Abstract
Problem-solving is considered one of the most important skills to retain in the coming decades for building a modern and proactive society. In this realm, computer programming learning is vital to enrich those skills. Practicing in this area boils down to solve programming exercises. In order to foster this practice, it is necessary to provide students with the best of the breed automated tools and a good set of exercises in a fair quantity covering the curricula of a typical programming course. Despite the increasing appearance of automated tools such as program evaluators, gamification engines and sophisticated web environments, access to exercises remains problematic. In fact, although the existence of several code repositories (most for feed computer programming contests), the majority of them store the exercises in proprietary formats and without any access facilities hindering their use. This leaves no other option to teachers but to manually create programming exercises which is time-consuming and error prone, or simply, reuse the same exercises, from previous years, which is considered as a detrimental and limiting approach to enhance multi-faceted and creative programmers. The article surveys the current interoperability efforts on programming exercises, more precisely, in terms of serialization formats and communication protocols. This study will sustain the selection of an API to feed a code playground called LearnJS with random programming exercises.

Abstract
Designing a viable carsharing system in a competitive environment is challenging and often dependent on a myriad of decisions. This paper establishes and presents an integrated conceptual decision-support framework for carsharing systems, encompassing critical decisions that should be made by carsharing organizations and users. To identify the main decisions in a carsharing system, and the inputs and interactions among them, it is crucial to obtain a comprehensive understanding of the current state of the literature as well as the business practices and context. To this aim, a holistic and in-depth literature review is conducted to structure distinct streams of literature and their main findings. Then, we describe some of the key decisions and business practices that are often oversimplified in the literature. Finally, we propose a conceptual decision-support framework that systematizes the interactions between the usually isolated problems in the academic literature and business practices, integrating the perspectives of carsharing organizations and of their users. From the proposed framework, we identify relevant research gaps and ways to bridge them in the future, toward more realistic and applicable research.

Abstract
Immersion is a proven method of learning a foreign language and using Virtual Reality to achieve that immersion has high potential educational benefits. However, there are no recent systematic reviews that combine both foreign language education and immersive Virtual Reality. This systematic review aims to identify features, educational methods, technologies, and gaps of immersive virtual reality for foreign and second language education. PRISMA method was followed to carry out the systematic review. From the analysis of the results, two main conclusions were drawn. Firstly, the relation between immersive Virtual Reality and foreign language learning is quite positive, particularly when compared with conventional pedagogical practices. Not only that, the connection between immersive Virtual Reality and the user's motivation and satisfaction is also quite positive. Lastly, limitations such as the low sample of studies and gaps in the literature are addressed, and directions for future work and the area's progress are pointed out.

Abstract
Practice is pivotal in learning programming. As many other automated assessment tools for programming assignments, Mooshak has been adopted by numerous educational practitioners to support them in delivering timely and accurate feedback to students during exercise solving. These tools specialize in the delivery and assessment of blank-sheet coding questions. However, the different phases of a student's learning path may demand distinct types of exercises (e.g., bug fix and block sorting) to foster new competencies such as debugging programs and understanding unknown source code or, otherwise, to break the routine and keep engagement. Recently, a format for describing programming exercises - YAPExIL -, supporting different types of activities, has been introduced. Unfortunately, no automated assessment tool yet supports this novel format. This paper describes a JavaScript library to transform YAPExIL packages into Mooshak problem packages (i.e., MEF format), keeping support for all exercise types. Moreover, its integration in an exercise authoring tool is described.

Abstract
We present SyVMO, an algorithmic extension of the Variable Markov Oracle algorithm, to model and predict multi-part dependencies from symbolic music manifestations. Our model has been implemented as a software application named INCITe for computer-assisted algorithmic composition. It learns variable amounts of musical data from style-agnostic music represented as multiple viewpoints. To evaluate the SyVMO model within INCITe, we adopted the Creative Support Index survey and semi-structured interviews. Four expert composers participated in the evaluation using both personal and exogenous music corpus of variable size. The results suggest that INCITe shows great potential to support creative music tasks, namely in assisting the composition process. The use of SyVMO allowed the creation of polyphonic music suggestions from style-agnostic sources while maintaining a coherent melodic structure. © 2021, Springer Nature Switzerland AG.

Abstract
Wikipedia is an online, free, multi-language, and collaborative encyclopedia, currently one of the most significant information sources on the web. The open nature of Wikipedia contributions raises concerns about the quality of its information. Previous studies have addressed this issue using manual evaluations and proposing generic measures for quality assessment. In this work, we focus on the quality of health-related content. For this purpose, we use general and health-specific features from Wikipedia articles to propose health-specific metrics. We evaluate these metrics using a set of Wikipedia articles previously assessed by WikiProject Medicine. We conclude that it is possible to combine generic and specific metrics to determine health-related content's information quality. These metrics are computed automatically and can be used by curators to identify quality issues. Along with the explored features, these metrics can also be used in approaches that automatically classify the quality of Wikipedia health-related articles.