Adaptive Learning
Adaptive Learning - Related Videos
08:04
Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation
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Cited by 14 •
2017
We describe a protocol for investigating human locomotor adaptation using the split-belt treadmill, which has two belts that can drive each leg at a different speed. We specifically focus on a paradigm designed to test the generalization of adapted locomotor patterns to different walking contexts (e.g., gait speeds, walking environments).
08:16
Adaptation of a Haptic Robot in a 3T fMRI
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Cited by 2 •
2011
The adaptation and use of a haptic robot in a 3T fMRI is described.
05:18
Mutual Exclusivity: How Children Learn the Meanings of Words
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2023
Source: Laboratories of Nicholaus Noles and Judith Danovitch—University of Louisville Humans are different from other animals in many ways, but perhaps the most important differentiating factor is their ability to use language. Other animals can communicate and even understand and use language in limited ways, but trying to teach human language to a chimp or a dog takes a great deal of time and effort. In contrast, young humans acquire their native language easily, and they learn linguistic...
11:30
A Molecular Readout of Long-term Olfactory Adaptation in C. elegans
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Cited by 2 •
2012
Here we describe a molecular readout of long-term olfactory adaptation in Caenorhabditis elegans. The Protein Kinase G, EGL-4, is necessary for stable adaptation responses in the primary sensory neuron pair called AWC. During prolonged odor exposure EGL-4 translocates from the cytosol to nucleus of the AWC.
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...
FAQs
Performance data gives the system a basis for identifying where a learner is progressing or making errors. Responses, errors, and progress can be interpreted together rather than as isolated scores, allowing subsequent activities to address an apparent knowledge gap. In engineering learning, this supports focused reinforcement of difficult concepts and helps connect assessment evidence with instructional decisions.
Rules and computational models serve as decision mechanisms for choosing what comes next. A rule-based approach can link observed performance conditions to a specified activity, while a computational model can use performance information to make the selection. Both approaches turn learner evidence into an instructional pathway, but the source of the decision differs, which matters when educators interpret how activities were assigned.
Targeted feedback matters because it connects a learner’s observed difficulty with a response intended to address it. When errors reveal that a concept remains difficult, the learning pathway can reinforce that concept rather than advancing without support. This makes feedback a mechanism for correction and helps learners move toward stronger understanding of technical material.
An adaptive learning workflow begins with collecting learner performance information, including responses, errors, and progress. The system then uses rules or computational models to select a subsequent activity and provide feedback directed at the learner’s needs. Continued performance evidence can guide later selections. This sequence creates an iterative pathway in which each activity is informed by what the learner has already demonstrated.
In engineering education and professional training, adaptive learning can support learners working toward technical skill mastery. It can identify gaps in understanding, reinforce concepts that cause difficulty, and adjust the sequence of activities using performance evidence. This is relevant when learners have different starting points or need different amounts of practice on technical content.
Educators can use accumulated performance information to evaluate learner progress and examine whether difficult concepts are receiving adequate reinforcement. The same evidence can inform the design of more effective learning pathways for diverse learners. In this role, the system provides more than activity selection: it also supplies a data-informed basis for instructional planning and progress review.