scholarly journals An open web‐based module developed to advance data‐driven hydrologic process learning

2021 ◽  
Author(s):  
Belize Lane ◽  
Irene Garousi‐Nejad ◽  
Melissa A. Gallagher ◽  
David G. Tarboton ◽  
Emad Habib
Keyword(s):  
Data Driven ◽  
Web Based ◽  
Hydrologic Process ◽  
Process Learning

scholarly journals Better understanding of hydrologic process through data-driven learning facilitated by collaborative open web-based platforms

2021 ◽  
Author(s):  
Belize Lane ◽  
Irene Garousi-Nejad ◽  
Melissa Gallagher ◽  
Dave Tarboton ◽  
Emad Habib
Keyword(s):  
Big Data ◽  
Critical Evaluation ◽  
Open Data ◽  
Real Data ◽  
Learning Activities ◽  
Data Driven ◽  
Reproducible Research ◽  
Web Based ◽  
Hydrologic Process ◽  
Data Services

The era of ”big data” promises to provide new hydrologic insights, and open web-based platforms are being developed and adopted by the hydrologic science community to harness these datasets and data services. This shift accompanies advances in hydrology education and the growth of web-based hydrology learning modules, but their capacity to utilize emerging open platforms and data services to enhance student learning through data-driven activities remains largely untapped. Given that generic equations may not easily translate into local or regional solutions, teaching students to explore how well models or equations work in particular settings or to answer specific problems using real data is essential. This paper introduces an open web-based learning module developed to advance data-driven hydrologic process learning, targeting upper level undergraduate and early graduate students in hydrology and engineering. The module was developed and deployed on the HydroLearn open educational platform, which provides a formal pedagogical structure for developing effective problem-based learning activities. We found that data-driven learning activities utilizing collaborative open web platforms like HydroShare and CUAHSI JupyterHub computational notebooks allowed students to access and work with datasets for systems of personal interest and promoted critical evaluation of results and assumptions. Initial student feedback was generally positive, but also highlights challenges including trouble-shooting and future-proofing difficulties and some resistance to open-source software and programming. Opportunities to further enhance hydrology learning include better articulating the myriad benefits of open web platforms upfront, incorporating additional user-support tools, and focusing methods and questions on implementing and adapting notebooks to explore fundamental processes rather than tools and syntax. The profound shift in the field of hydrology toward big data, open data services and reproducible research practices requires hydrology instructors to rethink traditional content delivery and focus instruction on harnessing these datasets and practices in the preparation of future hydrologists and engineers.

scholarly journals Design of a joint research data platform: A use case for severity assessment

2019 ◽  
Vol 54 (1) ◽  
pp. 33-39
Author(s):  
Steven R Talbot ◽  
Stefan Bruch ◽  
Fabian Kießling ◽  
Michael Marschollek ◽  
Branko Jandric ◽  
...  
Keyword(s):  
Human Factor ◽  
Data Driven ◽  
Use Case ◽  
Severity Assessment ◽  
Joint Research ◽  
Web Based ◽  
Specific Content ◽  
Data Platform ◽  
Basic Understanding ◽  
Minimal Effort

Severity assessment in animal models is a data-driven process. We therefore present a use case for building a repository for interlaboratory collaboration with the potential of uploading specific content, making group announcements and internal prepublication discussions. We clearly show that it is possible to offer such a structure with minimal effort and a basic understanding of web-based services, also taking into account the human factor in individual data collection. The FOR2591 Online Repository serves as a blueprint for other groups, so that one day not only will data sharing among consortium members be improved but the transition from the private to the persistent domain will also be easier.

scholarly journals Defining the key issues discussed by problematic gamblers on web-based forums: a data-driven approach

2020 ◽  
pp. 1-15
Author(s):  
Alex Bradley ◽  
Richard J. E. James
Keyword(s):  
Data Driven ◽  
Web Based ◽  
Data Driven Approach ◽  
Key Issues

scholarly journals Near real-time monitoring of the April-May 2010 Eyjafjallajökull ash cloud: an example of a web-based, satellite data-driven, reporting system

