Gamification as a tool of enhancing teaching and learning effectiveness in higher education: Needs analysis

Valentinovna, Zolkina Anna; Lomonosova, Natalia; Petrusevich, D A

doi:10.15293/2658-6762.2003.07

Cited by 10 publications

(2 citation statements)

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“…Is one takes into account that information criteria values depend not only on model quality (as difference between model's prediction and real value) but requirement of model simplicity is also used [1], other metrics of time series quality can be used: for example, only likelihood function value of each time series. At this stage even game-based techniques [16,17] can be used.…”

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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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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“…Учитывая, что на сегодняшний момент геймификация в образовании активно используется, не смотря на то, что этот процесс не всегда очевиден [2], важно изучить уровень когнитивной нагрузки в процессе применения цифровой геймификации и изменения психоэмоционального состояния студентов вуза, поскольку в литературе имеются неоднозначные данные. Одновременно, при рассмотрении вопроса о когнитивной нагрузке в процессе применения цифровой геймификации важно учитывать этап усвоения знания, которое проходит через первичное усвоение, где когнитивная нагрузка должна быть сниженной, закрепление -на этапе которого когнитивная нагрузка может быть умеренной, позднее -увеличиваться.…”

Section: Introductionunclassified

Digital gamification and knowledge consolidation: revealing changes in the psychoemotional state of university students taking into account cognitive load

Krezhevskikh,

Mikhaylova

2024

P Sci Edu

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Introduction. Currently, there is conflicting evidence regarding the effects of digital gamification in education. The purpose of this study is to identify changes in the psycho-emotional state of university students in the conditions of using digital gamified resources in comparison with traditional training (face-to-face training), taking into account the cognitive load and if the resource itself is considered as a means of consolidating the studied material. Materials and methods. The study involved 80 students, as well as 17 experts. The study was accompanied by an initial diagnosis using the same test that was used in the final tests, and an analysis of the students' well-being, activity, and mood, according to the SAN Yu questionnaire. Doskin, self-assessment of the psycho-emotional state of students from pre- to post-test. The level of cognitive load was assessed by the expert group according to its three types: extraneous (external), basic (internal) and generative (relevant), according to the methodology of the Delphi method. The concordance coefficient was used to determine the consistency of expert opinions. Results. Digital gamification is effective in consolidating knowledge previously acquired by students if it has an optimal level of cognitive load, at the same time as the traditional teaching method: significant differences from pre- to post-test were observed in the control and experimental groups: the control group (t = -8.619, p < 0.0001) and the experimental group (t = -11.108, p < 0.0001). Activity (t = -3.021, p = 0.04) and selfassessment indicators of psychoemotional state (t = -2.908, p = 0.06) increased in the experimental group after using the digital corporate game, while the same changes were not observed in the control group. Conclusion. Forecasts regarding changes in the psycho-emotional state of students when using digital gamification, taking into account the optimal cognitive load, can be presented as cautiously optimistic. Keywords: digital gamification, psychoemotional state of university students, cognitive load, Delphi method, consolidation of students' knowledge

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A Gamification Conceptual Framework for Marketing Courses

Firsova,

Vasbieva,

Abaev

2023

Lecture Notes in Networks and Systems

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Gamification as a tool of enhancing teaching and learning effectiveness in higher education: Needs analysis

Cited by 10 publications

References 0 publications

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

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

Digital gamification and knowledge consolidation: revealing changes in the psychoemotional state of university students taking into account cognitive load

A Gamification Conceptual Framework for Marketing Courses

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