An Analysis of Concentration Region on Powerpoint Slides using Eye Tracking

Powerpoint slides have become one of the essential teaching  tools in academic  for both  offline and  online modes. It may  play  a  useful  role to facilitate  discussion  and  information exchange.  However,  in  our  teaching  experience,  we find  many students utilizing Powerpoint slides beyond their traditional functions.  Many  students   fully  rely on  the  slides  as  the  main learning   materials   and,  in  some  cases,  substituting  textbooks. This  study  intends  to  understand how  students   interact   with the   learning   materials   presented  on  Powerpoint   slides.  The interaction is measured using  an  eye tracker device called  the Eye  Tribe  Tracker. Thirty  sophomore  and  junior  students  are asked  to participate. They are  instructed to learn  a topic in the subject  of Introduction to Algorithm  and  Programming, a basic course  in the  computer science field. During  the  process,  their fixation points  are  monitored  and  are  related to the contents  on the slides. The results are rather surprising. Many students  read the  slides  in  unexpected  manners that  may  compromise   their understanding and  may lead to inaccurate interpretations

Detecting the Early Drop of Attention using EEG Signal

The capability   to detect the drop of attention as early as possible has many practical applications including for the development of the early warning system for those who involve in high-risk works that  require a constant level of concentration. This study intends to  develop such the capability on the basis of the data of the brain   waves: delta, theta, alpha, beta, and gamma. For the purpose, a number of participants are asked  to participate in the study where their  brain waves are recorded by using a low-cost Neurosky Mindwave EEG sensor. In the process, the  participants are performing a continuous performance test from which their attention levels are directly measured in  the form of the response time in conjunction to those waves. When the response time is much longer than  a normal one, the participant attention is assumed  to be dropped. A simple k-NN classification method is used with the k = 3. The results are the following. The best detection of the attention drop is achieved when  the attention features are extracted   from the earliest stage of the brain wave signals. The brain wave signal should be  recorded longer than 1 s since the time the stimulus is presented as a short signal  leads to a poor categorization. A significant drop in the level of response time is required to provide the brain signal that better predicts the change of the attention