Data Work in Education: Enacting and Negotiating Care and Control in Teachers' Use of Data-Driven Classroom Surveillance Technology

2021 ◽  
Vol 5 (CSCW2) ◽  
pp. 1-26
Author(s):  
Alex Jiahong Lu ◽  
Tawanna R. Dillahunt ◽  
Gabriela Marcu ◽  
Mark S. Ackerman
Keyword(s):  
Data Driven ◽  
Use Of Data ◽  
Surveillance Technology ◽  
And Control

scholarly journals Homophonie et maîtrise du français écrit : apport de l’apprentissage sur corpus

2016 ◽  
Vol 78 (4) ◽  
Author(s):  
Maï Leray ◽  
Henry Tyne
Keyword(s):  
Primary Schools ◽  
Data Driven ◽  
Traditional Teaching ◽  
Control Groups ◽  
Test Stage ◽  
Learning Group ◽  
Use Of Data ◽  
Post Test ◽  
And Control ◽  
Made In

Mastery of spelling in L1 French is notoriously complex, in particular due to morphographic difficulties and grammatical homophony. This study investigates the use of data-driven learning in French primary schools for the homophonous set /sE/ (c’est, s’est, ces, ses, sais/t). The results show that whereas initially there is no difference in comparison to traditional teaching (i.e. following pre-test, fewer errors are made in general at the time of learning by both experimental and control groups), there is less attrition for the data-driven learning group, with some spellings progressing even at the post-test stage. We suggest that data-driven learning may be used for developing spelling in L1 French as learners are exposed to enriched input. The question of how a predominantly L2-based method can be applied to L1 pedagogy is raised.

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

scholarly journals On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1310
Author(s):  
Pablo Torres ◽  
Soledad Le Clainche ◽  
Ricardo Vinuesa
Keyword(s):  
Physical Phenomenon ◽  
Urban Pollution ◽  
Pollutant Dispersion ◽  
Urban Environments ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Thermal Fields ◽  
Use Of Data ◽  
Sustainability Challenges ◽  
New Research

Understanding the flow in urban environments is an increasingly relevant problem due to its significant impact on air quality and thermal effects in cities worldwide. In this review we provide an overview of efforts based on experiments and simulations to gain insight into this complex physical phenomenon. We highlight the relevance of coherent structures in urban flows, which are responsible for the pollutant-dispersion and thermal fields in the city. We also suggest a more widespread use of data-driven methods to characterize flow structures as a way to further understand the dynamics of urban flows, with the aim of tackling the important sustainability challenges associated with them. Artificial intelligence and urban flows should be combined into a new research line, where classical data-driven tools and machine-learning algorithms can shed light on the physical mechanisms associated with urban pollution.

A Data-Driven Electric Water Heater Scheduling and Control System

2021 ◽  
pp. 110924
Author(s):  
Gulai Shen ◽  
Zachary E. Lee ◽  
Ali Amadeh ◽  
K. Max Zhang
Keyword(s):  
Control System ◽  
Data Driven ◽  
Water Heater ◽  
And Control

Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Nurali Virani ◽  
Devesh K. Jha ◽  
Zhenyuan Yuan ◽  
Ishana Shekhawat ◽  
Asok Ray
Keyword(s):  
Particle Filtering ◽  
Dynamic Models ◽  
Training Data ◽  
Data Driven ◽  
Conditional Density ◽  
Bayesian Filtering ◽  
State Dependent ◽  
State Evolution ◽  
And Control ◽  
Nonparametric Kernel

This paper addresses the problem of learning dynamic models of hybrid systems from demonstrations and then the problem of imitation of those demonstrations by using Bayesian filtering. A linear programming-based approach is used to develop nonparametric kernel-based conditional density estimation technique to infer accurate and concise dynamic models of system evolution from data. The training data for these models have been acquired from demonstrations by teleoperation. The trained data-driven models for mode-dependent state evolution and state-dependent mode evolution are then used online for imitation of demonstrated tasks via particle filtering. The results of simulation and experimental validation with a hexapod robot are reported to establish generalization of the proposed learning and control algorithms.

scholarly journals Disciplinary Power, the Oligopticon and Rhizomatic Surveillance in Elite Sports Academies

2012 ◽  
Vol 10 (3/4) ◽  
pp. 303-319 ◽  
Author(s):  
Andrew Manley ◽  
Catherine Palmer ◽  
Martin Roderick
Keyword(s):  
Identity Construction ◽  
Sense Of Self ◽  
Elite Sport ◽  
Surveillance Networks ◽  
Disciplinary Power ◽  
Surveillance Technology ◽  
And Control ◽  
Elite Sports

This article aims to apply a post-panoptic view of surveillance within the context of elite sport. Latour’s (2005) ‘oligopticon’ and Deleuze and Guttari’s (2003) ‘rhizomatic’ notion of surveillance networks are adopted to question the relevance and significance of Foucault’s (1979) conceptualisation of surveillance within an elite sports academy setting. A contemporary representation of bio-politics (Rose 1999, 2001) is further utilised to discern the mode of governance and control effective within such institutions. In so doing, this article seeks to understand the evolving methods of surveillance technology and governance and how they are situated within the setting of a contemporary institution. Such considerations aim to provoke a line of questioning surrounding the normalisation of intrusive surveillance practices and their impact upon identity construction and an authentic sense of self.

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