Setting up and managing course social learning and networking in Moodle involves integrating various tools and features that allow students to interact, collaborate, and share knowledge. Below is a step-by-step guide to help you create a socially engaging and networked learning environment in your Moodle course:

1. Set up the Moodle Course

• Create a New Course: Start by creating a course in Moodle. Go to your Moodle homepage, select “Site administration” > “Courses” > “Add a new course”, and fill out the necessary course information such as course name, summary, and format.
• Choose the Format: Select a course format that supports collaboration. Social format or Weekly format can be good options for this.
  • Social format: Displays only one forum but allows for social interaction and can be great for courses focusing on social learning.
  • Weekly format: Divides the course into weekly blocks, useful for managing course progression with social learning tools attached.

2. Enable Social Learning Tools

Moodle offers several tools to encourage social learning. You can integrate these tools into your course to enhance networking and collaboration.

a) Forums

• Use forums for discussion: Moodle forums are excellent for fostering social learning and networking. You can set up various types of forums such as Standard forum for general use, Q&A forums, or Single simple discussion forums.
  • Create discussion threads: Encourage learners to share ideas, ask questions, or respond to prompts.
  • Use group forums: If you divide the students into groups, you can create forums where students from the same group can collaborate.
• Moderate forums: Enable forum moderation to ensure that discussions stay on topic and are respectful.

b) Messaging and Chat

• Direct messaging: Students can send private messages to each other. Encourage students to communicate through messaging for questions and feedback.
• Chat rooms: Set up synchronous chat rooms for real-time discussions. You can create a scheduled chat session for students to join, allowing for group conversations or Q&A sessions.

c) Blogs

• Student Blogs: Moodle allows students to create their own blogs where they can post reflections, projects, or learning milestones. This gives students a platform for self-expression and interaction.
• Peer feedback: Students can comment on each other’s blog posts to encourage feedback and engagement.

d) Wikis

• Collaborative Content Creation: Set up a wiki where students can collaborate on creating documents, notes, or content for the course. This tool is useful for team projects and research.

e) Glossaries

• Create a Collaborative Glossary: Allow students to contribute to a shared glossary of terms related to the course. They can define terms and concepts, creating a useful reference that everyone in the course can benefit from.

f) Peer and Self-Assessment

• Enable peer assessments: Allow students to assess their peers’ work, projects, or contributions. This can be done through the Assignment activity or Workshop activity.
• Reflection activities: Encourage self-reflection where students can track their learning progress and share insights with their peers.

3. Organize Group Work

• Group Activities: Divide your class into groups for collaboration and learning. Group work can be done using forums, wikis, assignments, or file-sharing tasks.
• Group Discussions: Use group forums or group chat to allow students within the same group to collaborate, share ideas, and discuss their projects.
• Group Wikis: Let each group create and manage their own wiki. They can collaborate on research, write group reports, and provide peer feedback on each other’s content.

4. Create and Manage Community Building Tools

• Course Announcements: Use the News Forum for important announcements and updates. You can use this to foster communication between the instructor and students.
• Social Networking Plugins: Consider installing third-party plugins for more advanced networking features such as Mahara for ePortfolios or integration with social media platforms.

5. Gamification and Badges

• Use Gamification: To keep learners engaged and motivated, you can implement gamification tools such as badges, points, and leaderboards.
• Award Badges: Assign badges for milestones, achievements, or contributions to discussions, wikis, or blogs. This can help encourage student participation and engagement.
• Activity Completion: Use the Activity Completion feature to set requirements for completing tasks or activities. This ensures that students stay on track while fostering interaction.

6. Encourage Active Participation

• Interactive Activities: Make use of Moodle’s interactive features such as quizzes, polls, and surveys to stimulate learning and social interaction.
• Invite Guest Speakers: Invite guest speakers or experts to join your course through video conferencing (using BigBlueButton or Zoom integration) to provide an opportunity for students to interact and network with professionals in the field.
• Interactive Media: Encourage students to share videos, podcasts, or multimedia content on topics related to the course.

7. Monitor and Assess Social Learning

• Monitor Student Engagement: Use Moodle’s reports and logs to monitor student engagement in discussions, forums, and other social learning tools. This helps you identify students who may need additional support.
• Track Collaboration and Contributions: Through activity completion, forum participation, or peer assessments, you can keep track of how students are collaborating with one another.

8. Customize Your Moodle for Social Learning

• Personalized Dashboards: Allow students to personalize their Moodle dashboard to see updates and notifications related to their social learning activities.
• Profiles and Badges: Encourage students to complete their profiles, add photos, and list their achievements. This helps build an online learning community and facilitates networking among learners.
• External Social Media Integration: If desired, integrate Moodle with external social media tools (e.g., Twitter, Facebook) for extended networking opportunities.

9. Foster a Positive Online Learning Culture

• Set Ground Rules: Define clear guidelines for online communication, discussions, and collaboration to ensure a positive and respectful environment.
• Encourage Collaboration: Promote collaboration over competition. Give students opportunities to work together, share ideas, and learn from one another.
• Provide Regular Feedback: Keep the communication channels open by offering constructive feedback on their interactions, work, and contributions.

By using these strategies, Moodle can become an effective platform for social learning and networking, encouraging active engagement, collaboration, and peer-to-peer support throughout the course.

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Network interconnect supports command and control, Internet of Things, cloud, storage replication, artificial intelligence (AI) and machine learning.

ASHBURN, Va. – Curtiss-Wright Corp. Defense Solutions division in Ashburn, Va., is introducing the PacStar 448 10-Gigabit Ethernet switch module for military, intelligence, and commercial applications.

The Ethernet switch delivers a 10x increase in networking speed to its PacStar Modular Data Center (MDC) 2.0. The PacStar 448 module, based on Cisco ESS 9300 technology, supports high-speed switching for the MDC’s servers and storage devices.

It enables PacStar MDC to perform compute and network tasks in tactical and expeditionary settings. The module features ten 10 Gigabit Ethernet SFP+ enhanced small form-factor pluggable transceiver ports that deliver speed and density.

The enhanced network interconnect performance provided by PacStar 448 supports command and control, Internet of Things, cloud, storage replication, artificial intelligence (AI) and machine learning. The module also is a drop-in replacement for previous Ethernet switch modules. Curtiss-Wright acquired PacStar last fall.

PacStar MDC is based on the PacStar 400-series commercial off-the-shelf (COTS) small-form-factor modules. It is a tactical and expeditionary rugged data center capable of hosting mission command, cloud storage, sensor fusion, AI, and analytics applications.

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DriveNets Ltd., a startup developing a software container-based networking platform for carriers, today announced that it has raised $208 million in funding at a valuation exceeding $1 billion. 

D1 Capital Partners led the round with participation from Atreides Management as well as returning DriveNets backers Bessemer and Pitango. The last two firms have made “significant follow-on investments,” according to the startup. Bessemer and Pitango previously jointly led a $110 million funding round for DriveNets in early 2019.

Israel-based DriveNets provides a software platform that carriers and cloud providers can use to manage their networks. AT&T, one of the largest carriers in the U.S., disclosed late last year that it has adopted the platform to help manage its data traffic. DriveNets is hoping to use the new $208 million in funding to expand geographically and win deals with additional service providers around the world.

Large organizations such as carriers traditionally built their networks using pricey proprietary appliances that combine hardware and software in a single integrated package. DriveNets’ networking platform, in contrast, doesn’t come as part of a traditional appliance but can be deployed on low-cost commodity gear that’s more affordable than proprietary hardware. The result, DriveNets says, is a reduction in infrastructure expenses for customers.

The startup implemented its platform as a set of container-based microservices to allow for efficient scaling. When carriers using DriveNets’ platform need more capacity to meet user demand, they simply add more commodity networking gear to their network. The equipment comes from hardware makers that DriveNets itself certifies and the startup also trains the channel partners charged with setting up the hardware at carriers’ sites.

After the deployment is complete, a carrier can deploy its own custom networking applications on top of the platform. The startup says the platform allows customers to run, among other things, core routing applications, which are the applications responsible for managing the most important and highest capacity data routes in a carrier’s network. Those data routes are collectively often referred to as the internet backbone.

Customers can also use DriveNets’ software to build other kinds of applications, such as services to handle so-called edge provider routing. That’s a process wherein a carrier forwards data traffic to another internet provider, which is often necessary to enable connections between devices far apart from each other.

