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PREDICTING LEARNING OUTCOMES IN HIGHER EDUCATION WITH SEMANTIC METHODS

Management of Educational Organizations , UDC: 378 DOI: 10.25688/2072-9014.2022.59.1.05

Authors

  • Yarmakhov Boris Borisovich PhD (Philosophical Science), Associate Professor
  • Bosenko Timur Murtazovich PhD (Technical Sciences)
  • Lavrenova Ekaterina Vladimirovna PhD (Pedagogy)

Annotation

The article discusses the practice of using semantic methods to predict students’ learning outcomes.

How to link insert

Yarmakhov, B. B., Bosenko, T. M. & Lavrenova, E. V. (2022). PREDICTING LEARNING OUTCOMES IN HIGHER EDUCATION WITH SEMANTIC METHODS Bulletin of the Moscow City Pedagogical University. Series "Pedagogy and Psychology", 2022, №1 (59), 47. https://doi.org/10.25688/2072-9014.2022.59.1.05
References
1. 1. Aguiar E., Ambrose G. Engagement vs performance: Using electronic portfolios to predict first semester engineering student persistence // Journal of Learning Analytics. 2014. № 1 (3).
2. 2. Al-Barrak M. A., Al-Razgan M. Predicting students final gpa using decision trees: a case study // International Journal of Information and Education Technology. 2016. № 7. P. 528.
3. 3. Grivokostopoulou F., Perikos I., Hatzilygeroudis I. Utilizing semantic web technologies and data mining techniques to analyze students learning and predict final performance // International Conference on Teaching, Assessment and Learning (TALE). 2014. P. 488–494.
4. 4. Hellas A., Ihantola I., Predicting academic performance: A systematic literature review // Proc. Companion 23rd Annu. ACM Conf. Innov. Technol. Comput. Sci. Edu. 2018. P. 175–199.
5. 5. Khanna L., Singh S. Educational data mining and its role in determining factors affecting students academic performance: A systematic review // 1st India International Conference on Information Processing (IICIP). 2016. P. 1–7.
6. 6. Murtaugh P. A., Burs L. D., Schuster J. Predicting the retention of university students // Research in Higher Education. 1999. № 40 (3). P. 355–371.
7. 7. Musso M., Hernández C., Cascallar C. Predicting key educational outcomes in academic trajectories: A machine-learning approach // Higher Educ.2020. Vol. 80. № 5. P. 1–20.
8. 8. Perez C. Different Tests, Same Flaws: Examining the SAT I, SAT II, and ACT // Journal of College Admission. 2002. № 177. P. 20–25.
9. 9. Shahiri M., Husain W. A review on predicting student’s performance using data mining techniques // Procedia Comput. Sci. 2015. Vol. 72. P. 414–422.
10. 10. Siemens G., Long P. Penetrating the fog: Analytics in learning and education // EDUCAUSE Review. 2011. № 46 (5). P. 31–40.
11. 11. Tapia-Leon M., Carrera Rivera A. Representation of Latin American University Syllabuses in a Semantic Network // 2nd International Conference on Information Systems and Computer Science (INCISCOS). 2017. P. 295–301.
12. 12. Thiele T., Singleton A., Pope D., Stanistreet D. Predicting students’ academic performance based on school and socio-demographic characteristics // Studies in Higher Education. 2015. № 27. P. 1–23.
13. 13. Wymore A. Wayne A Mathematical Theory of Systems Engineering // The Elements. John Wiley & Sons: New York, 1967.
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