Matching Items (4)
Filtering by

Clear all filters

151684-Thumbnail Image.png

Maximizing the benefits of collaborative learning in the college classroom

Description
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
149465-Thumbnail Image.png

Implementation of the Teamwork Skills Inventory among adolescents

Description
Individual and group accountability is an important part of productive group work. However, classroom evaluation of teamwork often relies on top-down assessment of group product by the teacher. Other methods include averaging group grades, group discussions, evaluative essays and random selection and application of one member's grade to the entire…

Individual and group accountability is an important part of productive group work. However, classroom evaluation of teamwork often relies on top-down assessment of group product by the teacher. Other methods include averaging group grades, group discussions, evaluative essays and random selection and application of one member's grade to the entire team. In contrast, the Teamwork Skills Inventory (TSI) developed by Strom and Strom provides assessment of individual conduct and contributions as observed by peers. The instrument also affords students with the opportunity to judge their own performance. Team members are responsible for their own behavior and skill development but are not held accountable for the actions taken by others. The TSI provides criteria for productive teamwork skills and behaviors. Students know in advance the criteria by which they and their teammates will judge each other's behavior skills. In turn, students have the opportunity to practice self-evaluation as they apply the same criteria to assess their own conduct. Self-evaluation compared with peer-evaluation provides support for confidence in behavioral strengths and can guide goal setting in areas where skills are weak and need adjustment. The TSI gives teachers an insider's view of group dynamics: the obstacles and benefits groups may encounter. Since team members have the vantage point of close interaction with peers they are more likely to know how individuals affect the thinking of others in a group. This frees teachers from the difficult task of judging group dynamics. TSI results can guide teachers in developing lessons that address the needs of individuals and groups. Data derived from the TSI can help schools provide for the needs of subgroups, such as special education and gifted classes. It can also help schools detect in-service needs for faculty and provide schools with a method of community accountability for use with cooperative learning methods and social skill achievement.
ContributorsBrown, Denise (Author) / Strom, Robert D. (Thesis advisor) / Stamm, Jill (Committee member) / Strom, Paris S. (Committee member) / Arizona State University (Publisher)
Created2010
148300-Thumbnail Image.png

Association between Student Engagement and Resilience in the Context of COVID-19

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
147645-Thumbnail Image.png

Using Logistic Regression to Predict Stock Trends Based on Bag-of-Words Representations of News Article Headlines

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