J. García-Peñalvo, "Predicting student failure in an introductory programming course with multiple back-propagation," in Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality, 2019, pp. 44-49.

H. Chanlekha and J. Niramitranon, "Student performance prediction model for early-identification of at-risk students in traditional classroom settings," in Proceedings of the 10th International Conference on Management of Digital EcoSystems, 2018, pp. 239-245.

C. I. P. Benablo, E. T. Sarte, J. M. D. Dormido, and T. Palaoag, "Higher Education Student's Academic Performance Analysis through Predictive Analytics," in Proceedings of the 2018 7th International Conference on Software and Computer Applications, 2018, pp. 238-242.

B. Sekeroglu, K. Dimililer, and K. Tuncal, "Student performance prediction and classification using machine learning algorithms," in Proceedings of the 2019 8th International Conference on Educational and Information Technology, 2019, pp. 7-11.

F. Okubo, T. Yamashita, A. Shimada, and H. Ogata, "A neural network approach for students' performance prediction," in Proceedings of the seventh international learning analytics & knowledge conference, 2017, pp. 598-599.

K. Kang and S. Wang, "Analyze and predict student dropout from online programs," in Proceedings of the 2nd International Conference on Compute and Data Analysis, 2018, pp. 6-12.

S. N. Liao, D. Zingaro, K. Thai, C. Alvarado, W. G. Griswold, and L. Porter, "A robust machine learning technique to predict low-performing students," ACM Transactions on Computing Education (TOCE), vol. 19, pp. 1-19, 2019.

M. V. Amazona and A. A. Hernandez, "Modelling Student Performance Using Data Mining Techniques: Inputs for Academic Program Development," in Proceedings of the 2019 5th International Conference on Computing and Data Engineering, 2019, pp. 36-40.

S. Sisovic, M. Matetic, and M. B. Bakaric, "Mining student data to assess the impact of moodle activities and prior knowledge on programming course success," in Proceedings of the 16th International Conference on Computer Systems and Technologies, 2015, pp. 366-373.

A. Daud, N. R. Aljohani, R. A. Abbasi, M. D. Lytras, F. Abbas, and J. S. Alowibdi, "Predicting student performance using advanced learning analytics," in Proceedings of the 26th international conference on world wide web companion, 2017, pp. 415-421.

Y. Arora, A. Singhal, and A. Bansal, "PREDICTION & WARNING: a method to improve student's performance," ACM SIGSOFT Software Engineering Notes, vol. 39, pp. 1-5, 2014.

M. Ashraf, M. Zaman, and M. Ahmed, "An Intelligent Prediction System for Educational Data Mining Based on Ensemble and Filtering approaches," Procedia Computer Science, vol. 167, pp. 1471-1483, 2020.

J. D. Gobert, Y. J. Kim, M. A. Sao Pedro, M. Kennedy, and C. G. Betts, "Using educational data mining to assess students’ skills at designing and conducting experiments within a complex systems microworld," Thinking Skills and Creativity, vol. 18, pp. 81-90, 2015.

C. Romero, M.-I. López, J.-M. Luna, and S. Ventura, "Predicting students' final performance from participation in on-line discussion forums," Computers & Education, vol. 68, pp. 458-472, 2013.

W. Xing, R. Guo, E. Petakovic, and S. Goggins, "Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory," Computers in Human Behavior, vol. 47, pp. 168-181, 2015.

A. A. Yahya and A. Osman, "Using Data Mining Techniques to Guide Academic Programs Design and Assessment," Procedia Computer Science, vol. 163, pp. 472-481, 2019.

P. Kaur, M. Singh, and G. S. Josan, "Classification and prediction based data mining algorithms to predict slow learners in education sector," Procedia Computer Science, vol. 57, pp. 500-508, 2015.

C. Burgos, M. L. Campanario, D. de la Peña, J. A. Lara, D. Lizcano, and M. A. Martínez, "Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout," Computers & Electrical Engineering, vol. 66, pp. 541-556, 2018.

S. Natek and M. Zwilling, "Student data mining solution–knowledge management system related to higher education institutions," Expert systems with applications, vol. 41, pp. 6400-6407, 2014.

N. Thai-Nghe, L. Drumond, A. Krohn-Grimberghe, and L. Schmidt-Thieme, "Recommender system for predicting student performance," Procedia Computer Science, vol. 1, pp. 2811-2819, 2010.

