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Themain technique in Computational Neuroscience is imperative programming, which is often used to implement simulations of dynamics, and describes how a computation is performed. A complement to this approach is declarative programming. Declarations provide descriptions of relationships between elements, effectively describing what the computation should accomplish. The use of declarative programming for modeling biological processes is still in its infancy (Fisher and Henzinger 2007) yet has shown itself to be a valuable first step for analysis of the seemingly impenetrable complexity of molecular interactomics: the interplay of the myriad proteins and signaling species in the cell. Declarative programming can also be used at the connectomic level of understanding connections among neurons or among brain areas.

Since the introduction of general-purpose computers during the early 1950's, competing techniques of mathematical programming have been developed, analyzed empirically, and compared. In this paper we survey fifty articles (spanning the period 1953-1977) which report the computational testing of mathematical programming algorithms. Our intention is to document the performance measures which were used, the standards which were maintained, and the forms in which the results were reported for these experiments. Trends in methodology are noted, and suggestions for improving the current state of affairs are offered.

Bakurov, I., Buzzelli, M., Castelli, M., Vanneschi, L., & Schettini, R. (2021). General purpose optimization library (Gpol): A flexible and efficient multi-purpose optimization library in python. Applied Sciences (Switzerland), 11(11), 1-34. [4774]. https://doi.org/10.3390/app11114774 Several interesting libraries for optimization have been proposed. Some focus on individual optimization algorithms, or limited sets of them, and others focus on limited sets of problems. Frequently, the implementation of one of them does not precisely follow the formal definition, and they are difficult to personalize and compare. This makes it difficult to perform comparative studies and propose novel approaches. In this paper, we propose to solve these issues with the General Purpose Optimization Library (GPOL): a flexible and efficient multipurpose optimization library that covers a wide range of stochastic iterative search algorithms, through which flexible and modular implementation can allow for solving many different problem types from the fields of continuous and combinatorial optimization and supervised machine learning problem solving. Moreover, the library supports full-batch and mini-batch learning and allows carrying out computations on a CPU or GPU. The package is distributed under an MIT license. Source code, installation instructions, demos and tutorials are publicly available in our code hosting platform (the reference is provided in the Introduction). publishersversion published

