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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- All Subjects: Active learning
- Creators: Turaga, Pavan
- Creators: W. P. Carey School of Business
Description
We solve the problem of activity verification in the context of sustainability. Activity verification is the process of proving the user assertions pertaining to a certain activity performed by the user. Our motivation lies in incentivizing the user for engaging in sustainable activities like taking public transport or recycling. Such incentivization schemes require the system to verify the claim made by the user. The system verifies these claims by analyzing the supporting evidence captured by the user while performing the activity. The proliferation of portable smart-phones in the past few years has provided us with a ubiquitous and relatively cheap platform, having multiple sensors like accelerometer, gyroscope, microphone etc. to capture this evidence data in-situ. In this research, we investigate the supervised and semi-supervised learning techniques for activity verification. Both these techniques make use the data set constructed using the evidence submitted by the user. Supervised learning makes use of annotated evidence data to build a function to predict the class labels of the unlabeled data points. The evidence data captured can be either unimodal or multimodal in nature. We use the accelerometer data as evidence for transportation mode verification and image data as evidence for recycling verification. After training the system, we achieve maximum accuracy of 94% when classifying the transport mode and 81% when detecting recycle activity. In the case of recycle verification, we could improve the classification accuracy by asking the user for more evidence. We present some techniques to ask the user for the next best piece of evidence that maximizes the probability of classification. Using these techniques for detecting recycle activity, the accuracy increases to 93%. The major disadvantage of using supervised models is that it requires extensive annotated training data, which expensive to collect. Due to the limited training data, we look at the graph based inductive semi-supervised learning methods to propagate the labels among the unlabeled samples. In the semi-supervised approach, we represent each instance in the data set as a node in the graph. Since it is a complete graph, edges interconnect these nodes, with each edge having some weight representing the similarity between the points. We propagate the labels in this graph, based on the proximity of the data points to the labeled nodes. We estimate the performance of these algorithms by measuring how close the probability distribution of the data after label propagation is to the probability distribution of the ground truth data. Since labeling has a cost associated with it, in this thesis we propose two algorithms that help us in selecting minimum number of labeled points to propagate the labels accurately. Our proposed algorithm achieves a maximum of 73% increase in performance when compared to the baseline algorithm.
ContributorsDesai, Vaishnav (Author) / Sundaram, Hari (Thesis advisor) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
The purpose of this study was to determine whether a peer nursing student who presents a longitudinal case study on warfarin in a pharmacology course classroom influences prelicensure and postbaccalaureate nursing students' knowledge and perceived knowledge about warfarin. The study was a descriptive design that used a convenience sample of baccalaureate nursing students enrolled in two pharmacology courses. All participating students answered warfarin case-study questions and completed a self-demographic questionnaire, a knowledge pretest and posttest, and a self-efficacy questionnaire after the activity, which evaluated students' knowledge and perceived knowledge on 11 warfarin concepts. For all students (N = 89), the number of correct answers improved significantly between pretests and posttests for Items 2-11 (p < .0001; Wilcoxon signed-rank tests), which evaluated students' knowledge on warfarin's site of action, associated laboratory values, use of vitamin K, and food-drug interactions. However, no significant difference was seen in the number of correct answers for warfarin's mechanism of action. Comparing prelicensure and postbaccalaureate groups by Mann-Whitney tests, no significant difference was seen for pretest total scores (median 7.00, n = 55; median 7.50, n = 34; respectively; p = .399). Similarly, no difference was seen for posttest total scores by groups (prelicensure: median = 9.00, n =54; postbaccalaureate: median = 10.00, n = 32; p = .344). Overall, students in both groups agreed that they could identify and explain all 11 warfarin concepts. The Pearson correlation between the total posttest and total self-efficacy scores for the combined group was .338 (p = .003), demonstrating a low but significant correlation between students' posttest total scores and their perceived warfarin knowledge, as evaluated by the self-efficacy questionnaire.
ContributorsLam, Wing Tung (Author) / Vana, Kimberly (Thesis director) / Holcomb, Cynthia (Committee member) / Silva, Graciela (Committee member) / Barrett, The Honors College (Contributor) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor) / W. P. Carey School of Business (Contributor)
Created2014-12