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- Member of: ASU Electronic Theses and Dissertations
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ABSTRACT
To remain competitive on local, state, and national levels and to achieve future economic and social goals, Imperial and Yuma County need an educated workforce. The primary industries supporting the desert region are technical, science, technology, enginnering and mathematics (STEM)-based, and require a highly skilled and educated workforce. There continue to be vast disparities in terms of numbers of students declared and enrolled in STEM transfer degree programs and the number of students completing STEM bachelor’s degrees.
Perceptions regarding post-secondary education start to develop at a young age and can prevent or enable a student’s development of post-secondary aspirations. Understanding a student’s perceptions of barriers are important because they can prevent students from completing a four-year degree. The pilot research provided in the study are the first steps in helping educators and community leaders understand what drives and form student perceived educational barriers and student perceptions of self, and then provide a better understanding of first-generation Hispanic students’ value of higher education.
As part of the study, I designed the science, technology, engineering, agriculture and mathematics (“STEAM”) College Success Program to help college students overcome the perceived barriers intervening with the completion of a bachelor’s degree. The program involved community, industry, and college students in a unique experience of incorporating a one-week camp, academic year of mentorship, STEM education, and college support. Pilot results of the “STEAM” College Success Program indicate the innovation was effective in reducing perceived barriers relating to college success and bachelor’s degree completion.and was most effective in the area of self-efficacy and personal achievement.
To remain competitive on local, state, and national levels and to achieve future economic and social goals, Imperial and Yuma County need an educated workforce. The primary industries supporting the desert region are technical, science, technology, enginnering and mathematics (STEM)-based, and require a highly skilled and educated workforce. There continue to be vast disparities in terms of numbers of students declared and enrolled in STEM transfer degree programs and the number of students completing STEM bachelor’s degrees.
Perceptions regarding post-secondary education start to develop at a young age and can prevent or enable a student’s development of post-secondary aspirations. Understanding a student’s perceptions of barriers are important because they can prevent students from completing a four-year degree. The pilot research provided in the study are the first steps in helping educators and community leaders understand what drives and form student perceived educational barriers and student perceptions of self, and then provide a better understanding of first-generation Hispanic students’ value of higher education.
As part of the study, I designed the science, technology, engineering, agriculture and mathematics (“STEAM”) College Success Program to help college students overcome the perceived barriers intervening with the completion of a bachelor’s degree. The program involved community, industry, and college students in a unique experience of incorporating a one-week camp, academic year of mentorship, STEM education, and college support. Pilot results of the “STEAM” College Success Program indicate the innovation was effective in reducing perceived barriers relating to college success and bachelor’s degree completion.and was most effective in the area of self-efficacy and personal achievement.
ContributorsHodges, Tanya Marie (Author) / Bernstein, Katie (Thesis advisor) / Dyer, Penny (Committee member) / Montopoli, George (Committee member) / Schaal, Mary (Committee member) / Arizona State University (Publisher)
Created2019
Description
Significance of real-world knowledge for Natural Language Understanding(NLU) is well-known for decades. With advancements in technology, challenging tasks like question-answering, text-summarizing, and machine translation are made possible with continuous efforts in the field of Natural Language Processing(NLP). Yet, knowledge integration to answer common sense questions is still a daunting task. Logical reasoning has been a resort for many of the problems in NLP and has achieved considerable results in the field, but it is difficult to resolve the ambiguities in a natural language. Co-reference resolution is one of the problems where ambiguity arises due to the semantics of the sentence. Another such problem is the cause and result statements which require causal commonsense reasoning to resolve the ambiguity. Modeling these type of problems is not a simple task with rules or logic. State-of-the-art systems addressing these problems use a trained neural network model, which claims to have overall knowledge from a huge trained corpus. These systems answer the questions by using the knowledge embedded in their trained language model. Although the language models embed the knowledge from the data, they use occurrences of words and frequency of co-existing words to solve the prevailing ambiguity. This limits the performance of language models to solve the problems in common-sense reasoning task as it generalizes the concept rather than trying to answer the problem specific to its context. For example, "The painting in Mark's living room shows an oak tree. It is to the right of a house", is a co-reference resolution problem which requires knowledge. Language models can resolve whether "it" refers to "painting" or "tree", since "house" and "tree" are two common co-occurring words so the models can resolve "tree" to be the co-reference. On the other hand, "The large ball crashed right through the table. Because it was made of Styrofoam ." to resolve for "it" which can be either "table" or "ball", is difficult for a language model as it requires more information about the problem.
