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- Genre: Doctoral Dissertation
- Creators: Arizona State University
- Creators: Brewer, Gene A
- Resource Type: Text
Description
An intervention study was conducted with elementary physical education teachers and their use of a newly developed series of fitness segments called Knowledge in Action (KIA). This study was designed to enable teachers to teach healthy behavior knowledge (HBK) in their classes without sacrificing physical activity levels. This study has two phases. First, the intervention was conducted to determine the effectiveness of the KIA fitness segment intervention. Second, teachers' perceptions of both teaching HBK and the KIA fitness segments were investigated. Ten teacher participants were randomly assigned to the intervention or control group. Intervention teachers participated in professional development, provided with all teaching materials, and YouTube videos that modeled the teaching of the KIA fitness segments. Teacher fidelity was measured through observations. Student physical activity patterns were measured in randomly selected teachers' classes (both intervention and control) to determine potential physical activity pattern differences between groups. Teachers were interviewed from one to three times across the project in order to determine perceptions of teaching HBK and the KIA fitness segments. Researchers used constant comparison method to uncover possible common themes. Student knowledge was assessed pre/post using PE Metrics Standard 3 cognitive test to determine HBK changes. Data analysis included General liner models (GLM) at the student level (gender) and Hierarchical linear models (HLM) at the school level (treatment, school). There was a moderate mean teacher fidelity score (77.9%) found among the intervention teachers. HLM results showed students in the intervention group had a 3.4(20%) greater improvement in HBK scores when compared with their control counterparts (p<0.001). Student activity levels were found to be similar in both groups with 871.33 and 822.22 steps in the intervention and control groups, respectively. Although all of the teachers thought it was important to teach HBK they were not spending time on it during classes at pretest. Three common themes were discovered: (a) Effective Teacher Training of the Segments, (b), Teachers Learned a Novel Strategy, and (c) Teachers Recommended Modifications. In summary, the KIA fitness segments received favorable views and gave teachers a way to teach HBK without reducing physical activity time.
ContributorsHodges, Michael (Author) / Kulinna, Pamela (Thesis advisor) / Van Der Mars, Hans (Committee member) / Lee, Chong (Committee member) / Arizona State University (Publisher)
Created2013
Description
Affirmative action is an education policy adopted by higher education institutions in the 1960s, where an applicant’s race is taken into account to some degree when being evaluated for admission to a college or university. The practice of affirmative action, or race conscious-admissions, has been repeatedly challenged in the legal system and remains a controversial and polarizing topic amongst the general public, campus leaders, and policy makers. Despite a vast amount of research on the effects of affirmative action policies on student and institutional behaviors and outcomes, such as college applications and enrollments, considerably less research has examined students’ attitudes towards race-conscious admissions policies. Even less research has focused on students in academic disciplines, especially STEM or engineering. Likewise, there is a paucity of research that explores students’ perceptions and knowledge of how affirmative action is implemented in practice. To address these gaps, this study investigates undergraduate engineering students’ knowledge of and attitudes towards affirmative action admissions policies in higher education. The Student Attitudes Towards Admissions Policies Survey (SATAPS) was designed to assess students’ knowledge of and attitudes regarding affirmative action practices in higher education admissions. This survey was administered to undergraduate engineering students and a comparison group of education students at 42 colleges/universities in the United States. Data were analyzed utilizing confirmatory factor analysis and hierarchical regression. Results demonstrated that students have low levels of knowledge about affirmative action, and have misconceptions about how the policy functions in practice. There was no difference in engineering and education students’ level of support for affirmative action; however, underrepresented minority students in engineering were more supportive of affirmative action. Results also indicated that students’ beliefs and values were the strongest predictors of attitude towards affirmative action, so much so that this negated the significance of demographic and personal characteristics, which was observed in the majority of previous studies. Results highlight a complicated relationship between demographic characteristics, personal variables, knowledge, institutional context, beliefs/values, and attitude towards affirmative action admissions policies in higher education.
