Students randomized to the in sequence condition did a relevant computer tutorial just after having seen the index patient (See Figure 2).
Petrosyan et al. [38], on the other hand, proposed a classification-based solution to discover tutorial sections that explain a given API type (classes and interfaces), i.e., relevant tutorial sections to API types.
In the context of our work, however, the concepts of salient code element [37] and relevant tutorial sections to API types [38] are not exactly adequate.
Rather than allowing customers to waste their time and money, Unified's StumbleUpon feature only turns on after they've completed the relevant tutorial in the University.
Each delegate was given a brief, general introduction on the purpose of the study and instructions on how to use the simulator and the relevant tutorials.
After encountering a patient with either FWS or a patient needing ORS, the student logged into a computer that randomly assigned them to either a) completing a relevant computer tutorial (e.g. FWS patient + FWS tutorial = "in sequence") or b) completing the non-relevant tutorial (e.g. FWS patient + ORS tutorial = "out of sequence").
For the 'out of sequence' subjects, the timing of when they did the tutorial was variable with 26 of 51 (49%) doing the non-relevant tutorial immediately after seeing the index patient while the rest did the tutorial some convenient time later in their 8-hour shift.
Adjusting for pre-test score, the model showed significant main effects for Tutorial, Patient Exposure and the interaction term between Patient Exposure and Tutorial suggesting that in sequence condition has a significant effect on post-test score over and above that independently predicted by seeing a relevant patient or doing the tutorial in isolation.
A computer tutorial done in conjunction with seeing a relevant patient (i.e. the Patient-Teacher-Tutorial sequence) is more effective in raising test scores than the same tutorial done separate from a patient encounter.
In this section of the tutorial, we will take you through the process of integrating an expression data set with your newly constructed interactome, and then extracting a relevant subnetwork.
