Feedback is essential to guide performance in simulation-based training (SBT) and to refine learning. Generally outcomes improve when feedback is delivered with personalized tutoring that tailors specific guidance and adapts feedback to the learner in a one-to-on environment. Therefore, emulating by automation these adaptive aspects of human tutors in SBT systems should be an effective way to train individuals. This study investigates the efficacy of automating different types of feedback in a SBT system. These include adaptive bottom-up feedback (i.e., detailed feedback, changing to general as proficiency develops) and adaptive top-down feedback (i.e., general feedback, changing to detailed if performance fails to improve). Other types of non-adaptive feedback were included for performance comparisons as well as to examine the overall cognitive load. To test hypotheses, 130 participants were randomly assigned to five conditions. Two feedback conditions employed adaptive approaches (bottom-up and top-down), two used non-adaptive approaches (constant detailed and constant general), and one functioned as a control group (i.e., only a performance score was given). After preliminary training on the simulator system, participants completed four simulated search and rescue missions (three training missions and one transfer mission). After each training mission, all participants received feedback relative to the condition they were assigned. Overall performance on missions, knowledge post-test scores, and subjective cognitive load were measured and analyzed to determine the effectiveness of the type of feedback. Results indicate that: (1) feedback generally improves performance, confirming prior research; (2) performance for the two adaptive approaches (bottom-up vs. top-down did not differ significantly at the end of training, but the bottom-up group achieved higher performance levels significantly sooner; (3) performance for the bottom-up and constant detailed groups did not differ significantly, although the trend suggests that adaptive bottom-up feedback may yield significant results in further studies. Overall, these results have implications for the implementation of feedback in SBT and beyond for other computer-based training systems.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-5271 |
| Date | 01 January 2010 |
| Creators | Billings, Deborah |
| 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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