Abstract

Abstract

Abstract

Abstract
Being able to capture the characteristics of a time series with a feature vector is a very important task with a multitude of applications, such as classification, clustering or forecasting. Usually, the features are obtained from linear and nonlinear time series measures, that may present several data related drawbacks. In this work we introduce NetF as an alternative set of features, incorporating several representative topological measures of different complex networks mappings of the time series. Our approach does not require data preprocessing and is applicable regardless of any data characteristics. Exploring our novel feature vector, we are able to connect mapped network features to properties inherent in diversified time series models, showing that NetF can be useful to characterize time data. Furthermore, we also demonstrate the applicability of our methodology in clustering synthetic and benchmark time series sets, comparing its performance with more conventional features, showcasing how NetF can achieve high-accuracy clusters. Our results are very promising, with network features from different mapping methods capturing different properties of the time series, adding a different and rich feature set to the literature.

Abstract
E-learning tools are gaining increasing relevance as facilitators in the task of learning how to program. This is mainly a result of the pandemic situation and consequent lockdown in several countries, which forced distance learning. Instant and relevant feedback to students, particularly if coupled with gamification, plays a pivotal role in this process and has already been demonstrated as an effective solution in this regard. However, teachers still struggle with the lack of tools that can adequately support the creation and management of online gamified programming courses. Until now, there was no software platform that would be simultaneously open-source and general-purpose (i.e., not integrated with a specific course on a specific programming language) while featuring a meaningful selection of gamification components. Such a solution has been developed as a part of the Framework for Gamified Programming Education (FGPE) project. In this paper, we present its two front-end components: FGPE AuthorKit and FGPE PLE, explain how they can be used by teachers to prepare and manage gamified programming courses, and report the results of the usability evaluation by the teachers using the platform in their classes.

Abstract
Computer science is a skill that will continue to be in high demand in the foreseeable future. Despite this trend, automated assessment in computer science is often hampered by the lack of systems supporting a wide range of topics. While there is a number of open software systems and programming exercise collections supporting automated assessment, up to this date, there are few systems that offer a diversity of exercises ranging from computer programming exercises to markup and databases languages. At the same time, most of the best-of-breed solutions force teachers and students to alternate between the Learning Management System - a pivotal piece of the e-learning ecosystem - and the tool providing the exercises. This issue is addressed by JuezLTI, an international project whose goal is to create an innovative tool to allow the automatic assessment of exercises in a wide range of computer science topics. These topics include different languages used in computer science for programming, markup, and database management. JuezLTI borrows part of its name from the IMS Learning Tools Interoperability (IMS LTI) standard. With this standard, the tool will interoperate with reference systems such as Moodle, Sakai, Canvas, or Blackboard, among many others. Another contribution of JuezLTI will be a pool of exercises. Interoperability and content are expected to foster the adoption of JuezLTI by many institutions. This paper presents the JuezLTI project, its architecture, and its main components. © Carrillo, Juan V.; Sierra, Alberto; Leal, Jose Paulo; Queirs, Ricardo; Pellicer, Salvador; Primo, Marco; licensed under Creative Commons License CC-BY 4.0