In this work, I have built an end-to-end framework, which uses the automatically extracted knowledge based on the problem. This knowledge is augmented with the language models using an explicit reasoning module to resolve the ambiguity. This system is built to improve the accuracy of the language models based approaches for commonsense reasoning. This system has proved to achieve the state of the art accuracy on the Winograd Schema Challenge.
In this work, I have built an end-to-end framework, which uses the automatically extracted knowledge based on the problem. This knowledge is augmented with the language models using an explicit reasoning module to resolve the ambiguity. This system is built to improve the accuracy of the language models based approaches for commonsense reasoning. This system has proved to achieve the state of the art accuracy on the Winograd Schema Challenge.
ContributorsPrakash, Ashok (Author) / Baral, Chitta (Thesis advisor) / Devarakonda, Murthy (Committee member) / Anwar, Saadat (Committee member) / Arizona State University (Publisher)
Created2019
Description
Live streaming has risen to significant popularity in the recent past and largely this live streaming is a feature of existing social networks like Facebook, Instagram, and Snapchat. However, there does exist at least one social network entirely devoted to live streaming, and specifically the live streaming of video games, Twitch. This social network is unique for a number of reasons, not least because of its hyper-focus on live content and this uniqueness has challenges for social media researchers.
Despite this uniqueness, almost no scientific work has been performed on this public social network. Thus, it is unclear what user interaction features present on other social networks exist on Twitch. Investigating the interactions between users and identifying which, if any, of the common user behaviors on social network exist on Twitch is an important step in understanding how Twitch fits in to the social media ecosystem. For example, there are users that have large followings on Twitch and amass a large number of viewers, but do those users exert influence over the behavior of other user the way that popular users on Twitter do?
This task, however, will not be trivial. The same hyper-focus on live content that makes Twitch unique in the social network space invalidates many of the traditional approaches to social network analysis. Thus, new algorithms and techniques must be developed in order to tap this data source. In this thesis, a novel algorithm for finding games whose releases have made a significant impact on the network is described as well as a novel algorithm for detecting and identifying influential players of games. In addition, the Twitch network is described in detail along with the data that was collected in order to power the two previously described algorithms.
Despite this uniqueness, almost no scientific work has been performed on this public social network. Thus, it is unclear what user interaction features present on other social networks exist on Twitch. Investigating the interactions between users and identifying which, if any, of the common user behaviors on social network exist on Twitch is an important step in understanding how Twitch fits in to the social media ecosystem. For example, there are users that have large followings on Twitch and amass a large number of viewers, but do those users exert influence over the behavior of other user the way that popular users on Twitter do?
This task, however, will not be trivial. The same hyper-focus on live content that makes Twitch unique in the social network space invalidates many of the traditional approaches to social network analysis. Thus, new algorithms and techniques must be developed in order to tap this data source. In this thesis, a novel algorithm for finding games whose releases have made a significant impact on the network is described as well as a novel algorithm for detecting and identifying influential players of games. In addition, the Twitch network is described in detail along with the data that was collected in order to power the two previously described algorithms.
ContributorsJones, Isaac (Author) / Liu, Huan (Thesis advisor) / Maciejewski, Ross (Committee member) / Shakarian, Paulo (Committee member) / Agarwal, Nitin (Committee member) / Arizona State University (Publisher)
Created2019
Description
The purpose of this mixed-methods action research study was to discover the hindrances and apply new innovative ideas to the problematic stages of student acclimatization and acculturation to an American education and Taiwanese host culture. The goal was to improve academic success during the initial first year, improve the acclimatization process, and stimulate the acculturation process.
The study applied a mixed-methods approach. Four new foreign students participated in a 12-week innovation. This innovation consisted of establishing a protocol for school staff, creating and implementing a student-led Welcoming Committee, training at the beginning of the school year, establishing guidelines and expectations for participating Welcoming Committee members, assigning peer mentors to new students, and providing opportunities for socializing and meeting people. The participants took pre and post cultural self-efficacy tests. In addition, qualitative data was collected from the interviews of the four participants.
The new foreign students showed an increase in cultural self-efficacy from the beginning of the innovation to the conclusion of it. Findings of this study found that students used past experiences in creating initial perceptions, these perceptions changed after interactions with the Welcoming Committee, ample assistance was given to the new foreign students throughout the innovation, and Welcoming Committee members were relied on to make initial contact with others due to initial difficulties in this area.
The study applied a mixed-methods approach. Four new foreign students participated in a 12-week innovation. This innovation consisted of establishing a protocol for school staff, creating and implementing a student-led Welcoming Committee, training at the beginning of the school year, establishing guidelines and expectations for participating Welcoming Committee members, assigning peer mentors to new students, and providing opportunities for socializing and meeting people. The participants took pre and post cultural self-efficacy tests. In addition, qualitative data was collected from the interviews of the four participants.
