Abstract
JavaScript is currently one of the most popular languages worldwide. Its meteoric rise is mainly due to the fact that the language is no longer bound to the limits of the browser and can now be used on several platforms. This growth has led to its increasing use by companies and, consequently, to become part of the curriculum in schools. Meanwhile, in the teaching-learning process of computer programming, teachers continue to use automatic code evaluation systems to relieve their time-consuming and error prone evaluation work. However, these systems reveal a number of issues: they are very generic (one size fits all), they have scarce features to foster exercises authoring, they do not adhere to interoperability standards (e.g. LMS communication), they rely solely on remote evaluators being exposed to single point of failure problems and reducing application performance and user experience, which is a feature well appreciated by the mobile users. In this context, LearnJS is presented as a Web playground for practicing the JavaScript language. The system uses a local evaluator (the user’s own browser) making response times small and thus benefiting the user experience. LearnJS also uses a sophisticated authoring system that allows the teacher to quickly create new exercises and aggregate them into gamified activities. Finally, LearnJS includes universal LMS connectors based on international specifications. In order to validate its use, an evaluation was made by a group of students of Porto Polytechnic aiming to validate the usability of its graphical user interface. © Ricardo Queirós.

Abstract
Background: Blended treatments, combining digital components with face-to-face (FTF) therapy, are starting to find their way into mental health care. Knowledge on how blended treatments should be set up is, however, still limited. To further explore and optimize blended treatment protocols, it is important to obtain a full picture of what actually happens during treatments when applied in routine mental health care. Objective: The aims of this study were to gain insight into the usage of the different components of a blended cognitive behavioral therapy (bCBT) for depression and reflect on actual engagement as compared with intended application, compare bCBT usage between primary and specialized care, and explore different usage patterns. Methods: Data used were collected from participants of the European Comparative Effectiveness Research on Internet-Based Depression Treatment project, a European multisite randomized controlled trial comparing bCBT with regular care for depression. Patients were recruited in primary and specialized routine mental health care settings between February 2015 and December 2017. Analyses were performed on the group of participants allocated to the bCBT condition who made use of the Moodbuster platform and for whom data from all blended components were available (n=200). Included patients were from Germany, Poland, the Netherlands, and France; 64.5% (129/200) were female and the average age was 42 years (range 18-74 years). Results: Overall, there was a large variability in the usage of the blended treatment. A clear distinction between care settings was observed, with longer treatment duration and more FTF sessions in specialized care and a more active and intensive usage of the Web-based component by the patients in primary care. Of the patients who started the bCBT, 89.5% (179/200) also continued with this treatment format. Treatment preference, educational level, and the number of comorbid disorders were associated with bCBT engagement. Conclusions: Blended treatments can be applied to a group of patients being treated for depression in routine mental health care. Rather than striving for an optimal blend, a more personalized blended care approach seems to be the most suitable. The next step is to gain more insight into the clinical and cost-effectiveness of blended treatments and to further facilitate uptake in routine mental health care.

Abstract
In public transport, smart card-based ticketing system allows to redesign the UPT network, by providing customized transport services, or incentivize travelers to change specific patterns. However, in open systems, to develop personalized connections the journey destination must be known before the end of the travel. Thus, to obtain that knowledge, in this study three models (Top-K, NB, and J48) were applied using different groups of travelers of an urban public transport network located in a medium-sized European metropolitan area (Porto, Portugal). Typical travelers were selected from the segmentation of transportation card signatures, and groups were defined based on the traveler age or economic conditions. The results show that is possible to predict the journey’s destination based on the past with an accuracy rate that varies, on average, from 20% in the worst scenarios to 65% in the best. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
Trusted open data can be used for auditing, accountability, business development, or as an anti-corruption mechanism. Metadata information can address provenance concerns, and trust issues can somehow be mitigated by digital signatures. Those approaches can trace the data origin, but usually lack information about the transformation process. Creating trust in an open data service through technology can reduce the need for third-party certifications, and creating a distributed consensus mechanism capable of validating all the transformations can guarantee that the datasets are reliable and easy to use. This work aims to leverage blockchain technologies to track open data transformations, allowing consumers to verify the data using a distributed ledger, and providing a mechanism capable of publishing trusted transformed data without relying on third-party certifications. To validate the proposed approach, use cases for data transformation will be used. The consensus protocol must be capable of validating the transformations according to a predefined algorithm, the provider must be capable of publishing verifiable transformed data, and the consumer should be able to check if a dataset originated by a transformation is legit. © 2020, Springer Nature Switzerland AG.

Abstract
Software analytics supports data-driven decision making, which allows software practitioners to leverage valuable insights from software data to improve their processes and many quality aspects of the software. In this paper, we present an artifact designed from a set of patterns to support agile teams to plan and manage software analysis activities, named Software Analytics Canvas. Further, we report the study undertaken to evaluate the ease of use and the utility of our canvas from the practitioners' viewpoint, and a participatory design session carried out to collect information about possible artifact improvements. In general, subjects found the artifact useful, but some of them reported difficulties in learning and understanding how to use it. In the participatory design, they pointed out improvement points and a new layout for the canvas components. The results of both studies helped us refine the proposed artifact, improving both the terms used in each element and the layout of the blocks to make more sense for its users.

Abstract
We are assisting the rise of online coding environments as a strategy to promote youth tech employment. With the growing importance of the technology sector, these type of technical training programs give learners emergent tech skills with a big impact and relevance to the current professional market needs. In this realm, MOOCs (massive open online courses) and online coding bootcamps are two increasingly popular options for learners to improve their code development skills and find work within a relatively short amount of time. Among all the features available on these environments, one stands out, which is the code generation. This chapter aims to detail and compare the most popular solutions for both learning contexts based on several criteria such as impact and maturity, user groups, and tools and features. In the features field, the authors highlight the code generation feature as an efficient way to enhance exercise resolution.