Updated Evaluation Plan
Gene and I met this morning to draw up a revised plan for the WiiCane data analysis. We see this taking form in several ways.
First, Gene has identified some case studies from our first exploratory trials with the WiiCane that suggest ways that the device is beneficial for student learning. He has posted some of these findings on this blog. One set of graphs plots the number of corrective feedbacks given over successive trials, showing that the frequency of these feedbacks may decrease with practice. This suggests that students are learning something from repeated use of the device that carries over into later trials.
A second set of graphs Gene has created shows an effect on veering. When the number of nonveering messages (indicating correct positioning) exceeds the number of corrective messages (indicating veering error), improvement has occurred. A plot of the arithmetic difference (nonveering minus veering messages) shows that this balance may improve with practice. Again, this demonstrates the potential for learning with practice using the device.
The cases analyzed so far took place during the exploratory phase of our trials, when we were experimenting with the optimal number of trials and adjustment of parameters such as the tolerance threshold for veering (which varied from 12 to 18 inches). Our next step would be to set up more consistent trials with several participants, in which the number of trials and the veering tolerance are held constant. Thus, we plan to select three representative students from NYISE and to test them under these conditions: three 30-trial sessions over a period of three days, for a total of 90 trials; and a veering tolerance level of 12”. These conditions are consistent with those identified by Guth in his previous research, which demonstrated that improvement in veering could be observed after several successive days of sessions consisting of 30 trials each.
This experiment would provide a more systematic evaluation of the potential of the WiiCane device to reduce the incidence of veering. With more reliable data from such an experiment, we will be better positioned to design future research with larger samples and to advocate for the benefits of using an automated tool to improve mobility training. This knowledge will be beneficial both for instructors who wish to evaluate how the device will help their students and for our ability to market the device.
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