Technology-enhanced Learning
Technology-enhanced Learning - Related Videos
05:59
Ultra-High-Speed Western Blot using Immunoreaction Enhancing Technology
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Cited by 4 •
2020
An ultra-high-speed western blotting technique is developed by improving the kinetics of antigen-antibody binding through cyclic draining and replenishing (CDR) technology in conjunction with an immunoreaction enhancing agent.
10:06
An Introduction to Learning and Memory
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2023
Learning is the process of acquiring new information and memory is the retention or storage of that information. Different types of learning, such as non-associative and associative learning, and different types of memory, such as long-term and short-term memory, have been associated with human behaviors. Studying these components in detail helps behavioral scientists understand the neural mechanisms behind these two complex phenomena. JoVE's overview on learning and memory introduces common...
08:13
Development and Implementation of a Multi-Disciplinary Technology Enhanced Care Pathway for Youth and Adults with Concussion
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Cited by 13 •
2019
A multidisciplinary carepath to standardize disparate concussion care was developed and implemented at the Cleveland Clinic. The Cleveland Clinic Concussion (C3) mobile application was used to enable the carepath through biomechanical outcomes characterizing cognitive and motor function. Patient outcomes improved while cost of care was reduced following implementation.
06:02
Visual Statistical Learning
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University The visual environment contains massive amounts of information involving the relations between objects in space and time; certain objects are more likely to appear in the vicinity of other objects. Learning these regularities can support a wide array of visual processing, including object recognition. Unsurprisingly, then, humans appear to learn these regularities automatically, quickly, and without conscious awareness. The name...
10:43
Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
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Cited by 9 •
2021
We present a protocol for a behavioral analysis of adults (ages 18 to 70-year-old) engaged in learning processes, undertaking tasks designed for Self-Regulated Learning (SRL). The participants, university teachers and students, and adults from the University of Experience, were monitored with eye-tracking devices and the data were analyzed with data-mining techniques.
FAQs
Learning analytics use information from digital learning activities to support feedback and personalization. By showing how students access content, practice skills, or respond within a platform, they can help shape subsequent learning experiences around individual needs. In environmental education, this may guide learners toward additional practice or explanation when interpreting ecosystem, climate, or resource-management material.
These approaches represent processes that students may not be able to observe directly. Multimedia can combine different forms of explanation, while interactive simulations allow learners to examine ecosystem behavior, climate processes, or resource-management scenarios within a digital environment. This creates opportunities to connect scientific concepts with situations that are difficult to reproduce through classroom observation alone.
Digital environments allow students to work together online while receiving feedback during the learning process. Collaboration can support shared problem-solving, and feedback helps learners refine their understanding or skills as they engage with content and activities. For environmental topics, these features extend discussion beyond the classroom and support collective examination of real-world scientific challenges.
A learning sequence can begin with accessing digital content, continue through multimedia instruction or an interactive simulation, and then move to practice and feedback. Students may next analyze environmental data or collaborate on a problem-solving activity. This progression links conceptual learning with application while allowing digital tools to support work inside or beyond the classroom.
It is particularly useful when learners need to explore ecosystems, climate processes, or resource-management scenarios that are difficult to observe directly. Digital tools can provide simulations, remote fieldwork opportunities, and resources for data analysis. These applications make it possible to study environmental systems and challenges even when direct classroom observation is limited.
Digital approaches can help learners connect scientific concepts with real-world environmental challenges. Through remote fieldwork, data analysis, and collaborative problem-solving, students can work with environmental information in ways that extend beyond classroom instruction. The resulting learning experience combines conceptual understanding with opportunities to examine situations involving ecosystems, climate, and resource management.