Abstract
Recently, machine learning (ML) has evolved and finds its application in higher education (HE) for various data analysis. Studies have shown that such an emerging field in educational technology provides meaningful insights into several dimensions of educational quality. An in-depth analysis of the application of ML could have a positive impact on the HE sector. However, there is a scarcity of a systematic review of HE literature to gain from the overarching trends and patterns discovered using ML. This paper conducts a systematic review and meta-analyses of research studies that have reported on the application of ML in HE. The differentiating factors of this study are primarily vested in the meta-analyses including a specific focus on student academic performance, at-risk, and attrition in HE. Our detailed investigation adopts an evidence-based framework called PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for reporting the findings of our systematic review and meta-analyses of literature on the use of ML models, algorithms, evaluation metrics, and other criteria, including demographics for assessing student academic performance, at-risk and attrition in HE. After undergoing the PRISMA steps such as selection criteria and filtering, we arrive at a small-scale dataset of 89 relevant studies published from 2010 to 2020 for an in-depth analysis. The results show the outcomes of the quantitative analysis of the application of ML types, models, evaluation metrics, and other related demographics and provide quality insights of publication patterns and future trends towards predicting and monitoring student academic progress in HE.
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References
Adejo, O. W., & Connolly, T. (2018). Predicting student academic performance using multi-model heterogeneous ensemble approach. Journal of Applied Research in Higher Education, 10(1), 61–75. https://doi.org/10.1108/JARHE-09-2017-0113
Adekitan, A. I., & Salau, O. (2019). The impact of engineering students' performance in the first three years on their graduation result using educational data mining. Heliyon, 5(2), e01250. https://doi.org/10.1016/j.heliyon.2019.e01250
Aderibigbe, I. A., & Noma-Osaghae, E. (2019). Data mining approach to predicting the performance of first year student in a university using the admission requirements. Education and Information Technologies, 24(2), 1527–1543. https://doi.org/10.1007/s10639-018-9839-7
Ajoodha, R., Jadhav, A., & Dukhan, S. (2020). Forecasting learner attrition for student success at a South African University. In Conference of the South African Institute of Computer Scientists and Information Technologists 2020 (SAICSIT ’20), September 14–16, 2020, Cape Town, South Africa
Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13–49. https://doi.org/10.1016/j.tele.2019.01.007
Al-kmali, M., Mugahed, H., Boulila, W., Al-Sarem, M., & Abuhamdah, A. (2020). A machine-learning based approach to support academic decision-making at higher educational institutions. 2020 international symposium on networks, computers and communications (ISNCC),
Allah, A. G. F. (2020). Using machine learning to support students’ academic decisions [article]. Journal of Theoretical and Applied Information Technology, 8(10), 3778–3796 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092618542&partnerID=40&md5=3fe24360f3fd83bb2c814129418b629e
Alshanqiti, A., & Namoun, A. (2020). Predicting student performance and its influential factors using hybrid regression and multi-label classification [article]. IEEE Access, 8, 203827–203844. https://doi.org/10.1109/ACCESS.2020.3036572
Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: Literature review and best practices. International Journal of Educational Technology in Higher Education, 17(1). https://doi.org/10.1186/s41239-020-0177-7
Ashraf, M., Zaman, M., & Ahmed, M. (2018). Using ensemble StackingC method and base classifiers to ameliorate prediction accuracy of pedagogical data. Procedia Computer Science, 132, 1021–1040. https://doi.org/10.1016/j.procs.2018.05.018
Aydogdu, S. (2020). Predicting student final performance using artificial neural networks in online learning environments. Education and Information Technologies, 25(3), 1913–1927. https://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=eric&AN=EJ1253324&site=ehost-live&custid=s1145751. https://doi.org/10.1007/s10639-019-10053-x
Baneres, D., Rodríguez-Gonzalez, M. E., & Serra, M. (2019). An early feedback prediction system for learners at-risk within a first-year higher education course. IEEE Transactions on Learning Technologies, 12(2), 249–263. https://doi.org/10.1109/TLT.2019.2912167
Baranyi, M., Gál, K., Molontay, R., & Szabó, M. (2019). Modeling students’ academic performance using Bayesian networks. 2019 17th international conference on emerging eLearning technologies and applications (ICETA).