2012 ◽  
Vol 48 (1/2/3/4) ◽  
pp. 262 ◽  
Author(s):  
P. Labazuy ◽  
M. Gouhier ◽  
A. Harris ◽  
Y. Guéhenneux ◽  
M. Hervo ◽  
...  
Keyword(s):  
Real Time ◽  
Satellite Data ◽  
Reporting System ◽  
Data Driven ◽  
Real Time Monitoring ◽  
Web Based ◽  
Ash Cloud

5.25 DATA-DRIVEN CLUSTERING OF PSYCHIATRIC AND BEHAVIORAL SYMPTOMS USING A WEB-BASED SYMPTOM CHECKER

2016 ◽  
Vol 55 (10) ◽  
pp. S191-S192
Author(s):  
Zahra Kadkhodaie ◽  
Aki Nikolaidis ◽  
Lindsay Alexander ◽  
Curt White ◽  
Barbara Clement ◽  
...  
Keyword(s):  
Data Driven ◽  
Behavioral Symptoms ◽  
Web Based

scholarly journals Validation of the Learning Machine

10.28945/2869 ◽  
2005 ◽  
Author(s):  
Jo-Mae Maris
Keyword(s):  
Data Driven ◽  
Course Development ◽  
Web Based ◽  
Interactive Instruction ◽  
Education Philosophy ◽  
Behavioral Education ◽  
Learning Machine

As Web-based courses become more prevalent, tools need to be created that go beyond electronic page turning. The tools should allow for easy development of Web-based interactive instruction. The Learning Machine is data-driven tutorial software that is based on behavioral education philosophy. Development and presentation use the same database, but separate scripts, so that changes to content do not require changes to the presentation script. This decoupling enables content providers to concentrate on course development. This paper validates the effectiveness of Learning Machine tutorials as compared with classroom lectures. The experiment conducted to validate the Learning Machine tutorials showed that the tutorials were at least as good as classroom lectures.

Agent-based Collaboration Between Distributed Web Applications: Case Study on 'Collaborative Design for X' Using CyberCO

2002 ◽  
Vol 10 (4) ◽  
pp. 279-290
Author(s):  
G Q Huang ◽  
K L Mak
Keyword(s):  
Collaborative Design ◽  
Web Applications ◽  
Workflow Management ◽  
Data Driven ◽  
Prototype System ◽  
Engineering Systems ◽  
Web Based ◽  
Agent Based ◽  
Systems Collaboration

This paper presents a case study on the collaboration between a set of distributed web applications developed and deployed for carrying out a 'Design for Assembly' analysis. The case study is conducted using a web-based prototype system called CyberCO. The system is based on a theoretical framework which has been formed through an innovative combination of a number of concepts such as agents and workflows. Unlike previous attempts in computer supported concurrent engineering systems, collaboration in this framework between distributed web applications is achieved through workflows between their representative agents. The flows of data between agents are guided by the associated constraints. The flows of controls are somewhat data-driven in the sense that agents start and stop themselves whenever the predefined conditions are satisfied. The data-driven flows of controls are different from those widely used in workflow management where flows of controls are determined by the precedence relationships. The key purpose of this case study demonstration is to extend our knowledge and insights into this emerging field where increasing number of web applications are developed and deployed for collaborative product development and realization projects.

scholarly journals CAiRE: An End-to-End Empathetic Chatbot

2020 ◽  
Vol 34 (09) ◽  
pp. 13622-13623
Author(s):  
Zhaojiang Lin ◽  
Peng Xu ◽  
Genta Indra Winata ◽  
Farhad Bin Siddique ◽  
Zihan Liu ◽  
...  
Keyword(s):  
User Interface ◽  
Active Learning ◽  
Transfer Learning ◽  
User Feedback ◽  
Data Driven ◽  
Dialogue Systems ◽  
Generation Task ◽  
Web Based ◽  
End To End ◽  
Negative Training

We present CAiRE, an end-to-end generative empathetic chatbot designed to recognize user emotions and respond in an empathetic manner. Our system adapts the Generative Pre-trained Transformer (GPT) to empathetic response generation task via transfer learning. CAiRE is built primarily to focus on empathy integration in fully data-driven generative dialogue systems. We create a web-based user interface which allows multiple users to asynchronously chat with CAiRE. CAiRE also collects user feedback and continues to improve its response quality by discarding undesirable generations via active learning and negative training.

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