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This study tested the effects of two kinds of cognitive, domain-based preparation tasks on learning outcomes after engaging in a collaborative activity with a partner. The collaborative learning method of interest was termed "preparing-to-interact," and is supported in theory by the Preparation for Future Learning (PFL) paradigm and the Interactive-Constructive-Active-Passive

This study tested the effects of two kinds of cognitive, domain-based preparation tasks on learning outcomes after engaging in a collaborative activity with a partner. The collaborative learning method of interest was termed "preparing-to-interact," and is supported in theory by the Preparation for Future Learning (PFL) paradigm and the Interactive-Constructive-Active-Passive (ICAP) framework. The current work combined these two cognitive-based approaches to design collaborative learning activities that can serve as alternatives to existing methods, which carry limitations and challenges. The "preparing-to-interact" method avoids the need for training students in specific collaboration skills or guiding/scripting their dialogic behaviors, while providing the opportunity for students to acquire the necessary prior knowledge for maximizing their discussions towards learning. The study used a 2x2 experimental design, investigating the factors of Preparation (No Prep and Prep) and Type of Activity (Active and Constructive) on deep and shallow learning. The sample was community college students in introductory psychology classes; the domain tested was "memory," in particular, concepts related to the process of remembering/forgetting information. Results showed that Preparation was a significant factor affecting deep learning, while shallow learning was not affected differently by the interventions. Essentially, equalizing time-on-task and content across all conditions, time spent individually preparing by working on the task alone and then discussing the content with a partner produced deeper learning than engaging in the task jointly for the duration of the learning period. Type of Task was not a significant factor in learning outcomes, however, exploratory analyses showed evidence of Constructive-type behaviors leading to deeper learning of the content. Additionally, a novel method of multilevel analysis (MLA) was used to examine the data to account for the dependency between partners within dyads. This work showed that "preparing-to-interact" is a way to maximize the benefits of collaborative learning. When students are first cognitively prepared, they seem to make the most efficient use of discussion towards learning, engage more deeply in the content during learning, leading to deeper knowledge of the content. Additionally, in using MLA to account for subject nonindependency, this work introduces new questions about the validity of statistical analyses for dyadic data.
ContributorsLam, Rachel Jane (Author) / Nakagawa, Kathryn (Thesis advisor) / Green, Samuel (Committee member) / Stamm, Jill (Committee member) / Arizona State University (Publisher)
Created2013
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Description
The Japanese American internment in Arizona proved to be one of the greatest assaults on the civil liberties of American citizens in the 20th century. Families lived in shabby facilities, had meager food, fought isolation, and strict military control. However, they overcame these challenges and built a strong community relationshi

The Japanese American internment in Arizona proved to be one of the greatest assaults on the civil liberties of American citizens in the 20th century. Families lived in shabby facilities, had meager food, fought isolation, and strict military control. However, they overcame these challenges and built a strong community relationship and courageously sought to prove their loyalty to a government that deemed them untrustworthy. With time, their fortitude and solidarity helped bring an end to World War II and create new lives afterwards.
ContributorsPaasch, Natalie Jean (Author) / Longley, Rodney (Thesis director) / Nakagawa, Kathryn (Committee member) / Rondilla, Joanne (Committee member) / Barrett, The Honors College (Contributor) / School of Historical, Philosophical and Religious Studies (Contributor)
Created2013-05
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Description
Asian Americans have a unique relationship with food. From the moment they landed on American soil, their history and experiences have been tied to food, and not entirely by their own will. Now, the general American population enjoys foods from a multitude of ethnic groups, but in America’s early history,

Asian Americans have a unique relationship with food. From the moment they landed on American soil, their history and experiences have been tied to food, and not entirely by their own will. Now, the general American population enjoys foods from a multitude of ethnic groups, but in America’s early history, these foods were abhorred and used as justifications for legal discrimination, murders, massacres, and banishment. These struggles forced Asian Americans to work in the food industry (the only work they could do without as much backlash), further promoting the association of Asian Americans and food. While working in the food industry in order to find passage into America and to survive, many Asian dishes had to be assimilated to the palette of the general White American population and many dishes were made up and presented as authentically Asian. Some of these dishes have become iconic when thinking of classic American foods—chow mein, orange chicken, and more. For many non-Asian Americans, these popular dishes contribute to the pairing of Asian Americans with food and the food industry. But for Asian Americans, these dishes symbolize their struggles—leaving their homes and families behind, trying to live out the American dream, assimilating and changing their foods in just the right way in order to fit in, be accepted, and to survive. This project, in the form of a cookbook, examines the significance of food in the Chinese American, Japanese American, and Filipino American experiences in America while looking at the histories of those specific foods as well as histories of each group.
ContributorsTo, Lennie (Author) / Yoo, Hyung Chol (Thesis director) / Nakagawa, Kathryn (Committee member) / Department of Psychology (Contributor) / School of Life Sciences (Contributor) / School for the Science of Health Care Delivery (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description

During the global COVID-19 pandemic in 2020, many universities shifted their focus to hosting classes and events online for their student population in order to keep them engaged. The present study investigated whether an association exists between student engagement (an individual’s engagement with class and campus) and resilience. A single-shot

During the global COVID-19 pandemic in 2020, many universities shifted their focus to hosting classes and events online for their student population in order to keep them engaged. The present study investigated whether an association exists between student engagement (an individual’s engagement with class and campus) and resilience. A single-shot survey was administered to 200 participants currently enrolled as undergraduate students at Arizona State University. A multiple regression analysis and Pearson correlations were calculated. A moderate, significant correlation was found between student engagement (total score) and resilience. A significant correlation was found between cognitive engagement (student’s approach and understanding of his learning) and resilience and between valuing and resilience. Contrary to expectations, participation was not associated with resilience. Potential explanations for these results were explored and practical applications for the university were discussed.

ContributorsEmmanuelli, Michelle (Author) / Jimenez Arista, Laura (Thesis director) / Sever, Amy (Committee member) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones

We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.

ContributorsBarolli, Adeiron (Author) / Jimenez Arista, Laura (Thesis director) / Wilson, Jeffrey (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05