Baranyi, M., Nagy, M., & Molontay, R. (2020). Interpretable deep learning for university dropout prediction. In the Proceedings of the 21st Annual Conference on Information Technology Education (SIGITE '20), October 2020 pp. 13–19. https://doi.org/10.1145/3368308.3415382. accessed on 30 Aug, 2021
Beaulac, C., & Rosenthal, J. S. (2019). Predicting University students’ academic success and major using random forests [article]. Research in Higher Education, 60(7), 1048–1064. https://doi.org/10.1007/s11162-019-09546-y
Beer, C., & Lawson, C. (2016). The problem of student attrition in higher education: An alternative perspective. Journal of Further and Higher Education, 41(6), 773–784. https://doi.org/10.1080/0309877x.2016.1177171
Berens, J., Schneider, K., Gortz, S., Oster, S., & Burghoff, J. (2019). Early detection of students at risk -- predicting student dropouts using administrative student data from German universities and machine learning methods. Journal of Educational Data Mining, 11(3), 1–41 https://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=eric&AN=EJ1241620&site=ehost-live&custid=s1145751
Berriri, M., Djema, S., Rey, G., & Dartigues-Pallez, C. (2021). Multi-class assessment based on random forests [article]. Education in Science, 11(3), 1–12, article 92. https://doi.org/10.3390/educsci11030092
Biradar, U. G. (2015). Knowledge Discovery to Analyze Student Performance using k-mean Clustering depend upon various mean values input methods: A Case Study. International Journal of Advanced Research in Computer Science, 6(2). https://www.proquest.com/scholarly-journals/knowledge-discovery-analyze-student-performance/docview/1682542664/se-2?accountid=14844. accessed on 30 Aug, 2021
Borrella, I., Caballero-Caballero, S., & Ponce-Cueto, E. (2019). Predict and intervene: Addressing the dropout problem in a MOOC-based program. In the Proceedings of Sixth (2019) ACM Conference on Learning @ Scale, Chicago, IL, USA, June 24–25, pp. 9
Buenaño-Fernández, D., Gil, D., & Luján-Mora, S. (2019). Application of machine learning in predicting performance for computer engineering students: A case study [article]. Sustainability (Switzerland), 11(10), article 2833. https://doi.org/10.3390/su11102833
Chanamarn, N., & Tamee, K. (2017). Enhancing efficient study plan for student with machine learning techniques. International Journal of Modern Education and Computer Science, 9(3), 1 https://www.proquest.com/scholarly-journals/enhancing-efficient-study-plan-student-with/docview/1886772482/se-2?accountid=14844
Chen, F., & Cui, Y. (2020). Utilizing student time series behaviour in learning management systems for early prediction of course performance [article]. Journal of Learning Analytics, 7(2), 1–17. https://doi.org/10.18608/JLA.2020.72.1
Chen, Y., Zheng, Q., Ji, S., Tian, F., Zhu, H., & Liu, M. (2020). Identifying at-risk students based on the phased prediction model. Knowledge and Information Systems, 62(3), 987–1003. https://doi.org/10.1007/s10115-019-01374-x
Choi, S. P. M., Lam, S. S., Li, K. C., & Wong, B. T. M. (2018). Learning analytics at low cost: At-risk student prediction with clicker data and systematic proactive interventions. Journal of Educational Technology & Society, 21(2), 273–290 https://www.proquest.com/scholarly-journals/learning-analytics-at-low-cost-risk-student/docview/2147868992/se-2?accountid=14844
