Mathematical Modeling and Forecasting of Student’s Academic Performance on Massive Online Courses
- 10.2991/aebmr.k.201205.019How to use a DOI?
- online courses, probability model, assessing of tests’ quality, asymptotically steady scores distribution, forecasting of test results
Mathematical model for calculating the scores’ distributions in massive open online courses is proposed. The model is based on the theory of Markov processes. It allows to calculate the probability to find a student in one of the groups according to the results of passing the tests: unsuccessful students, performing satisfactorily and doing well and excellent. It is shown that in the limit of a sufficiently long history of teaching the course on the educational platform, the distribution of scores for the course becomes asymptotically steady. It is shown also that such asymptotically steady distributions, can be calculated on the base of the model proposed, even for the courses without a long history. Such asymptotically steady distributions can be indicators of the quality of control materials and approaches to student scoring. As an example, several courses of Ural Federal University (UrFU), posted on the National Platform of Open Education have been analyzed. The possibility of using the model to predict the results of control tests based on the data on the current progress of students before passing them is shown.
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - A.V. Tolmachev AU - E.V. Sinitsyn AU - G.V. Astratova PY - 2020 DA - 2020/12/07 TI - Mathematical Modeling and Forecasting of Student’s Academic Performance on Massive Online Courses BT - Proceedings of the 2nd International Scientific and Practical Conference on Digital Economy (ISCDE 2020) PB - Atlantis Press SP - 121 EP - 127 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.201205.019 DO - 10.2991/aebmr.k.201205.019 ID - Tolmachev2020 ER -