[ES] El proceso de ciencia de datos es esencial para extraer valor de los datos. Sin embargo, la parte más tediosa del proceso, la preparación de los datos, implica una serie de formateos, limpieza e identificación de problemas que principalmente son tareas manuales. La preparación de datos todavía se resiste a la automatización en parte porque el problema depende en gran medida de la información del dominio, que se convierte en un cuello de botella para los sistemas de última generación a medida que aumenta la diversidad de dominios, formatos y estructuras de los datos. En esta tesis nos enfocamos en generar algoritmos que aprovechen el conocimiento del dominio para la automatización de partes del proceso de preparación de datos. Mostramos la forma en que las técnicas generales de inducción de programas, en lugar de los lenguajes específicos del dominio, se pueden aplicar de manera flexible a problemas donde el conocimiento es importante, mediante el uso dinámico de conocimiento específico del dominio. De manera más general, sostenemos que una combinación de enfoques de aprendizaje dinámicos y basados en conocimiento puede conducir a buenas soluciones. Proponemos varias estrategias para seleccionar o construir automáticamente el conocimiento previo apropiado en varios escenarios de preparación de datos. La idea principal se basa en elegir las mejores primitivas especializadas de acuerdo con el contexto del problema particular a resolver. Abordamos dos escenarios. En el primero, manejamos datos personales (nombres, fechas, teléfonos, etc.) que se presentan en formatos de cadena de texto muy diferentes y deben ser transformados a un formato unificado. El problema es cómo construir una transformación compositiva a partir de un gran conjunto de primitivas en el dominio (por ejemplo, manejar meses, años, días de la semana, etc.). Desarrollamos un sistema (BK-ADAPT) que guía la búsqueda a través del conocimiento previo extrayendo varias meta-características de los ejemplos que caracterizan el dominio de la columna. En el segundo escenario, nos enfrentamos a la transformación de matrices de datos en lenguajes de programación genéricos como R, utilizando como ejemplos una matriz de entrada y algunas celdas de la matriz de salida. También desarrollamos un sistema guiado por una búsqueda basada en árboles (AUTOMAT[R]IX) que usa varias restricciones, probabilidades previas para las primitivas y sugerencias textuales, para aprender eficientemente las transformaciones. Con estos sistemas, mostramos que la combinación de programación inductiva, con la selección dinámica de las primitivas apropiadas a partir del conocimiento previo, es capaz de mejorar los resultados de otras herramientas actuales específicas para la preparación de datos., [CA] El procés de ciència de dades és essencial per extraure valor de les dades. No obstant això, la part més tediosa del procés, la preparació de les dades, implica una sèrie de transformacions, neteja i identificació de problemes que principalment són tasques manuals. La preparació de dades encara es resisteix a l'automatització en part perquè el problema depén en gran manera de la informació del domini, que es converteix en un coll de botella per als sistemes d'última generació a mesura que augmenta la diversitat de dominis, formats i estructures de les dades. En aquesta tesi ens enfoquem a generar algorismes que aprofiten el coneixement del domini per a l'automatització de parts del procés de preparació de dades. Mostrem la forma en què les tècniques generals d'inducció de programes, en lloc dels llenguatges específics del domini, es poden aplicar de manera flexible a problemes on el coneixement és important, mitjançant l'ús dinàmic de coneixement específic del domini. De manera més general, sostenim que una combinació d'enfocaments d'aprenentatge dinàmics i basats en coneixement pot conduir a les bones solucions. Proposem diverses estratègies per seleccionar o construir automàticament el coneixement previ apropiat en diversos escenaris de preparació de dades. La idea principal es basa a triar les millors primitives especialitzades d'acord amb el context del problema particular a resoldre. Abordem dos escenaris. En el primer, manegem dades personals (noms, dates, telèfons, etc.) que es presenten en formats de cadena de text molt diferents i han de ser transformats a un format unificat. El problema és com construir una transformació compositiva a partir d'un gran conjunt de primitives en el domini (per exemple, manejar mesos, anys, dies de la setmana, etc.). Desenvolupem un sistema (BK-ADAPT) que guia la cerca a través del coneixement previ extraient diverses meta-característiques dels exemples que caracteritzen el domini de la columna. En el segon escenari, ens enfrontem a la transformació de matrius de dades en llenguatges de programació genèrics com a R, utilitzant com a exemples una matriu d'entrada i algunes dades de la matriu d'eixida. També desenvolupem un sistema guiat per una cerca basada en arbres (AUTOMAT[R]IX) que usa diverses restriccions, probabilitats prèvies per a les primitives i suggeriments textuals, per aprendre eficientment les transformacions. Amb aquests sistemes, mostrem que la combinació de programació inductiva amb la selecció dinàmica de les primitives apropiades a partir del coneixement previ, és capaç de millorar els resultats d'altres enfocaments de preparació de dades d'última generació i més específics., [EN] Data science is essential for the extraction of value from data. However, the most tedious part of the process, data wrangling, implies a range of mostly manual formatting, identification and cleansing manipulations. Data wrangling still resists automation partly because the problem strongly depends on domain information, which becomes a bottleneck for state-of-the-art systems as the diversity of domains, formats and structures of the data increases. In this thesis we focus on generating algorithms that take advantage of the domain knowledge for the automation of parts of the data wrangling process. We illustrate the way in which general program induction techniques, instead of domain-specific languages, can be applied flexibly to problems where knowledge is important, through the dynamic use of domain-specific knowledge. More generally, we argue that a combination of knowledge-based and dynamic learning approaches leads to successful solutions. We propose several strategies to automatically select or construct the appropriate background knowledge for several data wrangling scenarios. The key idea is based on choosing the best specialised background primitives according to the context of the particular problem to solve. We address two scenarios. In the first one, we handle personal data (names, dates, telephone numbers, etc.) that are presented in very different string formats and have to be transformed into a unified format. The problem is how to build a compositional transformation from a large set of primitives in the domain (e.g., handling months, years, days of the week, etc.). We develop a system (BK-ADAPT) that guides the search through the background knowledge by extracting several meta-features from the examples characterising the column domain. In the second scenario, we face the transformation of data matrices in generic programming languages such as R, using an input matrix and some cells of the output matrix as examples. We also develop a system guided by a tree-based search (AUTOMAT[R]IX) that uses several constraints, prior primitive probabilities and textual hints to efficiently learn the transformations. With these systems, we show that the combination of inductive programming with the dynamic selection of the appropriate primitives from the background knowledge is able to improve the results of other state-of-the-art and more specific data wrangling approaches., This research was supported by the Spanish MECD Grant FPU15/03219;and partially by the Spanish MINECO TIN2015-69175-C4-1-R (Lobass) and RTI2018-094403-B-C32-AR (FreeTech) in Spain; and by the ERC Advanced Grant Synthesising Inductive Data Models (Synth) in Belgium.

Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods become crucial. Inductive logic programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the processing of data. Learning from interpretation transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given black-box system (under certain conditions). The present work takes a first step to a general methodology to incorporate accurate declarative explanations to classic machine learning by checking the viability of LFIT in a specific AI application scenario: fair recruitment based on an automatic tool generated with machine learning methods for ranking Curricula Vitae that incorporates soft biometric information (gender and ethnicity). We show the expressiveness of LFIT for this specific problem and propose a scheme that can be applicable to other domains. In order to check the ability to cope with other domains no matter the machine learning paradigm used, we have done a preliminary test of the expressiveness of LFIT, feeding it with a real dataset about adult incomes taken from the US census, in which we consider the income level as a function of the rest of attributes to verify if LFIT can provide logical theory to support and explain to what extent higher incomes are biased by gender and ethnicity, This work was supported by projects: PRIMA (H2020-MSCA-ITN-2019-860315), TRESPASS-ETN(H2020-MSCA-ITN-2019-860813), IDEA-FAST (IMI2-2018-15-853981), BIBECA(RTI2018-101248-B-I00MINECO/FEDER), RTI2018-095232-B-C22MINECO, PLeNTaS project PID2019-111430RBI00MINECO; and also by Pays de la Loire Region through RFI Atlanstic 2020

This chapter is devoted to the dynamic programming approach for solving (separable) programs for both discrete and continuous cases. The Lagrange multipliers method for reducing the dimensionality of the problem is discussed, and some separable and other models are reviewed at the end of the chapter.

It is highly recognized that difficulties involved in teaching programming in an introductory course, arise from the cognitive process complexity that is necessary for developing this ability. Previous studies on the best first programming language or the best first programming paradigm have been conducted. Even, the most appropriate integrated development environment (IDE) for novice programmers has been inquired, and the most appropriate problem solving strategy to teach to write computable solutions has been researched. From the investigation in a sample of Bogota universities, and from a holistic perspective, this paper presents an overview of the most common practices and difficulties in teaching these courses, providing important information to help diagnose possible weaknesses in the current process of teaching as well as providing some elements to consider in the design of new strategies, resources and planning criteria for these formative scenarios.

Hybrid Diversity-aware Collective Adaptive Systems (HDA-CAS) are a new generation of socio-technical systems where both human and machine peers collectively participate in complex cognitive and physical tasks. These systems are characterized by the fundamental properties of hybridity and collectiveness, hiding from users the complexities associated with managing the collaboration and coordination of human-machine teams. The SmartSociety platform is a set of integrated software components that jointly provide a number of advanced HDA-CAS functionalities. As part of the CAS initiative, we have developed a programming model and Java APIs that make the use of those functionalities easy and accessible to application developers. In this paper we present the SmartSociety programming model elements, including the principal contributions – Collectives and Collective-based Tasks. We describe and discuss their functionality, implementation and runtime environment. Finally, we qualitatively evaluate the programming model and the language constructs with respect to the desired HDA-CAS properties.

The technical goal of the Fifth Generation Computer Systems (FGCS) project (1982–1993) was to develop Parallel Inference technologies, namely systematized technologies for realizing knowledge information processing on top of parallel computer architecture. The Logic Programming paradigm was adopted as the central working hypothesis of the project. At the same time, building a large-scale Parallel Inference Machine (PIM) meant to develop a novel form of general-purpose computing technologies that are powerful enough to express various parallel algorithms and to describe a full operating system of PIM. Accordingly, the research goal of the Kernel Language was set to designing a concurrent and parallel programming language under the working hypothesis of Logic Programming. The aim of this article is to describe the design process of the Kernel Language (KL1) in the context of related programming models in the 1980s, the essence of Concurrent Logic Programming and Constraint-Based Concurrency, and how the technologies we developed in those days evolved after their conception.