ContributorsRoss, Lydia (Author) / Judson, Eugene (Thesis advisor) / Dorn, Sherman (Committee member) / Powers, Jeanne M. (Committee member) / Arizona State University (Publisher)
Created2019
Description
The principle purpose of this research was to compare two definitions and assessments of Mathematics Pedagogical Content Knowledge (PCK) and examine the development of that knowledge among pre-service and current math teachers. Seventy-eight current and future teachers took an online version of the Measures of Knowledge for Teaching (MKT) - Mathematics assessment and nine of them took the Cognitively Activating Instruction in Mathematics (COACTIV) assessment. Participants answered questions that demonstrated their understanding of students' challenges and misconceptions, ability to recognize and utilize multiple representations and methods of presenting content, and understanding of tasks and materials that they may be using for instruction. Additionally, participants indicated their college major, institution attended, years of experience, and participation in various other learning opportunities. This data was analyzed to look for changes in knowledge, first among those still in college, then among those already in the field, and finally as a whole group to look for a pattern of growth from pre-service through working in the classroom. I compared these results to the theories of learning espoused by the creators of these two tests to see which model the data supports. The results indicate that growth in PCK occurs among college students during their teacher preparation program, with much less change once a teacher enters the field. Growth was not linear, but best modeled by an s-curve, showing slow initial changes, substantial development during the 2nd and 3rd year of college, and then a leveling off during the last year of college and the first few years working in a classroom. Among current teachers' the only group that demonstrated any measurable growth were teachers who majored in a non-education field. Other factors like internships and professional development did not show a meaningful correlation with PCK. Even though some of these models were statistically significant, they did not account for a substantial amount of the variation among individuals, indicating that personal factors and not programmatic ones may be the primary determinant of a teachers' knowledge.
ContributorsJohnson, Jeffrey (Author) / Middleton, James A. (Thesis advisor) / Marsh, Josephine P (Committee member) / Sloane, Finbarr (Committee member) / Arizona State University (Publisher)
Created2016
Description
How is knowledge created at the intersections between basic science, biotechnology, and industry? Gene drives are an interesting example, as they combine a long-standing interest with a recent technological breakthrough and a new set of commercial applications. Gene drives are genes engineered such that they are preferentially inherited at a frequency greater than the typical Mendelian fifty percent ratio. During the historical and conceptual evolution of gene drives beginning in the 1960s, there have been many innovations and publications. Along with that, gene drive science developed considerable public attention, explosion of new scientists, and variation in the way the topic is discussed. It is now time to look at this new organization of science using a systematic approach to characterize the system that has enabled knowledge to grow in this scientific field. This project breaks new ground in how knowledge advances in genetic engineering science, and how scientists understand what a “gene drive” is through analysis of language, communities, and other social factors. In effect, this research will advance multiple fields and enable a deeper understanding of knowledge and complexity. This project documents patterns of publication, collaborative relationships, linguistic variation, innovation, and knowledge expansion. The results of computational analysis provide an in-depth and complete characterization of the structure, dynamics, and evolution of scientific knowledge found in the gene drive technology. Further, time series analysis of the multiple layers of discourse enabled a diachronic connective mapping of collaborative relationships and tracked linguistic variation and change, highlighting where ambiguous language may appear, improving and creating more cohesive scientific language.
Overall, depicting the structure, dynamics, and evolution of scientific knowledge during a novel eruption of scientific complexity can shed light on the factors that can lead to: (1) improved scientific communication, (2) reduction of scientific progress, (3) new knowledge, and (4) novel collaborative relationships. Therefore, characterizing the current technological, methodological, and social contexts that can influence scientific knowledge.
ContributorsOToole, Cody Lane (Author) / Laubichler, Manfred (Thesis advisor) / Collins, James P (Committee member) / Simeone, Michael (Committee member) / Evans, James (Committee member) / Arizona State University (Publisher)
Created2021
Description
In natural language processing, language models have achieved remarkable success over the last few years. The Transformers are at the core of most of these models. Their success can be mainly attributed to an enormous amount of curated data they are trained on. Even though such language models are trained on massive curated data, they often need specific extracted knowledge to understand better and reason. This is because often relevant knowledge may be implicit or missing, which hampers machine reasoning. Apart from that, manual knowledge curation is time-consuming and erroneous. Hence, finding fast and effective methods to extract such knowledge from data is important for improving language models. This leads to finding ideal ways to utilize such knowledge by incorporating them into language models. Successful knowledge extraction and integration lead to an important question of knowledge evaluation of such models by developing tools or introducing challenging test suites to learn about their limitations and improve them further. So to improve the transformer-based models, understanding the role of knowledge becomes important. In the pursuit to improve language models with knowledge, in this dissertation I study three broad research directions spanning across the natural language, biomedical and cybersecurity domains: (1) Knowledge Extraction (KX) - How can transformer-based language models be leveraged to extract knowledge from data? (2) Knowledge Integration (KI) - How can such specific knowledge be used to improve such models? (3) Knowledge Evaluation (KE) - How can language models be evaluated for specific skills and understand their limitations?
