Today's best intelligent, adaptive, multimedia trainers have shown excellent performance; however, their results still fall far-short of what good human tutors can achieve. The overarching thesis of this paper is that future intelligent, adaptive systems will be improved by taking into account relevant, consistent, and meaningful individual differences. Specifically, responding to individual differences among trainees will (a) form more accurate individual baselines within a training system, and (b) better inform system responses (so that they interpret and respond to observable data more appropriately). One variable to consider is trait arousability, which describes individual differences in sensitivity to stimuli. Individuals' arousability interacts with the arousal inherent to a task/environment to create a person's arousal state. An individual's arousal state affects his/her attentional capacity, working memory function, and depth of processing. In this paper, two studies are presented. The purpose of the first study was to evaluate existing subjective measures of trait arousability and then develop a new measure by factor analyzing existing apparatus. From this well-populated (N = 622) study, a new reliable ([alpha] = .91) 35-item scale was developed. This scale includes two factors, negative emotionality and orienting sensitivity, which have been previously theorized but not yet so reliably measured. The purposes of the second study were to (a) validate the measure developed in the first investigation and (b) demonstrate the applied value of the arousability construct in the context of training. Results from the second study (N=45) demonstrated significant main effects, but the interaction effects were inconclusive. They neither clearly confirm nor invalidate the hypotheses, but they do raise further questions.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-4711 |
| Date | 01 January 2008 |
| Creators | Schatz, Sae |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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