Chui, K. T., Fung, D. C. L., Lytras, M. D., & Lam, T. M. (2020). Predicting at-risk university students in a virtual learning environment via a machine learning algorithm [article]. Computers in Human Behavior, 107, article 105584. https://doi.org/10.1016/j.chb.2018.06.032
Ciolacu, M., Tehrani, A. F., Binder, L., & Svasta, P. M. (2019). Education 4.0 - artificial intelligence assisted higher education: Early recognition system with machine learning to support Students' success. In the 26th IEEE International Symposium, Pitesti, Romania. https://doi.org/10.1109/SIITME50350.2020.9292148. accessed on 30 Aug, 2021
Dalipi, F., Imran, A. S., & Kastrati, Z. (2018). MOOC dropout prediction using machine learning techniques: Review and research challenges. Social Media and Web Technologies for Innovation and Growth Conference, Tenerife, Spain. https://doi.org/10.1109/EDUCON.2018.8363340. accessed 20 Aug, 2021
Delen, D. (2010). A comparative analysis of machine learning techniques for student retention management [article]. Decision Support Systems, 49(4), 498–506. https://doi.org/10.1016/j.dss.2010.06.003
Delen, D. (2011). Predicting student attrition with data mining methods. Journal of College Student Retention, 13(1), 17–35 https://www.proquest.com/scholarly-journals/predicting-student-attrition-with-data-mining/docview/883238524/se-2?accountid=14844
Figueroa-Cañas, J., & Sancho-Vinuesa, T. (2019). Predicting early dropout students is a matter of checking completed quizzes: The case of an online statistics module, Working paper. http://ceur-ws.org/Vol-2415/paper09.pdf. accessed on 20 Aug 2021
Francis, B. K., & Suvanam Sasidhar, B. (2019). Predicting academic performance of students using a hybrid data mining approach. Journal of Medical Systems, 43(6), 1–15. https://doi.org/10.1007/s10916-019-1295-4
Freitas, F. A. D. S., Vasconcelos, F. F. X., Peixoto, S. A., Hassan, M. M., Ali Akber Dewan, M., de Albuquerque, V. H. C., & Rebouças Filho, P. P. (2020). IoT system for school dropout prediction using machine learning techniques based on socioeconomic data [Article]. Electronics (Switzerland), 9(10), 1–14, Article 1613. https://doi.org/10.3390/electronics9101613
Gamao, A. O., & Gerardo, B. D. (2019). Prediction-based model for student dropouts using modified mutated firefly algorithm [article]. International Journal of Advanced Trends in Computer Science and Engineering, 8(6), 3461–3469, article 122. https://doi.org/10.30534/ijatcse/2019/122862019
Gamie, E. A., El-Seoud, M. S. A., Salama, M. A., & Hussein, W. (2019a). Multi-dimensional analysis to predict students' grades in higher education [article]. International Journal of Emerging Technologies in Learning, 14(2), 4–15. https://doi.org/10.3991/ijet.v14i02.9905
Gamie, E. A., Samir Abou El-Seoud, M., & Salama, M. A. (2019b). A layered-analysis of the features in higher education data set. In the International Journal of Emerging Technologies in Learning (iJET) 14(02):4. https://doi.org/10.3991/ijet.v14i02.9905
Genemo, H., Miah, S.J., & McAndrew, A. (2015). A Design Science Research Methodology for developing a Computer-Aided Assessment Approach using Method Marking Concept, Education and Information Technologies, 1769–1784
Goker, H., & Bulbul, H. I. (2014). Improving an early warning system to prediction of student examination achievement. 2014 13th international conference on machine learning and applications,
Gray, C. C., & Perkins, D. (2019). Utilizing early engagement and machine learning to predict student outcomes [article]. Computers & Education, 131, 22–32. https://doi.org/10.1016/j.compedu.2018.12.006
