Breaking Out of the Box Teaching Biology with Web-Based Active Learning Modules

Ecological research like wildlife censuses increasingly relies on data on the scale of Terabytes. For example, modern camera trap datasets contain millions of images that require prohibitive amounts of manual labour to be annotated with species, bounding boxes, and the like. Machine learning, especially deep learning [3], could greatly accelerate this task through automated predictions, but involves expansive coding and expert knowledge.In this abstract we present AIDE, the Annotation Interface for Data-driven Ecology [2]. In a first instance, AIDE is a web-based annotation suite for image labelling with support for concurrent access and scalability, up to the cloud. In a second instance, it tightly integrates deep learning models into the annotation process through active learning [7], where models learn from user-provided labels and in turn select the most relevant images for review from the large pool of unlabelled ones (Fig. 1). The result is a system where users only need to label what is required, which saves time and decreases errors due to fatigue.<img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.0402be60f60062057601161/sdaolpUECMynit/12UGE&app=m&a=0&c=131251398e575ac9974634bd0861fadc&ct=x&pn=gnp.elif&d=1" alt="">Fig. 1: AIDE offers concurrent web image labelling support and uses annotations and deep learning models in an active learning loop.AIDE includes a comprehensive set of built-in models, such as ResNet [1] for image classification, Faster R-CNN [5] and RetinaNet [4] for object detection, and U-Net [6] for semantic segmentation. All models can be customised and used without having to write a single line of code. Furthermore, AIDE accepts any third-party model with minimal implementation requirements. To complete the package, AIDE offers both user annotation and model prediction evaluation, access control, customisable model training, and more, all through the web browser.AIDE is fully open source and available under https://github.com/microsoft/aerial_wildlife_detection.&#160;References

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Cultural Adaptation of E-Learning Courseware

Cases on Cultural Implications and Considerations in Online Learning ◽

10.4018/978-1-4666-1885-5.ch010 ◽

2012 ◽

pp. 192-209

Author(s):

Randall Stieghorst ◽

Andrea L. Edmundson

Keyword(s):

Instructional Design ◽

Design Process ◽

Ethical Dilemmas ◽

Code Of Conduct ◽

Corporate Environment ◽

Web Based ◽

Global Organizations ◽

Learning Modules ◽

Instructional Design Process ◽

E Learning

Web-based and self-paced learning modules have become a common-and sometimes primary-tool used by the Ethics & Compliance departments of global organizations to educate employees worldwide. These e-learning modules provide guidance around such topics as the company’s Code of Conduct, specific policies or laws, globally applicable corporate standards, and how best to manage ethical dilemmas in a corporate environment. In this case, the authors describe the instructional design process that were used on various ethics and compliance courses to achieve a more global, regional, or country-specific applicability, including an overview of changes made to content and methodology that was originally perceived as “very American.”

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Utilizing Web Tools for Computer-Mediated Communication to Enhance Team-Based Learning

Human Computer Interaction ◽

10.4018/978-1-87828-991-9.ch086 ◽

2009 ◽

pp. 1334-1349

Author(s):

Elizabeth Avery Gomez ◽

Dezhi Wu ◽

Katia Passerini ◽

Michael Bieber

Keyword(s):

Active Learning ◽

Computer Mediated Communication ◽

Mediated Communication ◽

Web Based ◽

Team Members ◽

Face To Face ◽

Computer Mediated ◽

Team Based Learning ◽

Team Interactions ◽

The Face

Team-based learning is an active learning instructional strategy used in the traditional face-to-face classroom. Web-based computer-mediated communication (CMC) tools complement the face-toface classroom and enable active learning between face-to-face class times. This article presents the results from pilot assessments of computer-supported team-based learning. The authors utilized pedagogical approaches grounded in collaborative learning techniques, such as team-based learning, and extended these techniques to a Web-based environment through the use of computer-mediated communications tools (discussion Web-boards). This approach was examined through field studies in the course of two semesters at a US public technological university. The findings indicate that the perceptions of team learning experience such as perceived motivation, enjoyment, and learning in such a Web-based CMC environment are higher than in traditional face-to-face courses. In addition, our results show that perceived team members’ contributions impact individual learning experiences. Overall, Web-based CMC tools are found to effectively facilitate team interactions and achieve higher-level learning.

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