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Paper assessment remains to be an essential formal assessment method in today's classes. However, it is difficult to track student learning behavior on physical papers. This thesis presents a new educational technology—Web Programming Grading Assistant (WPGA). WPGA not only serves as a grading system but also a feedback delivery tool

Paper assessment remains to be an essential formal assessment method in today's classes. However, it is difficult to track student learning behavior on physical papers. This thesis presents a new educational technology—Web Programming Grading Assistant (WPGA). WPGA not only serves as a grading system but also a feedback delivery tool that connects paper-based assessments to digital space. I designed a classroom study and collected data from ASU computer science classes. I tracked and modeled students' reviewing and reflecting behaviors based on the use of WPGA. I analyzed students' reviewing efforts, in terms of frequency, timing, and the associations with their academic performances. Results showed that students put extra emphasis in reviewing prior to the exams and the efforts demonstrated the desire to review formal assessments regardless of if they were graded for academic performance or for attendance. In addition, all students paid more attention on reviewing quizzes and exams toward the end of semester.
ContributorsHuang, Po-Kai (Author) / Hsiao, I-Han (Thesis advisor) / Nelson, Brian (Committee member) / VanLehn, Kurt (Committee member) / Arizona State University (Publisher)
Created2017
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In this study, I used two computational models (state-space model and simple DIVA model) to determine the speech motor system’s sensitivity to auditory errors that are relevant vs. irrelevant and introduced gradually or suddenly. I applied formant perturbations (first and second formants of /ɛ/ were shifted toward formants of /æ/)

In this study, I used two computational models (state-space model and simple DIVA model) to determine the speech motor system’s sensitivity to auditory errors that are relevant vs. irrelevant and introduced gradually or suddenly. I applied formant perturbations (first and second formants of /ɛ/ were shifted toward formants of /æ/) to generate auditory errors. Then I measured subjects’ adaptive responses to the formant perturbations. I examined (a) the accuracy of models in explaining the adaptive responses (b) the relationship between the models’ parameters and the adaptive responses. My results showed that both models predict the adaptive responses to errors. However, the models’ parameters differently correlated with the adaptive responses, suggesting that while the models perform similarly, they provide different insights about adaptive responses to auditory errors. These results have important implications for speech motor learning and production models and shed light on neural processes involved in generating adaptive responses.
ContributorsKasraeian, Kimiya (Author) / Daliri, Ayoub AD (Thesis advisor) / Luo, Xin XL (Committee member) / Rogalsky, Corianne CR (Committee member) / Arizona State University (Publisher)
Created2021