Personalization of gamification is an alternative to overcome the shortcomings of the one-size-fits-all approach, but the few empirical studies analyzing its effects do not provide conclusive results. While many user and contextual information affect gamified experiences, prior personalized gamification research focused on a single user characteristic/dimension. Therefore, we hypothesize if a multidimensional approach for personalized gamification, considering multiple (user and contextual) information, can improve user motivation when compared to the traditional implementation of gamification. In this paper, we test that hypothesis through a mixed-methods sequential explanatory study. First, 26 participants completed two assessments using one of the two gamification designs and self-reported their motivations through the Situational Motivation Scale. Then, we conducted semi-structured interviews to understand learners' subjective experiences during these assessments. As result, the students using the personalized design were more motivated than those using the one-size-fits-all approach regarding intrinsic motivation and identified regulation. Furthermore, we found the personalized design featured game elements suitable to users' preferences, being perceived as motivating and need-supporting. Thus, informing i) practitioners on the use of a strategy for personalizing gamified educational systems that is likely to improve students' motivations, compared to OSFA gamification, and ii) researchers on the potential of multidimensional personalization to improve single-dimension strategies. For transparency, dataset and analysis procedures are available at https://osf.io/grzhp.

Background Metagenomics is an expanding field within microbial ecology, microbiology, and related disciplines. The number of metagenomes deposited in major public repositories such as Sequence Read Archive (SRA) and Metagenomic Rapid Annotations using Subsystems Technology (MG-RAST) is rising exponentially. However, data mining and interpretation can be challenging due to mis-annotated and misleading metadata entries. In this study, we describe the Marine Metagenome Metadata Database (MarineMetagenomeDB) to help researchers identify marine metagenomes of interest for re-analysis and meta-analysis. To this end, we have manually curated the associated metadata of several thousands of microbial metagenomes currently deposited at SRA and MG-RAST. Results In total, 125 terms were curated according to 17 different classes (e.g., biome, material, oceanic zone, geographic feature and oceanographic phenomena). Other standardized features include sample attributes (e.g., salinity, depth), sample location (e.g., latitude, longitude), and sequencing features (e.g., sequencing platform, sequence count). MarineMetagenomeDB version 1.0 contains 11,449 marine metagenomes from SRA and MG-RAST distributed across all oceans and several seas. Most samples were sequenced using Illumina sequencing technology (84.33%). More than 55% of the samples were collected from the Pacific and the Atlantic Oceans. About 40% of the samples had their biomes assigned as ‘ocean’. The ‘Quick Search’ and ‘Advanced Search’ tabs allow users to use different filters to select samples of interest dynamically in the web app. The interactive map allows the visualization of samples based on their location on the world map. The web app is also equipped with a novel download tool (on both Windows and Linux operating systems), that allows easy download of raw sequence data of selected samples from their respective repositories. As a use case, we demonstrated how to use the MarineMetagenomeDB web app to select estuarine metagenomes for potential large-scale microbial biogeography studies. Conclusion The MarineMetagenomeDB is a powerful resource for non-bioinformaticians to find marine metagenome samples with curated metadata and stimulate meta-studies involving marine microbiomes. Our user-friendly web app is publicly available at https://webapp.ufz.de/marmdb/.

Recently, data mining studies are being successfully conducted to estimate several parameters in a variety of domains. Data mining techniques have attracted the attention of the information industry and society as a whole, due to a large amount of data and the imminent need to turn it into useful knowledge. However, the effective use of data in some areas is still under development, as is the case in sports, which in recent years, has presented a slight growth; consequently, many sports organizations have begun to see that there is a wealth of unexplored knowledge in the data extracted by them. Therefore, this article presents a systematic review of sports data mining. Regarding years 2010 to 2018, 31 types of research were found in this topic. Based on these studies, we present the current panorama, themes, the database used, proposals, algorithms, and research opportunities. Our findings provide a better understanding of the sports data mining potentials, besides motivating the scientific community to explore this timely and interesting topic.

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RESUMOEste artigo tem como objetivo demonstrar a eficácia de uma ferramenta de apoio ao controle social da gestão pública, denominada SICM-Educação (Sistema Integrado de Custos Municipais -Educação), que foi desenvolvida pela Universidade Estadual de Londrina em um trabalho conjunto do Departamento de Computação e do Departamento de Administração. O propósito dessa ferramentaé possibilitar a visualização e a comparação dos custos das escolas, por exemplo, dentro de um município. O artigo apresenta um estudo de caso utilizando a ferramenta proposta sobre dados obtidos a partir de um dos levantamentos realizados, referentes ao município de Assaí/PR. Tambémé mostrado como os dados podem ser visualizados através da ferramenta e como eles podem ser utilizados em tomadas de decisão por parte do poder público, o que se torna um poderoso meio de impor a transparência e a fiscalização sobre a administração pública. Palavras-ChaveControle social, tomada de decisão, governo eletrônico * Este trabalho teve apoio financeiro da SETI -Secretaria da Ciência, Tecnologia e Ensino Superior do Paraná. ABSTRACTThis paper aims at demonstrating the effectiveness of a support tool for the social control of public management, the so-called SICM-Educação (Integrated System of Municipal Costs -Education), which has been developed at the University of Londrina in a joint effort of the Department of Computing and the Department of Administration. The purpose of such a tool is to allow visualizing and comparing costs of schools, e.g. within a city. The paper shows a case study using the proposed tool over data obtained from one of the conducted surveys, regarding the city of Assaí/PR. It is also shown how the tool can be used to visualize the collected data and how it can be used to aid decision-making in public power, which enforces transparency and citizens control over the public administration.

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In recent years, there has been an exponential growth in sequencing projects due to accelerated technological advances, leading to a significant increase in the amount of data and resulting in new challenges for biological sequence analysis. Consequently, the use of techniques capable of analyzing large amounts of data has been explored, such as machine learning (ML) algorithms. ML algorithms are being used to analyze and classify biological sequences, despite the intrinsic difficulty in extracting and finding representative biological sequence methods suitable for them. Thereby, extracting numerical features to represent sequences makes it statistically feasible to use universal concepts from Information Theory, such as Tsallis and Shannon entropy. In this study, we propose a novel Tsallis entropy-based feature extractor to provide useful information to classify biological sequences. To assess its relevance, we prepared five case studies: (1) an analysis of the entropic index q; (2) performance testing of the best entropic indices on new datasets; (3) a comparison made with Shannon entropy and (4) generalized entropies; (5) an investigation of the Tsallis entropy in the context of dimensionality reduction. As a result, our proposal proved to be effective, being superior to Shannon entropy and robust in terms of generalization, and also potentially representative for collecting information in fewer dimensions compared with methods such as Singular Value Decomposition and Uniform Manifold Approximation and Projection.

Gamified learning has the potential to improve learning outcomes, but most strategies are focused on using Points-Badges-Leaderboards (PBL) while neglecting other game elements. Drawing upon research discussing nostalgia’s potential to improve user experience and engagement, we hypothesized affective memory could aid in designing gamification’s visual aesthetics. To start testing that hypothesis, we conducted a moderated usability study aiming to understand how Pokémon-based gamification compares to a trophy-based approach. Nine undergraduate students used two versions of a gamified quiz prototype (Pokémon and trophy-based) to complete multiple-choice items, then discussed their experiences in a semi-structured interview. Our findings suggest affective memory plays a significant role in gamified learning and indicate Pokémon-based gamification might maximize intrinsic motivation. Thus, informing practitioners that gamification targeting affective memory can enhance learning by increasing engagement and intrinsic motivation, and calling researchers to further investigate affective memory and other brands in gamification research.