This paper introduces a strategy for identifying design patterns in quantum circuits. The foundation of this approach relies on using the information procured from both the segmentation and the analysis of these circuits as primary data. The approach of the methodology is based on the novel interpretation of quantum circuit components through the lens of an automaton. Additionally, the method entails the generation of input symbols for this finite automaton. The symbols are derived from the matching process between design patterns and components of quantum circuits. Two tool prototypes, QPainter and QCDPDTool, have been developed to represent quantum circuits graphically and automatically detect quantum patterns. Using them, the primary reasoning process is carried out by an automaton that can manage representations of quantum circuit components. A suite of experiments on a set of quantum circuit sequences reveals promising results and offers empirical support for our approach. Furthermore, we explore how these experimental findings can be leveraged to improve the efficacy of design pattern identification in quantum circuits.

Data quality should be at the core of many Artificial Intelligence initiatives from the very first moment in which data is required for a successful analysis. Measurement and evaluation of the level of quality are crucial to determining whether data can be used for the tasks at hand. Conscientious of this importance, industry and academia have proposed several data quality measurements and assessment frameworks over the last two decades. Unfortunately, there is no common and shared vocabulary for data quality terms. Thus, it is difficult and time-consuming to integrate data quality analysis within a (Big) Data workflow for performing Artificial Intelligence tasks. One of the main reasons is that, except for a reduced number of proposals, the presented vocabularies are neither machine-readable nor processable, needing human processing to be incorporated. This paper proposes a unified data quality measurement and assessment information model. This model can be used in different environments and contexts to describe data quality measurement and evaluation concerns. The model has been developed as an ontology to make it interoperable and machine-readable. For better interoperability and applicability, this ontology, BIGOWL4DQ, has been developed as an extension of a previously developed ontology for describing knowledge management in Big Data analytics. This extended ontology provides a data quality measurement and assessment framework required when designing Artificial Intelligence workflows and integrated reasoning capacities. Thus, BIGOWL4DQ can be used to describe Big Data analysis and assess the data quality before the analysis. Our proposal has been validated with two use cases. First, the semantic proposal has been assessed using an academic use case. And second, a real-world case study within an Artificial Intelligence workflow has been conducted to endorse our work.

Context: Programming courses are compulsory for most engineering degrees, but students’ performance on these courses is often not as good as expected. Programming is difficult for students to learn, given that it includes a lot of new, complex, and abstract topics. All of this has led experts to the conclusion that new teaching techniques are required if students are to be motivated and engaged in learning on programming courses. Gamification has come to be an effective technique in education in general, and is especially useful in programming courses. This motivated us to develop an open source gamified platform, called UDPiler, for use in a programming course. Objective: The main goal of this article is to obtain empirical evidence on the improvement of students’ learning performance when using UDPiler in comparison to a non-gamified compiler. Method: A quasi-experiment was performed with two groups of first-year engineering students at Diego Portales University in Chile, using a non-gamified compiler and a gamified platform, respectively. Results: The results reveal that the students obtained better marks when the gamified platform was used to learn C programming. In addition, there is statistical significance in favor of there being a positive effect on the learning performance of those students who used the gamified platform. Conclusions: The results allow us to conclude that gamification is an encouraging approach with which to teach C programming, a finding that is aligned with previous empirical studies concerning gamification on programming courses, carried out in academic contexts. Nonetheless, we are aware that further validation is also required to corroborate and strengthen the findings obtained and to investigate whether the kind of gamified elements (mechanics, dynamics, and aesthetics) used have any influence on students’ performance, among other issues that deserve further investigation and that are explained throughout this article.

Student dropout in Engineering Education is an important problem which has been studied from different perspectives, as well as using different techniques. This manuscript describes the methodology used in order to address this question in the context of learning analytics. Bayesian networks (BNs) have been used as they provide adequate methods for the representation, interpretation and contextualisation of data. The proposed approach is illustrated through a case study about Computer Science (CS) dropout at the University of Castilla-La Mancha (Spain), which is close to 40%. To that end, several BNs were obtained from a database which contained 383 records representing both academic and social data of the students enrolled in the CS degree during four courses. Then, these probabilistic models were interpreted and evaluated. The results obtained revealed that the best model that fits the data is provided by the K2 algorithm although the great heterogeneity of the data studied did not permit the adjustment of the dropout profile of the student too accurately. Nonetheless, the methodology described here can be taken as a reference for future works.

We carried out a family of experiments to investigate whether the use of UML models produced in the requirements analysis process helps in the comprehensibility and modifiability of source code. The family consists of a controlled experiment and 3 external replications carried out with students and professionals from Italy and Spain. 86 participants with different abilities and levels of experience with UML took part. The results of the experiments were integrated through the use of meta-analysis. The results of both the individual experiments and meta-analysis indicate that UML models produced in the requirements analysis process influence neither the comprehensibility of source code nor its modifiability.

This paper reports on a controlled experiment that investigates the influence of stereotypes in UML sequence diagrams. The comprehension of UML sequence diagrams with and without stereotypes is analyzed from three different perspectives: semantic comprehension, retention and transfer. The experiment was carried out with 77 undergraduate students of Computer Science from the University of Bari in Italy. The results obtained show a slight tendency in favor of the use of stereotypes in facilitating the comprehension of UML sequence diagrams. Further replications are needed to obtain more conclusive results.