A. M. Ahmed, A. Rizaner, and A. H. Ulusoy, "Using data mining to predict instructor performance," Procedia Computer Science, vol. 102, pp. 137-142, 2016.

S. Oeda and M. Chieda, "Visualization of Programming Skill Structure by Log-Data Analysis with Decision Tree," Procedia Computer Science, vol. 159, pp. 582-589, 2019.

H. Zeineddine, U. Braendle, and A. Farah, "Enhancing prediction of student success: Automated machine learning approach," Computers & Electrical Engineering, vol. 89, p. 106903.

F. Marbouti, H. A. Diefes-Dux, and K. Madhavan, "Models for early prediction of at-risk students in a course using standards-based grading," Computers & Education, vol. 103, pp. 1-15, 2016.

M. Bennett, L. Bormann, S. Lovan, and B. Cobb, "Preadmission predictors of student success in a baccalaureate of science in nursing program," Journal of Nursing Regulation, vol. 7, pp. 11-18, 2016.

T. A. Cardona, "Predicting Student Retention Using Support Vector Machines," Procedia Manufacturing, vol. 39, pp. 1827-1833, 2019.

A.-S. Hoffait and M. Schyns, "Early detection of university students with potential difficulties," Decision Support Systems, vol. 101, pp. 1-11, 2017.

A. A. Kardan, H. Sadeghi, S. S. Ghidary, and M. R. F. Sani, "Prediction of student course selection in online higher education institutes using neural network," Computers & Education, vol. 65, pp. 1-11, 2013.

S. Helal, J. Li, L. Liu, E. Ebrahimie, S. Dawson, D. J. Murray, et al., "Predicting academic performance by considering student heterogeneity," Knowledge-Based Systems, vol. 161, pp. 134-146, 2018.

G. Lesinski and S. Corns, "Multi-objective evolutionary neural network to predict graduation success at the United States military academy," Procedia Computer Science, vol. 140, pp. 196-205, 2018.

E. Howard, M. Meehan, and A. Parnell, "Contrasting prediction methods for early warning systems at undergraduate level," The Internet and Higher Education, vol. 37, pp. 66-75, 2018.

G. Badr, A. Algobail, H. Almutairi, and M. Almutery, "Predicting students’ performance in university courses: a case study and tool in KSU mathematics department," Procedia Computer Science, vol. 82, pp. 80-89, 2016.

Y. Altujjar, W. Altamimi, I. Al-Turaiki, and M. Al-Razgan, "Predicting critical courses affecting students performance: a case study," Procedia Computer Science, vol. 82, pp. 65-71, 2016.

A. Yağci and M. Çevik, "Prediction of academic achievements of vocational and technical high school (VTS) students in science courses through artificial neural networks (comparison of Turkey and Malaysia)," Education and Information Technologies, vol. 24, pp. 2741-2761, 2019.

C. Beaulac and J. S. Rosenthal, "Predicting university students’ academic success and major using random forests," Research in Higher Education, vol. 60, pp. 1048-1064, 2019.

M. Ezz and A. Elshenawy, "Adaptive recommendation system using machine learning algorithms for predicting student’s best academic program," Education and Information Technologies, pp. 1-14, 2019.

M. Hussain, W. Zhu, W. Zhang, S. M. R. Abidi, and S. Ali, "Using machine learning to predict student difficulties from learning session data," Artificial Intelligence Review, vol. 52, pp. 381-407, 2019.

M. F. Musso, C. F. R. Hernández, and E. C. Cascallar, "Predicting key educational outcomes in academic trajectories: a machine-learning approach," Higher Education, pp. 1-20, 2020.

Y. Nieto, V. García-Díaz, C. Montenegro, and R. G. Crespo, "Supporting academic decision making at higher educational institutions using machine learning-based algorithms," Soft Computing, vol. 23, pp. 4145-4153, 2019.

Z. Nudelman, D. Moodley, and S. Berman, "Using bayesian networks and machine learning to predict computer science success," in Annual Conference of the Southern African Computer Lecturers' Association, 2018, pp. 207-222.

D. Canagareddy, K. Subarayadu, and V. Hurbungs, "A machine learning model to predict the performance of university students," in International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering, 2018, pp. 313-322.

J. Nouri, K. Larsson, and M. Saqr, "Identifying factors for master thesis completion and non-completion through learning analytics and machine learning," in European Conference on Technology Enhanced Learning, 2019, pp. 28-39.

