A mobile phone application was designed as part of an X-Prize challenge at Arizona State University (ASU). The team created an application that displays 4D visualization of time sensitive contagion data, specifically focusing on the Ebola Virus Disease. The application, named “Ebola Resource Decision Evaluator” (ERDE), is a tool to aid in resource allocation for decision-makers during epidemics and outbreaks. The predictive algorithm was based on the SIR Model—susceptible, infected, and recovered (or immune). We adapted this predictive model into our application to forecast weeks forward the Ebola incidence in three cities in the Democratic Republic of Congo (DRC).
The current 2D map used by the Center for Disease Control (CDC) displays only the number of deaths in a city caused by the outbreak. But, the cities differ in ways 2D cannot convey. We implemented the augmented reality (AR) aspect to give more meaning to data and to give decision-makers interactive 4D city-by-city comparisons. The outbreak is ongoing as of September 2019 and ASU has committed to hosting the application for other healthcare workers to use. The application incorporates the most recent data on the disease and updates to visualize how many are predicted to become infected given X units of vaccine. We are able to use the data and compare the effectiveness to other cities. After this collection of data, professionals would determine the most efficient action to take against the spread of the disease.

The purpose of this study was to investigate the impact of immersion on knowledge, cognitive load, and presence in a simulation designed to deliver a lesson on science lab safety training. 108 participants were randomly assigned to one of three conditions: high immersion (played an interactive simulation about lab safety in a VR headset), medium immersion (played the same interactive simulation on the computer), or low immersion (watched a video and read about lab safety procedures). Participants completed a pretest, a science lab safety training, a posttest (same as the pretest), a questionnaire with subjective presence questions, and a questionnaire with subjective cognitive load questions. Participants were again asked to complete a follow-up test (same as the pretest and posttest) a week later.

The results revealed three significant findings:

(a) Participants in the high and medium immersion conditions had significantly higher knowledge scores at posttest and follow-up than their peers in the low immersion condition,

(b) Participants in the high and medium immersion conditions reported higher presence scores than participants in the low immersion conditions.

(c) Correlation coefficients suggested that the higher the immersion and presence, the higher the knowledge scores are at posttest and follow-up.

In addition, multiple hierarchical linear regression models were conducted out of which one was significant.

This study explored three methods to measure cognitive load in a learning environment using four logic puzzles that systematically varied in level of intrinsic cognitive load. Participants' perceived intrinsic load was simultaneously measured with a self-report measure--a traditional subjective measure--and two objective, physiological measures based on eye-tracking and EEG technology. In addition to gathering self-report, eye-tracking data, and EEG data, this study also captured data on individual difference variables and puzzle performance. Specifically, this study addressed the following research questions: 1. Are self-report ratings of cognitive load sensitive to tasks that increase in level of intrinsic load? 2. Are physiological measures sensitive to tasks that increase in level of intrinsic load? 3. To what extent do objective physiological measures and individual difference variables predict self-report ratings of intrinsic cognitive load? 4. Do the number of errors and the amount of time spent on each puzzle increase as the puzzle difficulty increases? Participants were 56 undergraduate students. Results from analyses with inferential statistics and data-mining techniques indicated features from the physiological data were sensitive to the puzzle tasks that varied in level of intrinsic load. The self-report measures performed similarly when the difference in intrinsic load of the puzzles was the most varied. Implications for these results and future directions for this line of research are discussed.