Recent technological advances have led to an exponential expansion of biological sequence data and extraction of meaningful information through Machine Learning (ML) algorithms. This knowledge has improved the understanding of mechanisms related to several fatal diseases, e.g. Cancer and coronavirus disease 2019, helping to develop innovative solutions, such as CRISPR-based gene editing, coronavirus vaccine and precision medicine. These advances benefit our society and economy, directly impacting people's lives in various areas, such as health care, drug discovery, forensic analysis and food processing. Nevertheless, ML-based approaches to biological data require representative, quantitative and informative features. Many ML algorithms can handle only numerical data, and therefore sequences need to be translated into a numerical feature vector. This process, known as feature extraction, is a fundamental step for developing high-quality ML-based models in bioinformatics, by allowing the feature engineering stage, with design and selection of suitable features. Feature engineering, ML algorithm selection and hyperparameter tuning are often manual and time-consuming processes, requiring extensive domain knowledge. To deal with this problem, we present a new package: BioAutoML. BioAutoML automatically runs an end-to-end ML pipeline, extracting numerical and informative features from biological sequence databases, using the MathFeature package, and automating the feature selection, ML algorithm(s) recommendation and tuning of the selected algorithm(s) hyperparameters, using Automated ML (AutoML). BioAutoML has two components, divided into four modules: (1) automated feature engineering (feature extraction and selection modules) and (2) Metalearning (algorithm recommendation and hyper-parameter tuning modules). We experimentally evaluate BioAutoML in two different scenarios: (i) prediction of the three main classes of noncoding RNAs (ncRNAs) and (ii) prediction of the eight categories of ncRNAs in bacteria, including housekeeping and regulatory types. To assess BioAutoML predictive performance, it is experimentally compared with two other AutoML tools (RECIPE and TPOT). According to the experimental results, BioAutoML can accelerate new studies, reducing the cost of feature engineering processing and either keeping or improving predictive performance. BioAutoML is freely available at https://github.com/Bonidia/BioAutoML.

Programming is a complex, not trivial to learn and teach task, which gamification can facilitate. However, how gamification affects learning and the influence of context-related aspects on that effect demand research to better understand how and to whom gamification enhances programming learning. Therefore, we conducted an experimental study analyzing how gamification worked and the role of context-related aspects in terms of intervention duration and learners' familiarity with programming (i.e., the task's topic). It was a six-week study with 19 undergraduate students from an Algorithms class that measured their learning gains, intrinsic motivation, and number of completed quizzes. Mainly, we found gamification affected learning via intrinsic motivation, effect that depended on intervention duration and learners' familiarity with programming. That is, intrinsic motivation strongly predicted learning gains and gamification's effect on intrinsic motivation changed over time, decreasing from positive to negative as learners had less familiarity with programming. Thus, showing gamification can positively impact programming learning by improving students' intrinsic motivation, although that effect changes over time depending on one's previous familiarity with programming.

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.