Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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Human Auditory Biases Match Natural Regularities Found With Animal Calls

Description
Human perceptual dimensions of sound are not necessarily simple representations of the actual physical dimensions that make up sensory input. In particular, research on the perception of interactions between acoustic frequency and intensity has shown that people exhibit a bias to expect the perception of pitch and loudness to change…

Human perceptual dimensions of sound are not necessarily simple representations of the actual physical dimensions that make up sensory input. In particular, research on the perception of interactions between acoustic frequency and intensity has shown that people exhibit a bias to expect the perception of pitch and loudness to change together. Researchers have proposed that this perceptual bias occurs because sound sources tend to follow a natural regularity of a correlation between changes in intensity and frequency of sound. They postulate that the auditory system has adapted to expect this naturally occurring relationship to facilitate auditory scene analysis, the tracking and parsing sources of sound as listeners analyze their auditory environments. However, this correlation has only been tested with human speech and musical sounds. The current study explores if animal sounds also exhibit the same natural correlation between intensity and frequency and tests if people exhibit a perceptual bias to assume this correlation when listening to animal calls. Our principal hypotheses are that animal sounds will tend to exhibit a positive correlation between intensity and frequency and that, when hearing such sounds change in intensity, listeners will perceive them to also change in frequency and vice versa. Our tests with 21 animal calls and 8 control stimuli along with our experiment with participants responding to these stimuli supported these hypotheses. This research provides a further example of coupling of perceptual biases with natural regularities in the auditory domain, and provides a framework for understanding perceptual biases as functional adaptations that help perceivers more accurately anticipate and utilize reliable natural patterns to enhance scene analyses in real world environments.
ContributorsWilkinson, Zachary David (Author) / McBeath, Michael (Thesis director) / Glenberg, Arthur (Committee member) / Rutowski, Ronald (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor)
Created2014-05
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What Predicts Student Comprehension in Language Learning? Augmenting Student Action with Elapsed Time in an Educational Data Mining Approach

Description
Reading comprehension is a critical aspect of life in America, but many English language learners struggle with this skill. Enhanced Moved by Reading to Accelerate Comprehension in English (EMBRACE) is a tablet-based interactive learning environment is designed to improve reading comprehension. During use of EMBRACE, all interactions with the system…

Reading comprehension is a critical aspect of life in America, but many English language learners struggle with this skill. Enhanced Moved by Reading to Accelerate Comprehension in English (EMBRACE) is a tablet-based interactive learning environment is designed to improve reading comprehension. During use of EMBRACE, all interactions with the system are logged, including correct and incorrect behaviors and help requests. These interactions could potentially be used to predict the child’s reading comprehension, providing an online measure of understanding. In addition, time-related features have been used for predicting learning by educational data mining models in mathematics and science, and may be relevant in this context. This project investigated the predictive value of data mining models based on user actions for reading comprehension, with and without timing information. Contradictory results of the investigation were obtained. The KNN and SVM models indicated that elapsed time is an important feature, but the linear regression models indicated that elapsed time is not an important feature. Finally, a new statistical test was performed on the KNN algorithm which indicated that the feature selection process may have caused overfitting, where features were chosen due coincidental alignment with the participants’ performance. These results provide important insights which will aid in the development of a reading comprehension predictor that improves the EMBRACE system’s ability to better serve ELLs.
ContributorsDexheimer, Matthew Scott (Author) / Walker, Erin (Thesis advisor) / Glenberg, Arthur (Committee member) / VanLehn, Kurt (Committee member) / Arizona State University (Publisher)
Created2017