Gray, G., McGuinness, C., Owende, P., & Hofmann, M. (2016). Learning factor models of students at risk of failing in the early stage of tertiary education. Journal of Learning Analytics, 3(2), 330–372 https://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=eric&AN=EJ1126865&site=ehost-live&custid=s1145751
Guan, C., Mou, J., & Jiang, Z. (2020). Artificial intelligence innovation in education: A twenty-year data-driven historical analysis. International Journal of Innovation Studies, 4(4), 134–147. https://doi.org/10.1016/j.ijis.2020.09.001
Hoffait, A.-S., & Schyns, M. (2017). Early detection of university students with potential difficulties. Decision Support Systems, 101, 1–11. https://doi.org/10.1016/j.dss.2017.05.003
Huang, A. Y. Q., Lu, O. H. T., Huang, J. C. H., Yin, C. J., & Yang, S. J. H. (2020). Predicting Students' academic performance by using educational big data and learning analytics: Evaluation of classification methods and learning logs. Interactive Learning Environments, 28(2), 206–230. https://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=eric&AN=EJ1249916&site=ehost-live&custid=s1145751. https://doi.org/10.1080/10494820.2019.1636086
Hussain, M., Zhu, W., Zhang, W., Syed Muhammad Raza, A., & Sadaqat, A. (2019). Using machine learning to predict student difficulties from learning session data. The Artificial Intelligence Review, 52(1), 381–407. https://doi.org/10.1007/s10462-018-9620-8
Hutagaol, N., & Suharjito. (2019). Predictive modelling of student dropout using ensemble classifier method in higher education [Article]. Advances in Science, Technology and Engineering Systems, 4(4), 206–211. https://doi.org/10.25046/aj040425
Iatrellis, O., Savvas, I. K., Fitsilis, P., & Gerogiannis, V. C. (2021). A two-phase machine learning approach for predicting student outcomes. Education and Information Technologies, 26(1), 69–88. https://doi.org/10.1007/s10639-020-10260-x
Ifenthaler, D., & Yau, J. Y.-K. (2020). Utilising learning analytics to support study success in higher education: a systematic review. Educational Technology Research and Development, 68(4), 1961–1990. https://doi.org/10.1007/s11423-020-09788-z
Iqbal, Z., Qayyum, A., Latif, S., & Qadir, J. (2019). Early student grade prediction: An empirical study. 2019 2nd international conference on advancements in computational sciences (ICACS),
Iyanda, A. R., Ninan, O. D., Ajayi, A. O., & Anyabolu, O. G. (2018). Predicting Student Academic Performance in Computer Science Courses: A Comparison of Neural Network Models. International Journal of Modern Education and Computer Science, 11(6), 1. https://doi.org/10.5815/ijmecs.2018.06.01
Jayaraman, J. D., Gerber, S., & Garcia, J. (2019). Supporting minority student success by using machine learning to identify at-risk students.Poster presented at the 12th International Educational Data Mining (EDM) conference, Montreal, Canada. https://www.njcu.edu/sites/default/files/2021-01/JD_Resume%20V2.pdf. accessed on 20 Aug 2021
Jembere, E., Rawatlal, R., & Pillay, A. W. (2017). Matrix factorisation for predicting student performance. 2017 7th world engineering education forum (WEEF),
Kadhim, M. K., & Hassan, A. K. (2020). Towards intelligent E-learning systems: A hybrid model for Predicatingthe learning continuity in Iraqi higher education [article]. Webology, 17(2), 172–188. https://doi.org/10.14704/WEB/V17I2/WEB17023
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Einforcement learning: A survey. The Journal of Artificial Intelligence Research, 4, 237–285.
