Stat Edu 21 New Perspectives In Statistics Education Proceedings Of The International Conference Stat Edu 21

[Italiano]: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. /[English]: The volume collects the papers presented at the Conference “Stat.Edu’21 -New Perspectives in Statistics Education”. The Conference was held at the Department of Political Sciences of the University of Naples Federico II (25-26 March 2021). The conference was the final event of the “ALEAS - Adaptive LEArning in Statistics”, an ERASMUS+ project (https://aleas-project.eu) developed in the period 2018-2021 to design and implement an Adaptive LEArning system able to offer personalised learning paths to students, with the purpose to provide them remedial advice to deal with the “statistics anxiety”. Stat.Edu’21 aimed at stimulating discussions, solicitations and contributions around the central theme of ALEAS, the development of adaptive learning systems in the field of Higher Education as a complementary tool for traditional courses and promote a community of practice in this field. The volume collects 12 papers reporting reflections and quantitative studies covering mainly three topics: the assessment of the effects of anxiety or more generally of a different attitude in the study of Statistics, tools and methods for the assessment of training paths and technology-based learning experiences.

This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2023), held at London Metropolitan University, London, UK, during June 2023. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students. The book is divided into four volumes.

This book contains a selection of articles from The 2014 World Conference on Information Systems and Technologies (WorldCIST'14), held between the 15th and 18th of April in Funchal, Madeira, Portugal, a global forum for researchers and practitioners to present and discuss recent results and innovations, current trends, professional experiences and challenges of modern Information Systems and Technologies research, technological development and applications. The main topics covered are: Information and Knowledge Management; Organizational Models and Information Systems; Intelligent and Decision Support Systems; Software Systems, Architectures, Applications and Tools; Computer Networks, Mobility and Pervasive Systems; Radar Technologies; Human-Computer Interaction; Health Informatics and Information Technologies in Education.

This modern approach integrates classical and contemporary methods, fusing theory and practice and bridging the gap to statistical learning.

The first OZCOTS conference in 1998 was inspired by papers contributed by Australians to the 5th International Conference on Teaching Statistics. In 2008, as part of the program of one of the first National Senior Teaching Fellowships, the 6th OZCOTS was held in conjunction with the Australian Statistical Conference, with Fellowship keynotes and contributed papers, optional refereeing and proceedings. This venture was so successful that the 7th and 8th OZCOTS were similarly run, conjoined with Australian Statistical Conferences in 2010 and 2012. Authors of papers from these OZCOTS conferences were invited to develop chapters for refereeing and inclusion in this volume. There are sections on keynote topics, undergraduate curriculum and learning, professional development, postgraduate learning, and papers from OZCOTS 2012. Because OZCOTS aim to unite statisticians and statistics educators, the approaches this volume takes are immediately relevant to all who have a vested interest in good teaching practices. Globally, statistics as a discipline, statistical pedagogy and statistics in academia and industry are all critically important to the modern information society. This volume addresses these roles within the wider society as well as questions that are specific to the discipline itself. Other chapters share research on learning and teaching statistics in interdisciplinary work and student preparation for futures in academia, government and industry.

This work presents link prediction similarity measures for social networks that exploit the degree distribution of the networks. In the context of link prediction in dense networks, the text proposes similarity measures based on Markov inequality degree thresholding (MIDTs), which only consider nodes whose degree is above a threshold for a possible link. Also presented are similarity measures based on cliques (CNC, AAC, RAC), which assign extra weight between nodes sharing a greater number of cliques. Additionally, a locally adaptive (LA) similarity measure is proposed that assigns different weights to common nodes based on the degree distribution of the local neighborhood and the degree distribution of the network. In the context of link prediction in dense networks, the text introduces a novel two-phase framework that adds edges to the sparse graph to forma boost graph.