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Investigation of the use of navigation tools in web-based learning: A data mining approach
Web-based learning is widespread in educational settings. The popularity of Web-based learning is in great measure because of its flexibility. Multiple navigation tools provided some of this flexibility. Different navigation tools offer different functions. Therefore, it is important to understand how the navigation tools are used by learners with different backgrounds, knowledge, and skills. This article presents two empirical studies in which data-mining approaches were used to analyze learners' navigation behavior. The results indicate that prior knowledge and subject content are two potential factors influencing the use of navigation tools. In addition, the lack of appropriate use of navigation tools may adversely influence learning performance. The results have been integrated into a model that can help designers develop Web-based learning programs and other Web-based applications that can be tailored to learners' needs
Flow Navigation by Smart Microswimmers via Reinforcement Learning
Smart active particles can acquire some limited knowledge of the fluid
environment from simple mechanical cues and exert a control on their preferred
steering direction. Their goal is to learn the best way to navigate by
exploiting the underlying flow whenever possible. As an example, we focus our
attention on smart gravitactic swimmers. These are active particles whose task
is to reach the highest altitude within some time horizon, given the
constraints enforced by fluid mechanics. By means of numerical experiments, we
show that swimmers indeed learn nearly optimal strategies just by experience. A
reinforcement learning algorithm allows particles to learn effective strategies
even in difficult situations when, in the absence of control, they would end up
being trapped by flow structures. These strategies are highly nontrivial and
cannot be easily guessed in advance. This Letter illustrates the potential of
reinforcement learning algorithms to model adaptive behavior in complex flows
and paves the way towards the engineering of smart microswimmers that solve
difficult navigation problems.Comment: Published on Physical Review Letters (April 12, 2017
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
This paper was motivated by the problem of how to make robots fuse and
transfer their experience so that they can effectively use prior knowledge and
quickly adapt to new environments. To address the problem, we present a
learning architecture for navigation in cloud robotic systems: Lifelong
Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge
fusion algorithm for upgrading a shared model deployed on the cloud. Then,
effective transfer learning methods in LFRL are introduced. LFRL is consistent
with human cognitive science and fits well in cloud robotic systems.
Experiments show that LFRL greatly improves the efficiency of reinforcement
learning for robot navigation. The cloud robotic system deployment also shows
that LFRL is capable of fusing prior knowledge. In addition, we release a cloud
robotic navigation-learning website based on LFRL
Navigation: am I really lost or virtually there?
Data is presented from virtual environment (VE) navigation studies that used building- and chessboard-type layouts. Participants learned by repeated navigation, spending several hours in each environment. While some participants quickly learned to navigate efficiently, others remained almost totally disoriented. In the virtual buildings this disorientation was illustrated by mean direction estimate errors of approximately 90°, and in the chessboard VEs disorientation was highlighted by the large number of rooms that some participants visited. Part of the cause of disorientation, and generally slow spatial learning, lies in the difficulty participants had learning the paths they had followed through the VEs
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