Supervising attention in an E-learning system

Until now, the level of attention of a worker has been evaluated through his/her productivity: the more one produces, the better his/her attention at work. First, the worst aspect about this approach is that it only points out a potential decrease of attention after a productivity loss. An approach that could point out, in advance, upcoming breaks in attention could allow active/preventive interventions rather than reactive ones. In this paper we present a distributed system for monitoring attention in teams (of people). It is especially suited for people working with computers and it can be interesting for domains such as the workplace or the classroom. It constantly analyzes the behavior of the user while interacting with the computer and together with knowledge about the task, is able to temporally classify attention.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 andFCT –Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013

Assess and enhancing attention in learning activities

The rapid progress of technologies has enabled the development of innovative environment in learning activities when the student used computer devices with access to Internet. The goal of this paper is to propose an ambient intelligent (AmI) system, directed at the teacher that indicates the level of attention of the students in the class when it requires the use of the computer connected to the Internet. This AmI system captures, measures, and supervises the interaction of each student with the computer (or laptop) and indicates the level of attention of students in the activities proposed by the teacher. When the teacher has big class, he/she can visualize in real time the level of engagement of the students in the proposed activities and act accordingly when necessary. 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 behaviour and change the learning styles.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Analysis learning styles though attentiveness

Attention is one of the most widely misused and overgeneralized constructs found in the educational, learning, instructional, and psychological sciences. It would be convenient for teachers if they could grasp the attentiveness states of learners in their classes precisely so that they could try to improve the way to deliver the course material in a manner that could attract more learners. When students are doing learning activities using the news technologies is very hard for the teacher detected if each student her/his level of attentiveness. Furthermore, different student learn in different ways, each one preferring a different learning style. This paper presents an experience using different learning styles with a system that monitoring attention, with the aim of providing a nonintrusive and non-invasive way, reliable and easy tool that can be used freely in schools, 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.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Performance analysis of models used to predict failure in secondary school

According to data made available by Pordata for the year 2021, about 8.3% of medium school students in Portuguese schools fail or drop out of the educational system, another 9.8% of students in this situation are still in basic education. Since education is one of the pillars of a country’s development, it is important to understand the reasons behind these statistics and discover what leads students to such failure in order to try to mitigate these results. In order to do so, it is necessary to acquire data about the students, thus emerging the area of Educational Data Mining. Early prediction of school failure can be the key piece of the effort to avoid it. So, this paper present a comparative study of machine learning models to indicate the best model to predict school failure.FCT - Fundação para a Ciência e a Tecnologia (UIDB/00319/2020

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 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.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Modelling an intelligent interaction system for increasing the level of attention

Learning activities using technologies is one of the common education methods. Its advantages allow that students can learn with concepts more practical’s. However in this environment not all the students can be attentive. In this research an Ambient Intelligent System has been designed using biometrics behaviors for detecting learner inattentiveness. The learning attentiveness of a student can be determined precisely and the teacher has access to these results and might improve news strategies.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Non-intrusive monitoring of attentional behavior in teams

Attention is a very important cognitive and behavioral process, by means of which an individual is able to focus on a single aspect of information, while ignoring others. In a time in which we drawn in notifications, beeps, vibrations and blinking messages, the ability to focus becomes increasingly important. This is true in many diferent domains, from the workplace to the classroom. In this paper we present a nonintrusive distributed system for monitoring attention in teams of people. It is especially suited for teams working at the computer. The presented system is able to provide real-time information about each individual as well as information about the team. It can be very useful for team managers to identify potentially distracting events or individuals, as well as to detect the onset of mental fatigue or boredom, which significantly influence attention. In the overall, this tool may prove very useful for team managers to implement better human resources management strategies.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013. The work of Davide Carneiro is supported by a PostDoctoral Grant by FCT (SFRH/BPD/109070/2015).info:eu-repo/semantics/publishedVersio

Supervising and improving attentiveness in human computer interaction

The collection, storage, management, and anticipation of contextual information about the user to support decision-making constitute some of the key operations in most Ambient Intelligent (AmI) systems. When the instructor has a computer-based class it is often difficult to confirm if the students are working in the proposed activities. In order to mitigate problems that might occur in an environment with learning technologies we suggest an AmI system aimed at capturing, measuring, and supervising the students’ level of attentiveness in real scenarios and dynamically provide recommendations to the instructor. With this system it is possible to assess both individual and group attention, in real-time, providing a measure of the level of engagement of each student in the proposed activities and allowing the instructor to better steer teaching methodologies.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

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.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Emotion extraction from Likert-Scale questionnaires: an additional dimension to psychology instruments

Sentiment analysis tasks are used in various domains, including education. Likert-scale questionnaires are often used to gain insights into the respondents’ views in various contexts. However, these questionnaires can allow for more information than they are designed for. This research paper explores an emotion classification technique for extracting emotional information from likert-scale questionnaires. A case study is presented in which a tailored questionnaire was employed to gather students’ opinions on school-related matters, such as learning importance, academic performance and family and peer involvement and support. The students (n = 845) answered the questionnaire using a scale from totally disagree to totally agree. Through this questionnaire-based approach, data on students’ emotions was collected.FCT - Fundação para a Ciência e a Tecnologia(UIDB/00319/2020)This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project 2022.06822.PTD