In this paper, we introduce Manim-DFA, an extension of the Manim library for generating video visualisations to teach data flow analysis and abstract interpretation. Despite the importance of data flow analysis in static program analysis, educational visualisation tools remain scarce. Manim-DFA addresses this gap by enabling educators to animate control flow graphs and lattice structures, illustrating their transformation during program analysis. Currently, the tool supports automated animation of the worklist algorithm, as well as lattice visu-alisation. Designed with established pedagogical principles, Manim-DFA promotes active learning, reduces cognitive load, and enhances conceptual understanding. Preliminary evaluations suggest that it effectively complements traditional resources and supports autonomous learning.

While there is consensus that educational escape games have a beneficial impact on student learning in computer science, this hypothesis is not empirically demonstrated because the evaluation methods used by researchers in the field are carried out in an ad hoc manner, lack reproducibility and often rely on confidential samples. We introduce EvscApp, a standard methodology for evaluating educational escape games intended for the learning of computer science at the undergraduate level. Based on a state of the art in the realm of educational escape games and on the different associated pedagogical approaches existing in the literature, we arrive at a general-purpose experimental process divided in fifteen steps. The evaluation criteria used for assessing an escape game’s efficiency concern the aspects of motivation, user experience and learning. The EvscApp methodology has been implemented as an open source Web dashboard that helps researchers to carry out structured experimentations of educational escape games designed to teach computer science. The tool allows designers of educational computer escape games to escape the ad hoc construction of evaluation methods while gaining in methodological rigor and comparability. All the results collected through the experiments carried out with EvscApp are scheduled to be compiled in order to be able to rule empirically as to the pedagogical effectiveness of pedagogical escape games for computer science in general. A few preliminary experiments indicate positive early results of the method.

Anti-unification in logic programming refers to the process of capturing common syntactic structure among given goals, computing a single new goal that is more general called a generalization of the given goals. Finding an arbitrary common generalization for two goals is trivial, but looking for those common generalizations that are either as large as possible (called largest common generalizations) or as specific as possible (called most specific generalizations) is a non-trivial optimization problem, in particular when goals are considered to be \textit{unordered} sets of atoms. In this work we provide an in-depth study of the problem by defining two different generalization relations. We formulate a characterization of what constitutes a most specific generalization in both settings. While these generalizations can be computed in polynomial time, we show that when the number of variables in the generalization needs to be minimized, the problem becomes NP-hard. We subsequently revisit an abstraction of the largest common generalization when anti-unification is based on injective variable renamings, and prove that it can be computed in polynomially bounded time.

We introduce, describe and discuss the potentialities of Moulinog, a tool created during the COVID-19 lockdown, designed to generate individual questionnaires for the remote evaluation of large classrooms. Starting with a list of students and a series of predicates constituting a pool of parametric questions along with rules for their parametrization, Mouling generates a list of individual questionnaires, together with a shell script allowing an easy emailing of the (password-protected) questionnaires to the students. The tool’s use in practice is illustrated on a particular course case for which it has proven to be both useful and time-saving.

Abstract Anti-unification refers to the process of generalizing two (or more) goals into a single, more general, goal that captures some of the structure that is common to all initial goals. In general one is typically interested in computing what is often called a most specific generalization, that is a generalization that captures a maximal amount of shared structure. In this work we address the problem of anti-unification in CLP, where goals can be seen as unordered sets of atoms and/or constraints. We show that while the concept of a most specific generalization can easily be defined in this context, computing it becomes an NP-complete problem. We subsequently introduce a generalization algorithm that computes a well-defined abstraction whose computation can be bound to a polynomial execution time. Initial experiments show that even a naive implementation of our algorithm produces acceptable generalizations in an efficient way.