Special Libraries, October 1963

Volume 54, Issue 8https://scholarworks.sjsu.edu/sla_sl_1963/1007/thumbnail.jp

Trialing project-based learning in a new EAP ESP course: A collaborative reflective practice of three college English teachers

Currently in many Chinese universities, the traditional College English course is facing the risk of being ‘marginalized’, replaced or even removed, and many hours previously allocated to the course are now being taken by EAP or ESP. At X University in northern China, a curriculum reform as such is taking place, as a result of which a new course has been created called ‘xue ke’ English. Despite the fact that ‘xue ke’ means subject literally, the course designer has made it clear that subject content is not the target, nor is the course the same as EAP or ESP. This curriculum initiative, while possibly having been justified with a rationale of some kind (e.g. to meet with changing social and/or academic needs of students and/or institutions), this is posing a great challenge for, as well as considerable pressure on, a number of College English teachers who have taught this single course for almost their entire teaching career. In such a context, three teachers formed a peer support group in Semester One this year, to work collaboratively co-tackling the challenge, and they chose Project-Based Learning (PBL) for the new course. This presentation will report on the implementation of this project, including the overall designing, operational procedure, and the teachers’ reflections. Based on discussion, pre-agreement was reached on the purpose and manner of collaboration as offering peer support for more effective teaching and learning and fulfilling and pleasant professional development. A WeChat group was set up as the chief platform for messaging, idea-sharing, and resource-exchanging. Physical meetings were supplementary, with sound agenda but flexible time, and venues. Mosoteach cloud class (lan mo yun ban ke) was established as a tool for virtual learning, employed both in and after class. Discussions were held at the beginning of the semester which determined only brief outlines for PBL implementation and allowed space for everyone to autonomously explore in their own way. Constant further discussions followed, which generated a great deal of opportunities for peer learning and lesson plan modifications. A reflective journal, in a greater or lesser detailed manner, was also kept by each teacher to record the journey of the collaboration. At the end of the semester, it was commonly recognized that, although challenges existed, the collaboration was overall a success and they were all willing to continue with it and endeavor to refine it to be a more professional and productive approach

Special Libraries, December 1962

Volume 53, Issue 10https://scholarworks.sjsu.edu/sla_sl_1962/1009/thumbnail.jp

Special Libraries, January 1967

Volume 58, Issue 1https://scholarworks.sjsu.edu/sla_sl_1967/1000/thumbnail.jp

Objective Measurement of Physician Stress in the Emergency Department Using a Wearable Sensor

Physician stress, and resultant consequences such as burnout, have become increasingly recognized pervasive problems, particularly within the specialty of Emergency Medicine. Stress is difficult to measure objectively, and research predominantly relies on self-reported measures. The present study aims to characterize digital biomarkers of stress as detected by a wearable sensor among Emergency Medicine physicians. Physiologic data was continuously collected using a wearable sensor during clinical work in the emergency department, and participants were asked to self-identify episodes of stress. Machine learning algorithms were used to classify self-reported episodes of stress. Comparing baseline sensor data to data in the 20-minute period preceding self-reported stress episodes demonstrated the highest prediction accuracy for stress. With further study, detection of stress via wearable sensors could be used to facilitate evidence-based stress research and just-in-time interventions for emergency physicians and other high-stress professionals

Special Libraries, September 1976

Volume 67, Issue 9https://scholarworks.sjsu.edu/sla_sl_1976/1007/thumbnail.jp

The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?

This book is a reprint of the Special Issue entitled "The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?". Artificial intelligence is extending into the world of both digital radiology and digital pathology, and involves many scholars in the areas of biomedicine, technology, and bioethics. There is a particular need for scholars to focus on both the innovations in this field and the problems hampering integration into a robust and effective process in stable health care models in the health domain. Many professionals involved in these fields of digital health were encouraged to contribute with their experiences. This book contains contributions from various experts across different fields. Aspects of the integration in the health domain have been faced. Particular space was dedicated to overviewing the challenges, opportunities, and problems in both radiology and pathology. Clinal deepens are available in cardiology, the hystopathology of breast cancer, and colonoscopy. Dedicated studies were based on surveys which investigated students and insiders, opinions, attitudes, and self-perception on the integration of artificial intelligence in this field

The potential of AI in health higher education to increase the students’ learning outcomes

The main goal of this article is to understand the potential learning applications based on AI technologies for health higher education students. We employed a Systematic Literature Review, contributing to explore to what extent AI technologies are currently influencing the Health learning processes in higher education and the skills developed during the learning path. The intent is to contribute to a more profound understanding of learning contexts, methodologies, technologies, and pedagogical processes with the application of AI technologies. The literature emphasizes that AI can be used to potentiate the learning process and the learning outcomes, especially in laboratory classes, and such contexts are still largely unstudied. To fulfil this gap, some practical applications based on AI technologies applied to health higher education studies were identified, highlighting AI's innovations and possible opportunities for health higher education.info:eu-repo/semantics/publishedVersio

The Potential of AI in Health Higher Education to Increase the Students’ Learning Outcomes

The main goal of this article is to understand the potential learning applications based on AI technologies for health higher education students. We employed a Systematic Literature Review, contributing to explore to what extent AI technologies are currently influencing the Health learning processes in higher education and the skills developed during the learning path. The intent is to contribute to a more profound understanding of learning contexts, methodologies, technologies, and pedagogical processes with the application of AI technologies. The literature emphasizes that AI can be used to potentiate the learning process and the learning outcomes, especially in laboratory classes, and such contexts are still largely unstudied. To fulfil this gap, some practical applications based on AI technologies applied to health higher education studies were identified, highlighting AI's innovations and possible opportunities for health higher education