Setting up and managing virtual classrooms and webinars in Moodle involves using plugins and tools that integrate with the platform. Below is a step-by-step guide on how to set up and manage virtual classrooms and webinars:

1. Set Up Moodle for Virtual Classrooms

Moodle doesn’t have built-in tools for virtual classrooms and webinars, but it supports integrations with third-party services such as BigBlueButton, Zoom, and Jitsi Meet. These tools can be used to create a virtual classroom experience.

A. BigBlueButton Plugin

BigBlueButton is a popular choice for Moodle users who want to conduct live virtual classes or webinars.

1. Install the BigBlueButton Plugin:
   • Download the BigBlueButton plugin from the Moodle plugin repository.
   • Go to Site administration > Plugins > Install plugins in Moodle and upload the plugin zip file.
   • After installation, configure the plugin by entering the BigBlueButton server URL in Site administration > Plugins > Activity modules > BigBlueButtonBN.
   • If you don’t have a BigBlueButton server, you can use their cloud hosting service.
2. Add a BigBlueButton Activity:
   • Go to your course and turn editing on.
   • In the course section where you want the virtual classroom, click Add an activity or resource.
   • Select BigBlueButtonBN.
   • Configure the settings such as meeting name, description, start time, duration, and access settings.
3. Invite Students:
   • Students will see a link to the BigBlueButton session on the course page.
   • They can join the session by clicking the link at the scheduled time.
4. Manage Sessions:
   • As an instructor, you can manage the session (start, end, share resources, use whiteboard features) from within Moodle or the BigBlueButton interface.

B. Zoom Integration

Zoom is another widely used tool for virtual classrooms and webinars. You can integrate Zoom with Moodle using a Zoom plugin.

1. Install the Zoom Plugin:
   • Go to the Zoom Moodle Plugin page and download the plugin.
   • Install the plugin by going to Site administration > Plugins > Install plugins and uploading the zip file.
   • After installation, go to Site administration > Plugins > Activity modules > Zoom meetings and configure your Zoom API key and secret.
2. Create Zoom Sessions:
   • Once the plugin is installed, in your course, click on Add an activity or resource and select Zoom meeting.
   • Configure the settings such as meeting time, duration, password protection, etc.
3. Join and Manage Zoom Meetings:
   • Students can join the session by clicking the link on the course page.
   • Instructors can start the meeting from the Zoom interface or Moodle, and manage attendees, share content, and more.

C. Jitsi Meet Integration

Jitsi is a free, open-source alternative to Zoom and BigBlueButton. It can also be integrated with Moodle through the Jitsi plugin.

1. Install the Jitsi Plugin:
   • Download the Jitsi plugin from the Moodle plugin repository.
   • Install the plugin by navigating to Site administration > Plugins > Install plugins.
2. Add a Jitsi Activity:
   • In the course, click Add an activity or resource and select Jitsi Meet.
   • Configure settings like room name, meeting description, and set a password for the room.
3. Join the Meeting:
   • Students can join the Jitsi session directly from the Moodle course page by clicking the meeting link.

2. Manage Virtual Classrooms and Webinars

After setting up the tools, you’ll need to manage the virtual classroom or webinar experience:

A. Scheduling Sessions

1. Create Recurring Sessions: For ongoing virtual classrooms, you can schedule recurring sessions in BigBlueButton, Zoom, or Jitsi.
2. Set Notifications: Configure automatic notifications for students to remind them of upcoming sessions.

B. Managing Participants

• Role-based Permissions: Define roles such as moderator or participant and assign specific permissions (e.g., who can share the screen, use the microphone, or chat).
• Monitor Attendance: Most tools have attendance tracking features. Ensure this is enabled to monitor student participation.
• Control Access: Set meeting passwords, waiting rooms, and access settings to control who can enter the meeting.

C. Recording Sessions

• Most tools (Zoom, BigBlueButton, Jitsi) support recording. This allows students who missed a session to catch up.
• In BigBlueButton, recordings are saved and available directly on the course page.
• In Zoom, recordings can be saved in the cloud or on your local device and shared as a link.

D. Collaborate During Sessions

1. Whiteboard and Screen Sharing: Use the whiteboard, screen sharing, and annotation tools to make the class interactive.
2. Breakout Rooms: Both Zoom and BigBlueButton offer breakout rooms, where you can split participants into smaller groups for discussions or activities.
3. Chat and Polling: Engage students using in-meeting chat and polls.

3. Post-Class Management

1. Grade Integration: Moodle allows you to integrate activities with grading. For instance, you can create quizzes or assignments during or after the webinar to assess student participation and understanding.
2. Discussion Forums: Set up forums for post-session discussions, where students can ask questions and reflect on the session.

4. Additional Tips

• Mobile Access: Ensure students know they can access the virtual classroom via the Moodle mobile app and the corresponding mobile apps (Zoom, BigBlueButton, or Jitsi).
• Tech Support: Provide students with a quick guide to troubleshooting common technical issues like microphone setup, camera permissions, or connection issues.
• Recording Access: Allow students to review the recording after class if they miss a session.