Jutge.org is an open educational online programming judge designed for students and instructors, featuring a repository of problems that is well organized by courses, topics, and difficulty. Internally, Jutge.org uses a secure and efficient architecture and integrates modern verification techniques, formal methods, static code analysis, and data mining. Jutge.org has exhaustively been used during the last decade at the Universitat Politecnica de Catalunya to strengthen the learning-by-doing approach in several courses. This paper presents the main characteristics of Jutge.org and shows its use and impact on a wide range of courses covering basic programming, data structures, algorithms, artificial intelligence, functional programming, and circuit design.

We describe the Inspire system which participated in the first competition on inductive logic programming (ILP). Inspire is based on answer set programming (ASP). The distinguishing feature of Inspire is an ASP encoding for hypothesis space generation: given a set of facts representing the mode bias, and a set of cost configuration parameters, each answer set of this encoding represents a single rule that is considered for finding a hypothesis that entails the given examples. Compared with state-of-the-art methods that use the length of the rule body as a metric for rule complexity, our approach permits a much more fine-grained specification of the shape of hypothesis candidate rules. The Inspire system iteratively increases the rule cost limit and thereby increases the search space until it finds a suitable hypothesis. The system searches for a hypothesis that entails a single example at a time, utilizing an ASP encoding derived from the encoding used in XHAIL. We perform experiments with the development and test set of the ILP competition. For comparison we also adapted the ILASP system to process competition instances. Experimental results show that the cost parameters for the hypothesis search space are an important factor for finding hypotheses to competition instances within tight resource bounds.

In this paper, a novel method is proposed to support the process of solving multi-objective nonlinear programming problems subject to strict or flexible constraints. This method assumes that the practical problems are expressed in the form of geometric programming problems. Integrating the concept of intuitionistic fuzzy sets into the solving procedure, a rich structure is provided which can include the inevitable uncertainties into the model regarding different objectives and constraints. Another important feature of the proposed method is that it continuously interacts with the decision maker. Thus, the decision maker could learn about the problem, thereby a compromise solution satisfying his/hers preferences could be obtained. Further, a new two-step geometric programming approach is introduced to determine Pareto-optimal compromise solutions for the problems defined during different iterative steps. Employing the compensatory operator of “weighted geometric mean”, the first step concentrates on finding an intuitionistic fuzzy efficient compromise solution. In the cases where one or more intuitionistic fuzzy objectives are fully achieved, a second geometric programming model is developed to improve the resulting compromise solution. Otherwise, it is concluded that the resulting solution vectors simultaneously satisfy both of the conditions of intuitionistic fuzzy efficiency and Pareto-optimality. The models forming the proposed solving method are developed in a way such that, the posynomiality of the defined problem is not affected. This property is of great importance when solving nonlinear programming problems. A numerical example of multi-objective nonlinear programming problem is also used to provide a better understanding of the proposed solving method.

In this paper, an interactive approach for solving multi-level multi-objective fractional programming (ML-MOFP) problems with fuzzy parameters is presented. The proposed interactive approach makes an extended work of Shi and Xia (1997). In the first phase, the numerical crisp model of the ML-MOFP problem has been developed at a confidence level without changing the fuzzy gist of the problem. Then, the linear model for the ML-MOFP problem is formulated. In the second phase, the interactive approach simplifies the linear multi-level multi-objective model by converting it into separate multi-objective programming problems. Also, each separate multi-objective programming problem of the linear model is solved by the ∊-constraint method and the concept of satisfactoriness. Finally, illustrative examples and comparisons with the previous approaches are utilized to evince the feasibility of the proposed approach.

This paper was motivated by a practical optimization problem formulated at the Erdenet Mining Corporation (Mongolia). By solving an identification problem for a chosen design of experiment we developed a quadratic model that quite adequately represents the experimental data. The problem obtained turned out to be the indefinite quadratic program, which we solved by applying the global search theory for a d.c. programming developed by A.S. Strekalovsky [ 13 ]-[ 15 ]. According to this d.c. optimization theory, we performed a local search that takes into account the structure of the problem in question, and constructed procedures of escaping critical points provided by the local search. The algorithms proposed for d.c. programming were verified using a set of test problems as well as a copper content maximization problem arising at the mining factory.

