This research paper explores the effectiveness of live coding as an active learning methodology in teaching programming, particularly in the context of diverse learning styles. Live coding, characterized by real-time coding demonstrations by instructors, has been increasingly adopted to enhance the learning experience in programming education. It offers immediate feedback, demonstrates problem-solving in action, and allows instructors to incorporate student suggestions, making it a dynamic and engaging teaching tool. However, its effectiveness varies among students with different learning preferences. This study investigates the impact of various learning style dimensions, as defined by the Felder–Silverman model, on the effectiveness of live coding in an introductory object-oriented programming course. The study was conducted at Aalborg University, Denmark, with students from the BSc Software program. It aims to provide empirical evidence on how different learning style dimensions influence student preferences and the effectiveness of live coding, offering insights to educators for tailoring active learning methodologies in programming courses to diverse learner needs.

Socio-ecological systems are recognized as complex adaptive systems whose multiple interactions might change as a response to external or internal changes. Due to its complexity, the behavior of the system is often uncertain. Bayesian networks provide a sound approach for handling complex domains endowed with uncertainty. The aim of this paper is to analyze the impact of the Bayesian network structure on the uncertainty of the model, expressed as the Shannon entropy. In particular, three strategies for model structure have been followed: naive Bayes (NB), tree augmented network (TAN) and network with unrestricted structure (GSS). Using these network structures, two experiments are carried out: (1) the impact of the Bayesian network structure on the entropy of the model is assessed and (2) the entropy of the posterior distribution of the class variable obtained from the different structures is compared. The results show that GSS constantly outperforms both NB and TAN when it comes to evaluating the uncertainty of the entire model. On the other hand, NB and TAN yielded lower entropy values of the posterior distribution of the class variable, which makes them preferable when the goal is to carry out predictions.

Since the Bologna Process was adopted, continuous assessment has been a cornerstone in the curriculum of most of the courses in the different degrees offered by the Spanish Universities. Continuous assessment plays an important role in both students’ and lecturers’ academic lives. In this study, we analyze the effect of the continuous assessment on the performance of the students in their final exams in courses of Statistics at the University of Almería. Specifically, we study if the performance of a student in the continuous assessment determines the score obtained in the final exam of the course in such a way that this score can be predicted in advance using the continuous assessment performance as an explanatory variable. After using and comparing some powerful statistical procedures, such as linear, quantile and logistic regression, artificial neural networks and Bayesian networks, we conclude that, while the fact that a student passes or fails the final exam can be properly predicted, a more detailed forecast about the grade obtained is not possible.