Innovating Pedagogy 2015: Open University Innovation Report 4

This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This fourth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Center for Technology in Learning at SRI International. We proposed a long list of new educational terms, theories, and practices. We then pared these down to ten that have the potential to provoke major shifts in educational practice, particularly in post-school education. Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education. These are summarised below in an approximate order of immediacy and timescale to widespread implementation

NATIONAL "E-EXTENSION" PROGRAMS: FEASIBILITY AND STRUCTURE

Illinois agriculture and natural resource e-Extension programs have exceeded expectations. National e-Extension programs will also succeed given they explicitly help clients make decisions, make available user-friendly decision tools and data, use GIS, reward the programs' interdisciplinary teams, and use the web for seamless integration across states.Teaching/Communication/Extension/Profession,

Student Research Symposium Schedule 2023

Student Research Symposium Schedule 2023, including abstracts and faculty advisor information

Estimating animal pose using deep learning a trained deep learning model outperforms morphological analysis

INTRODUCTION: Analyzing animal behavior helps researchers understand their decision-making process and helper tools are rapidly becoming an indispensable part of many interdisciplinary studies. However, researchers are often challenged to estimate animal pose because of the limitation of the tools and its vulnerability to a specific environment. Over the years, deep learning has been introduced as an alternative solution to overcome these challenges. OBJECTIVES: This study investigates how deep learning models can be applied for the accurate prediction of animal behavior, comparing with traditional morphological analysis based on image pixels. METHODS: Transparent Omnidirectional Locomotion Compensator (TOLC), a tracking device, is used to record videos with a wide range of animal behavior. Recorded videos contain two insects: a walking red imported fire ant (Solenopsis invicta) and a walking fruit fly (Drosophila melanogaster). Body parts such as the head, legs, and thorax, are estimated by using an open-source deep-learning toolbox. A deep learning model, ResNet-50, is trained to predict the body parts of the fire ant and the fruit fly respectively. 500 image frames for each insect were annotated by humans and then compared with the predictions of the deep learning model as well as the points generated from the morphological analysis. RESULTS: The experimental results show that the average distance between the deep learning-predicted centroids and the human-annotated centroids is 2.54, while the average distance between the morphological analysis-generated centroids and the human-annotated centroids is 6.41 over the 500 frames of the fire ant. For the fruit fly, the average distance of the centroids between the deep learning- predicted and the human-annotated is 2.43, while the average distance of the centroids between the morphological analysis-generated and the human-annotated is 5.06 over the 477 image frames. CONCLUSION: In this paper, we demonstrate that the deep learning model outperforms traditional morphological analysis in terms of estimating animal pose in a series of video frames

Exploring Immersive Learning Experiences: A Survey

Immersive technologies have been shown to significantly improve learning as they can simplify and simulate complicated concepts in various fields. However, there is a lack of studies that analyze the recent evidence-based immersive learning experiences applied in a classroom setting or offered to the public. This study presents a systematic review of 42 papers to understand, compare, and reflect on recent attempts to integrate immersive technologies in education using seven dimensions: application field, the technology used, educational role, interaction techniques, evaluation methods, and challenges. The results show that most studies covered STEM (science, technology, engineering, math) topics and mostly used head-mounted display (HMD) virtual reality in addition to marker-based augmented reality, while mixed reality was only represented in two studies. Further, the studies mostly used a form of active learning, and highlighted touch and hardware-based interactions enabling viewpoint and select tasks. Moreover, the studies utilized experiments, questionnaires, and evaluation studies for evaluating the immersive experiences. The evaluations show improved performance and engagement, but also point to various usability issues. Finally, we discuss implications and future research directions, and compare our findings with related review studies

Smithsonian Biodiversity Outreach Program in Gabon

This report was prepared for the Smithsonian Institution\u27s Monitoring and Assessment of Biodiversity Program (MAB). Our project created lesson plans regarding ecological problems in Gabon and the research of the Smithsonian to mitigate these problems. The report describes the methods used in creating the lesson plans, and an analysis of the structure, content, activities developed, and recommendations for future lesson plans. The implementation of these lesson plans will provide the Gabonese youth awareness of the ecological issues of their country as well as knowledge of Smithsonian Institution research