Cybersecurity Specialists’ E-learning Problems

Birukov, Svyatoslav; Vasilev, Dmitry; Kokoreva, Lubov; Shmarion, Polina; Nikonovich, Sergey

doi:10.1007/978-3-030-87034-8_41

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…Online learning is a novel learning pattern developing rapidly based on online education platforms in recent years, and it has been taken as a major development direction in the field of education [1][2][3][4][5]. This new style learning pattern can satisfy college students' personalized learning requirements in diverse directions, help improve their learning efficiency, and solve problems such as the time and space limits, as well as the unbalanced learning resource allocation [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Learning Interest Model for a Personalized Learning Requirement Analysis of College Students

Wang¹,

Han²,

Wei³

2022

Int. J. Emerg. Technol. Learn.

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The randomness in college students’ participation of online learning causes common problems such as improper learning resource allocation or unbalanced supply and demand to online education platforms, thus scientifically predicting college students’ learning requirements, triggering their dynamic learning interest, and effectively improving their learning effect are important prerequisites for ensuring these platforms to develop stably, efficiently, and sustainably. Existing prediction models generally ignored the progressing process of learning and failed to comprehensively model or analyze from multiple dimensions such as resource utilization, learning behavior, learning pattern, teaching mode, and teaching management, etc. To fill in this research gap, this paper proposed a Dynamic Learning Interest (DLI) model for analyzing the personalized learning requirements of college students. Based on the college students’ online learning cycles and the learning cycle division results, a time series DLI model was established for studying the prediction of college students’ personalized learning requirements, and short-term prediction and comparative experiment were carried out accordingly. Then, the proposed model was subject to stability test and white noise test to determine the model structure and estimate it parameters. At last, significance test was performed on the proposed model and its parameters, and the results verified the effectiveness of the model.

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