Dalila Durães

Monitoring Level Attention Approach in Learning Activities

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 archi-tecture 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.

Using Computer Peripheral Devices to Measure Attentiveness

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.

Spiral inductors on silicon for wireless communications

This paper presents the design and fabrication of integrated micromachined inductors on silicon substrates. In order to reduce eddy currents in silicon substrates, bulk-micromachining technology is used to etch the silicon wafer. In this way, aluminum spiral micromachined inductors can achieve a quality factor Q of approximately 30 (0.6-2 nH @ 2-15 GHz). Also, the resistivity of the inductors material is discussed.

Detection of Behavioral Patterns for Increasing Attentiveness Level

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.

Supervising and improving attentiveness in human computer interaction

This work has been supported by COMPETE: POCI-01-0145-FEDER-007043and FCT – Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013.

Characterize a Human-Robot Interaction: Robot Personal Assistance

In the last years, the development of robots is entering a new stage where the focus is placed on interaction with people in their daily environments. With the improvement of more and more complex robots to be used in rehabilitation, heath care, service or other applications, robot-human interaction is a rapidly growing area of research. This chapter explores the topic of human-robot interaction. Finally, we presented a proposed framework design that will operate with a person. The system considers the person attitudes level while interact with. The goal is to propose an architecture that monitoring person attitudes in real scenario, and detect patterns of behavior in different occasions. The robot will interact with a person and its training a decision support system that in a real scenario that provide the robot to makes interactions with a person.

Characterizing attentive behavior in intelligent environments

Abstract Learning styles are strongly connected with learning and when it comes to acquiring new knowledge, attention is one the most important mechanisms. The learners attention affects learning results and can define the success or failure of a student. When students are carrying out 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 and then to choose the appropriate teaching methods. In this paper we present a nonintrusive distributed system for monitoring the attention level in students. It is especially suited for classes working at the computer. The presented system is able to provide real-time information about each student as well as information about the class, and make predictions about the best learning style for a student using an ensemble of neural networks. It can be very useful for teachers to identify potentially distracting events and this system might be very useful to the teacher to implement more suited teaching strategies.

Predicting completion time in high-stakes exams

Abstract 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.

Quantifying the effects of learning styles on attention

Monitoring and managing attention in the classroom is nowadays an important aspect where the level of learner’s attention affects learning results. When students are using devices connected to the Internet in learning activities in which they send and received notifications, beeps, and vibrations and blinking messages, the ability to focus becomes increasingly important. This is true in many different domains, from the classroom to the workplace. This paper deals with the issue of attention monitoring, with the aim of providing a non-intrusive, reliable and easy tool that can be used freely in schools or organizations, without changing or interfering with the established working routines. Specifically, we look at desk students in learning activities, in which the student spends long time interacting with the computer.

Learning Frequent Behaviors Patterns in Intelligent Environments for Attentiveness Level

Nowadays, when it comes to achieving goals in business environments or educational environments, the performance successfully has an important role in performing a task. However, this performance can be affected by several factors. One of the most common is the lack of attention. The individual’s attention in performing a task can be determinant for the final quality or even at the task’s conclusion. In this paper is intended to design a solution that can reduce or even eliminate the lack of attention on performing a task. The idea consist on develop an architecture that capture the user behavior through the mouse and keyboard usage. Furthermore, the system will analyze how the devices are used.