Abstract

Abstract
This paper addresses the problem of course (path) generation when a learner's available time is not enough to follow the complete course. We propose a method to recommend successful paths regarding a learner's available time and his/her knowledge background. Our recommender is an instance of long term goal recommender systems (LTRS). This method, after locating a target learner in a course graph, applies a depth-first search algorithm to find all paths for the learner given a time limitation. In addition, our method estimates learning time and score for all paths. It also indicates the probability of error for the estimated time and score for each path. Finally, our method recommends a path that satisfies the learner's time restriction while maximizing expected learning score. In order to evaluate our proposals for time and score estimation, we used the mean absolute error and average MAE. We have evaluated time and score estimation methods, including one proposed in the literature, on two E-learning datasets.

Abstract
Quarmic is a web framework for rapid prototyping of web applications. Its main goal is to facilitate the development of web applications by providing a high level of abstraction that hides Web communication complexities. This framework allows developers to build scalable applications capable of handling data communication in different models, data persistence and authentication, requiring them just to use simple annotations. Quarmic’s approach consists of the replication of the shared object among clients and server in order to communicate through its methods execution. Where the annotations, namely decorators, are used to indicate the concern (model or view) that each method addresses and to implement the framework’s inversion of control. By indicating the method concern, it enables the separation of its execution across the clients (responsible for the view) and the server (responsible for the model) which facilitates the state management and code maintenance. © Pedro M. P. Cunha and José P. Leal.

Abstract
Graphs with a large number of nodes and edges are difficult to visualize. Semantic graphs add to the challenge since their nodes and edges have types and this information must be mirrored in the visualization. A common approach to cope with this difficulty is to omit certain nodes and edges, displaying sub-graphs of smaller size. However, other transformations can be used to summarize semantic graphs and this research explores a particular one, both to reduce the graph's size and to focus on its path patterns. A-graphs are a novel kind of graph designed to highlight path patterns using this kind of summarization. They are composed of a-nodes connected by a-edges, and these reflect respectively edges and nodes of the semantic graph. A-graphs trade the visualization of nodes and edges by the visualization of graph path patterns involving typed edges. Thus, they are targeted to users that require a deep understanding of the semantic graph it represents, in particular of its path patterns, rather than to users wanting to browse the semantic graph's content. A-graphs help programmers querying the semantic graph or designers of semantic measures interested in using it as a semantic proxy. Hence, a-graphs are not expected to compete with other forms of semantic graph visualization but rather to be used as a complementary tool. This paper provides a precise definition both of a-graphs and of the mapping of semantic graphs into a-graphs. Their visualization is obtained with a-graphs diagrams. A web application to visualize and interact with these diagrams was implemented to validate the proposed approach. Diagrams of well-known semantic graphs are presented to illustrate the use of agraphs for discovering path patterns in different settings, such as the visualization of massive semantic graphs, the codification of SPARQL or the definition of semantic measures. The validation with large semantic graphs is the basis for a discussion on the insights provided by a-graphs on large semantic graphs: the difference between a-graphs and ontologies, path pattern visualization using a-graphs and the challenges posed by large semantic graphs.

Abstract
One of the great challenges in programming education is to keep students motivated while working on their programming assignments. Of the techniques proposed in the literature to engage students, gamification is arguably the most widely spread and effective method. Nevertheless, gamification is not a panacea and can be harmful to students. Challenges comprising intrinsic motivators of games, such as graphical feedback and game-thinking, are more prone to have longterm positive effects on students, but those are typically complex to create or adapt to slightly distinct contexts. This paper presents Asura, a game-based programming assessment environment providing means to minimize the hurdle of building game challenges. These challenges invite the student to code a Software Agent to solve a certain problem, in a way that can defeat every opponent. Moreover, the experiment conducted to assess the difficulty of authoring Asura challenges is described.

Abstract
Computer programming is a complex domain both to teach and learn. This incited endeavors to find methods that could mitigate at least some of the existing barriers. In the last years, automatic assessment has been playing an important role in reducing the burden of teachers in the assessment of students' attempts to solve programming exercises and fostering the autonomy of students by allowing them to practice in any place and at any time with timely feedback. Even more recent development is the use of gamification in computer programming education in order to raise the enjoyment and engagement of students. Despite its rising spread, until now, there is not a programming exercise specification format addressing the needs of gamification, such as the definition of challenges, the underlying storyline, including the links to other exercises, or the rewards for solving challenges in form of points, badges or virtual items. Such a data format would allow the exchange of ready-to-use programming exercises along with the gamification-related data among different educational institutions and courses, providing instructors a possibility to make use of gamification in their courses without having to invest their own time in defining gamification rules themselves. In this paper, we analyze a set of concepts related to programming gamification developed in our previous work to identify the requirements for the specification of a gamified exercise format. (C) 2019 The Authors. Published by Elsevier B.V.