Abstract
The digital transformation of industry poses new challenges for related industrial fields, in particular for education. Universities must transform accordingly and provide the digital economy with qualified personal. Online technologies, including online learning, play an important role in digital transformation, opening up new opportunities and posing challenges that the education system has never faced before. The problem of making justified decisions on the selection and assessment of the quality of online courses and their control materials is one of these challenges. Data mining and machine learning analytics tools that work with learners’ digital footprint data can be used to solve this problem. To do so, this work uses the methods of information theory. The indicator of the informativeness of control materials provides differentiation of standalone checkpoints and online courses in general and minimizes the subjective factor present when using existing assessment methods. Another indicator that assesses the mutual informativeness of checkpoints allows determining how much information about the final test results can be obtained by monitoring current academic performance. This indicator is applicable both to individual checkpoints and their series (assembled by type—test assignments, homework assignments, etc.; or by the chronology of the course—¼, ½, etc. of the course length). With its help, it is possible to evaluate the course checkpoints and make a decision on how to improve the course materials. In addition, this indicator can be useful for assessing the marginal accuracy of the prediction of the test results according to the current academic performance of a student. The proposed methods have been tested on online courses of the Ural Federal University with positive approbation results.
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References
Hollands, F. M., & Tirthali, D. (2014). MOOCs: Expectations and Reality. Full report. Online Submission. Retrieved October 24, 2020, from https://files.eric.ed.gov/fulltext/ED547237.pdf
Abrami, P. C., Bernard, R. M., Bures, E. M., et al. (2011). Interaction in distance education and online learning: using evidence and theory to improve practice. Journal of Computing in Higher Education, 23, 82–103. https://doi.org/10.1007/s12528-011-9043-x
Aguaded Gómez, J. I. (2013). The MOOC revolution: A new form of education from the technological paradigm? Comunicar, 41, 7–8
Fidalgo-Blanco, Á., Sein-Echaluce, M.L. & García-Peñalvo, F.J. (2016). From massive access to cooperation: Lessons learned and proven results of a hybrid xMOOC/cMOOC pedagogical approach to MOOCs. International Journal of Educational Technology in Higher Education 13(24). DOI: https://doi.org/10.1186/s41239-016-0024-z
Filatova, L., Abankina, I., Abankina, T., & Nikolayenko, E. (2012). Education development trends in Russia. Journal of US-China Public Administration, 9(10), 1198–1214
Jacoby, J. (2014). The disruptive potential of the massive open online course: A literature review. Journal of Open, Flexible and Distance Learning, 18, 73–85
Bystrova, T., Larionova, V., Sinitsyn, E., & Tolmachev, A. (2018). Learning analytics in massive open online courses as a tool for predicting learner performance. Educational Studies Moscow, 4, 139–166. https://doi.org/10.17323/1814-9545-2018-4-139-166
O’Farrell, L. (2017). Using learning analytics to support the enhancement of teaching and learning in higher education. National Forum for the Enhancement of Teaching and Learning in Higher Education. Retrieved from https://www.teachingandlearning.ie/publication/using-learning-analytics-to-support-the-enhancement-of-teaching-and-learning-in-higher-education/
Abbakumov, D., Desmet, P., & Van den Noortgate, W. (2020). Psychometrics of MOOCs: Measuring learners’ proficiency. Psychologica Belgica, 60(1), 115–131. https://doi.org/10.5334/pb.515
Crocker, L. M., & Algina, J. (1986). Introduction to Classical and Modern Test Theory. Holt, Rinehart, and Winston.
Wentzel, E. S. (1969). Probability theory, Main edition of physical and mathematical literature. Nauka.
Acknowledgements
This work was carried out within the framework of an internal UrFU grant on the topic “Modeling the process of forming the values of University students in the context of implementing mass open online courses (MOOCs)”.
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Tolmachev, A., Astratova, G. (2021). Assessment of Online Courses Control Materials Using Information Theory Methods. In: Kumar, V., Rezaei, J., Akberdina, V., Kuzmin, E. (eds) Digital Transformation in Industry. Lecture Notes in Information Systems and Organisation, vol 44. Springer, Cham. https://doi.org/10.1007/978-3-030-73261-5_24
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