The new foreign students showed an increase in cultural self-efficacy from the beginning of the innovation to the conclusion of it. Findings of this study found that students used past experiences in creating initial perceptions, these perceptions changed after interactions with the Welcoming Committee, ample assistance was given to the new foreign students throughout the innovation, and Welcoming Committee members were relied on to make initial contact with others due to initial difficulties in this area.
ContributorsAyers, Aaron (Author) / Bernstein, Katie (Thesis advisor) / Koro-Ljungberg, Mirka (Committee member) / McGrath, John (Committee member) / Arizona State University (Publisher)
Created2019
Description
Over the last decade, deep neural networks also known as deep learning, combined with large databases and specialized hardware for computation, have made major strides in important areas such as computer vision, computational imaging and natural language processing. However, such frameworks currently suffer from some drawbacks. For example, it is generally not clear how the architectures are to be designed for different applications, or how the neural networks behave under different input perturbations and it is not easy to make the internal representations and parameters more interpretable. In this dissertation, I propose building constraints into feature maps, parameters and and design of algorithms involving neural networks for applications in low-level vision problems such as compressive imaging and multi-spectral image fusion, and high-level inference problems including activity and face recognition. Depending on the application, such constraints can be used to design architectures which are invariant/robust to certain nuisance factors, more efficient and, in some cases, more interpretable. Through extensive experiments on real-world datasets, I demonstrate these advantages of the proposed methods over conventional frameworks.
ContributorsLohit, Suhas Anand (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Li, Baoxin (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2019
Description
Longitudinal recursive partitioning (LRP) is a tree-based method for longitudinal data. It takes a sample of individuals that were each measured repeatedly across time, and it splits them based on a set of covariates such that individuals with similar trajectories become grouped together into nodes. LRP does this by fitting a mixed-effects model to each node every time that it becomes partitioned and extracting the deviance, which is the measure of node purity. LRP is implemented using the classification and regression tree algorithm, which suffers from a variable selection bias and does not guarantee reaching a global optimum. Additionally, fitting mixed-effects models to each potential split only to extract the deviance and discard the rest of the information is a computationally intensive procedure. Therefore, in this dissertation, I address the high computational demand, variable selection bias, and local optimum solution. I propose three approximation methods that reduce the computational demand of LRP, and at the same time, allow for a straightforward extension to recursive partitioning algorithms that do not have a variable selection bias and can reach the global optimum solution. In the three proposed approximations, a mixed-effects model is fit to the full data, and the growth curve coefficients for each individual are extracted. Then, (1) a principal component analysis is fit to the set of coefficients and the principal component score is extracted for each individual, (2) a one-factor model is fit to the coefficients and the factor score is extracted, or (3) the coefficients are summed. The three methods result in each individual having a single score that represents the growth curve trajectory. Therefore, now that the outcome is a single score for each individual, any tree-based method may be used for partitioning the data and group the individuals together. Once the individuals are assigned to their final nodes, a mixed-effects model is fit to each terminal node with the individuals belonging to it.
I conduct a simulation study, where I show that the approximation methods achieve the goals proposed while maintaining a similar level of out-of-sample prediction accuracy as LRP. I then illustrate and compare the methods using an applied data.
I conduct a simulation study, where I show that the approximation methods achieve the goals proposed while maintaining a similar level of out-of-sample prediction accuracy as LRP. I then illustrate and compare the methods using an applied data.
ContributorsStegmann, Gabriela (Author) / Grimm, Kevin (Thesis advisor) / Edwards, Michael (Committee member) / MacKinnon, David (Committee member) / McNeish, Daniel (Committee member) / Arizona State University (Publisher)
Created2019
Description
Disentangling latent spaces is an important research direction in the interpretability of unsupervised machine learning. Several recent works using deep learning are very effective at producing disentangled representations. However, in the unsupervised setting, there is no way to pre-specify which part of the latent space captures specific factors of variations. While this is generally a hard problem because of the non-existence of analytical expressions to capture these variations, there are certain factors like geometric
transforms that can be expressed analytically. Furthermore, in existing frameworks, the disentangled values are also not interpretable. The focus of this work is to disentangle these geometric factors of variations (which turn out to be nuisance factors for many applications) from the semantic content of the signal in an interpretable manner which in turn makes the features more discriminative. Experiments are designed to show the modularity of the approach with other disentangling strategies as well as on multiple one-dimensional (1D) and two-dimensional (2D) datasets, clearly indicating the efficacy of the proposed approach.
transforms that can be expressed analytically. Furthermore, in existing frameworks, the disentangled values are also not interpretable. The focus of this work is to disentangle these geometric factors of variations (which turn out to be nuisance factors for many applications) from the semantic content of the signal in an interpretable manner which in turn makes the features more discriminative. Experiments are designed to show the modularity of the approach with other disentangling strategies as well as on multiple one-dimensional (1D) and two-dimensional (2D) datasets, clearly indicating the efficacy of the proposed approach.
