In this article we focus on a new field of application of ICT techniques and technologies in learning activities. With these activities with computer platforms, attention allows us to break down the problem of understanding a speculative scenario into a series of computationally less demanding and localized lack of attention. The system considers the students' attention level while performing a task in learning activities. The goal is to propose an architecture that measures the level of attentiveness in real scenario, and detect patterns of behavior in different attention levels among different students. Measurements of attention level are obtained by a proposed model, and user for training a decision support system that in a real scenario makes recommendations for the teachers so as to prevent undesirable behavior.

Attention is strongly connected with learning and when it comes to acquiring new knowledge, attention is one the most important mechanisms. The learner's attention affects learning results and can define the success or failure of a student. The negative effects are especially significant when carrying out long or demanding tasks, as often happens in an assessment. This paper presents a monitoring system using computer peripheral devices. Two classes were monitored, a regular one and an assessment one. Results show that it is possible to measure attentiveness in a non-intrusive way.

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Violence detection behavior is a particular problem regarding the great problem action recognition. In recent years, the detection and recognition of violence has been studied for several applications, namely in surveillance. In this paper, we conducted a recent systematic review of the literature on this subject, covering a selection of various researched papers. The selected works were classified into three main approaches for violence detection: video, audio, and multimodal audio and video. Our analysis provides a roadmap to guide future research to design automatic violence detection systems. Techniques related to the extraction and description of resources to represent behavior are also reviewed. Classification methods and structures for behavior modelling are also provided.

Nowadays, the world is getting increasingly competitive and the quality and the amount of the work presented are one of the decisive factors when choosing an employee. It is no longer necessary to only perform but, to achieve a product with quality, on time, at the lowest possible cost and with the minimum resources. For this reason, the employee must have a high score of attention when performing a task, and the factors that influence attention negatively must be reduced. This is true in many different domains, from the workplace to the classroom. In this paper, we present a nonintrusive smart environment for monitoring people's attention when working in teams. The presented system provides real time information about each individual and information about the team. It can be very useful for team managers to identify potentially distracting events or individuals because when the attention of an individual is not at its best when performing the proposed task, her/his performance will be negatively affected, with consequences for the individual and for the organization.

In the current world, performance is one of the most important issues concerning work and competition. Performance is strongly connected with learning and when it comes to acquiring new knowledge, attention is one the most important mechanisms as the level of the learner's attention affects learning results. When students are doing learning activities using new technologies, it is extremely important that the teacher has some feedback from the students' work in order to detect potential learning problems at an early stage. The goal of this research is to propose a system that measures the level of attentiveness in real scenarios, and detects patterns of behavior associated to different attention levels among different students. This system measures attention and uses this information for training a decision support system that shows the level of attention of a group of students in real time.

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For the majority of students, assessment moments are associated with significant levels of stress and anxiety. While a certain amount of stress motivates the individual and improves performance, too much stress will have the contrary effect. Stress has therefore a fundamental role on student performance. It should be the educational organizations' mission to understand the underlying mechanisms that lead to performance anxiety and provide their students with the best coping tools and strategies. In the present study we analyze student behavior during e-assessment in terms of mouse dynamics. Two major behavioral patterns can be identified, based on ten features that quantify the performance of the student's interaction with the computer: (1) students who are able to sustain performance during the exam and (2) students whose performance varies significantly. Data shows that the behavior of each student during the exam correlates strongly with the time it takes the student to complete it. Several classifiers were trained that predict the completion time of each exam based on the students' interaction patterns. Two of them do it with an average error of around twelve minutes. Results show that there are still mechanisms that can be explored to better understand the complex relationship between stress, performance and human behavior, that can be used for the implementation of better stress detection, monitoring and coping strategies.