87,246 research outputs found
Capturing "attrition intensifying" structural traits from didactic interaction sequences of MOOC learners
This work is an attempt to discover hidden structural configurations in
learning activity sequences of students in Massive Open Online Courses (MOOCs).
Leveraging combined representations of video clickstream interactions and forum
activities, we seek to fundamentally understand traits that are predictive of
decreasing engagement over time. Grounded in the interdisciplinary field of
network science, we follow a graph based approach to successfully extract
indicators of active and passive MOOC participation that reflect persistence
and regularity in the overall interaction footprint. Using these rich
educational semantics, we focus on the problem of predicting student attrition,
one of the major highlights of MOOC literature in the recent years. Our results
indicate an improvement over a baseline ngram based approach in capturing
"attrition intensifying" features from the learning activities that MOOC
learners engage in. Implications for some compelling future research are
discussed.Comment: "Shared Task" submission for EMNLP 2014 Workshop on Modeling Large
Scale Social Interaction in Massively Open Online Course
Statistical Analysis of Dynamic Actions
Real-world action recognition applications require the development of systems which are fast, can handle a large variety of actions without a priori knowledge of the type of actions, need a minimal number of parameters, and necessitate as short as possible learning stage. In this paper, we suggest such an approach. We regard dynamic activities as long-term temporal objects, which are characterized by spatio-temporal features at multiple temporal scales. Based on this, we design a simple statistical distance measure between video sequences which captures the similarities in their behavioral content. This measure is nonparametric and can thus handle a wide range of complex dynamic actions. Having a behavior-based distance measure between sequences, we use it for a variety of tasks, including: video indexing, temporal segmentation, and action-based video clustering. These tasks are performed without prior knowledge of the types of actions, their models, or their temporal extents
Unsupervised Video Understanding by Reconciliation of Posture Similarities
Understanding human activity and being able to explain it in detail surpasses
mere action classification by far in both complexity and value. The challenge
is thus to describe an activity on the basis of its most fundamental
constituents, the individual postures and their distinctive transitions.
Supervised learning of such a fine-grained representation based on elementary
poses is very tedious and does not scale. Therefore, we propose a completely
unsupervised deep learning procedure based solely on video sequences, which
starts from scratch without requiring pre-trained networks, predefined body
models, or keypoints. A combinatorial sequence matching algorithm proposes
relations between frames from subsets of the training data, while a CNN is
reconciling the transitivity conflicts of the different subsets to learn a
single concerted pose embedding despite changes in appearance across sequences.
Without any manual annotation, the model learns a structured representation of
postures and their temporal development. The model not only enables retrieval
of similar postures but also temporal super-resolution. Additionally, based on
a recurrent formulation, next frames can be synthesized.Comment: Accepted by ICCV 201
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