Integration of digital technology in mining and metallurgy industries

Sarkisov, S. S.; Lomonosova, Natalia; Zolkina, A. V.; Sarkisov, T. S.

doi:10.17580/tsm.2020.03.01

Cited by 5 publications

(3 citation statements)

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“…These values can be considered as quite small but the reduced model can be constructed. It's shown in the expression (4): 11 Hastie T., Tibshirani R., Friedman J.…”

Section: Resultsmentioning

confidence: 99%

“…But during the Covid-19 pandemic universities had only two possible ways: either to stop learning process, or to use distant learning techniques with support of e-learning systems. Thus, research of online, distant and blended learning systems are extremely actual and valuable [10][11][12][13][14]. During the investigated experiment students could discuss complex topics in forum of such system.…”

Section: Resultsmentioning

confidence: 99%

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The impact of e-learning and social parameters on students’ academic performance

Petrusevich¹

2020

Science for Education Today

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Introduction. The article examines the problem of assessing students' academic performance in the current situation. The purpose of the paper is to evaluate the influence of e-learning and some social and behavioral parameters on students’ academic performance. Materials and Methods. The author employed the machine learning procedures in order to identify and assess the current problems of the educational system, students’ behavior, and universities’ policy. Methods of mathematical analysis and statistics as well as ensemble methods (gradient boosting and the random forest algorithms) were used in order to achieve high accuracy of the research. Results. The author conducted the analysis of the following datasets devoted to academic performance at higher and secondary educational institutions in a number of countries: Students’ Performance in Portugal, E-learning Student Reactions and Students’ Academic Performance. The purposes of the current study were to identify statistical correlations between social parameters of students and the level of their academic performance and to understand how academic performance is determined by the implementation of online learning and blended learning. The research findings suggest that mathematical statistics and data analysis methods allow to identify correlations between students’ performance data and reveal hidden relationships which can be important for university staff. Conclusions. In conclusion, the author summarizes the results of evaluating the impact of the introduction of e-learning elements and some social parameters on students’ academic performance. Keywords Clustering students; Blended learning; Academic performance evaluation; Digitalization of education; Digital technologies in education; Correlation of features; performance improvement.

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“…These values can be considered as quite small but the reduced model can be constructed. It's shown in the expression (4): 11 Hastie T., Tibshirani R., Friedman J.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

The impact of e-learning and social parameters on students’ academic performance

Petrusevich¹

2020

Science for Education Today

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“…At the same time there's idea of averaging forecasts made by means of a few models. If all the models make good forecasts it's obvious that combination of their forecasts should behave well also [4][5][6][7]10]. But this result needs more thorough check and strict confirmation.…”

Section: Time Series Forecast Errors and Prediction Intervalsmentioning

confidence: 99%

Improvement of time series forecasting quality by means of multiple models prediction averaging

2021

Proceedings of the III International Workshop on Modeling, Information Processing and Computing (MIP: Computing-2021)

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Construction of time series models is usually based on Akaike and Bayes information criteria. Requirement of model simplicity is built inside the information criteria structure and the best fitted models aren't always best in terms of criteria values. Often there are a few best models that fit investigated time series well and there's problem of choice between them. Usually information criteria values allow to choose among them. But if one needs the best model by forecasts quality, best fitted models, or there are other thoughts a research has to choose and test models manually. At the same time when a few models are chosen it's possible to construct their combination. The simplest way is to count mean value of their forecasts and to use it as a combined prediction. Practical researches confirm that forecast error gets lower in this approach. Also, more complex construction than averaging of forecasts can be used (for example, weighted voting that is widely used in bagging technique in solution of classification problems). But this approach hasn't got enough theoretical base. From theoretical point of view confidence intervals of time series forecasts (usually here they're considered as prediction intervals) is also very complex task. Intervals tend to be very wide even for models with good prediction quality in terms of mean forecast errors. Thus, prediction intervals are rare in use. In this paper a few time series models are constructed for wage and income indices of Russian macroeconomic time series. Their predictions are combined into one forecast and it's quality is compared to individual ones. Transformation of forecast variance and prediction intervals in case of simple (moving averages MA(q) and autoregressions AR(p) of low order) models is also considered but is a part of further work.

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Modern trends in the digitalization of education

Petrusevich

2020

J. Phys.: Conf. Ser.

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The process of education digitalization has been on its way for a long time. However, a strong surge of interest arose during the period of temporary communication restrictions associated with Covid-19 and the widespread transition to a completely remote format of interaction between all participants in educational process. Digitalization has affected all levels of education and directly the activities of students, teachers, administrators, or stakeholders. The purpose of the study is to evaluate the current state of education digitalization. The objectives of the research are to study trends in the global and domestic online educational market, identify the features of digital educational activities implementation in the Russian Federation, and comparatively analyze the willingness of secondary and higher education to put digital distance learning into practice.

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Integration of digital technology in mining and metallurgy industries

Cited by 5 publications

References 0 publications

The impact of e-learning and social parameters on students’ academic performance

The impact of e-learning and social parameters on students’ academic performance

Improvement of time series forecasting quality by means of multiple models prediction averaging

Modern trends in the digitalization of education

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