Academic Performance Evaluation Using Data Mining in Times of Pandemic
Relationship between Access to the Virtual Classroom and Grades of University Students
DOI:
https://doi.org/10.37467/gkarevtechno.v11.3324Keywords:
Learning analytics, Data analysis, Machine learning, Academic performance, Virtual educationAbstract
This work focuses on studying the relationship that existed between the use of the learning management system (LMS) and the academic performance of the students of the Jorge Basadre Grohmann National University of Tacna-Perú. For this, we use the data provided by the LMS (access virtual classroom) and the university's academic management system (grades). For that, we perform various classification machine learning algorithms to predict academic performance with two classes SATISFACTORY or POOR where Gradient Boosted Trees algorithm had the best accuracy 91.79%. However, with three classes, SATISFACTORY, REGULAR AND POOR, Random Forest algorithm had the best accuracy of 89.26%.
References
Ahmed, N., & Hikmat, M. (2018). Clarify of the Random Forest Algorithm in an Educational Field. ICOASE 2018 - International Conference on Advanced Science and Engineering, 179–184. https://doi.org/10.1109/ICOASE.2018.8548804
Alamri, L., Almuslim, R., Alotibi, M., Alkadi, D., Ullah, I., & Aslam, N. (2020). Predicting Student Academic Performance using Support Vector Machine and Random Forest. ACM International Conference Proceeding Series, PartF16898, 100–107. https://doi.org/10.1145/3446590.3446607
Alloghani, M., Al-Jumeily, D., Hussain, A., Aljaaf, A., Mustafina, J., & Petrov, E. (2019). Application of machine learning on student data for the appraisal of academic performance. Proceedings - International Conference on Developments in ESystems Engineering, DeSE, 2018-Septe, 157–162. https://doi.org/10.1109/DeSE.2018.00038
Altaf, S., Soomro, W., & Rawi, M. (2019). Student Performance Prediction using Multi-Layers Artificial Neural Networks: A case study on educational data mining. ACM International Conference Proceeding Series, 59–64. https://doi.org/10.1145/3325917.3325919
Arnold, K., & Pistilli, M. (2012). Learning Analytics to Increase Student Success. 2nd International Conference on Learning Analytics and Knowledge, May, 267–270.
Athani, S., Kodli, S., Banavasi, M., & Hiremath, P. (2018). Student performance predictor using multiclass support vector classification algorithm. Proceedings of IEEE International Conference on Signal Processing and Communication, ICSPC 2017, 2018-Janua(July), 341–346. https://doi.org/10.1109/CSPC.2017.8305866
Baker, R. (2010). Data Mining. International Encyclopedia of Education, 112–118. https://doi.org/10.1016/B978-0-08-044894-7.01318-X
Baker, R., & Yacef, K. (2009). The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1(1), 3–16.
Baranyi, M., Nagy, M., & Molontay, R. (2020). Interpretable Deep Learning for University Dropout Prediction. SIGITE 2020 - Proceedings of the 21st Annual Conference on Information Technology Education, 13–19. https://doi.org/10.1145/3368308.3415382
Bouchard, K., Gonzales, L., Maitre, J., & Gaboury, S. (2020). Features Exploration for Grades Prediction using Machine Learning. ACM International Conference Proceeding Series, 78–83. https://doi.org/10.1145/3411170.3411232
Bowers, A. J. (2010). Analyzing the longitudinal K-12 grading histories of entire cohorts of students: Grades, data driven decision making, dropping out and hierarchical cluster analysis. Practical Assessment, Research and Evaluation, 15(7).
Cavazos, R., & Garza, S. (2013). Learning Models for Student Performance Prediction. In Springer International Publishing, 17(12). https://doi.org/10.1007/s00500-013-1159-7
Chango, W., Cerezo, R., & Romero, C. (2019). Predicting academic performance of university students from multi-sources data in blended learning. ACM International Conference Proceeding Series. https://doi.org/10.1145/3368691.3368694
Chanlekha, H., & Niramitranon, J. (2018). Student performance prediction model for early-identification of at-risk students in traditional classroom settings. MEDES 2018 - 10th International Conference on Management of Digital EcoSystems, 239–245. https://doi.org/10.1145/3281375.3281403
Chatti, M., Dyckhoff, A., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5–6), 318–331. https://doi.org/10.1504/IJTEL.2012.051815
Chen, H., & Ward, P. (2020). Predicting student performance using data from an auto-grading system. CASCON 2019 Proceedings - Conference of the Centre for Advanced Studies on Collaborative Research - Proceedings of the 29th Annual International Conference on Computer Science and Software Engineering, 234–243. https://doi.org/10.5555/3370272.3370297
Chen, Y., Wang, Y., Xiao, X., & Shi, M. (2011). Knowledge discovery technology based on access information mining on knowledge warehouse. Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, 2, 1285–1288. https://doi.org/10.1109/FSKD.2011.6019701
Cobo, G., García-Solórzano, D., Morán, J., Santamaría, E., Monzo, C., & Melenchón, J. (2012). Using agglomerative hierarchical clustering to model learner participation profiles in online discussion forums. ACM International Conference Proceeding Series, May, 248–251. https://doi.org/10.1145/2330601.2330660
Cortez, P., & Silva, A. (2008). Using data mining to predict secondary school student performance. 15th European Concurrent Engineering Conference 2008, ECEC 2008 - 5th Future Business Technology Conference, FUBUTEC 2008, 2003(2000), 5–12.
