Improving Accessibility of Educational Content - An Exploratory Data Analysis

A recent increase in settlements resulting from violations of the Americans with Disabilities Act (ADA) has resulted in institutions developing processes for improving course material accessibility. We analyze data from about 1670 sections of courses offered at a US school of business, spanning over 9 semesters that include numerical accessibility scores for various components of the course material. We combine this data with student performance and faculty evaluation data from the same period. In our analysis we observed improvement in overall accessibility scores, yet noticed statistically significant reduction in student performance as well as instructor evaluations. We document that one possible explanation for this result can be linked to the drastic reduction of course materials. We conclude that instead of relying only on a measure of accessibility, faculty should be involved in a multi-faceted process that includes communication and training to identify and improve issues with accessibility in course content

Could Government Measures Crowd Out Grassroots Philanthropy? Empirical Evidence from an Education Crowdfunding Platform

Over the last two decades, grassroots altruism, enabled through platforms such as DonorsChoose.org, has resulted in successful funding of innumerable and essential public school projects across the country. While such channels become critical fundraising mechanisms, there is an unintended possibility of crowding out of these sources by governmental initiatives which aim to shed light on, and address public school resource deficits. In this study, with a focus on major public policy announcements, we examine whether there is an unintended effect of external measures, such as the signing of the Every Student Succeeds Act (ESSA), on grassroots altruism, which is possible to examine on online philanthropy platforms. We surmise that, in such platforms, donors could become complacent and take comfort in the cognizance of an external agency addressing the problems they care about -- we call this the “savior effect”. Importantly, from our analysis of panel data on the platform, we find that the savior effect: (a) results in declined donations toward under-served public school projects on the platform, and (b) makes donations more local, disproportionately impacting schools with high concentrations of low-income and minority students, which receive fewer instructional resources to begin with. Our work has important policy implications for public schools, donor communities, and online fundraising platforms

Infer and Adapt: Bipedal Locomotion Reward Learning from Demonstrations via Inverse Reinforcement Learning

Enabling bipedal walking robots to learn how to maneuver over highly uneven, dynamically changing terrains is challenging due to the complexity of robot dynamics and interacted environments. Recent advancements in learning from demonstrations have shown promising results for robot learning in complex environments. While imitation learning of expert policies has been well-explored, the study of learning expert reward functions is largely under-explored in legged locomotion. This paper brings state-of-the-art Inverse Reinforcement Learning (IRL) techniques to solving bipedal locomotion problems over complex terrains. We propose algorithms for learning expert reward functions, and we subsequently analyze the learned functions. Through nonlinear function approximation, we uncover meaningful insights into the expert's locomotion strategies. Furthermore, we empirically demonstrate that training a bipedal locomotion policy with the inferred reward functions enhances its walking performance on unseen terrains, highlighting the adaptability offered by reward learning

JGAT: a joint spatio-temporal graph attention model for brain decoding

The decoding of brain neural networks has been an intriguing topic in neuroscience for a well-rounded understanding of different types of brain disorders and cognitive stimuli. Integrating different types of connectivity, e.g., Functional Connectivity (FC) and Structural Connectivity (SC), from multi-modal imaging techniques can take their complementary information into account and therefore have the potential to get better decoding capability. However, traditional approaches for integrating FC and SC overlook the dynamical variations, which stand a great chance to over-generalize the brain neural network. In this paper, we propose a Joint kernel Graph Attention Network (JGAT), which is a new multi-modal temporal graph attention network framework. It integrates the data from functional Magnetic Resonance Images (fMRI) and Diffusion Weighted Imaging (DWI) while preserving the dynamic information at the same time. We conduct brain-decoding tasks with our JGAT on four independent datasets: three of 7T fMRI datasets from the Human Connectome Project (HCP) and one from animal neural recordings. Furthermore, with Attention Scores (AS) and Frame Scores (FS) computed and learned from the model, we can locate several informative temporal segments and build meaningful dynamical pathways along the temporal domain for the HCP datasets. The URL to the code of JGAT model: https://github.com/BRAINML-GT/JGAT

Disaster Management Through Digital Platforms: Online Crowdfunding Communities Respond to the COVID-19 Pandemic

We study how digital crowdfunding platforms can help replenish the sudden economic deficiencies that accompany a global crisis. Specifically, we examine whether public schools, which suffered severe setbacks during the COVID-19 crisis, were able to generate support from online fundraising communities. We study how the shutdown of schools and the shift to online learning in the United States affected private fundraising on the DonorsChoose.org platform. We find evidence that, after the exogenous shock caused by stay-at-home orders, donations to schools increased and the increased level of concern moves toward high-need schools. Moreover, we find a shift in donation patterns, wherein donors swiftly adapted to renewed priorities and redistributed their resources to immediate needs around digital learning infrastructure. Our findings reveal the pivotal role digital platforms can play in facilitating community resilience during times of crisis