50 research outputs found
Recognizing Multidimensional Engagement of E-learners Based on Multi-channel Data in E-learning Environment
Despite recent advances in MOOC, the current e-learning systems have
advantages of alleviating barriers by time differences, and geographically
spatial separation between teachers and students. However, there has been a
'lack of supervision' problem that e-learner's learning unit state(LUS) can't
be supervised automatically. In this paper, we present a fusion framework
considering three channel data sources: 1) videos/images from a camera, 2) eye
movement information tracked by a low solution eye tracker and 3) mouse
movement. Based on these data modalities, we propose a novel approach of
multi-channel data fusion to explore the learning unit state recognition. We
also propose a method to build a learning state recognition model to avoid
manually labeling image data. The experiments were carried on our designed
online learning prototype system, and we choose CART, Random Forest and GBDT
regression model to predict e-learner's learning state. The results show that
multi-channel data fusion model have a better recognition performance in
comparison with single channel model. In addition, a best recognition
performance can be reached when image, eye movement and mouse movement features
are fused.Comment: 4 pages, 4 figures, 2 table
CPS Data Streams Analytics based on Machine Learning for Cloud and Fog Computing: A Survey
Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture