The DOI matches its Crossref record; 87 of the 87 references it cites were found in a registry or an index.
Predicting academic success in higher education: literature review and best practices
Open accessAbstract
Student success plays a vital role in educational institutions, as it is often used as a metric for the institution’s performance. Early detection of students at risk, along with preventive measures, can drastically improve their success. Lately, machine learning techniques have been extensively used for prediction purpose. While there is a plethora of success stories in the literature, these techniques are mainly accessible to “computer science”, or more precisely, “artificial intelligence” literate educators. Indeed, the effective and efficient application of data mining methods entail many decisions, ranging from how to define student’s success, through which student attributes to focus on, up to which machine learning method is more appropriate to the given problem. This study aims to provide a step-by-step set of guidelines for educators willing to apply data mining techniques to predict student success. For this, the literature has been reviewed, and the state-of-the-art has been compiled into a systematic process, where possible decisions and parameters are comprehensively covered and explained along with arguments. This study will provide to educators an easier access to data mining techniques, enabling all the potential of their application to the field of education.
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Cites
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
Data Mining: Practical Machine Learning Tools and Techniques
(2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,437
(1992) · Cited by 23,431
Fast Algorithms for Mining Association Rules in Large Databases
Agrawal, R., & Srikant, R. (1994) · Very Large Data Bases · Cited by 9,419
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 8,947
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242
An Introduction to Variable and Feature Selection.
Guyon, I., & Elisseeff, A. (2003) · Cited by 6,576
Top 10 algorithms in data mining
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., et al. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-007-0114-2 · Cited by 5,667
A survey on feature selection methods
Chandrashekar, G., & Sahin, F. (2014) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2013.11.024 · Cited by 5,189
Practical Assessment, Research & Evaluation, 2000-2001.
Rudner, L. M., & Schafer, W. D. (2001) · Practical assessment, research & evaluation · Cited by 3,905
Research works printed but not linked: 20
- Aleryani, A., Wang, W., De, B., & Iglesia, L. (2018). Dealing with missing data and uncertainty in the context of data mining. In International Conference on Hybrid Artificial Intelligence Systems.
- Chotmongkol, V., & Jitpimolmard, S. (1993). Cryptococcal intracerebral mass lesions associated with cryptococcal meningitis. The Southeast Asian Journal of Tropical Medicine and Public Health, 24(1), 94–98.
- CrowdFlower (2016). Data Science Report, (pp. 8–9).
- Han, J., Kamber, M., & Pei, J. (2011). Data mining : concepts and techniques. Elsevier Science. Retrieved from https://www.elsevier.com/books/data-mining-concepts-and-techniques/han/978-0-12-381479-1.
- Jascaniene, N., Nowak, R., Kostrzewa-Nowak, D., & Kolbowicz, M. (2013). Selected aspects of statistical analyses in sport with the use of STATISTICA software. Central European Journal of Sport Sciences and Medicine, 3(3), 3–11.
- Kuh, G. D., Kinzie, J., Buckley, J. A., Bridges, B. K., & Hayek, J. C. (2006). What matters to student success: A review of the literature commissioned report for the National Symposium on postsecondary student success: Spearheading a dialog on student success.
- Mayhew, M. J., & Simonoff, J. S. (2015). Non-white, no more: Effect coding as an alternative to dummy coding with implications for higher education researchers. Journal of College Student Development, 56(2), 170–175. · doi:10.1353/csd.2015.0019
- McCarthy, R. V., McCarthy, M. M., Ceccucci, W., & Halawi, L. (2019). Introduction to Predictive Analytics. In Applying Predictive Analytics. Springer International Publishing. https://doi.org/10.1007/978-3-030-14038-0. · doi:10.1007/978-3-030-14038-0
- Mohamed, M. H., & Waguih, H. M. (2017). Early prediction of student success using a data mining classification technique. International Journal of Science and Research, 6(10), 126–131.
