Data science for analyzing and improving educational processes
Author:
Publisher
Springer Science and Business Media LLC
Subject
Education
Link
https://link.springer.com/content/pdf/10.1007/s12528-021-09299-7.pdf
Reference5 articles.
1. Klašnja-Milićević, A., Ivanović, M., & Budimac, Z. (2017). Data science in education: Big data and learning analytics. Computer Applications in Engineering Education, 25, 1066–1078. https://doi.org/10.1002/cae.21844
2. López-Zambrano, J., Lara, J. A., & Romero, C. (2021). Improving the portability of predicting students’ performance models by using ontologies. Journal of Computing in Higher Education. https://doi.org/10.1007/s12528-021-09273-3
3. Maatuk, A. M., Elberkawi, E. K., Aljawarneh, S., Rashaideh, H., & Alharbi, H. (2021). The COVID-19 pandemic and E-learning: Challenges and opportunities from the perspective of students and instructors. Journal of Computing in Higher Education. https://doi.org/10.1007/s12528-021-09274-2
4. Mitrofanova, Y. S., Sherstobitova, A. A., & Filippova, O. A. (2019). Modeling smart learning processes based on educational data mining tools. In V. Uskov, R. Howlett, & L. Jain (Eds.), Smart Education and e-Learning 2019. Smart Innovation, Systems and Technologies. (Vol. 144). Springer. https://doi.org/10.1007/978-981-13-8260-4_49
5. Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wires Data Mining and Knowledge Discovery, 10, e1355. https://doi.org/10.1002/widm.1355
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