Tools for Statistical Analysis of Online Student Testing Results

S. G. Magomedov, N.Sh. Gazanova, A. V. Tarasov, Ya. S. Gryukan, E.V. Nikulchev

Abstract


E-learning and online testing have become firmly embedded in the educational practice of higher education institutions, not just during the pandemic. Digital technologies provide new opportunities to improve learning methodology based on the analysis of student engagement in the educational process and rapid assessment of the amount of material being learned. Online e-learning systems store a large amount of data, such as logging of user actions, saving time spent on interaction with certain components, time to answer questions of control or final certification tests, time spent in the online learning system. These data, as a rule, are not available to teachers and are used to control access to data. However, such data can be the basis for methodological analysis. In this paper, a set of tools for statistical analysis of test response times is formed based on the available data in the online testing system. As we know, response time on cognitive tests is an important research tool in the field of psychology. With the rich data set collected during the pandemic in e-learning systems, it is possible to develop psychological and pedagogical digital techniques for data analysis, which can be applied both to improve e-learning and to identify non-self or non-involved responses during testing.

Full Text:

PDF (Russian)

References


Al Lily, A. E., Ismail, A. F., Abunasser, F. M., Alqahtani, R., Alshumaimeri, Y. A., Albugami, S. S. (2020). Distance education as a response to pandemics: Coronavirus and Arab culture //Technology in society. – 2020. – Т. 63. – С. 101317.

Zhang Y., Lin C. H. Student interaction and the role of the teacher in a state virtual high school: what predicts online learning satisfaction? //Technology, Pedagogy and Education. – 2020. – Т. 29. – №. 1. – С. 57-71.

Khalil R. et al. The sudden transition to synchronized online learning during the COVID-19 pandemic in Saudi Arabia: a qualitative study exploring medical students’ perspectives //BMC medical education. – 2020. – Т. 20. – С. 1-10.

Magomedov, S., Ilin, D., Silaeva, A., Nikulchev, E. (2020). Dataset of user reactions when filling out web questionnaires. Data, 5(4), 108. doi: https://doi.org/10.3390/data5040108

Nikulchev, E., Ilin, D., Silaeva, A., Kolyasnikov, P., Malykh, S. (2020). Digital Psychological Platform for Mass Web-Surveys. Data, 5(4), 95. doi: https://doi.org/10.3390/data5040095

Nikulchev, E.; Gusev, A.; Ilin, D.; Gazanova, N.; Malykh, S. Evaluation of User Reactions and Verification of the Authenticity of the User’s Identity during a Long Web Survey. Apply Sciences. 2021, 11, 11034. doi: https://doi.org/10.3390/app112211034

Nikulchev E., Gusev A., Gazanova N,, Magomedov S., Alexeenko A., Malykh A., Kolyasnikov P., Malykh S. Engagement assessment for the educational web-service based on largest Lyapunov exponent calculation for user reaction time series // Education Sciences, 2023. Vol. 13, No. 2, P. 141. https://doi.org/10.3390/educsci13020141

Komleva N. V. "Digital tutor"-platform for creating online courses // Plekhanov Scientific Bulletin. - 2021. - №. 1. - С. 35-44.

V. F. Ochkov, A. I. Tikhonov, D. S. Leonova et al. Mathematics and new information technologies. Ending // Mathematical Education. 2021. № 2 (98). С. 34-43.

Tikhomirova T.N., Malykh S.B. Cognitive development of schoolchildren: the effects of macro- and microenvironmental conditions of education // Voprosy psychologii. 2021. № 67(5). С. 30-43.

Mõttus, R., Kattai, K., Allik, J., Realo, A. (2021). Combining item response theory and multivariate density modeling for detecting cheating in tests. Journal of Educational Measurement, 58(2), 207-227. doi: 10.1111/jedm.12251

Cizek, G. J. (2020). Detecting cheating in tests: A review and critique. Educational Measurement: Issues and Practice, 39(1), 47-54. doi: 10.1111/emip.12316

Jiao, H., Zou, H., Chen, Y., Qian, M. (2021). The identification of cheating behavior based on multimodal test response data: A clustering approach. Journal of Educational Data Mining, 13(1), 1-24.

Boruch, R. F., & Gormley Jr, W. T. (2022). Statistical methods for detecting test fraud. Annual Review of Statistics and Its Application, 9, 325-346. doi: 10.1146/annurev-statistics-042821-110259

Bruce P., Bruce E., Gedek P. Practical Statistics for Data Science Specialists: - SPb.: BHV-Peterburg, 2021.

Kovalchuk A. O., Kapitan V. Yu. Fundamentals of statistical and research data analysis in Python. - Vladivostok : FEFU Publishing House, 2021.

Chen, C., Wu, Y., & Huang, S. (2020). Identifying cheating behavior in online tests based on time-series data analysis. Computers & Education, 153, 103933. doi: 10.1016/j.compedu.2020.103933

Chen, C., Wu, Y., & Huang, S. (2020). Identifying cheating behavior in online tests based on time-series data analysis. Computers & Education, 153, 103933. doi: 10.1016/j.compedu.2020.103933


Refbacks

  • There are currently no refbacks.


Abava  Кибербезопасность MoNeTec 2024

ISSN: 2307-8162