A Multisensory Monitoring and Interpretation Framework Based on the Model–View–Controller Paradigm

Advances in Intelligent Systems and Computing

et al. 2013

In traditional learning, teachers can easily get an insight into how their students work and learn and how they interact in the classroom. However, in online learning, it is more difficult for teachers to see how individual students behave. 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. In this paper we focus on the importance of stress in the learning process. Stress detection in an E-learning environment is an important and crucial factor to success. Estimating, in a non-invasive way, the students' levels of stress, and taking measures to deal with it, is then the goal of this paper. Moodle, by being one of the most used e-learning platforms is used to test the log tool referred in this work.

Section: Explicit and Implicit Mood Assumptionmentioning

confidence: 99%

Keystrokes and Clicks: Measuring Stress on E-learning Students

Rodrigues

Gonçalves

Advances in Intelligent Systems and Computing

et al. 2013

“…An estimation of the amount of movement from the video camera is built. The image processing stack uses the principles established by Castillo et al (2011) and uses image difference techniques to calculate the amount of movement between two consecutive frames (Fernández-Caballero et al, 2010). • Acceleration -the acceleration is measured from accelerometers in mobile devices.…”

Section: Functionalitiesmentioning

confidence: 99%

Multimodal behavioral analysis for non-invasive stress detection

Expert Systems with Applications

Castillo

Nováis

et al. 2012

Self Cite

125

This study presents and approach to measure the levels of acute stress in humans by analysing their behavioural patterns when interacting with technological devices. We study the effects of stress on eight behavioural, physical and cognitive features. The data was collected with the participation of 19 users in different phases, with different levels of stress induced. A non-parametric statistical hypothesis test is used to determine which features show statistically significant differences, for each user, when under stress. It is shown that the features more related to stress are the acceleration and the mean and maximum intensity of the touch. It is also shown that each user is affected by stress in a specific way. Moreover, all the process of estimating stress is undertaken in a non-invasive way. This work constitutes the foundation of a context layer for a virtual environment for conflict resolution. The main objective is to overcome some of the main drawbacks of communicating online, namely the lack of contextual information such as body language or gestures.

“…Information about the user's context is provided through a monitoring framework [7], which is customized to perform movement detection from a camera located in front of the user. Considering frame-to-frame movement information, the level of agitation or excitement of the user can be analyzed.…”

Section: System Overviewmentioning

confidence: 99%

Stress Monitoring in Conflict Resolution Situations

Ambient Intelligence - Software and Applications

Montotya

Nováis

et al. 2012

Self Cite

Online Dispute Resolution is steadily growing to become the major alternative to litigation in court. In fact, given the characteristics of current disputes, technology-based conflict resolution may be a quite efficient approach. However, in this shift of paradigm, there are also threats that should be considered. Specifically, in this paper we deal with the problem of the lack of important context information when parties communicate in a virtual setting. In that sense, we propose the addition of a monitoring framework capable of measuring the level of stress of the parties in a non-invasive way. This information will be used by the platform and the mediator throughout the complete conflict resolution process to adapt strategies in real-time, resulting in a context-aware and more efficient approach.