DriveNets positions its platform’s application capabilities as a way to ease 5G rollouts. For the telecommunications sector, a key benefit of 5G is that the technology can enable carriers to bring new revenue-generating offerings to market, such as  edge hosting services and highly customized internet packages tailored to specific customers’ needs. Providing such features requires deploying a fleet of network applications to manage the behind-the-scenes operational details. 

DriveNets has raised more than $320 million from investors to date.

The idea of replacing proprietary systems with lower-cost commodity gear is also gaining traction in other parts of the telecommunications market. Historically, the antennas and base stations that connect subscribers’ devices to carrier networks used proprietary computing hardware to process the incoming data traffic. Using a technology known as vRAN, carriers are now replacing that hardware with commodity servers capable of performing the task more efficiently.

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The networking industry in 2020 is not the same as it was in 2019, nor any year prior for that matter.
The global pandemic has changed the role of the network and networking technology from being an enabler for business to being the foundation on which all businesses, schools and homes run. That’s not just an idle observation – that’s also the view of the top networking industry CTOs.

At the annual Interop conference, which Enterprise Networking Planet has covered for decades (and like all other events, has gone digital), the CTOs of the largest and most influential networking vendors came together in a panel to talk about the future of networking.

No conversation in 2020 can start or end without discussing the pandemic, however.

John Apostolopoulos, VP & CTO at Cisco, said the pandemic has helped change the mindset that many have about IT. It’s now about how, under challenging circumstances, IT can make changes to help enable networks meet the demands of remote work. Raj Yavatkar, CTO at Juniper Networks, and Partha Narasimhan, CTO of HPE/Aruba, emphasized that now more than ever the network is the basis not just of IT but of business continuity.

While security has always had a role in networking, moving forward that role will become even more integrated than before, according to all three CTOs. With everyone working from home, the complexity of securing distributed organizations is exponentially more difficult than when everyone is inside an office. With security integrated into the fabric of the network, that same network that enables connectivity can and should enable secured connectivity.

The Growing Role of SD-WAN
Before the pandemic, and likely continuing after it’s over, SD-WAN has been a hot topic of conversation.

Yavatkar said he’s seen a lot of hype around SD-WAN and in his view, it’s a technology that’s still in its first generation. Over the next few years, Yavatkar expects that we’ll see a second generation of SD-WAN technology that provides a more integrated approach that combines security, networking, access and even artificial intelligence-powered insights.

Apostolopoulos sees SD-WAN evolving in the coming years to being more about enabling end-to-end application delivery and experiences. It’s an idea that has a lot of value given the extremely distributed nature of application deployments and microservices today.

Narasimhan observed that in the last few years there has been a lot of consolidation in the SD-WAN space and he expects there will be further consolidation in the years ahead. Ultimately he expects that there will only be five or six important SD-WAN vendors in the market.

How Networking will be Measured in the Future
While conversations about SD-WAN, security and remote work are interesting, perhaps the most insightful part of the CTO panel was about how networking – and by extension networking professionals – will be judged and evaluated in the future.

There was a time when the nuts and bolts, the so-called plumbing of the network, was what networking was all about. Networking conversations and professionals had the job of learning all the nuances of protocols and configuration.

The network today is more of a given, a foundation, and networking is judged by what it enables.

“It’s about the quality of experience,” Yavatkar said.

And that will keep pressure on networking vendors to continue evolving regardless of what the future of work looks like.

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Microservices-style applications rely on fast, dependable network infrastructure in order to respond quickly and reliably, and the service mesh can be a powerful enabler.

At the same time, service-mesh infrastructure can be difficult to deploy and manage at scale and may be too complex for smaller applications, so enterprises need to carefully consider its potential upsides and downsides in relation to their particular circumstances.

What is a service mesh?

A service mesh is infrastructure software that provides fast and reliable communications between the microservices that applications may need. Its networking features include application identification, load balancing, authentication, and encryption.

Network requests are routed between microservices via proxies that run alongside the service. These proxies form a mesh network to connect the individual microservices. A central controller provides for access control, as well as network and performance management.

A service mesh provides logical isolation of microservices applications from the complexity of network routing and security requirements. The abstraction provided by a service mesh enables rapid and flexible deployment of microservices without constantly requiring the data-center networking team to intervene.

Why do microservices-style apps need service mesh?

