Mental Models of On-line Learning: An Interview Study of University Decision-Makers

2008 ◽  
Author(s):  
Monica Guzman ◽  
Eric Amsel
Keyword(s):  
Mental Models ◽  
Interview Study ◽  
Decision Makers ◽  
On Line ◽  
On Line Learning

scholarly journals Specialization in Hierarchical Learning Systems

2020 ◽  
Vol 52 (3) ◽  
pp. 2319-2352
Author(s):  
Heinke Hihn ◽  
Daniel A. Braun
Keyword(s):  
Decision Making ◽  
Learning Rule ◽  
Decision Makers ◽  
Learning Problems ◽  
Meta Learning ◽  
Partitioning Problems ◽  
Complex Decision Making ◽  
On Line ◽  
The Individual ◽  
On Line Learning

AbstractJoining multiple decision-makers together is a powerful way to obtain more sophisticated decision-making systems, but requires to address the questions of division of labor and specialization. We investigate in how far information constraints in hierarchies of experts not only provide a principled method for regularization but also to enforce specialization. In particular, we devise an information-theoretically motivated on-line learning rule that allows partitioning of the problem space into multiple sub-problems that can be solved by the individual experts. We demonstrate two different ways to apply our method: (i) partitioning problems based on individual data samples and (ii) based on sets of data samples representing tasks. Approach (i) equips the system with the ability to solve complex decision-making problems by finding an optimal combination of local expert decision-makers. Approach (ii) leads to decision-makers specialized in solving families of tasks, which equips the system with the ability to solve meta-learning problems. We show the broad applicability of our approach on a range of problems including classification, regression, density estimation, and reinforcement learning problems, both in the standard machine learning setup and in a meta-learning setting.

Patterns of Implicit Learning Below the Level of Conscious Knowledge

2010 ◽  
Vol 24 (2) ◽  
pp. 91-101 ◽  
Author(s):  
Juliana Yordanova ◽  
Rolf Verleger ◽  
Ullrich Wagner ◽  
Vasil Kolev
Keyword(s):  
Implicit Learning ◽  
Explicit Knowledge ◽  
Implicit Knowledge ◽  
Knowledge Generation ◽  
Sleep Stages ◽  
Implicit Processing ◽  
Implicit Processes ◽  
Late Night ◽  
On Line ◽  
On Line Learning

The objective of the present study was to evaluate patterns of implicit processing in a task where the acquisition of explicit and implicit knowledge occurs simultaneously. The number reduction task (NRT) was used as having two levels of organization, overt and covert, where the covert level of processing is associated with implicit associative and implicit procedural learning. One aim was to compare these two types of implicit processes in the NRT when sleep was or was not introduced between initial formation of task representations and subsequent NRT processing. To assess the effects of different sleep stages, two sleep groups (early- and late-night groups) were used where initial training of the task was separated from subsequent retest by 3 h full of predominantly slow wave sleep (SWS) or rapid eye movement (REM) sleep. In two no-sleep groups, no interval was introduced between initial and subsequent NRT performance. A second aim was to evaluate the interaction between procedural and associative implicit learning in the NRT. Implicit associative learning was measured by the difference between the speed of responses that could or could not be predicted by the covert abstract regularity of the task. Implicit procedural on-line learning was measured by the practice-based increased speed of performance with time on task. Major results indicated that late-night sleep produced a substantial facilitation of implicit associations without modifying individual ability for explicit knowledge generation or for procedural on-line learning. This was evidenced by the higher rate of subjects who gained implicit knowledge of abstract task structure in the late-night group relative to the early-night and no-sleep groups. Independently of sleep, gain of implicit associative knowledge was accompanied by a relative slowing of responses to unpredictable items suggesting reciprocal interactions between associative and motor procedural processes within the implicit system. These observations provide evidence for the separability and interactions of different patterns of processing within implicit memory.

On line learning for Personal and Professional Development in Medicine

MedEdPublish ◽  
2015 ◽  
Author(s):  
Annemarie Hennessy ◽  
Brad Frankum
Keyword(s):  
Professional Development ◽  
On Line ◽  
On Line Learning

Implementing On-Line Learning and Performance Support Using an EPSS

10.28945/1520 ◽  
2011 ◽  
Vol 7 ◽  
pp. 213-224 ◽  
Author(s):  
Eran Gal ◽  
Rafi Nachmias
Keyword(s):  
Performance Support ◽  
On Line ◽  
And Performance ◽  
On Line Learning ◽  
Learning And Performance

SCROLLA — On-Line Learning and Assessment

2001 ◽  
Vol 1 (2) ◽  
pp. 27-27
Author(s):  
Cliff Beevers
Keyword(s):  
On Line ◽  
On Line Learning

On-line learning the graph edit distance costs

2021 ◽  
Author(s):  
Elena Rica ◽  
Susana Álvarez ◽  
Francesc Serratosa
Keyword(s):  
Edit Distance ◽  
Graph Edit Distance ◽  
On Line ◽  
On Line Learning

scholarly journals Global On-Line Learning: Hope or Hype?

2015 ◽  
Author(s):  
Lawrence Gladieux
Keyword(s):  
On Line ◽  
On Line Learning

Where is the body in the mental model for a story?

1997 ◽  
Vol 20 (1) ◽  
pp. 25-25 ◽  
Author(s):  
Arthur C. Graesser
Keyword(s):  
Mental Models ◽  
Mental Model ◽  
The Body ◽  
Discourse Processing ◽  
On Line

Researchers in the field of discourse processing have investigated how mental models are constructed when adults comprehend stories. They have explored the process of encoding various classes of inferences “on-line” when these mental microworlds are constructed during comprehension. This commentary addresses the extent to which these inferences and mental microworlds are “embodied.”

scholarly journals Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2848 ◽  
Author(s):  
Leonel Rosas-Arias ◽  
Jose Portillo-Portillo ◽  
Aldo Hernandez-Suarez ◽  
Jesus Olivares-Mercado ◽  
Gabriel Sanchez-Perez ◽  
...  
Keyword(s):  
Principal Component ◽  
Subspace Learning ◽  
Transport Systems ◽  
Video Sequences ◽  
Projected Image ◽  
The Road ◽  
Illumination Changes ◽  
On Line ◽  
Incremental Learning Method ◽  
On Line Learning

The counting of vehicles plays an important role in measuring the behavior patterns of traffic flow in cities, as streets and avenues can get crowded easily. To address this problem, some Intelligent Transport Systems (ITSs) have been implemented in order to count vehicles with already established video surveillance infrastructure. With this in mind, in this paper, we present an on-line learning methodology for counting vehicles in video sequences based on Incremental Principal Component Analysis (Incremental PCA). This incremental learning method allows us to identify the maximum variability (i.e., motion detection) between a previous block of frames and the actual one by using only the first projected eigenvector. Once the projected image is obtained, we apply dynamic thresholding to perform image binarization. Then, a series of post-processing steps are applied to enhance the binary image containing the objects in motion. Finally, we count the number of vehicles by implementing a virtual detection line in each of the road lanes. These lines determine the instants where the vehicles pass completely through them. Results show that our proposed methodology is able to count vehicles with 96.6% accuracy at 26 frames per second on average—dealing with both camera jitter and sudden illumination changes caused by the environment and the camera auto exposure.

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