In this article we perform a detailed statistical analysis of a large experiment that was carried out in two engineering schools at Universitat Politècnica de València. The goal of the study is to quantify how the distance of students to the professor affects their marks. In the experiment, we collected and processed data about the exact students' position in the lecture hall and in the computer lab for two academic years, their changes of position along the course, and their marks in various degrees, courses, and terms, for both lectures and practicals. Our experiments provide quantitative data that is analyzed using advanced statistical methods such as ANOVA, the TukeyHSD post-hoc test, and the Mantel test based on Pearson product-moment correlation coefficient.

Automated marking of multiple-choice exams is of great interest in university courses with a large number of students. For this reason, it has been systematically implanted in almost all universities. Automatic assessment of source code is however less extended. There are several reasons for that. One reason is that almost all existing systems are based on output comparison with a gold standard. If the output is the expected, the code is correct. Otherwise, it is reported as wrong, even if there is only one typo in the code. Moreover, why it is wrong remains a mystery. In general, assessment tools treat the code as a black box, and they only assess the externally observable behavior. In this work we introduce a new code assessment method that also verifies properties of the code, thus allowing to mark the code even if it is only partially correct. We also report about the use of this system in a real university context, showing that the system automatically assesses around 50% of the work.

Algorithmic debugging is a technique that uses an internal data structure to represent computations and ask about their correctness. The strategy used to explore this data structure is essential for the performance of the technique. The most efficient strategy in practice is Divide and Query that, until now, has been considered optimal in the worst case. In this paper we first show that the original algorithm is inaccurate and moreover, in some situations it is unable to find all possible solutions, thus it is incomplete. Then, we present a new version of the algorithm that solves these problems. Moreover, we introduce a counterexample showing that Divide and Query is not optimal, and we propose the first optimal strategy for algorithmic debugging with respect to the number of questions asked by the debugger.

This work presents DDJ, an algorithmic debugger for Java. The main advantage of DDJ with respect to previous algorithmic debuggers is its scalability. DDJ has a new architecture based on the use of cache memories that allows it to scale both in time and memory. In addition, it includes new techniques that allow the debugger to start the debugging session even before the execution tree has been produced. We present the new architecture, and describe the main features of this debugger together with a usage scenario.