Starting from the first investigations with strictly functional languages, reactive programming has been proposed as the programming paradigm for reactive applications. Over the years, researchers have enriched reactive languages with more powerful abstractions, embedded these abstractions into mainstream languages—including object-oriented languages—and applied reactive programming to several domains, such as GUIs, animations, Web applications, robotics, and sensor networks. However, an important assumption behind this line of research is that, beside other claimed advantages, reactive programming makes a wide class of otherwise cumbersome applications more comprehensible. This claim has never been evaluated. In this paper, we present the first empirical study that evaluates the effect of reactive programming on comprehension. The study involves 127 subjects and compares reactive programming to the traditional object-oriented style with the Observer design pattern. Our findings show that program comprehension is significantly enhanced by the reactive-programming paradigm—a result that suggests to further develop research in this field.

This is the second part of a two-part series that describes a pilot programming course in which high school students majoring in computer science were introduced to the visual, scenario-based programming language of live sequence charts. The main rationale for the course was that computer science students should be exposed to at least two very different programming paradigms and that LSCs, with their unique characteristics, can be a good vehicle for that. Part 1 (see the previous issue) focused on the pedagogic rationale of the pilot, on introducing LSC, and on the structure of the course. Part 2 centers on the evaluation of the pilot’s results.

Inductive Logic Programming (ILP) combines rule-based and statistical artificial intelligence methods, by learning a hypothesis comprising a set of rules given background knowledge and constraints for the search space. We focus on extending the XHAIL algorithm for ILP which is based on Answer Set Programming and we evaluate our extensions using the Natural Language Processing application of sentence chunking. With respect to processing natural language, ILP can cater for the constant change in how we use language on a daily basis. At the same time, ILP does not require huge amounts of training examples such as other statistical methods and produces interpretable results, that means a set of rules, which can be analysed and tweaked if necessary. As contributions we extend XHAIL with (i) a pruning mechanism within the hypothesis generalisation algorithm which enables learning from larger datasets, (ii) a better usage of modern solver technology using recently developed optimisation methods, and (iii) a time budget that permits the usage of suboptimal results. We evaluate these improvements on the task of sentence chunking using three datasets from a recent SemEval competition. Results show that our improvements allow for learning on bigger datasets with results that are of similar quality to state-of-the-art systems on the same task. Moreover, we compare the hypotheses obtained on datasets to gain insights on the structure of each dataset., Comment: 24 pages, preprint of article accepted at Expert Systems With Applications

Extensive research has shown that one of the benefits of programming to learn about scientific phenomena is that it facilitates learning about mechanisms underlying the phenomenon. However, using programming activities in classrooms is associated with costs such as requiring additional time to learn to program or students needing prior experience with programming. This paper presents a class of programming environments that we call quickstart: Environments with a negligible threshold for entry into programming and a modest ceiling. We posit that such environments can provide benefits of programming for learning without incurring associated costs for novice programmers. To make this claim, we present a design-based research study conducted to compare programming models of evolutionary processes with a quickstart toolkit with exploring pre-built models of the same processes. The study was conducted in six seventh grade science classes in two schools. Students in the programming condition used EvoBuild, a quickstart toolkit for programming agent-based models of evolutionary processes, to build their NetLogo models. Students in the exploration condition used pre-built NetLogo models. We demonstrate that although students came from a range of academic backgrounds without prior programming experience, and all students spent the same number of class periods on the activities including the time students took to learn programming in this environment, EvoBuild students showed greater learning about evolutionary mechanisms. We discuss the implications of this work for design research on programming environments in K-12 science education.

Games with educational purposes usually follow a computer-assisted instruction concept that is predefined and rigid, offering no adaptability to each student. To overcome such problem, some ideas from Intelligent Tutoring Systems have been used in educational games such as teaching introductory programming. The objective of this study was to advance Online Game-based Bayesian Intelligent Tutoring System (OGITS) to enhance programming acquisition and online information searching skills, thus improving students’ ability in web-based problem solving through board games. The study sample comprised 79 college students in introductory level Computer Science classes. Qualitative and quantitative data were then gathered. Results of this study revealed generally favorable opinions about OGITS. As OGITS targets individual knowledge acquisition of computer programming and web-based problem-solving skills, it offers a suitable learning environment for students both as a stand-alone course and as a supplement to traditional classroom settings.

