Double-Loop Learning: An Approach to Critical Thinking

There is a general consensus that critical thinking is an essential part of college. Instructors should therefore be aware of the following: How they define and conceptualize critical thinking, how they are teaching critical thinking to their students, expectations for how students can exhibit critical thinking. Research-based how to strategies would be an example of single-loop learning, one form of critical thinking.https://digitalscholarship.unlv.edu/btp_expo/1056/thumbnail.jp

Fare Thee Well My Old Kentucky

https://digitalcommons.library.umaine.edu/mmb-vp/2865/thumbnail.jp

The Broadway Girl

https://digitalcommons.library.umaine.edu/mmb-vp/4582/thumbnail.jp

The Old New England Homestead On The Hill

https://digitalcommons.library.umaine.edu/mmb-vp/3330/thumbnail.jp

The Old New England Homestead On The Hill

https://digitalcommons.library.umaine.edu/mmb-vp/3331/thumbnail.jp

Phi Delta Kappan - Special Section on Youth Service

This Special Section on Youth Service features: The Sleeping Giant of School Reform; School-Based Community Service: What We Know from Research and Theory; Project Service Leadership: School Service Projects In Washington State; Gadugi: A Model of Service-Learning for Native American Communities; Citizenship, Service, and School Reform in Pennsylvania; Community Service Learning And School Improvement in Springfield, Massachusetts; Community Service and Civic Education; SerVermont: The Little Initiative That Could; and National Service and Education for Citizenship

Counting Containment Partitions

The study of integer partitions has wide applications to mathematics, mathematical physics, and statistical mechanics. We consider the problem of ?nding a generalized ap- proach to counting the partitions of an integer n that contain a partition of a ?xed integer k. We use generating function techniques to count containment partitions and verify exper- imental results using a self-made in program Mathematica. We have found explicit solutions to the problem for general n with k=1, 2, 3, 4, 5, 6. We also discuss open questions and ideas for future work

For His Mother\u27s Sake

https://digitalcommons.library.umaine.edu/mmb-vp/4601/thumbnail.jp

Machine Learning Hub for Tapis

"Machine learning is indispensable for extracting insights from intricate datasets, expediting data analysis, and enabling cross-disciplinary decision-making. However, the complexity of machine learning models can hinder non-technical users, necessitating for user-friendly tools. Within the Cloud and Interactive Computing group (CIC) at the Texas Advanced Computing Center (TACC), we are actively developing a Machine Learning Hub (ML Hub) API for the Tapis Framework. Comprising accessible microservices, each with an independent REST API implemented in Python's Flask and codified with OpenAPI v3 definitions, our research aims to enhance the experiences of developers, scientists, and researchers. The integration of Hugging Face's API into ML Hub provides open-source pre-trained models for state-of-the-art AI capabilities. Currently, ML Hub's Models Overview and Models Download functions offer a gateway for non-technical users to explore and download machine learning models, authenticated using a JSON Web Token (JWT) from the Tapis Authenticator API. Future developments encompass implementing the Inference Client and Training Engine, seamlessly integrating with the Tapis UI in React and Typescript. Key features of ML Hub: 1. Models Overview: A portal showcasing top Hugging Face models with filtering options. 2. Models Download: Users can obtain specific models, with options to either download a binary file of the model or a zip file containing the model's repository, cached in a version-aware manner. 3. Inference Client: Facilitating server initiation for machine learning model inference on TACC's HPC cluster, enabling rapid prototyping. 4. Training Engine: Enabling users to fine-tune models and showcase them on TACC's HPC cluster, removing technical complexities. This research contributes to the broader discourse on democratizing machine learning's potential, by providing user- friendly access to state-of-the-art models and addressing non-technical users' challenges. We hope that this project will foster innovative collaboration and user engagement, paving the way for an inclusive and impactful future in machine learning research."Texas Advanced Computing Center (TACC