- “National Commission for Academic Accreditation & Assessment Standards for Quality Assurance and Accreditation of Higher Education Institutions,” 2015.
- Pérez, J., Iturbide, E., Olivares, V., Hidalgo, M., Almanza, N., & Martínez, A. (2015). A data preparation methodology in data mining applied to mortality population databases. Advances in Intelligent Systems and Computing, 353, 1173–1182. · doi:10.1007/978-3-319-16486-1_116
- Putpuek, N., Rojanaprasert, N., Atchariyachanvanich, K., & Thamrongthanyawong, T. (2018). Comparative Study of Prediction Models for Final GPA Score : A Case Study of Rajabhat Rajanagarindra University. In 2018 IEEE/ACIS 17th International Conference on Computer and Information Science, (pp. 92–97).
- Qazi, N., & Raza, K. (2012). Effect of Feature Selection, SMOTE and under Sampling on Class Imbalance Classification. In 2012 UKSim 14th International Conference on Computer Modelling and Simulation, (pp. 145–150). · doi:10.1109/uksim.2012.116
- Richard-Eaglin, A. (2017). Predicting student success in nurse practitioner programs. Journal of the American Association of Nurse Practitioners, 29(10), 600–605. · doi:10.1002/2327-6924.12502
- Ruano, M. V., Ribes, J., Sin, G., Seco, A., & Ferrer, J. (2010). A systematic approach for fine-tuning of fuzzy controllers applied to WWTPs. Environmental Modelling & Software, 25(5), 670–676. · doi:10.1016/j.envsoft.2009.05.008
- Salman, I., & Vomlel, J. (2017). A machine learning method for incomplete and imbalanced medical data.
- Sarala, V., & Krishnaiah, J. (2015). Empirical study of data mining techniques in education system. International Journal of Advances in Computer Science and Technology (IJACST), 4(1), 15–21.
- Sumathi, S., & Sivanandam, S. N. (2006). Introduction to data mining and its applications. Springer. Retrieved from https://www.springer.com/gp/book/9783540343509. · doi:10.1007/978-3-540-34351-6
- Umadevi, S., & Marseline, K. S. J. (2017). A survey on data mining classification algorithms. In 2017 International Conference on Signal Processing and Communication (ICSPC), (pp. 264–268). · doi:10.1109/cspc.2017.8305851
- Zhang, L., Niu, D., Li, Y., & Zhang, Z. (2018). A Survey on Privacy Preserving Association Rule Mining. In 2018 5th International Conference on Information Science and Control Engineering (ICISCE), (pp. 93–97). · doi:10.1109/icisce.2018.00029
Cited by
A Review of Artificial Intelligence (AI) in Education from 2010 to 2020
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021) · Complexity · doi:10.1155/2021/8812542 · Cited by 1,364
“…teachers Song and Wang [84] Analyzing analysis of worldwide educational artificial intelligence research development in recent twenty years Ulum [ [106] Constructing guidelines to apply data mining techniques to predict student success Renz and Hilbig [107] Analyzing the drivers and barriers that…”
MethodOuyang, F., Zheng, L., & Jiao, P. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-10925-9 · Cited by 831
Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00436-z · Cited by 852
Facilitating student engagement through the flipped learning approach in K-12: A systematic review
Bond, M. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.103819 · Cited by 380
Human-centered Artificial Intelligence in education: Seeing the invisible through the visible
Yang, S. J. H., Ogata, H., Matsui, T., & Chen, N.-S. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100008 · Cited by 379
Metadata
- Authors
- Alyahyan, E., & Düştegör, D.
- Venue
- International Journal of Educational Technology in Higher Education
- Published
- 2020-02-10
- Volume
- 17
- Issue
- 1
- ISSN
- 2365-9440 (ISSN-L)
- OpenAlex
- W3014421158
- Cited by
- 615
- References
- 87
- FWCI
- 64.85
- Type
- journal article
- Language
- en
- Open access
- diamond
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
