scholarly journals A Scalable Architecture for the Dynamic Deployment of Multimodal Learning Analytics Applications in Smart Classrooms

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2923
Author(s):  
Alberto Huertas Celdrán ◽  
José A. Ruipérez-Valiente ◽  
Félix J. García Clemente ◽  
María Jesús Rodríguez-Triana ◽  
Shashi Kant Shankar ◽  
...  
Keyword(s):  
Learning Analytics ◽  
Ad Hoc ◽  
Heterogeneous Data ◽  
Network Function Virtualization ◽  
Learning Tools ◽  
Multimodal Learning ◽  
Network Function ◽  
The Future ◽  
Multimodal Learning Analytics ◽  
Smart Classrooms

The smart classrooms of the future will use different software, devices and wearables as an integral part of the learning process. These educational applications generate a large amount of data from different sources. The area of Multimodal Learning Analytics (MMLA) explores the affordances of processing these heterogeneous data to understand and improve both learning and the context where it occurs. However, a review of different MMLA studies highlighted that ad-hoc and rigid architectures cannot be scaled up to real contexts. In this work, we propose a novel MMLA architecture that builds on software-defined networks and network function virtualization principles. We exemplify how this architecture can solve some of the detected challenges to deploy, dismantle and reconfigure the MMLA applications in a scalable way. Additionally, through some experiments, we demonstrate the feasibility and performance of our architecture when different classroom devices are reconfigured with diverse learning tools. These findings and the proposed architecture can be useful for other researchers in the area of MMLA and educational technologies envisioning the future of smart classrooms. Future work should aim to deploy this architecture in real educational scenarios with MMLA applications.

scholarly journals Using Aerial and Vehicular NFV Infrastructures to Agilely Create Vertical Services

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1342
Author(s):  
Borja Nogales ◽  
Miguel Silva ◽  
Ivan Vidal ◽  
Miguel Luís ◽  
Francisco Valera ◽  
...  
Keyword(s):  
Ad Hoc Network ◽  
Ad Hoc ◽  
Cost Effective ◽  
Network Services ◽  
Network Function Virtualization ◽  
Mobile Environments ◽  
Network Resources ◽  
The Public ◽  
Network Function ◽  
And Performance

5G communications have become an enabler for the creation of new and more complex networking scenarios, bringing together different vertical ecosystems. Such behavior has been fostered by the network function virtualization (NFV) concept, where the orchestration and virtualization capabilities allow the possibility of dynamically supplying network resources according to its needs. Nevertheless, the integration and performance of heterogeneous network environments, each one supported by a different provider, and with specific characteristics and requirements, in a single NFV framework is not straightforward. In this work we propose an NFV-based framework capable of supporting the flexible, cost-effective deployment of vertical services, through the integration of two distinguished mobile environments and their networks: small sized unmanned aerial vehicles (SUAVs), supporting a flying ad hoc network (FANET) and vehicles, promoting a vehicular ad hoc network (VANET). In this context, a use case involving the public safety vertical will be used as an illustrative example to showcase the potential of this framework. This work also includes the technical implementation details of the framework proposed, allowing to analyse and discuss the delays on the network services deployment process. The results show that the deployment times can be significantly reduced through a distributed VNF configuration function based on the publish–subscribe model.

scholarly journals Detecting Drowsy Learners at the Wheel of e-Learning Platforms with Multimodal Learning Analytics

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ryosuke Kawamura ◽  
Shizuka Shirai ◽  
Noriko Takemura ◽  
Mehrasa Alizadeh ◽  
Mutlu Cukurova ◽  
...  
Keyword(s):  
Learning Analytics ◽  
Multimodal Learning ◽  
E Learning ◽  
Learning Platforms ◽  
Multimodal Learning Analytics

scholarly journals Multimodal Learning Analytics and Education Data Mining: using computational technologies to measure complex learning tasks

2016 ◽  
Vol 3 (2) ◽  
pp. 220-238 ◽  
Author(s):  
Paulo Blikstein ◽  
Marcelo Worsley
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Online Courses ◽  
Educational Data Mining ◽  
Multimodal Learning ◽  
Learning Tasks ◽  
Education Data ◽  
Multimodal Learning Analytics ◽  
High Degree ◽  
Learning Analysis

