Mason Journals (George Mason Univ.)
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The Walrus and the Emperor: Materials, Miracles, and Memory in the Early Modern Persian Cosmopolis
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Early Signs as Effective Indicators of Long-term Procrastination using Machine Learning
No full textProcrastination is a widespread issue that has a detrimental impact on students’ academic outcomes and mental health. Thus, for successful intervention, it is vital to identify early signs that correlate with a greater risk of procrastination. Most early-detection procrastination research focuses on the analysis of student activity and interaction with course material. However, the analysis of student-specific traits, such as personality and learning approach, in relation to procrastination habits is less studied. We designed an experiment to analyze the relationship between behavioral factors, specifically a student’s personality and time perspective, and long-term procrastination habits using Machine Learning. Personality traits will be reported using the Big 5 Personality Trait test, a survey containing 50 items that measure one’s levels of extraversion, agreeableness, openness, conscientiousness, and neuroticism. Time perspective will be reported using Zimbardo's Time Perspective Inventory, a survey containing 56 items that compute one’s score for each time perspective type – past negative, past positive, present fatalistic, present hedonistic, and future. Both surveys will be distributed to students at the beginning of a course for self-evaluation. Standard correlation and regression analysis will be conducted on this data to determine the relationship between these early signs and procrastination habits based on student submission times. Then, back propagation with perceptrons will be used to build a predictive neural network model based on these early signs to identify at-risk students. An IRB has been submitted to administer this study on a population of college students taking a core undergraduate course in Fall 2024
Analyzing the Connection Between GitHub Activity of Open Source Software Projects and Relevant Funding & Governance Models
No full textOpen Source Software (OSS) enables modifications by contributors beyond the initial authors, exemplified by technologies like NFTs, blockchains, and cryptocurrencies. Publicly available code fosters development and advancement of the software. Despite the growth of OSS projects, the impact of varying funding and governance models on GitHub activity remains unclear. This study investigates the correlation between GitHub development activity and the associated funding and governance models of OSS projects. Utilizing Big Query and SQL scripts, extensive data on user activity from multiple terabytes of blockchain-related GitHub projects were analyzed. By examining project metrics and categorizing different project types, this research aims to identify patterns in development trends and activity. The results will offer insights into the factors that influence OSS project success and the effects of different governance structures.
This abstract is part of a collection in which the overarching large project under Dr. Jiasun Li was subdivided into discrete critical tasks that were carried out by multiple individuals or smaller teams. Abstracts in this collection read similarly given the shared project goals, but represent distinct tasks completed by the abstract authors towards finalizing the described analysis