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- All Subjects: Bayesian
- Creators: Arcuria, Philip
Description
Many methodological approaches have been utilized to predict student retention and persistence over the years, yet few have utilized a Bayesian framework. It is believed this is due in part to the absence of an established process for guiding educational researchers reared in a frequentist perspective into the realms of Bayesian analysis and educational data mining. The current study aimed to address this by providing a model-building process for developing a Bayesian network (BN) that leveraged educational data mining, Bayesian analysis, and traditional iterative model-building techniques in order to predict whether community college students will stop out at the completion of each of their first six terms. The study utilized exploratory and confirmatory techniques to reduce an initial pool of more than 50 potential predictor variables to a parsimonious final BN with only four predictor variables. The average in-sample classification accuracy rate for the model was 80% (Cohen's κ = 53%). The model was shown to be generalizable across samples with an average out-of-sample classification accuracy rate of 78% (Cohen's κ = 49%). The classification rates for the BN were also found to be superior to the classification rates produced by an analog frequentist discrete-time survival analysis model.
ContributorsArcuria, Philip (Author) / Levy, Roy (Thesis advisor) / Green, Samuel B (Committee member) / Thompson, Marilyn S (Committee member) / Arizona State University (Publisher)
Created2015
Description
Sound localization can be difficult in a reverberant environment. Fortunately listeners can utilize various perceptual compensatory mechanisms to increase the reliability of sound localization when provided with ambiguous physical evidence. For example, the directional information of echoes can be perceptually suppressed by the direct sound to achieve a single, fused auditory event in a process called the precedence effect (Litovsky et al., 1999). Visual cues also influence sound localization through a phenomenon known as the ventriloquist effect. It is classically demonstrated by a puppeteer who speaks without visible lip movements while moving the mouth of a puppet synchronously with his/her speech (Gelder and Bertelson, 2003). If the ventriloquist is successful, sound will be “captured” by vision and be perceived to be originating at the location of the puppet. This thesis investigates the influence of vision on the spatial localization of audio-visual stimuli. Participants seated in a sound-attenuated room indicated their perceived locations of either ISI or level-difference stimuli in free field conditions. Two types of stereophonic phantom sound sources, created by modulating the inter-stimulus time interval (ISI) or level difference between two loudspeakers, were used as auditory stimuli. The results showed that the light cues influenced auditory spatial perception to a greater extent for the ISI stimuli than the level difference stimuli. A binaural signal analysis further revealed that the greater visual bias for the ISI phantom sound sources was correlated with the increasingly ambiguous binaural cues of the ISI signals. This finding suggests that when sound localization cues are unreliable, perceptual decisions become increasingly biased towards vision for finding a sound source. These results support the cue saliency theory underlying cross-modal bias and extend this theory to include stereophonic phantom sound sources.
ContributorsMontagne, Christopher (Author) / Zhou, Yi (Thesis advisor) / Buneo, Christopher A (Thesis advisor) / Yost, William A. (Committee member) / Arizona State University (Publisher)
Created2015