the application of deep learning and planning techniques, with the aim of constructing generalized plans capable of solving multiple problem instances. We construct a Deep Neural Network that, given an abstract problem state, predicts both (i) the best action to be taken from that state and (ii) the generalized “role” of the object being manipulated. The neural network was tested on two classical planning domains: the blocks world domain and the logistic domain. Results indicate that neural networks are capable of making such
predictions with high accuracy, indicating a promising new framework for approaching generalized planning problems.
This study aims to produce efficient and effective group writing workshops for students within the Barrett Honors College at Arizona State University. To balance two opposing theories in writing center pedagogy - the direct instruction theory and the student-led/ collaborative theory - this study also aims to determine whether a balanced combination of these approaches in writing workshops will increase student confidence in their writing abilities. Several writing workshops were held over Zoom utilizing a combination of direct teaching methods and collaborative techniques. Students were then surveyed to determine whether they found the workshops helpful, learned new skills, and/or grew more confident in their abilities. The student responses proved the hypothesis that a combined approach leads to an increase in student confidence.
A reflection on my diverse educational experience as a sports journalism student, key lessons I learned about specific forms of communication and content creation within social media, written reporting and radio/podcasting and the demand for versatility among all modern journalists.
American Sign Language (ASL) is used for Deaf and Hard of Hearing (DHH) individuals to communicate and learn in a classroom setting. In ASL, fingerspelling and gestures are two primary components used for communication. Fingerspelling is commonly used for words that do not have a specifically designated sign or gesture. In technical contexts, such as Computer Science curriculum, there are many technical terms that fall under this category. Most of its jargon does not have standardized ASL gestures; therefore, students, educators, and interpreters alike have been reliant on fingerspelling, which poses challenges for all parties. This study investigates the efficacy of both fingerspelling and gestures with fifteen technical terms that do have standardized gestures. The terms’ fingerspelling and gesture are assessed based on preference, ease of use, ease of learning, and time by research subjects who were selected as DHH individuals familiar with ASL.
The data is collected in a series of video recordings by research subjects as well as a post-participation questionnaire. Each research subject has produced thirty total videos, two videos to fingerspell and gesture each technical term. Afterwards, they completed a post-participation questionnaire in which they indicated their preference and how easy it was to learn and use both fingerspelling and gestures. Additionally, the videos have been analyzed to determine the time difference between fingerspelling and gestures. Analysis reveals that gestures are favored over fingerspelling as they are generally preferred, considered easier to learn and use, and faster. These results underscore the significance for standardized gestures in the Computer Science curriculum for accessible learning that enhances communication and promotes inclusion.