ContributorsKoneripalli Seetharam, Kaushik (Author) / Turaga, Pavan (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2019
Description
The problem of practice addressed in this mixed methods action research study is the underachievement of fifth-grade students in mathematics. This study explores the effects of an innovation designed to help students develop a growth mindset by utilizing self-regulation strategies to improve academic growth in mathematics. Students’ underachievement in mathematics has been illustrated by both state and international assessments. Throughout the decades, mathematics instruction and reforms have varied, but overall students’ psychological needs have been neglected. This innovation was designed to develop students’ psychological characteristics regarding facing challenges in mathematics. For this purpose, two guiding theories were utilized to frame this research study, Dweck’s mindset theory and self-regulation theory. To address the research questions of this study, pre- and post-questionnaire data, observational data and student work was analyzed. Results of the qualitative data indicated that the innovation positively impacted students’ mindsets and use of self-regulation strategies. However, quantitative data indicated the innovation had no effect on students’ use of self-regulation strategies or academic growth, and a negative impact on students’ mindsets.
ContributorsManchester, Sarah (Author) / Judson, Eugene (Thesis advisor) / Moses, Lindsey (Committee member) / Ellis, Raquel (Committee member) / Arizona State University (Publisher)
Created2020
Description
Humans perceive the environment using multiple modalities like vision, speech (language), touch, taste, and smell. The knowledge obtained from one modality usually complements the other. Learning through several modalities helps in constructing an accurate model of the environment. Most of the current vision and language models are modality-specific and, in many cases, extensively use deep-learning based attention mechanisms for learning powerful representations. This work discusses the role of attention in associating vision and language for generating shared representation. Language Image Transformer (LIT) is proposed for learning multi-modal representations of the environment. It uses a training objective based on Contrastive Predictive Coding (CPC) to maximize the Mutual Information (MI) between the visual and linguistic representations. It learns the relationship between the modalities using the proposed cross-modal attention layers. It is trained and evaluated using captioning datasets, MS COCO, and Conceptual Captions. The results and the analysis offers a perspective on the use of Mutual Information Maximisation (MIM) for generating generalizable representations across multiple modalities.
ContributorsRamakrishnan, Raghavendran (Author) / Panchanathan, Sethuraman (Thesis advisor) / Venkateswara, Hemanth Kumar (Thesis advisor) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2020
Description
This study explores the impact of a professional development (PD) activity conducted for teachers of the Next Generation Science Standards (NGSS) at 15 American-curriculum international schools. The intervention involved teachers utilizing the 3D-PAST screening tool to systematically evaluate the alignment of teacher-designed assessments with the constructs of the NGSS and best practices in science instruction. Data about the way the intervention enhanced or challenged teachers’ understanding of the NGSS were collected via a multiple methods approach. The New Framework of Science Education Survey of Teacher Understanding (NFSE-STU) was used in a retrospective pretest-posttest fashion to assess changes in teachers’ understanding of NGSS constructs. Subsequently, interviews were conducted with participants which provided data that expanded upon the NFSE-STU findings. The Refined Consensus Model of Pedagogical Content Knowledge (RCM-PCK) was used to interpret the findings and situate the study within the extant literature on teacher PCK. The intervention was found to have a statistically significant effect on teachers’ understanding of the NGSS in all areas measured by the NFSE-STU. Additionally, data suggest that the intervention elicited changes in teachers’ classroom practices and improved collaborative professional practices. Also highlighted in the analysis was the significance of the relationship between the intervention moderator and the participants as a strong predictor of the way the intervention was perceived by teachers. The findings strongly support the suggestion that international school administrators seeking to maximize the impact of science teacher professional development should consider PD activities that train teachers in the use of aids to align NGSS assessments, because doing so simultaneously enhances teacher understanding of the NGSS while encouraging meaningful changes to professional practice. The study contributes to the nascent body of literature utilizing the RCM-PCK to situate understanding of science-teacher PCK, and fills a void in literature examining PD in American curriculum international schools, and highlights issues with potential to serve as foci for additional cycles of action research in the areas of international schools, science teacher and NGSS-related professional development, and the use of tools similar to 3D-PAST within other teaching disciplines.
ContributorsWilcox, Wyatt (Author) / Fischman, Gustavo (Thesis advisor) / Graves Wolf, Leigh (Committee member) / Droese, Shirley (Committee member) / Arizona State University (Publisher)
Created2020