Kamal, P., & Ahuja, S. (2019). An ensemble-based model for prediction of academic performance of students in undergrad professional course. Journal of Engineering, Design and Technology, 17(4), 769–781. https://doi.org/10.1108/JEDT-11-2018-0204
Kiss, B., Nagy, M., Molontay, R., & Csabay, B. (2019). Predicting dropout using high school and first-semester academic achievement measures.In Predicting dropout using high school and first-semester academic achievement measures. https://akjournals.com/view/journals/063/11/3/article-p297.xml. accessed on 30 Aug, 2021
Korkmaz, C., & Correia, A.-P. (2019). A review of research on machine learning in educational technology. Educational Media International, 56(3), 250–267. https://doi.org/10.1080/09523987.2019.1669875
Liao, S. N., Zingaro, D., Thai, K., Alvarado, C., Griswold, W. G., & Porter, L. (2019). A robust machine learning technique to predict low-performing students. ACM Transactions on Computing Education, 19(3) https://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=eric&AN=EJ1248795&site=ehost-live&custid=s1145751, 1–19. https://doi.org/10.1145/3277569
Lye, C.-T., Ng, L.-N., Hassan, M. D., Goh, W.-W., Law, C.-Y., & Ismail, N. (2010). Predicting Pre-university Student's Mathematics Achievement. Procedia - Social and Behavioral Sciences, 8, 299–306. https://doi.org/10.1016/j.sbspro.2010.12.041
Lynn, N. D., & Emanuel, A. W. R. (2021). Using Data Mining Techniques to Predict Students' Performance. a Review ICIMECE 2020, IOP Conf. Series: Materials Science and Engineering,
Mai, T. L., Do, P. T., Chung, M. T., & Thoai, N. (2019). An apache spark-based platform for predicting the performance of undergraduate students, working paper. https://arxiv.org/pdf/1708.08744.pdf. accessed on 30 Aug, 2021
Marbouti, F., Ulas, J., & Wang, C. H. (2020). Academic and demographic cluster analysis of engineering student success. IEEE Transactions on Education, 1–6. https://doi.org/10.1109/TE.2020.303682
Martins, L. C. B., Carvalho, R. N., Carvalho, R. S., Victorino, M. C., & Holanda, M. (2017). Early prediction of college attrition using data mining. In the Conference of the IEEE 8th International Advance Computing Conference (IACC). https://doi.org/10.1109/IADCC.2018.8692137. accessed on 30 Aug, 2021
Md Rifatul Islam, R., Abdullah Al, I., & Badrudduza, A. S. M. (2019). Educational Performance Analytics of Undergraduate Business Students. International Journal of Modern Education and Computer Science, 11(7), 44. https://doi.org/10.5815/ijmecs.2019.07.05
Miah, S.J. (2004). Accessibility Improvement of Multicultural Educational Web Interface by Using the User Centered Design (UCI) Approach, Information Science and Information Technology Education Joint Conference, Rockhampton, Australia
Miah, S. J. (2008). An ontology based design environment for rural decision support, Unpublished PhD Thesis. Brisbane, Australia: Griffith University
Miah, S.J. & Ahamed, R. (2011). A cloud-based DSS model for driver safety and monitoring on Australian roads, International Journal of Emerging Sciences 1 (4), 634–648
Miah, S.J., & Gammack, J. (2008). A Mashup architecture for web end-user application designs, In Proceedings of the 2nd IEEE International Conference on Digital Ecosystems and Technologies, p.532–537, IEEE Digital Library
Miah, S.J., Kerr, D., Gammack, J., & Cowan, T. (2008). A generic design environment for the rural industry knowledge acquisition, Knowledge-Based Systems, 21 (8), 892-899
Miah, S.J. (2009). End User as Application Developer for Decision Support, In the Proceedings of the Fifteenth American Conference on Information Systems, Aug 6-9 2009, San Francisco, California, USA, 142
Miah, S. J. (2010). A new semantic knowledge sharing approach for e-government systems. Paper presented at the 4th IEEE International Conference on Digital Ecosystems and Technologies (DEST), IEEE Digital Library, 457–462
Miah, S.J., Gammack, J., & McKay, J. (2019). A Meta design Theory for Tailorable Decision Support, Journal of Association for Information Systems 20 (5), 570-603
Mimis, M., Mohamed El, H., Es-saady, Y., Guejdi, A. O., Douzi, H., & Mammass, D. (2019). A framework for smart academic guidance using educational data mining. Education and Information Technologies, 24(2), 1379–1393. https://doi.org/10.1007/s10639-018-9838-8
Mngadi, N., Ajoodha, R., & Jadhav, A. (2020). A conceptual model to identify vulnerable undergraduate learners at higher-education institutions, Journal of Computer Science and Technology 4(2). https://arxiv.org/abs/1104.4163. accessed on 30 Aug, 2021
Monllaó Olivé, D., Huynh, D. Q., Reynolds, M., Dougiamas, M., & Wiese, D. (2020). A supervised learning framework: Using assessment to identify students at risk of dropping out of a MOOC. Journal of Computing in Higher Education, 32(1), 9–26. https://doi.org/10.1007/s12528-019-09230-1
Mousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., & Ghazi Saeedi, M. (2018). Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments, 29(1), 142–163. https://doi.org/10.1080/10494820.2018.1558257
Murphy, K. P. (2012). Machine learning A probabilistic perspective. The MIT Press Cambridge.
