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Intuitive decision making refers to decision making based on situational pattern recognition, which happens without deliberation. It is a fast and effortless process that occurs without complete awareness. Moreover, it is believed that implicit learning is one means by which a foundation for intuitive decision making is developed. Accordingly, the

Intuitive decision making refers to decision making based on situational pattern recognition, which happens without deliberation. It is a fast and effortless process that occurs without complete awareness. Moreover, it is believed that implicit learning is one means by which a foundation for intuitive decision making is developed. Accordingly, the present study investigated several factors that affect implicit learning and the development of intuitive decision making in a simulated real-world environment: (1) simple versus complex situational patterns; (2) the diversity of the patterns to which an individual is exposed; (3) the underlying mechanisms. The results showed that simple patterns led to higher levels of implicit learning and intuitive decision-making accuracy than complex patterns; increased diversity enhanced implicit learning and intuitive decision-making accuracy; and an embodied mechanism, labeling, contributes to the development of intuitive decision making in a simulated real-world environment. The results suggest that simulated real-world environments can provide the basis for training intuitive decision making, that diversity is influential in the process of training intuitive decision making, and that labeling contributes to the development of intuitive decision making. These results are interpreted in the context of applied situations such as military applications involving remotely piloted aircraft.
ContributorsCovas-Smith, Christine Marie (Author) / Cooke, Nancy J. (Thesis advisor) / Patterson, Robert (Committee member) / Glenberg, Arthur (Committee member) / Homa, Donald (Committee member) / Arizona State University (Publisher)
Created2011
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Description
When discussing human factors and performance, researchers recognize stress as a factor, but overlook mood as contributing factor. To explore the relationship between mood, stress and cognitive performance, a field study was conducted involving fire fighters engaged in a fire response simulation. Firefighter participants completed a stress questionnaire, an emotional

When discussing human factors and performance, researchers recognize stress as a factor, but overlook mood as contributing factor. To explore the relationship between mood, stress and cognitive performance, a field study was conducted involving fire fighters engaged in a fire response simulation. Firefighter participants completed a stress questionnaire, an emotional state questionnaire, and a cognitive task. Stress and cognitive task performance scores were examined before and after the firefighting simulation for individual cognitive performance depreciation caused by stress or mood. They study revealed that existing stress was a reliable predictor of the pre-simulation cognitive task score, that, as mood becomes more positive, perceived stress scores decrease, and that negative mood and pre-simulation stress are also positively and significantly correlated.
ContributorsGomez-Herbert, Maria Elena (Author) / Cooke, Nancy J. (Thesis advisor) / Becker, Vaughn (Committee member) / Branaghan, Russell (Committee member) / Hyunjin, Song (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Data and the use of data to make educational decisions have attained new-found prominence in K-12 education following the inception of high-stakes testing and subsequent linking of teacher evaluations and teacher-performance pay to students' outcomes on standardized assessments. Although the research literature suggested students' academic performance benefits were derived from

Data and the use of data to make educational decisions have attained new-found prominence in K-12 education following the inception of high-stakes testing and subsequent linking of teacher evaluations and teacher-performance pay to students' outcomes on standardized assessments. Although the research literature suggested students' academic performance benefits were derived from employing data-informed decision making (DIDM), many educators have not felt efficacious about implementing and using DIDM practices. Additionally, the literature suggested a five-factor model of teachers' efficacy and anxiety with respect to using DIDM practices: (a) identification of relevant information, (b) interpretation of relevant information, (c) application of interpretations of data to their classroom practices, (d) requisite technological skills, and (e) comfort with data and statistics.

This action research study was designed to augment a program of support focused on DIDM, which was being offered at a K-8 charter school in Arizona. It sought to better understand the relation between participation in professional development (PD) modules and teachers' self-efficacy for using DIDM practices. It provided an online PD component, in which 19 kindergarten through 8th-grade teachers worked through three self-guided online learning modules, focused sequentially on (a) identification of relevant student data, (b) interpretation of relevant student data, and (c) application of interpretations of data to classroom practices. Each module concluded with an in-person reflection session, in which teachers shared artifacts they developed based on the modules, discussed challenges, shared solutions, and considered applications to their classrooms.

Results of quantitative data from pre- and post-intervention assessments, suggested the intervention positively influenced participants' self-efficacy for (a) identifying and (b) interpreting relevant student data. Qualitative results from eight semi-structured interviews conducted at the conclusion of the intervention indicated that teachers, regardless of previous experience using data, viewed DIDM favorably and were more able to find and draw conclusions from their data than they were prior to the intervention. The quantitative and qualitative data exhibited complementarity pointing to the same conclusions. The discussion focused on explaining how the intervention influenced participants' self-efficacy for using DIDM practices, anxiety around using DIDM practices, and use of DIDM practices.
ContributorsNelson, Andrew (Author) / Buss, Ray R (Thesis advisor) / Preach, Deborah (Committee member) / Buchanan, James (Committee member) / Mertler, Craig A. (Committee member) / Arizona State University (Publisher)
Created2017