Abstract

Abstract
In this paper we examine the use of social media as a marketing channel by Higher Education Institutions (HEI) and its impact on the institution's brand, attracting top professionals and students. HEIs are annually evaluated globally based on various success parameters to be published in rankings. Specifically, we analyze the Twitter publishing strategies of the selected HEIs, and we compare the results with their overall ranking positions. Our study shows that there are no significant differences between the well-known university rankings based on Kendall t and RBO metrics. However, our data retrieval indicates a tendency for the top-ranked universities to adopt specific strategies, which are further emphasized when analyzing emotions and topics. Conversely, some universities have less prominent strategies that do not align with their ranking positions. This study provides insights into the role of social media in the marketing strategies of HEIs and its impact on their global rankings. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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.