Year: 2014
Type: dissertation
Abstract: With the enormous growing of e-learning platforms, as complementary or even primary tool to support learning in organizations, monitoring students success factors becomes a crucial issue. Such platforms, in order to be successful must deal with context information, regarding e-learning students, thus being context-aware. Stress is a normal part of studying, but if not managed it the proper way, stress can grow, become a problem and greatly influence learning success. This is particularly true when in an e-learning environment, where students typically may work alone, thus more susceptible to stress. This Phd thesis proposes a way to accommodate individualized support to e-learning students. It presents a dynamic student assessment module and latter a dynamic stress recognition module to detect stress in e-learning students, in a non-intrusive way. This detection is made using keyboard and mouse as sensors, thus hiding from the students that they are being monitored. Initially, some other devices wore considered to act as sensors, but due to some legal issues they wore not used. \n\nBy using common devices, that any user uses daily when working with computers, the monitoring process is hidden, and in this way, better and realistic results are obtained.\n\nThe research hypothesis justification is made through a case study in a public secondary school, and the results are then analysed and validated. From the obtained results, we wore able to establish that the way students behave when engaging e-learning platforms is influenced by stress, and that influence, in this case, allowed to group them in two types of students.
DOI: 10.35869/11093/215
Author: Manuel Fernando Silva Rodrigues
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7160067789