The study aims to propose and validate a new digital model based on the Zettelkasten and interval repetition system for comprehensive and full-cycle training of the students. The core idea is to enhance the learning experience and effectiveness of the given training course by enhancing the student's attention during the learning and long-term information retention. In pursuit of the aforementioned research aim, the study incorporates a quantitative research methodology by combining experiments and a survey. In particular, the effectiveness of the proposed model was assessed via an achievement assessment involving two groups of students and assessing their scores on the same test. This was followed by a metacognitive awareness survey of the two groups to investigate their perceived understanding and performance (with and without the use of a model). The proposed model was found to be effective in enhancing the learning experience and effectiveness of the students on the training course. The Zettelkasten facilitates the management of the student's attention, while the interval repetition system contributes to increased retention. The students that used this model in their learning and preparation scored better than their peers. Also, they reported a significantly higher understanding and awareness of their learning than their peers. The model can be incorporated into the learning process or the provision of training courses to the students. This study is the first to suggest the integration of Zettelkasten and the interval repetition system into one learning model for the students. The article proposes a practical model that can be incorporated by teachers to improve the learning effectiveness of their students. This article has some limitations as well that must be acknowledged. Doi: 10.28991/ESJ-2023-SIED2-01 Full Text: PDF
The article is devoted to an overview of measures to support backbone organizations during a pandemic. In this context, the issue has not been addressed in other studies. The peculiarities of the group of backbone organizations were a significant increase in their number, in the structure the largest share falls on the industrial sector, in the regional context — on the capital region. However, the status of backbone organizations is not sufficiently substantiated. The criteria for referring to this group are controversial and, at the same time, can be ignored by the decision of the government commission. The measures of state support related to subsidizing bank loans provided to systemically important organizations, tax deferrals and other payments are being studied. The problems and risks for counterparties are indicated, due to the conditionality of the interest rate on loans. The main limitations for the use of support tools are given. We can conclude that the backbone status does not affect the composition of the support measures that a legal entity has the right to use. The advantage is that financial support is given priority. However, there is a possibility of disruption of the competitive environment as a result of this preference, as well as if the counterparty company is not subject to the moratorium on bankruptcy. It is proposed to change the approach to grouping organizations for the purposes of state support, with the allocation of sectoral priority. The article critically assesses the relevance of the proposed measures. It is proposed to diversify them through the use of refinancing and restructuring of already received concessional loans.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.