Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models

The Nature of Statistical Learning Theory

Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138

Discovering Statistics Using SPSS

Field, A. P., & Miles, J. N. V. (2000) · Medical Entomology and Zoology · Cited by 28,265

Research Methods in Education

Cohen, L., Manion, L., & Morrison, K. (2007) · Routledge eBooks · doi:10.4324/9780203029053 · Cited by 20,105

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855

A tutorial on support vector regression

Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,387

Motivational and self-regulated learning components of classroom academic performance.

Pintrich, P. R., & De Groot, E. V. (1990) · Journal of Educational Psychology · doi:10.1037/0022-0663.82.1.33 · Cited by 8,332

The Nature of Statistical Learning Theory

Sain, S. R. (1996) · Technometrics · doi:10.1080/00401706.1996.10484565 · Cited by 5,434

Forecasting with artificial neural networks:

Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998) · International Journal of Forecasting · doi:10.1016/s0169-2070(97)00044-7 · Cited by 4,229

An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods

(2001) · Kybernetes · doi:10.1108/k.2001.30.1.103.6 · Cited by 3,308

Choosing Multiple Parameters for Support Vector Machines

Chapelle, O., Vapnik, V., Bousquet, O., & Mukherjee, S. (2002) · Machine Learning · doi:10.1023/a:1012450327387 · Cited by 2,206

Research methods in education, 6th ed.

Cohen, L., Manion, L., & Morrison, K. (2007) · Cited by 1,928

Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management

Linoff, G. S., & Berry, M. J. (2004) · Cited by 1,232

A Generalized Growing and Pruning RBF (GGAP-RBF) Neural Network for Function Approximation

Huang, G.-B., Saratchandran, P., & Sundararajan, N. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.836241 · Cited by 705

Cognitive Styles and Learning Strategies

Riding, R., & Rayner, S. G. (2013) · David Fulton Publishers eBooks · doi:10.4324/9781315068015 · Cited by 543

Data Mining Methods for Knowledge Discovery

Cios, K. J., Pedrycz, W., & Swiniarsk, R. M. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.728406 · Cited by 452

Assessment of student achievement

Gronlund, N. E. (1997) · Cited by 421

Predicting achievement early and late in the semester: The role of motivation and use of learning strategies.

Pokay, P., & Blumenfeld, P. C. (1990) · Journal of Educational Psychology · doi:10.1037//0022-0663.82.1.41 · Cited by 393

Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms

Pai, P.-F., & Hong, W.-C. (2005) · Electric Power Systems Research · doi:10.1016/j.epsr.2005.01.006 · Cited by 371

Assessment of Student Achievement

Brown, G. T. L. (2017) · Routledge eBooks · doi:10.4324/9781315162058 · Cited by 240

Noncognitive Variables in Predicting Academic Success by Race

Tracey, T. J., & Sedlacek, W. E. (1984) · Measurement and Evaluation in Guidance · doi:10.1080/00256307.1984.12022352 · Cited by 237

Predicting Academic Performance by Data Mining Methods

Vandamme, J. ‐., Meskens, N., & Superby, J. ‐. (2007) · Education Economics · doi:10.1080/09645290701409939 · Cited by 190

Support vector fuzzy regression machines

Hong, D. H., & Hwang, C. (2003) · Fuzzy Sets and Systems · doi:10.1016/s0165-0114(02)00514-6 · Cited by 143

Engineering Mechanics Dynamics

Hibbeler, R. C. (1983) · International Linear Collider · Cited by 134

Soft Computing for Knowledge Discovery and Data Mining

Maimon, O., & Rokach, L. (2007) · Springer eBooks · doi:10.1007/978-0-387-69935-6 · Cited by 123

Early and dynamic student achievement prediction in e‐learning courses using neural networks

Lykourentzou, I., Giannoukos, I., Mpardis, G., Nikolopoulos, V., & Loumos, V. G. (2008) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20970 · Cited by 105

Is Modeling of Freshman Engineering Success Different from Modeling of Non‐Engineering Success?

Veenstra, C. P., Dey, E. L., & Herrin, G. D. (2008) · Journal of Engineering Education · doi:10.1002/j.2168-9830.2008.tb00993.x · Cited by 99

Computational Intelligence: A Compendium

Fulcher, J., & Jain, L. C. (2008) · Studies in computational intelligence · doi:10.1007/978-3-540-78293-3 · Cited by 91

Artificial Neural Networks

(1992) · Elsevier eBooks · doi:10.1016/c2009-0-09010-2 · Cited by 83

Predicting college success: the importance of ability and non-cognitive variables

Ransdell, S. (2001) · International Journal of Educational Research · doi:10.1016/s0883-0355(01)00032-5 · Cited by 62

Prediction of University Students' Academic Achievement by Linear and Logistic Models

Ayán, M. N. R., & García, M. T. C. (2008) · The Spanish Journal of Psychology · doi:10.1017/s1138741600004315 · Cited by 58

Predicting academic success of first-year engineering students from standardized test scores and psychosocial variables

Ting, S. R. (2001) · International journal of engineering education · Cited by 54

Data mining: Methods for knowledge discovery

(1999) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(99)90357-7 · Cited by 41

Student Retention Modelling: An Evaluation of Different Methods and their Impact on Prediction Results

Lin, J. J.-J., Imbrie, P. K., & Reid, K. (2009) · Cited by 37

Research and Practice of Active Learning in Engineering Education

Graaf, D. E., Saunders-Smits, G., & Nieweg, M. (2005) · Amsterdam University Press eBooks · doi:10.5117/9789085550914 · Cited by 29

Using Correlational and Prediction Data to Enhance Student Achievement in K-12 Schools: A Practical Application for School Counselors

Ware, W., & Galassi, J. P. (2006) · Professional School Counseling · doi:10.5330/prsc.9.5.73184524064708t7 · Cited by 19

Educational data mining: prediction of students' academic performance using machine learning algorithms

Yağcı, M. (2022) · Smart Learning Environments · doi:10.1186/s40561-022-00192-z · Cited by 698

Untitled work

(2014) · Educational Technology & Society · Cited by 598

Analyzing undergraduate students' performance using educational data mining

Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017) · Computers & Education · doi:10.1016/j.compedu.2017.05.007 · Cited by 586

Identifying significant indicators using LMS data to predict course achievement in online learning

You, J. W. (2016) · The Internet and Higher Education · doi:10.1016/j.iheduc.2015.11.003 · Cited by 388

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

Rastrollo-Guerrero, J. L., Gómez-Pulido, J. A., & Durán-Domínguez, A. (2020) · Applied Sciences · doi:10.3390/app10031042 · Cited by 367