Application of Affective Computing in the Analysis of Emotions of Educational Content for the Prevention of COVID-19

Application of Parallel Computing in the Analysis of Usability Tests, Under the Mouse Tracking Approach

“…Furthermore, it is evident through the calculation of MSE and the standard error of the estimated values that the model describing the parallel approach exhibits less variability and, therefore, greater consistency than the model corresponding to the sequential approach. The aforementioned is a relevant contribution to enhance the efficiency of the tools proposed in [21] and [25], where tools based on sequential computing are proposed for the analysis of zones of interest in images derived from mouse tracking tests, involving pixel counting by zone and respective clustering techniques.…”

“…One of the challenges of mouse tracking tests is the efficient analysis of the interaction zones in the images with the mouse trace, given the amount of data or pixels contained in the increasingly high quality images and the number of users performing the test [24], [25]. In this regard, the conventional or sequential computing approach relies on pixel counting for areas of interest by iterating through the image from left to right and top to bottom.…”

“…In [21], a tool is proposed for the analysis of interaction zones in mouse tracking tests using sequential computing. Similarly, in [25], a tool was developed for the analysis of mouse traces using unsupervised learning models, such that the main interaction zones are detected by obtaining clusters and their associated centroids. In [29] a study based on eye tracking and mouse tracking was developed on commercial and educational portals in Nigeria, using OGAMA software, in order to identify search and browsing patterns employed by users interacting with these web portals.…”