Titlebook: Visualizations and Dashboards for Learning Analytics; Muhittin Sahin,Dirk Ifenthaler Book 2021 The Editor(s) (if applicable) and The Autho

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Sebastian Hobert,Florian Berenscia, Spain, in November 2020. The conference was held virtually..The 53 full papers were carefully reviewed and selected from 135 submissions. They provide a comprehensive overview of major challenges and recent advances in various domains related to the digital transformation and collaborative netw

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Fatma Bayrak,Furkan Aydın,Halil Yurdugüliew various approaches to face detection developed in the past decade, with more emphasis on boosting-based learning algorithms. We then present a series of algorithms that are empowered by the statistical view of boosting and the concept of multiple instance learning. We start by describing a boost

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omputer‘s state. We examine research on securely capturing a computer‘s state, and consider the utility of this information both for improving security on the local computer (e.g., to convince the user that her computer is not infected with malware) and for communicating a remote computer‘s state (e

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Rogers Kaliisa,Anna Gillespie,Christothea Herodotou,Anders Kluge,Bart Rientiesroblems a prominent place is taken by the determination of statistical properties of complex methods for multivariate data analysis. Here we may think of multiparameter models for the exploratory analysis of multivariate data of mixed measurement level, which are usually applied in several steps (cf

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Students’ Emotional Reactions to Social Comparison via a Learner Dashboardnse to social comparison is a result of personality rather than attainment. Implications for the design of dashboards are considered, and in particular we argue that it is important to ensure that an institution has a well-embedded approach to student well-being before social comparison is implemented.

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Visualizations and Dashboards for Learning Analytics: A Systematic Literature Reviewrning analytics dashboards are customizable control panels displaying personalized learning analytics features which adapt to the learning process in real time. Seventy six studies were selected as the primary studies. These studies consist of journal articles and conference papers. Detailed informa

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Designing Theory-Driven Analytics-Enhanced Self-Regulated Learning Applicationsindicators were proposed to support different crucial SRL processes, such as planning, awareness, self-reflection, self-monitoring, and feedback. However, the design of these dashboards and indicators is often without reference to theories in learning science, human-computer interaction (HCI), and i