Devasia, T., Vinushree, T., & Hegde, V. (2016). Prediction of students performance using Educational Data Mining. Proceedings of 2016 International Conference on Data Mining and Advanced Computing, SAPIENCE 2016, 91–95. https://doi.org/10.1109/SAPIENCE.2016.7684167
ElGamal, A. (2013). An Educational Data Mining Model for Predicting Student Performance in Programming Course. International Journal of Computer Applications, 70(17), 22–28. https://doi.org/10.5120/12160-8163
Feng, M., Heffernan, N., & Koedinger, K. (2006). Predicting State Test Scores Better with Intelligent Tutoring Systems: Developing Metrics to Measure Assistance Required. In In M. Ikeda, K. D. Ashley, y T.-W. Chan (Eds.), 8th International Conference, ITS 2006 (Vol. 3744).
Gašević, D., Dawson, S., Siemens, G., Gašević, B., & Dawson, S. (2015). art%3A10.1007%2Fs11528-014-0822-x. TechTrends, 59(1), 64–71. https://bit.ly/3zyx4qP
Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers.
Hardman, J., Paucar-Caceres, A., & Fielding, A. (2013). Predicting Students’ Progression in Higher Education by Using the Random Forest Algorithm. Systems Research and Behavioral Science, 30(2), 194–203. https://doi.org/10.1002/sres.2130
Huang, S., & Fang, N. (2013). Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. Computers and Education, 61(1), 133–145. https://doi.org/10.1016/j.compedu.2012.08.015
Johnson, L., Adams, S., & Cummins, M. (2012). The NMC Horizon Report: 2012 Higher Education Edition.
Johnson, L., Adams-Becker, S., Cummins, M., Freeman, A., Ifenthaler, D., & Vardaxis, N. (2013). Technology Outlook for Australian Tertiary Education 2013-2018: An NMC Horizon Project Regional Analysis. In New Media Consortium. https://eric.ed.gov/?id=ED559378
Jones, K. (2019). Learning analytics and higher education: a proposed model for establishing informed consent mechanisms to promote student privacy and autonomy. International Journal of Educational Technology in Higher Education, 16(1). https://bit.ly/3b9IJ6b
Moulet, M., & Kodratoff, Y. (1995). From machine learning towards knowledge discovery in databases. IEE Colloquium on Knowledge Discovery in Databases (Digest No. 1995/021 (A)), 1–3.
Pardos, Z., Heffernan, N., Anderson, B., & Heffernan, C. (2007). The Effect of Model Granularity on Student Performance Prediction Using Bayesian Networks. In In C. Conati, K. McCoy, y G. Paliouras (Eds.), User Modeling 2007: Vol. 4511 LNCS. https://bit.ly/3zv9prp
Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems with Applications, 41(4 PART 1), 1432–1462. https://doi.org/10.1016/j.eswa.2013.08.042
Ramírez, P., & Grandón, E. (2018). Prediction of student dropout in a Chilean public university through classification based on decision trees with optimized parameters. Formacion Universitaria, 11(3), 3–10. https://doi.org/10.4067/S0718-50062018000300003
Riquelme, J., Ruiz, R., & Gilbert, K. (2006). Minería de Datos: Conceptos y Tendencias. Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial, 11–18.
Romero, C., López, M., Luna, J., & Ventura, S. (2013). Predicting students’ final performance from participation in on-line discussion forums. Computers and Education, 68, 458–472. https://doi.org/10.1016/j.compedu.2013.06.009
Shah, S., Shah, A., Memon, F., Kemal, A., & Soomro, A. (2021). Online learning during the COVID-19 pandemic: Applying the self-determination theory in the ‘new normal.’ Revista de Psicodidactica, 26(2), 169–178. https://doi.org/10.1016/j.psicod.2020.12.004
Siemens, G., & Long, P. (2014). Penetrare la nebbia: tecniche di analisi per l’apprendimento. Italian Journal of Educational Technology, 22(3), 132–137. https://ijet.itd.cnr.it/article/view/195
Strecht, P., Cruz, L., Soares, C., Mendes-Moreira, J., & Abreu, R. (2015). A Comparative Study of Classification and Regression Algorithms for Modelling Students’ Academic Performance. Proceedings of the 8th International Conference on Educational Data Mining, 392–395. http://www.educationaldatamining.org/EDM2015/proceedings/short392-395.pdf
Talavera, L., & Gaudioso, E. (2004). Mining student data to characterize similar behavior groups in unstructured collaboration spaces. Proceedings of Workshop on Artificial Intelligence in CSCL, 17–23.
Taya, E. (2021). Modelo de minería y datos para evaluar el efecto del uso del aula virtual sobre el rendimiento académico de los estudiantes de la Facultad de Ingeniería de la Universidad Nacional Jorge Basadre Grohmann de Tacna, en tiempos de pandemia, 2020. Repositorio Institucional de La Universidad Nacional Jorge Basadre Grohmann.
World Bank. (2020). The COVID-19 Crisis Response: Supporting tertiary education for continuity , adaptation , and innovation. World Bank, April, 1–9. https://bit.ly/3PYp2x4
Published
Issue
Section
License
All articles are published under an Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0) license. Authors retain copyright over their work.
