Abstract

Abstract
Keeping students engaged while learning programming is becoming more and more imperative. Of the several proposed techniques, gamification is presumably the most widely studied and has already proven as an effective means to engage students. However, there is a complete lack of public and customizable solutions to gamified programming education that can be reused with personalized rules and learning material. FGPE Gamification Service (FGPE GS) is an open-source GraphQL service that transforms a package containing the gamification layer – adhering to a dedicated open-source language, GEdIL – into a game. The game provides students with a gamified experience leveraging on the automatically-assessable activities referenced by the challenges. This paper presents FGPE GS, its architecture, data model, and validation. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
This paper presents ongoing research aiming at the automatic assessment of simple web applications, like those used in introductory web technologies courses. The distinctive feature of the proposed approach is a web interface matching procedure. This matching procedure verifies if the web interface being assessed corresponds to that of a reference application; otherwise, provides detailed feedback on the detected differences. Since web interfaces are event-driven, this matching is instrumental to assess the functionality. After mapping web interface elements from two applications, these can be targeted with events and property changes can be compared. This paper details the proposed matching algorithm and the current state of its implementation. It also discusses future work to embed this approach in a web environment for solving web application exercises with automatic assessment. © Marco Primo and José Paulo Leal; licensed under Creative Commons License CC-BY 4.0 Second International Computer Programming Education Conference (ICPEC 2021).

Abstract
The importance of e-learning tools facilitating the process of learning to program is growing, especially as the pandemic-caused lockdown enforced distance learning in many countries. The key success factor in this process is the provision of an instant and relevant feedback to students. In this paper, we describe a novel open-source programming learning environment featuring automatic assessment of students' solutions and customized gamification. This environment has been developed as a part of the FGPE framework. © José Carlos Paiva, Ricardo Queirós, José Paulo Leal, Jakub Swacha, and Filip Miernik; licensed under Creative Commons License CC-BY 4.0 Second International Computer Programming Education Conference (ICPEC 2021).

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
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.