Nagy, M., & Molontay, R. (2018). Predicting dropout in higher education based on secondary school performance. 2016 ICML Workshop on #Data4Good: Machine Learning in Social Good Applications, New York, NY, USA. https://arxiv.org/pdf/1606.06364.pdf. accessed on 30 Aug, 2021
Namoun, A., & Alshanqiti, A. (2021). Predicting student performance using data mining and learning analytics techniques: A systematic literature review. Applied Sciences, 11(1). https://doi.org/10.3390/app11010237
Nandy, A., & Biswas, M. (2018). Reinforcement learning - with open AI, TensorFlow and Keras Using Python. Apress. https://doi.org/10.1007/978-1-4842-3285-9
Naseem, M., Chaudhary, K., Sharma, B., & Lal, A. G. (2019). Using ensemble decision tree model to predict student dropout in computing science. 2019 IEEE Asia-Pacific conference on computer science and data engineering (CSDE)
Ndou, N., Ajoodha, R., & Jadhav, A. (2020). Educational data-mining to determine student success at higher education institutions.School of Computer Science & Applied Mathematics University of the Witwatersrand. https://riteshajoodha.co.za/sitepaddata/uploads/2021/02/2020-Ndou.pdf. accessed on 30 Aug, 2021
Oreshin, S., Filchenkov, A., Petrusha, P., Krasheninnikov, E., Panfilov, A., Glukhov, I., Kaliberda, Y., Masalskiy, D., Serdyukov, A., Kazakovtsev, V., Khlopotov, M., Podolenchuk, T., Smetannikov, I., & Kozlova, D. (2020). Implementing a machine learning approach to predicting students academic outcomes. in the 2020 International Conference on Control, Robotics and Intelligent SystemOctober 2020 Pages 78–83. https://doi.org/10.1145/3437802.3437816. accessed on 30 Aug, 2021
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. British Medical Journal, 372, n71. https://doi.org/10.1136/bmj.n71
Palacios, C. A., Reyes-Suárez, J. A., Bearzotti, L. A., Leiva, V., & Marchant, C. (2021). Knowledge discovery for higher education student retention based on data mining: Machine learning algorithms and case study in Chile [article]. Entropy, 23(4), article 485. https://doi.org/10.3390/e23040485
Pang, Y., Judd, N., O'Brien, J., & Ben-Avie, M. (2017). Predicting students' graduation outcomes through support vector machines. In the Conference: 2017 IEEE Frontiers in Education Conference (FIE). https://doi.org/10.1109/FIE.2017.8190666. accessed on 30 Aug, 2021
Philippou, N., Ajoodha, R., & Jadhav, A. (2020). Using machine learning techniques and matric grades to predict the success of first Year University students. 2020 2nd international multidisciplinary information technology and engineering conference (IMITEC)
Quan, G., Minghua, C., Yueli, D., Du, A., & Linlei, Y. (2019). Prediction of Students' Course Failure Based on Campus Card Data. 2019 International Conference on Robots & Intelligent System (ICRIS)
Ramaswami, G., Susnjak, T., Mathrani, A., Lim, J., & Garcia, P. (2019). Using educational data mining techniques to increase the prediction accuracy of student academic performance. Information and Learning Science, 120(7/8), 451–467. https://doi.org/10.1108/ILS-03-2019-0017
Ran, J., Zhang, G., Zheng, T., & Wang, W. (2018). Logistic regression analysis on learning behavior and learning effect based on SPOC data. 2018 13th international conference on Computer Science & Education (ICCSE)
Raza, H., Palaniappan, S., Mahmood, S., Abbas, A., Kamal Uddin, S., & Mian Usman, S. (2020). Predicting student performance in higher educational institutions using video learning analytics and data mining techniques. Applied Sciences, 10(11), 3894. https://doi.org/10.3390/app10113894