Applications based on microservices have a different architecture from hypervisor-based applications. They have numerous services running in individual containers on different servers or cores, and the frequency of transactions between these microservices within a single application may require low latency and significant bandwidth. Plus more than one application may need to access the same microservices.

Container-based micro services can often move their physical location from server to server yet provide only limited data about where they’ve moved to and that their status has changed. This makes it difficult for IT professionals to “find” them to resolve application-performance issues.

Meanwhile, DevOps teams require logical isolation from network complexity. They want to rapidly develop and change applications, and they require networking teams to provide networking and security adjustments such as provisioning vLANs in order to do their work.

Service mesh enables significant networking and security benefits for microservices applications. It abstracts the networking infrastructure, thus enabling microservices applications to maintain networking and security polices without requiring the intervention of the data-center networking team for each change.

Key requirements for networking microservices include:

• Network performance at scale
• Ease of provisioning networking, compute, and storage resources for new applications
• Ability to rapidly scale bandwidth by application
• Workload migration between internal data centers and public cloud
• Application isolation to enhance security and support multi-tenancy

To meet these requirements IT organizations will need to integrate service-mesh automation and management information into a more comprehensive data-center networking-management system–especially as container deployments become more numerous, complex and strategic.

For applications that are well suited to service mesh deployments, IT organizations will need to plan integration of the technology into their overall management/automation platforms. To prepare, IT teams must evaluate the range of service-mesh options–cloud, open source, vendor-supplied–as the technology continues to mature.

Service-mesh technology options can be vendor-supported or open source. Istio is a leading open-source service-mesh option driven by Google. Other open-source projects include Linkerd, HAProxy and Envoy. Leading IaaS suppliers have their own service mesh offerings. Leading network and IT suppliers and start-ups also have service mesh offerings.

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Machine learning is a branch of artificial intelligence that focuses on getting a computer to figure out how to solve a problem, instead of humans telling it how to do so.

In the case of networking, machine learning can be used to improve analytics, management and security. But, to fully understand how machine learning in networking can work, it’s helpful to understand a couple of machine learning models.

Machine learning tools embody one or more computational models, such as neural networks and genetic algorithms.

Neural networks are inspired by the behavior of biological neurons. Artificial neurons — software — are connected to each other in layers. Each can send signals to neurons in the next layer along connections that are weighted based on input importance from a previous layer. Receiving signals of sufficient strength triggers a neuron to send its own signals. The machine learning algorithm tunes the signals sent and the weightings on the connections through a training process.

Genetic algorithms also draw inspiration from nature. Developers start with multiple methods of identifying the correct output based on input data. They then use machine learning to mimic what nature does: Weed out the least fit options, mix and mutate the survivors, and repeat the cycle to improve results over time.

Applying machine learning in networking

Machine-learning-driven analytics tools are great at learning what normal network behavior looks like and highlighting anomalies relative to it. This awareness drives the utility of machine learning in networking for three areas: performance management, health management and security.

Performance management. Tools equipped with machine learning can help both with moment-by-moment traffic management and with longer-range capacity planning and management. These tools can see if traffic is spiking in some places or failing to flow in others, and they can direct automated or manual management responses.

Machine-learning-driven route analytics, for example, might shift traffic from connections using an internet provider experiencing a brownout to connections using a different provider. Machine learning management tools might shift half of the traffic headed for a back-end system from one data center to another based on traffic conditions.

Tools with machine learning can project traffic trends and help guide future decisions, like in the following examples:

• Is traffic in the data center shifting between rack to rack and rack unit to rack unit within a rack?
• Is traffic shifting from large numbers of small-packet flows to smaller numbers of large-packet flows?

Answers to these questions can help determine what kinds of networks an IT team designs — e.g., leaf-spine, switch-based mesh or host-based mesh.

Health management. Similarly, machine-learning-driven analytics can help spot when a network component is in the initial stages of failure and predict when those initial stages will appear for currently healthy nodes. Network equipment vendors are increasingly weaving analytics like this into management tools, especially those built around a SaaS offering.

Security. Spotting anomalies in network behavior can help cybersecurity teams find everything from a compromised hardware node to an employee going rogue on the company network. Machine learning techniques have vastly improved the behavioral threat analytics space, as well as distributed denial-of-service detection and remediation.

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