Ample research has been carried out in the area of collusion, plagiarism and e-learning. Collusion is a form of active cheating where two or more parties secretly or illegally corporate. Collusion is at the root of common knowledge plagiarism. While plagiarism requires two or more entities to compare, collusion can be determined in isolation. It is also possible that collusion do not lead to positive plagiarism checks. It is therefore the aims of this preliminary study to: (i) identify the factors responsible for collusion in e-learning (ii) determine the prominent factor that is representative of collusion and (iii) through user behaviour including, but not limited to, application switching time, determine collusion. We claim that user computer activities and application processes can help understand user behaviour during assessment task. It is on this premise that we develop a machine learning model to predict collusion through user behaviour during assessment task

Complex problems involve a concerted effort by the software team and can absorb vital resources, but our understanding of how the software team forms and succeeds has been minimal. It is not possible to explain the relationships between team achievement and scale, concentration, and especially team expertise by confound- ing elements, such as age group, additional participation from other individuals who are not in the team, or by team structures. This generates a need to understand software teams using systemic theory. This position paper presents the efforts we have undertaken to study the impact of systemic factors on software development teams and how systemic theory can be used to understand software teams. Our approach looks at the effect of psychological and sociological systemic variables on software teams to identify a way to represent software teams as systems

Developers are indeed the most important resource in software production, and the individual developers are hard to substitute. The core of the work of the developers of knowledge intensive systems is in their mind, and this now a growing interest in understanding how to detect and model the state of their mind. Such analysis would enable to determine and model the optimal situation to develop in terms, for instance, of speed of development, minimization of errors, etc., and, to the extent possible, to recreate or to get close to such situation while organizing the work, the processes, the tools, and so on. The problem of performing such analysis is that the most refined equipment to model the work of the mind, like the fMRI, is very expensive and not movable. However, a tool, MNE, has been developed who is able to recreate accurate approximations using EEG of the data coming from an accurate wearable device that would come from the fMRI. This is a major enabler of the research in this area and in this paper details on why to select it and how to use it are provided. Current paper makes proof-of-concept to show that EEG is applicable for for retrieving information about functional state of the brain especially with a help of MNE tool.

Researchers have largely identified that interactions and communications pose major challenges in software development, especially when extracting requirements. However, they have not appreciated the sources and the depth of them, thus approaching them with mechanisms that have not (fully) achieved the desired objectives. In this position, we claim that such challenges can be explained using three major theories coming from social sciences: the theory of verbal and nonverbal communication, systemic theory, and democratic theory. We also argue that some of the successful practices of agile methods can be explained in terms of these theories. Finally, we stipulate that a full appreciation of these theories can result in a significant leap forward in the discipline, identifying new mechanisms that can help to overcome the mentioned challenges, understanding fully what we are doing and why

The use of biological signals to understand software development has become more popular in the last few years but poses new challenges with respect to the overall experimental settings.In this paper we present such challenges and the approach we took to overcome them.We illustrate our approach by evaluating two programming situations: pair programming and programming with music.The subjects involved in the experimentation are mostly students, however, in the largest case we involved graduate students coming from industry with at least three years of working experience.The results in general support the validity of this approach and encourage to go further in this research line.Moreover, as a byproduct, the analysis of pair programming confirms, from a biological perspective, early hypotheses that pair programming induces higher level of concentration.