2021
Autores
Henriques, PR; Portela, F; Queirós, R; Simões, A;
Publicação
ICPEC
Abstract
2021
Autores
Queirós, R; Pinto, M; Terroso, T;
Publicação
Second International Computer Programming Education Conference, ICPEC 2021, May 27-28, 2021, University of Minho, Braga, Portugal.
Abstract
Learning computer programming is a complex activity and requires a lot of practice. The viral pandemic that we are facing has intensified these difficulties. In this context, programming learning platforms play a crucial role. Most of them are characterized by providing a wide range of exercises with progressive complexity, multi-language support, sophisticated interfaces and automatic evaluation and gamification services. Nevertheless, despite the various features provided, others features, which influence user experience, are not emphasized, such as performance and usability. This article presents an user experience evaluation of the LearnJS playground, a JavaScript learning platform which aims to foster the practice of coding. The evaluation highlights two facets of the code playground: performance and a usability. In the former, lab and field data were collected based on Google Lighthouse and PageSpeed Insights reports. In the later, an inquiry was distributed among students from a Web Technologies course with a set of questions based on flexibility, usability and consistency heuristics. Both evaluation studies have a twofold goal: to improve the learning environment in order to be officially used in the next school year and to foster the awareness of user experience in all phases of the software development life-cycle as a key facet in Web applications engagement and loyalty. © Ricardo Queirós, Mário Pinto, and Teresa Terroso; licensed under Creative Commons License CC-BY 4.0 Second International Computer Programming Education Conference (ICPEC 2021).
2021
Autores
Alves, S; Wassermann, R;
Publicação
Math. Struct. Comput. Sci.
Abstract
2021
Autores
Alves, S; Ramos, M;
Publicação
Electronic Proceedings in Theoretical Computer Science, EPTCS
Abstract
In this work, we develop a polymorphic record calculus with extensible records. Extensible records are records that can have new fields added to them, or preexisting fields removed from them. We also develop a static type system for this calculus and a sound and complete type inference algorithm. Most ML-style polymorphic record calculi that support extensible records are based on row variables. We present an alternative construction based on the polymorphic record calculus developed by Ohori. Ohori based his polymorphic record calculus on the idea of kind restrictions. This allowed him to express polymorphic operations on records such as field selection and modification. With the addition of extensible types, we were able to extend Ohori’s original calculus with other powerful operations on records such as field addition and removal. © S. Alves & M. Ramos
2021
Autores
Alves, S; Iglésias, J;
Publicação
CoRR
Abstract
2021
Autores
Alves, S; Fernández, M; Ramos, M;
Publicação
CoRR
Abstract
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