S. Almutairi, H. Shaiba, and M. Bezbradica, "Predicting Students’ Academic Performance and Main Behavioral Features Using Data Mining Techniques," in International Conference on Computing, 2019, pp. 245-259.

K. Agarwal, E. Maheshwari, C. Roy, M. Pandey, and S. S. Rautray, "Analyzing Student Performance in Engineering Placement Using Data Mining," in Proceedings of International Conference on Computational Intelligence and Data Engineering, 2019, pp. 171-181.

S. Sivakumar and R. Selvaraj, "Predictive modeling of students performance through the enhanced decision tree," in Advances in electronics, communication and computing, ed: Springer, 2018, pp. 21-36.

M. Petticrew and H. Roberts, Systematic reviews in the social sciences: A practical guide: John Wiley & Sons, 2008.

C. Salvador, A. Nakasone, and J. A. Pow-Sang, "A systematic review of usability techniques in agile methodologies," in Proceedings of the 7th Euro American Conference on Telematics and Information Systems, 2014, pp. 1-6.

C. C. Aggarwal, Data mining: the textbook: Springer, 2015.

M. Bramer, Principles of data mining vol. 180: Springer, 2007.

J. Han, M. Kamber, and J. Pei, "Data mining concepts and techniques third edition," The Morgan Kaufmann Series in Data Management Systems, vol. 5, pp. 83-124, 2011.

I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, "Practical machine learning tools and techniques," Morgan Kaufmann, p. 578, 2005.

I. A. Najm, A. K. Hamoud, J. Lloret, and I. Bosch, "Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment," Electronics, vol. 8, p. 607, 2019.

A. S. Hashim, W. A. Awadh, and A. K. Hamoud, "Student Performance Prediction Model based on Supervised Machine Learning Algorithms," in IOP Conference Series: Materials Science and Engineering, 2020, p. 032019.

A. Hamoud, "Selection of best decision tree algorithm for prediction and classification of students’ action," American International Journal of Research in Science, Technology, Engineering & Mathematics, vol. 16, pp. 26-32, 2016.

A. Hamoud, A. Humadi, W. A. Awadh, and A. S. Hashim, "Students’ success prediction based on Bayes algorithms," International Journal of Computer Applications, vol. 178, pp. 6-12, 2017.

N. Ye, Data mining: theories, algorithms, and examples: CRC press, 2013.

W. S. Noble, "What is a support vector machine?," Nature biotechnology, vol. 24, pp. 1565-1567, 2006.

L. Wang, Support vector machines: theory and applications vol. 177: Springer Science & Business Media, 2005.

L. J. Cao, S. S. Keerthi, C. J. Ong, J. Q. Zhang, U. Periyathamby, X. J. Fu, et al., "Parallel sequential minimal optimization for the training of support vector machines," IEEE Trans. Neural Networks, vol. 17, pp. 1039-1049, 2006.

O. Kramer, Dimensionality reduction with unsupervised nearest neighbors: Springer, 2013.

A. Hamoud, "Applying association rules and decision tree algorithms with tumor diagnosis data," International Research Journal of Engineering and Technology, vol. 3, pp. 27-31, 2017.

R. Agrawal, T. Imieliński, and A. Swami, "Mining association rules between sets of items in large databases," in Proceedings of the 1993 ACM SIGMOD international conference on Management of data, 1993, pp. 207-216.

A. S. Hashima, A. K. Hamoud, and W. A. Awadh, "Analyzing students’ answers using association rule mining based on feature selection," Journal of Southwest Jiaotong University, vol. 53, 2018.

A. DeMaris, "A tutorial in logistic regression," Journal of Marriage and the Family, pp. 956-968, 1995.


R. Xu and D. Wunsch, Clustering vol. 10: John Wiley & Sons, 2008.

C. Verma, Z. Illés, and V. Stoffová, "Age group predictive models for the real time prediction of the university students using machine learning: Preliminary results," in 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2019, pp. 1-7.

H. Guruler, A. Istanbullu, and M. Karahasan, "A new student performance analysing system using knowledge discovery in higher educational databases," Computers & Education, vol. 55, pp. 247-254, 2010.

G. J. Baars, T. Stijnen, and T. A. Splinter, "A model to predict student failure in the first year of the undergraduate medical curriculum," Health Professions Education, vol. 3, pp. 5-14, 2017.



Copyright (c) 2021 Alaa Khalaf

ISSN 2233 -1859

Digital Object Identifier DOI: 10.21533/scjournal

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License