Attentiveness and engagement in learning activities

• Autores: Dalila Durães
• Directores de la Tesis: Javier Bajo Pérez (dir. tes.) , Paulo Novais (codir. tes.)
• Lectura: En la Universidad Politécnica de Madrid ( España ) en 2018
• Idioma: español
• Tribunal Calificador de la Tesis: Vicente J. Julián Inglada (presid.) , Emilio Serrano Fernández (secret.) , Maria Goreti Carvalho Marreiros (voc.) , Josefa Zuleide Hernández Diego (voc.) , Jesus Garcia Herrero (voc.)
• Enlaces
  • Tesis en acceso abierto en: Archivo Digital UPM
• Resumen

  • The Human being is currently under increased demand for attention, a result of a society that is moving faster. In most of the so-called developed countries, workers have nowadays increasingly busier activities. This makes them stretch their limits to find time for children, sports activities, and other. This necessary extra time is frequently obtained at the expense of shorter periods of sleep or rest. Although this effect may not be readily visible, they may have consequences at many other levels than health, including emotions, results, attention and social behavior, among others.

    For these reasons, school and systems of e-learning must create environments that can involve students and capture their attention and engagement. Advances in computers and wireless technologies have also had an impact on the educational system, thus generating a new approach for Ambient Intelligent Systems (AmI systems). The rapid development of these technologies combined with the access to content in a wide variety of settings, allows learners to experience new learning situations beyond the school’s walls.

    The need for qualified people is growing exponentially, requiring limited resources allocated to education/training to be used most efficiently. Learning and e-learning systems can allow some flexibility for students who have complicated schedules and obligations. However, some problems can occur: (1) relying on learning theories, it is crucial to improve the learning process and mitigate the issues that may arise from technologically enhanced learning environments; (2) each student presents a particular way of assimilating knowledge, i.e. his/her learning style. It is essential that these systems adapt to the learning preferences of the students.

    This work deals with the issue of attention monitoring as a form of engagement, 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. We propose an intelligent learning system able to monitor the patterns of students’ behavior during lessons, to support the teaching procedure within school environments. The system used behavior patterns based on mouse dynamics, keystroke dynamics, student’s attention, and lessons activity. The idea highlights the main biometric behavioral variations for each activity and bases the set of attributes relevant to the development of machine learning classifiers for the prediction of students’ learning preference. The objective is to show that there are still mechanisms that can be explored to understand better the complex relationship between human behavior, attention, and learning, which could be used for the implementation of better learning strategies. After improving learning systems in a learning and e-learning environment it is possible to predict students’ behavior in an (e)learning lesson, based on their interaction with technological devices.