Programming technique is one of the subjects at Vocational High School in Indonesia. This subject contains theory and application of programming utilizing Visual Programming. Students experience some difficulties to learn textual learning. Therefore, it is necessary to develop media as a tool to transfer learning materials. The objectives of this study are to determine the feasibility of learning media based on Visual Programming and to investigate the response of students who utilize the learning media. Learning media is firstly validated by experts before it is applied to students. This research implemented rating validation with fuzzy rating based on fuzzy inference system. Fuzzy benefits in translating a crisp form into the preference of linguistic variable form and reprocess into a crisp form as a value decision. The results revealed that fuzzy rating can be implemented to measure the feasibility of learning media and students' responses to the use of learning media.

Goal representation globalized dual heuristic dynamic programming (Gr-GDHP) method is proposed in this paper. A goal neural network is integrated into the traditional GDHP method providing an internal reinforcement signal and its derivatives to help the control and learning process. From the proposed architecture, it is shown that the obtained internal reinforcement signal and its derivatives can be able to adjust themselves online over time rather than a fixed or predefined function in literature. Furthermore, the obtained derivatives can directly contribute to the objective function of the critic network, whose learning process is thus simplified. Numerical simulation studies are applied to show the performance of the proposed Gr-GDHP method and compare the results with other existing adaptive dynamic programming designs. We also investigate this method on a ball-and-beam balancing system. The statistical simulation results are presented for both the Gr-GDHP and the GDHP methods to demonstrate the improved learning and controlling performance.

The knowledge of the most common programming paradigms, and the basic abstractions provided by each paradigm, are competencies to be attained by Software Engineering undergraduate students. These abstractions also include the basis of concurrent and parallel programming, present in different programming paradigms. In an existing Software Engineering degree, these competencies were assigned to the Programming Technology and Paradigms course. We present the approach followed in the design of that course to teach object-oriented, functional, concurrent and parallel programming to second year undergraduate students with basic knowledge of Java. The time limitations of the course prevented us from using various programming languages. After analyzing different alternatives, we chose C# to teach the course. We describe the most important challenges faced and how we addressed them. The course success rate is slightly greater than the rest of courses in the same year and degree, while performance rates and average marks are analogous. There is no influence of age and gender on the final mark, but students retaking the course have significantly worse evaluation than those enrolled for the first time. The students’ self-evaluation revealed that the proposed course has a strong influence on the achievement of the expected learning outcomes, and their satisfaction with the course was significantly higher than with the rest of courses in the same degree.

We present seven programming challenges in Racket, and an elegant, unified approach to solving them using constraint logic programming in miniKanren.

Systematic endeavors to take computer science (CS) and computational thinking (CT) to scale in middle and high school classrooms are underway with curricula that emphasize the enactment of authentic CT skills, especially in the context of programming in block-based programming environments. There is, therefore, a growing need to measure students’ learning of CT in the context of programming and also support all learners through this process of learning computational problem solving. The goal of this research is to explore hypothesis-driven approaches that can be combined with data-driven ones to better interpret student actions and processes in log data captured from block-based programming environments with the goal of measuring and assessing students’ CT skills. Informed by past literature and based on our empirical work examining a dataset from the use of the Fairy Assessment in the Alice programming environment in middle schools, we present a framework that formalizes a process where a hypothesis-driven approach informed by Evidence-Centered Design effectively complements data-driven learning analytics in interpreting students’ programming process and assessing CT in block-based programming environments. We apply the framework to the design of Alice tasks for high school CS to be used for measuring CT during programming.

Static-type systems are a major topic in programming language research and the software industry because they should reduce the development time and increase the code quality. Additionally, they are predicted to decrease the number of defects in a code due to early error detection. However, only a few empirical experiments exist on the potential benefits of static-type systems in programming activities. This paper describes an experiment that tests whether static-type systems help developers create solutions for certain programming tasks. The results indicate that although the existence of a static-type system has no positive impact when subjects code a program from scratch, it does allow more errors in program debugging to be fixed.

This paper presents an experiment in which visual programming environments have been used in programming courses aiming at helping students to acquire the competencies of a course on object-oriented programming. This paper is centered on the analysis of the acceptance by the students of this type of environment, as well as its effect on the motivation of the students. The results obtained show differences in their answers according to the two possible characteristics of the students analyzed: their gender and the fact of being or not a newcomer.