Romero, C., López, M.-I., Luna, J.-M., & Ventura, S. (2013). Predicting students' final performance from participation in on-line discussion forums. Computers & Education, 68, 458–472. https://doi.org/10.1016/j.compedu.2013.06.009
Sajjadi, S., Shapiro, B., McKinlay, C., Sarkisyan, A., Shubin, C., & Osoba, E. (2017). Finding bottlenecks: Predicting student attrition with unsupervised classifier. 2017 intelligent systems conference (IntelliSys)
Sani, N. S., Nafuri, A. F. M., Othman, Z. A., Nazri, M. Z. A., & Nadiyah Mohamad, K. (2020). Drop-out prediction in higher education among B40 students [article]. International Journal of Advanced Computer Science and Applications, 11(11), 550–559. https://doi.org/10.14569/IJACSA.2020.0111169
Santos, G. A. S., Belloze, K. T., Tarrataca, L., Haddad, D. B., Bordignon, A. L., & Brandao, D. N. (2020). EvolveDTree: Analyzing student dropout in universities. https://www.researchgate.net/publication/339470641_A_Brief_Review_about_Educational_Data_Mining_applied_to_Predict_Student's_Dropout. accessed on 30 Aug, 2021
Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020). Cybersecurity data science: An overview from machine learning perspective. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-020-00318-5
Sarker, I. H., Furhad, M. H., & Nowrozy, R. (2021). AI-driven cybersecurity: An overview, security intelligence modeling and research directions. SN Computer Science, 2(3). https://doi.org/10.1007/s42979-021-00557-0
Segura-Morales, M., & Loza-Aguirre, E. (2018). Using decision trees for predicting academic performance based on socio-economic factors. In 2017 International Conference on Computational Science and Computational Intelligence (CSCI). https://doi.org/10.1109/CSCI.2017.197. accessed on 30 Aug, 2021
Shahiri, A. M., Husain, W., & Rashid, N. a. A. (2015). A review on predicting Student's performance using data mining techniques. Procedia Computer Science, 72, 414–422. https://doi.org/10.1016/j.procs.2015.12.157
Singh, W., & Kaur, P. (2016). Comparative Analysis of Classification Techniques for Predicting Computer Engineering Students’ Academic Performance. International Journal of Advanced Research in Computer Science, 7(6), 31–36 https://www.proquest.com/scholarly-journals/comparative-analysis-classification-techniques/docview/1912514690/se-2?accountid=14844
Soobramoney, R., & Singh, A. (2019). Identifying students at-risk with an Ensemble of Machine Learning Algorithms. 2019 conference on information communications technology and society (ICTAS).
Sravani, B., & Bala, M. M. (2020). Prediction of student performance using linear regression, International journal of engineering research and technology. https://www.semanticscholar.org/paper/Student-Performance-Prediction-using-Machine-Agrawal-Mavani/1cb8de070943349373fbf527e1abd8451903c6db. accessed on 30 Aug 2021
Tenpipat, W., & Akkarajitsakul, K. (2020). Student dropout prediction: A KMUTT case study.
Trandafili, E., Allkoçi, A., Kajo, E., & Xhuvani, A. (2012). Discovery and evaluation of student's profiles with machine learning.
Trstenjak, B., & Đonko, D. (2014). Determining the impact of demographic features in predicting student success in Croatia. 2014 37th international convention on information and communication technology, electronics and microelectronics (MIPRO).
Tsiakmaki, M., Kostopoulos, G., Koutsonikos, G., Pierrakeas, C., Kotsiantis, S., & Ragos, O. (2018). Predicting University Students' Grades Based on Previous Academic Achievements. 2018 9th international conference on information, Intelligence, Systems and Applications (IISA).
