Table 1 - uploaded by Rainer Knauf
Content may be subject to copyright.
Source publication
Due to the geographical distribution, different cognitive capacity, and different domain competency of workers or learners, many misunderstandings can occur during Web based remote collaboration, leading to inefficient discussions and undesired results. To overcome these problems, a “Multimedia adaptive utilization” framework for dependable web bas...
Similar publications
This research consists of a observation and collection of usability criteria with respect to the user's learning in multimedia applications used in distance education, through a empirical analysis, in which concepts and methods of software interface design are used. Grounded Theory and Case Study research methods have been used. The main goal of th...
Citations
... One of the promising approaches to detect learners' emotions is the use of biological signals [1]. In fact, biological signals such as eye movements, heart rate and skin temperature are controlled without human's consciousness [2]. Thus, this technique is considered as one of the best methods to detect learners' emotions [3]. ...
The trend of distance learning education has increased year by year because of the rapid advancement of information and communication technologies. Distance learning system can be regarded as one of ubiquitous computing applications since the learners can study anywhere even in mobile environments. However, the instructor cannot know if the learners comprehend the lecture or not since each learner is physically isolated. Therefore, a framework which detects the learners’ concentration condition is required. If a distance learning system obtains the information that many learners are not concentrated on the class due to the incomprehensible lecture style, the instructor can perceive it through the system and change the presentation strategy. This is a context-aware technology which is widely used for ubiquitous computing services. In this paper, an efficient distance learning system, which accurately detects learners’ concentration condition during a class, is proposed. The proposed system uses multiple biological information which are learners’ eye movement metrics, i.e. fixation counts, fixation rate, fixation duration and average saccade length obtained by an eye tracking system. The learners’ concentration condition is classified by using machine learning techniques. The proposed system has performed the detection accuracy of 90.7% when Multilayer Perceptron is used as a classifier. In addition, the effectiveness of the proposed eye metrics has been confirmed. Furthermore, it has been clarified that the fixation duration is the most important eye metric among the four metrics based on the investigation of evaluation experiment.
