331,854 research outputs found
A flexible open source web platform to facilitate Learning Object evaluation
Systematic evaluation of Learning Objects is essential to make high quality Web-based education possible. For this reason, several educational repositories and e-Learning systems have developed their own evaluation models and tools. However, the differences of the context in which Learning Objects are produced and consumed suggest that no single evaluation model is sufficient for all scenarios. Besides, no much effort has been put in developing open tools to facilitate Learning Object evaluation and use the quality information for the benefit of end users. This paper presents LOEP, an open source web platform that aims to facilitate Learning Object evaluation in different scenarios and educational settings by supporting and integrating several evaluation models and quality metrics. The work exposed in this paper shows that LOEP is capable of providing Learning Object evaluation to e-Learning systems in an open, low cost, reliable and effective way. Possible scenarios where LOEP could be used to implement quality control policies and to enhance search engines are also described. Finally, we report the results of a survey conducted among reviewers that used LOEP, showing that they perceived LOEP as a powerful and easy to use tool for evaluating Learning Objects
Towards a Learning Object pedagogical quality metric based on the LORI evaluation model
Evaluating and measuring the pedagogical quality of Learning Objects is essential for achieving a successful web-based education. On one hand, teachers need some assurance of quality of the teaching resources before making them part of the curriculum. On the other hand, Learning Object Repositories need to include quality information into the ranking metrics used by the search engines in order to save users time when searching. For these reasons, several models such as LORI (Learning Object Review Instrument) have been proposed to evaluate Learning Object quality from a pedagogical perspective. However, no much effort has been put in defining and evaluating quality metrics based on those models. This paper proposes and evaluates a set of pedagogical quality metrics based on LORI. The work exposed in this paper shows that these metrics can be effectively and reliably used to provide quality-based sorting of search results. Besides, it strongly evidences that the evaluation of Learning Objects from a pedagogical perspective can notably enhance Learning Object search if suitable evaluations models and quality metrics are used. An evaluation of the LORI model is also described. Finally, all the presented metrics are compared and a discussion on their weaknesses and strengths is provided
Efficient Asymmetric Co-Tracking using Uncertainty Sampling
Adaptive tracking-by-detection approaches are popular for tracking arbitrary
objects. They treat the tracking problem as a classification task and use
online learning techniques to update the object model. However, these
approaches are heavily invested in the efficiency and effectiveness of their
detectors. Evaluating a massive number of samples for each frame (e.g.,
obtained by a sliding window) forces the detector to trade the accuracy in
favor of speed. Furthermore, misclassification of borderline samples in the
detector introduce accumulating errors in tracking. In this study, we propose a
co-tracking based on the efficient cooperation of two detectors: a rapid
adaptive exemplar-based detector and another more sophisticated but slower
detector with a long-term memory. The sampling labeling and co-learning of the
detectors are conducted by an uncertainty sampling unit, which improves the
speed and accuracy of the system. We also introduce a budgeting mechanism which
prevents the unbounded growth in the number of examples in the first detector
to maintain its rapid response. Experiments demonstrate the efficiency and
effectiveness of the proposed tracker against its baselines and its superior
performance against state-of-the-art trackers on various benchmark videos.Comment: Submitted to IEEE ICSIPA'201
Component-based tools for educational simulations
e-Learning is an effective medium for delivering knowledge and skills. In spite of
improvements in electronic delivery technologies, e-Learning is still a long way away from offering anything close to efficient and effective learning environments. To improve e-Learning experiences, much literature supports simulation based e-Learning. This thesis begins identifying various types of simulation models and their features that induce experiential learning. We focus on designing and constructing an easy-to-use Discrete Event Simulation (DES) tool for building engaging and informative interactive DES models that allow learners to control the models' parameters and visualizations through runtime interactions. DES has long been used
to support analysis and design of complex systems but its potential to enhance learning has not yet been fully utilized. We first present an application framework and its resulting classes for better structuring DES models. However, importing relevant classes, establishing relationships between their objects and representing lifecycles of various types of active objects in a language that does not support concurrency demand a significant cognitive workload. To improve this situation, we utilize two design patterns to ease model structuring and logic representation (both in
time and space) through a drag and drop component approach. The patterns are the
Delegation Event Model, used for linking between components and delegating tasks of executing and updating active objects' lifecycles, and the MVC (Model-View-Controller)
pattern, used for connecting the components to their graphical instrumentations and GUIs. Components implementing both design patterns support the process-oriented approach, can easily be tailored to store model states and
visualizations, and can be extended to design higher level models through hierarchical
simulation development. Evaluating this approach with both teachers and learners using ActionScript as an implementation language in the Flash environment shows that the resulting components not only help model designers with few programming skills to construct DES models, but they also allow learners to conduct various
experiments through interactive GUIs and observe the impact of changes to model behaviour through a range of engaging visualizations. Such interactions can motivate
learners and make their learning an enjoyable experienc
Signature Activation: A Sparse Signal View for Holistic Saliency
The adoption of machine learning in healthcare calls for model transparency
and explainability. In this work, we introduce Signature Activation, a saliency
method that generates holistic and class-agnostic explanations for
Convolutional Neural Network (CNN) outputs. Our method exploits the fact that
certain kinds of medical images, such as angiograms, have clear foreground and
background objects. We give theoretical explanation to justify our methods. We
show the potential use of our method in clinical settings through evaluating
its efficacy for aiding the detection of lesions in coronary angiograms
Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks
Evaluating similarity between graphs is of major importance in several
computer vision and pattern recognition problems, where graph representations
are often used to model objects or interactions between elements. The choice of
a distance or similarity metric is, however, not trivial and can be highly
dependent on the application at hand. In this work, we propose a novel metric
learning method to evaluate distance between graphs that leverages the power of
convolutional neural networks, while exploiting concepts from spectral graph
theory to allow these operations on irregular graphs. We demonstrate the
potential of our method in the field of connectomics, where neuronal pathways
or functional connections between brain regions are commonly modelled as
graphs. In this problem, the definition of an appropriate graph similarity
function is critical to unveil patterns of disruptions associated with certain
brain disorders. Experimental results on the ABIDE dataset show that our method
can learn a graph similarity metric tailored for a clinical application,
improving the performance of a simple k-nn classifier by 11.9% compared to a
traditional distance metric.Comment: International Conference on Medical Image Computing and
Computer-Assisted Interventions (MICCAI) 201
Learning State-Space Models for Mapping Spatial Motion Patterns
Mapping the surrounding environment is essential for the successful operation
of autonomous robots. While extensive research has focused on mapping geometric
structures and static objects, the environment is also influenced by the
movement of dynamic objects. Incorporating information about spatial motion
patterns can allow mobile robots to navigate and operate successfully in
populated areas. In this paper, we propose a deep state-space model that learns
the map representations of spatial motion patterns and how they change over
time at a certain place. To evaluate our methods, we use two different
datasets: one generated dataset with specific motion patterns and another with
real-world pedestrian data. We test the performance of our model by evaluating
its learning ability, mapping quality, and application to downstream tasks. The
results demonstrate that our model can effectively learn the corresponding
motion pattern, and has the potential to be applied to robotic application
tasks.Comment: 6 pages, 5 figures, to be published in ECMR 2023 conference
proceeding
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