2,331 research outputs found
Embracing imperfection in learning analytics
© 2018 Copyright held by the owner/author(s). Learning Analytics (LA) sits at the confluence of many contributing disciplines, which brings the risk of hidden assumptions inherited from those fields. Here, we consider a hidden assumption derived from computer science, namely, that improving computational accuracy in classification is always a worthy goal. We demonstrate that this assumption is unlikely to hold in some important educational contexts, and argue that embracing computational “imperfection” can improve outcomes for those scenarios. Specifically, we show that learner-facing approaches aimed at “learning how to learn” require more holistic validation strategies. We consider what information must be provided in order to reasonably evaluate algorithmic tools in LA, to facilitate transparency and realistic performance comparisons
Implementing learning analytics for learning impact: Taking tools to task
© 2020 Elsevier Inc. Learning analytics has the potential to impact student learning, at scale. Embedded in that claim are a set of assumptions and tensions around the nature of scale, impact on student learning, and the scope of infrastructure encompassed by ‘learning analytics’ as a socio-technical field. Drawing on our design experience of developing learning analytics and inducting others into its use, we present a model that we have used to address five key challenges we have encountered. In developing this model, we recommend: A focus on impact on learning through augmentation of existing practice; the centrality of tasks in implementing learning analytics for impact on learning; the commensurate centrality of learning in evaluating learning analytics; inclusion of co-design approaches in implementing learning analytics across sites; and an attention to both social and technical infrastructure
Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications
Optical wireless communication (OWC) is a promising technology for future
wireless communications owing to its potentials for cost-effective network
deployment and high data rate. There are several implementation issues in the
OWC which have not been encountered in radio frequency wireless communications.
First, practical OWC transmitters need an illumination control on color,
intensity, and luminance, etc., which poses complicated modulation design
challenges. Furthermore, signal-dependent properties of optical channels raise
non-trivial challenges both in modulation and demodulation of the optical
signals. To tackle such difficulties, deep learning (DL) technologies can be
applied for optical wireless transceiver design. This article addresses recent
efforts on DL-based OWC system designs. A DL framework for emerging image
sensor communication is proposed and its feasibility is verified by simulation.
Finally, technical challenges and implementation issues for the DL-based
optical wireless technology are discussed.Comment: To appear in IEEE Communications Magazine, Special Issue on
Applications of Artificial Intelligence in Wireless Communication
Autoencoders for strategic decision support
In the majority of executive domains, a notion of normality is involved in
most strategic decisions. However, few data-driven tools that support strategic
decision-making are available. We introduce and extend the use of autoencoders
to provide strategically relevant granular feedback. A first experiment
indicates that experts are inconsistent in their decision making, highlighting
the need for strategic decision support. Furthermore, using two large
industry-provided human resources datasets, the proposed solution is evaluated
in terms of ranking accuracy, synergy with human experts, and dimension-level
feedback. This three-point scheme is validated using (a) synthetic data, (b)
the perspective of data quality, (c) blind expert validation, and (d)
transparent expert evaluation. Our study confirms several principal weaknesses
of human decision-making and stresses the importance of synergy between a model
and humans. Moreover, unsupervised learning and in particular the autoencoder
are shown to be valuable tools for strategic decision-making
Information Management Capability as Competitive Imperfection in the Strategic Factor Market of Big Data
The interest of the organizations in developing Big Data strategies is increasing significantly. However, the expectation of the value of these benefits and of the costs involved in acquiring or developing these solutions are not homogeneous for all of the firms, generating competitive imperfections in the market of strategic resources. Using Information Management Capability (IMC) as a premise to provide the required unique insight for Big Data strategies to be successful, this article proposes to analyze IMC as an imperfection agent in the market of strategic resources of Big Data. The formulated hypotheses were tested from a survey of 101 valid participants and analyzed with SEM-PLS. The results indicate a positive IMC influence on value expectation and a negative one on cost expectation. Cost expectation inversely affects the intent to purchase or develop the resources to implant Big Data strategies. Value expectation has a positive effect in both intents
Smart Grid Sensor Monitoring Based on Deep Learning Technique with Control System Management in Fault Detection
The smart grid environment comprises of the sensor for monitoring the environment for effective power supply, utilization and establishment of communication. However, the management of smart grid in the monitoring environment isa difficult process due to diversifieduser request in the sensor monitoring with the grid-connected devices. Presently, context-awaremonitoring incorporates effective management of data management and provision of services in two-way processing and computing. In a heterogeneous environment context-aware, smart grid exhibits significant performance characteristics with the grid-connected communication environment for effective data processing for sustainability and stability. Fault diagnoses in the automated system are formulated to diagnose the fault separately. This paper developed anoptimized power grid control model (OPGCM) model for fault detection in the control system model for grid-connected smart home appliances. OPGCM model uses the context-aware power-awarescheme for load management in grid-connected smart homes. Through the adaptive smart grid model,power-aware management is incorporated with the evolutionary programming model for context-awareness user communication. The OPGCM modelperforms fault diagnosis in the grid-connected control system initially, Fault diagnosis system comprises of a sequential process with the extraction of the statistical features to acquirea sustainable dataset with effective signal processing. Secondly, the features are extracted based on the sequential process for the acquired dataset with a reduction of dimensionality. Finally, the classification is performed with the deep learning model to predict or identify the fault pattern. With the OPGCM model, features are optimized with the whale optimization model to acquire features to perform fault diagnosis and classification. Simulation analysis expressed that the proposed OPGCM model exhibits ~16% improved classification accuracy compared with the ANN and HMM model
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