2As the technology that supports interactive systems advances, the possibility of leveraging a multitude of sensory systems becomes possible. By using multiple sensory processors, substantial gains in the infomation management capacity of the human-computer integral should be realized, and those with sensory losses can be better accommodated. The question becomes when multimodal information is presented, how should these multiple sources of information be coordmated, particularly when two or more tasks are performed simultaneously? While current design theories developed primarily for unimodal interaction can be drawn on, additional research is required to fully guide multimodal multitask interaction design. The current study seeks to extend unimodal design theories to multimodal systems and identifies some interesting differences in unimodal vs multimodal multitask interaction.
While previous research strongly indicates that training feedback is more effective when it includes information about the processes underlying performance, rather than only detailing the outcome of leamer actions, there are still many open questions about the content of optimally effective feedback. In particular, there is some disagreement in the literature as to whether process-based feedback should focus on a diagnostic critique of the student processes, or simply present information about the processes associated with skilled or expert performance. This research investigates the appropriate content of process-based feedback in a complex, dynamic, military task. Fuzzy logic was used to abstract pattems from performance data and form the basis of process-based feedback. We found that process-based feedback led to significant performance improvements, while outcome-based feedback did not, and that diagnostic feedback did not lead to greater performance improvement than providing information about expert processes.
While there are many different comput+tional modeling techniques capable of predicting human decision-making outcomes, training applications require modeling techniques that are also diagnostic of human decision-making processes. Multiple linear regression, a commonly used modeling technique in Psychology, makes overly restrictive processing assumptions such as that of additivity. A relatively new modeling approach, fuzzy system modeling, bears some striking similarities to current theories of categorization and cognition. In this research, we compare the diagnostic utility of multiple linear regression to fuzzy system models. Specifically, decisionmaking data are modeled using either linear regression or fuzzy system models, and trainee models are compared to an expert model built with the same technique. Discrepancies between the trainee and expert models are noted and qualitative feedback is generated. The diagnostic utility of each technique is evaluated by measuring changes in performance after model-based feedback is provided to the trainees.
This paper describes the results of data collection that occurred during the alternative format session presented to the 45th annual meeting of HFES. During the session, six participants were briefed on fuzzy logic as an alternative to regression for analyzing policy-capturing data and on usability issues associated with Advanced Distance Learning (ADL) applications. Participants then rated the extent to which usability violations impacted learning in three different ADL environments. After the conference, the validity of the regression and fuzzy models were assessed across the three ADL applications. In addition, exploratory analyses were performed in order to gain insight into the relative impact of usability principles on learning in ADL applications. Results revealed no statistically significant differences between the predictive validities of either modeling technique across the ADL applications. In addition, judgements of which usability violations had a more negative impact on learning did depend on the type of ADL application
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