Viloria, A., Padilla, J. G., Vargas-Mercado, C., Hernández-Palma, H., Llinas, N. O., & David, M. A. (2019). Integration of data Technology for Analyzing University Dropout. Procedia Computer Science, 155, 569–574. https://doi.org/10.1016/j.procs.2019.08.079
Waheed, H., Hassan, S. U., Aljohani, N. R., Hardman, J., Alelyani, S., & Nawaz, R. (2020). Predicting academic performance of students from VLE big data using deep learning models [article]. Computers in Human Behavior, 104, article 106189. https://doi.org/10.1016/j.chb.2019.106189
Wakelam, E., Jefferies, A., Davey, N., & Sun, Y. (2020). The potential for student performance prediction in small cohorts with minimal available attributes. British Journal of Educational Technology, 51(2), 347–370. https://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=eric&AN=EJ1243672&site=ehost-live&custid=s1145751. https://doi.org/10.1111/bjet.12836
Wham, D. (2017). Forecasting student outcomes at university-wide scale using machine learning. In the Proceedings of the Seventh International Learning Analytics & Knowledge Conference (LAK '17), March 2017, pp. 576–577. https://doi.org/10.1145/3027385.3029467. accessed on 20 July 2021
Wood, R., & Shirazi, S. (2020). A systematic review of audience response systems for teaching and learning in higher education: The student experience. Computers & Education, 153. https://doi.org/10.1016/j.compedu.2020.103896
Xu, X., Wang, J., Peng, H., & Wu, R. (2019). Prediction of academic performance associated with internet usage behaviors using machine learning algorithms [article]. Computers in Human Behavior, 98, 166–173. https://doi.org/10.1016/j.chb.2019.04.015
Yang, J., DeVore, S., Hewagallage, D., Miller, P., Ryan, Q. X., & Stewart, J. (2020). Using machine learning to identify the Most at-risk students in physics classes. Physical Review Physics Education Research, 16(2). https://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=eric&AN=EJ1274894&site=ehost-live&custid=s1145751. https://doi.org/10.1103/PhysRevPhysEducRes.16.020130
Yildiz Aybek, H. S., & Okur, M. R. (2018). Predicting achievement with artificial neural networks: The case of Anadolu University open education system. International Journal of Assessment Tools in Education, 5(3), 474–490 https://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=eric&AN=EJ1250252&site=ehost-live&custid=s1145751
Youssef, M., Mohammed, S., Hamada, E. K., & Wafaa, B. F. (2019). A predictive approach based on efficient feature selection and learning Algorithms' competition: Case of Learners' dropout in MOOCs. Education and Information Technologies, 24(6), 3591–3618. https://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=eric&AN=EJ1233573&site=ehost-live&custid=s1145751. https://doi.org/10.1007/s10639-019-09934-y
Zabriskie, C., Yang, J., DeVore, S., & Stewart, J. (2019). Using machine learning to predict physics course outcomes. Physical Review Physics Education Research, 15(2). https://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=eric&AN=EJ1228163&site=ehostlive&custid=s1145751. https://doi.org/10.1103/PhysRevPhysEducRes.15.020120
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16(1). https://doi.org/10.1186/s41239-019-0171-0
Zeineddine, H., Braendle, U., & Farah, A. (2021). Enhancing prediction of student success: Automated machine learning approach [article]. Computers and Electrical Engineering, 89, article 106903. https://doi.org/10.1016/j.compeleceng.2020.106903
Zhou, J., & Ye, J.-M. (2020). Sentiment analysis in education research: a review of journal publications. Interactive Learning Environments, 1–13. https://doi.org/10.1080/10494820.2020.1826985
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Fahd, K., Venkatraman, S., Miah, S.J. et al. Application of machine learning in higher education to assess student academic performance, at-risk, and attrition: A meta-analysis of literature. Educ Inf Technol 27, 3743–3775 (2022). https://doi.org/10.1007/s10639-021-10741-7
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DOI: https://doi.org/10.1007/s10639-021-10741-7