Multilevel IRT models for the analysis of satisfaction for distance learning during the Covid-19 pandemic
In: Socio-economic planning sciences: the international journal of public sector decision-making, Volume 86, p. 101467
ISSN: 0038-0121
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In: Socio-economic planning sciences: the international journal of public sector decision-making, Volume 86, p. 101467
ISSN: 0038-0121
Il volume raccoglie i contributi presentati alla conferenza "Stat.Edu'21 -New Perspectives in Statistics Education". La Conferenza è stata ospitata dal Dipartimento di Scienze Politiche dell'Università degli Studi di Napoli Federico II (25-26 marzo 2021). La conferenza è stata organizzata come evento finale del progetto ERASMUS+ "ALEAS - Adaptive LEArning in Statistics" (https://aleas-project.eu) che si è svolto dal 2018 al 2021. Il progetto ha avuto l'obiettivo di sviluppare e implementare un sistema di apprendimento adattivo che offra percorsi di apprendimento personalizzati agli studenti, con lo scopo ultimo di aiutare gli studenti a fronteggiare l'ansia statistica. Stat.Edu'21 ha stimolato riflessioni, discussioni e contributi sul tema di ALEAS e sullo sviluppo di sistemi di apprendimento adattivo in ambito universitario come strumenti complementari ai corsi tradizionali e contribuito lo scambio di buone pratiche. Il volume comprende 12 contributi che propongono riflessioni e studi quantitativi in particolare su 3 temi: la valutazione degli effetti dell'ansia o più generalmente lo studio di diverse attitudini nello studio della statistica, strumenti e metodi per la valutazione dei percorsi di insegnamento e le esperienze di apprendimento basate sulla tecnologia.
BASE
In: Advances in statistical analysis: AStA, Volume 107, Issue 1-2, p. 343-367
ISSN: 1863-818X
AbstractRecent studies have pointed out the effect of personality traits on athletes' performance and success; however, fewer analyses have focused the relation among these features and specific athletic behaviors, skills, and strategies to enhance performance. To fill this void, the present paper provides evidence on what personality traits mostly affect athletes' mental skills and, in turn, their effect on the performance of a sample of elite swimmers. The main findings were obtained by exploiting a component-based structural equation modeling which allows to analyze the relationships among some psychological constructs, measuring personality traits and mental skills, and a construct measuring sports performance. The partial least squares path modeling was employed, as it is the most recognized method among the component-based approaches. The introduced method simultaneously encompasses latent and emergent variables. Rather than focusing only on objective behaviors or game/race outcomes, such an approach evaluates variables not directly observable related to sport performance, such as cognition and affect, considering measurement error and measurement invariance, as well as the validity and reliability of the obtained latent constructs. The obtained results could be an asset to design strategies and interventions both for coaches and swimmers establishing an innovative use of statistical methods for maximizing athletes' performance and well-being.