New high-frequency multimodal data collection technologies and machine learning analysis techniques could offer new insights into learning, especially when students have the opportunity to generate unique, personalized artifacts, such as computer programs, robots, and solutions engineering challenges. To date most of the work on learning analytics and educational data mining has been focused on online courses and cognitive tutors, both of which provide a high degree of structure to the tasks, and are restricted to interactions that occur in front of a computer screen. In this paper, we argue that multimodal learning analytics can offer new insights into students’ learning trajectories in more complex and open-ended learning environments. We present several examples of this work and its educational application.

scholarly journals Designing Analytics for Collaboration Literacy and Student Empowerment

2021 ◽  
Vol 8 (1) ◽  
pp. 30-48
Author(s):  
Marcelo Worsley ◽  
Khalil Anderson ◽  
Natalie Melo ◽  
JooYoung Jang
Keyword(s):  
Student Perceptions ◽  
21St Century ◽  
Learning Analytics ◽  
Student Empowerment ◽  
Multimodal Learning ◽  
Theoretical Understanding ◽  
Student Collaboration ◽  
Communication Conflict ◽  
Multimodal Learning Analytics ◽  
The Moment

Collaboration has garnered global attention as an important skill for the 21st century. While researchers have been doing work on collaboration for nearly a century, many of the questions that the field is investigating overlook the need for students to learn how to read and respond to different collaborative settings. Existing research focuses on chronicling the various factors that predict the effectiveness of a collaborative experience, or on changing user behaviour in the moment. These are worthwhile research endeavours for developing our theoretical understanding of collaboration. However, there is also a need to centre student perceptions and experiences with collaboration as an important area of inquiry. Based on a survey of 131 university students, we find that student collaboration-related concerns can be represented across seven different categories or dimensions: Climate, Compatibility, Communication, Conflict, Context, Contribution, and Constructive. These categories extend prior research on collaboration and can help the field ensure that future collaboration analytics tools are designed to support the ways that students think about and utilize collaboration. Finally, we describe our instantiation of many of these dimensions in our collaborative analytics tool, BLINC, and suggest that these seven dimensions can be instructive for re-orienting the Multimodal Learning Analytics (MMLA) and collaboration analytics communities.

scholarly journals Artificial Intelligence Techniques for Information Security in 5G IoT Environments

2020 ◽  
Vol 5 (11) ◽  
pp. 1328-1333
Author(s):  
Ivan Petrov ◽  
Toni Janevski
Keyword(s):  
Artificial Intelligence ◽  
Industrial Revolution ◽  
Cost Benefit Analysis ◽  
Network Virtualization ◽  
Cost Benefit ◽  
Telecommunication Networks ◽  
Operation Management ◽  
Network Function Virtualization ◽  
Network Function ◽  
The Future

The development of the telecommunication networks observed in present and future time is impressive. Today we witness rapid implementation of 5G networks. We can say that this actually is the moment when (artificial intelligence) AI enters at small door but in the beyond 5G world it is expected to have the prime role in smart operation, management and maintenance of non-software defined networking (SDN), network function virtualization (NFV) and especially at SDN and NFV aware networks. Number of standardization body’s and work groups are focused in a way to create a framework that will define the future use of AI and security standards necessary to exist in order to create health environment for the next generation telecommunication infrastructure. In the wireless world AI/Machine learning (ML) has great potential to shake the way we operate and to become foundation of the transformation that leads to the next industrial revolution. Network virtualization gives flexibility and freedom of the telco operators to choose the hardware and network topology they need for AI/ML platforms and big data sets. 5G and IoT create positive environment for AI and ML development and usage. As the network requirements are developed and the number of the users raises, gains are expected to grow with the number of variables and the interactions among them so it becomes impossible to relay on humans to control the network for increased number of variables and this is why AI with ML and automation become beneficial and necessity to run the future networks. AI generally is defined as capacity of mind or ability to acquire and apply knowledge and skills while ML is defined as learning that does not require explicit programming. Combined usage of AI and ML can optimize almost any component of the wireless network, this does not mean that it should be used everywhere mainly because at the end of the day the cost benefit analysis of its usage must be positive. Smart operation, management and infrastructure maintenance (SOMM) networks are defined as: Intelligent, data driven, integrated and agile. Today AI is introduced but in future it will represent the network engine. It is interesting to mention that network security must be upgraded because the network will provide services for massive number of IoT devices that will have variety of functions and requests. AI/ML can improve the security services and to be used in order to elevate them at advanced level. In this text we focus our attention at AI/ML and security scenarios defined for IoT in 5G environment.

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