I propose methods to extract explicit textual, implicit structural, missing textual, and missing structural knowledge from natural language and binary programs using transformer-based language models. I develop ways to improve the language model’s multi-step and commonsense reasoning abilities using external knowledge. Finally, I develop challenging datasets which assess their numerical reasoning skills in both in-domain and out-of-domain settings.
ContributorsPal, Kuntal Kumar (Author) / Baral, Chitta (Thesis advisor) / Wang, Ruoyu (Committee member) / Blanco, Eduardo (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2023
Description
Physical activity (PA) has been shown to increase cognitive function, with higher test scores being reported for students engaged in higher levels PA. Additionally, the integration of the Common Core content into physical education allows for more Common Core content practice while students meet physical education objectives. Integration can be defined as the teaching of two or more subject areas simultaneously to enhance students’ learning and understanding. This novel shift to integration is underpinned by Fullan’s Change Theory where students may learn content in new and meaningful ways that meet the goals of multiple realms in education. The purpose of this crossover, replication design study was to investigate first-grade students’ enjoyment levels (enjoyment exit slips), attitudes (pre- & post-surveys), step counts (accelerometers), reading and listening comprehension (Accelerated Reader testing), as well as students’ and teachers’ perceptions (interviews & field notes) when integrating children’s literature into the fitness segment of physical education. Twenty-one first-grade students, two first-grade classroom teachers, and two physical education teachers from two different schools (Private and Public) in Southwestern, US participated in this study for six weeks each (12 weeks across the two schools). At each school, one first grade class participated as both the control and intervention groups. Overall, the results from integrating children’s literature into the physical education fitness segment were positive. Students’ enjoyment levels were high, their attitudes remained positive, they maintained similar step counts throughout the intervention periods, and the students scored similarly on the Accelerated Reader assessments from content taught in the classroom versus content presented in physical education. Additionally, students’ and teachers’ perceptions were positive, underpinned by Fullan’s Change Theory and resulted in the following three themes for students: (a) Motivation and engagement, (b) learning as perceived by students, and (c) home environment, as well as the following two themes for teachers: (a) Motivation and resources, and (b) stay the course. To my knowledge, this is the first experimental investigation of the integration of children’s literature into physical education which provides necessary evidence and an invaluable start to this important line of inquiry.
ContributorsGriffo, Janelle Marie (Author) / Kulinna, Pamela H. (Thesis advisor) / Van Der Mars, Hans (Committee member) / Marttinen, Risto H.J. (Committee member) / Johnston, Kelly (Committee member) / Moses, Lindsey (Committee member) / Arizona State University (Publisher)
Created2021
Description
Individuals encounter problems daily wherein varying numbers of constraints require delimitation of memory to target goal-satisfying information. Multiply-constrained problems, such as compound remote associates, are commonly used to study this type of problem solving. Since their development, multiply-constrained problems have been theoretically and empirically related to creative thinking, analytical problem solving, insight problem solving, intelligence, and a multitude of other cognitive abilities. Critically, in order to correctly solve a multiply-constrained problem the solver must have the solution available in memory and be able to target and access to that information. Experiment 1 determined that the cue – target relationship affects the likelihood that a problem is solved. Moreover, Experiment 2 identified that the association between cues and targets predicted inter- & intra-individual differences in multiply-constrained problem solving. Lastly, Experiment 3 found monetary incentives failed to improve problem solving performance likely due to knowledge serving as a limiting factor on performance. Additionally, problem solvers were shown to be able to reliably assess the likelihood they would solve a problem. Taken together all three studies demonstrated the importance of knowledge & knowledge structures on problem solving performance.
ContributorsEllis, Derek (Author) / Brewer, Gene A (Thesis advisor) / Homa, Donald (Committee member) / Blais, Chris (Committee member) / Goldinger, Stephen (Committee member) / Arizona State University (Publisher)
Created2021