By integrating one of these tools with Moodle and following these steps, you can effectively set up and manage virtual classrooms and webinars for your students.

The post How to set up and manage course virtual classrooms and webinars in Moodle? appeared first on Artificial Intelligence.

Step 1: Create a Quiz Activity

1. Log in to Moodle as an instructor or administrator.
2. Navigate to your course where you want to add the quiz.
3. Turn on editing mode (click the “Edit mode” toggle in the top-right corner).
4. Click “Add an activity or resource” in the desired section.
5. Select “Quiz” from the list and click Add.
6. Configure Quiz Settings:

• General Section: Set a name and description (optional).
• Timing: Set open/close dates and time limits.
• Grade: Define passing grade and attempts allowed.
• Layout: Choose question order (sequential or random).
• Question Behavior: Shuffle answers, allow multiple attempts, etc.
• Review Options: Control what students see after submission.
• Save and display when done.

Step 2: Add Questions to the Quiz

Moodle supports multiple question types. Here’s how to add them:

Option A: Create New Questions Directly in the Quiz

1. In the quiz page, click “Edit quiz”.
2. Click “Add” → “a new question”.
3. Choose a question type from the list (e.g., Multiple Choice, True/False, Short Answer, etc.).
4. Configure the question:

• Multiple Choice (MCQ):
  • Enter the question text.
  • Set one or multiple correct answers.
  • Assign grades for each choice.
  • Shuffle options if needed.
• True/False:
  • Enter the statement.
  • Select the correct answer (True or False).
• Short Answer:
  • Enter the question.
  • Provide possible correct answers (with partial grading if needed).
• Other types (Essay, Matching, Numerical, etc.) follow similar steps.

5. Save changes to add the question to the quiz.

Option B: Use the Question Bank

1. Go to Course Administration → “Question Bank” → “Questions”.
2. Click “Create a new question” and select a type (MCQ, True/False, etc.).
3. Configure the question (same as above) and save it.
4. In the quiz, click “Add” → “from question bank” to insert saved questions.

Option C: Import Questions (Bulk Upload)

1. Prepare questions in a supported format (e.g., Aiken, GIFT, XML).
2. Go to Question Bank → Import.
3. Upload the file and select the format.
4. Import questions and add them to the quiz.

Key Question Types in Moodle

Final Steps

• Preview the quiz to test questions.
• Set restrictions (password, time limit) if needed.
• Save and make the quiz available to students.

his process allows you to create a Moodle quiz with various question types efficiently.

The post How to Create Engaging Quizzes in Moodle: A Step-by-Step Guide appeared first on Artificial Intelligence.

Step 1: Log in as an Administrator or Course Creator

• Ensure you have the necessary permissions to create courses.

Step 2: Access the Course Creation Page

1. Go to Site administration (from the left sidebar or the gear icon).
2. Click on Courses > Manage courses and categories.
3. Select the category where you want to add the new course (or create a new category first).

Step 3: Add a New Course

1. Click Create new course.
2. Fill in the required details:

• Course full name (e.g., “Introduction to Python Programming”).
• Course short name (e.g., “PY101” – used for navigation).
• Course category (select from dropdown).
• Course summary (a brief description visible in course listings).
• Course format (Topics, Weekly, Social, etc.).
• Start and end dates (optional).
• Course ID number (optional, for external systems).

3. Click Save and display (or Save and return to go back to the course list).

Step 4: Configure Additional Course Settings (Optional)

After creation, you can further customize:

• Enrollment methods (e.g., manual, self-enrollment, guest access).
• Course completion settings (if tracking progress).
• Groups and groupings (if using collaborative features).
• Restrictions & access control (conditional availability).

Step 5: Add Content and Activities

• Click Turn editing on (top-right pencil icon).
• Use the “+ Add an activity or resource” button to add:
• Resources (Files, URLs, Pages).
• Activities (Assignments, Quizzes, Forums, etc.).

Step 6: Enroll Users

• Go to Course administration > Users > Enrolled users.
• Click Enroll users and select participants (students, teachers, etc.).

The post How to create a new course in Moodle? appeared first on Artificial Intelligence.

As larger neural networks with more layers and nodes are considered, reducing their storage and computational cost becomes critical, especially for some real-time applications such as online learning and incremental learning

In addition, recent years witnessed significant progress in virtual reality, augmented reality, and smart wearable devices, creating challenges in deploying deep learning systems to portable devices with limited resources (e.g. memory, CPU, energy, bandwidth).

Here are a few methods that are part of all compression techniques:

Parameter Pruning And Sharing

• Reducing redundant parameters which are not sensitive to the performance
• Robust to various settings
• Redundancies in the model parameters are explored and the uncritical yet redundant ones are removed

Low-Rank Factorisation

• Uses matrix decomposition to estimate the informative parameters of the deep convolutional neural networks

Transferred/Compact Convolutional Filters

• Special structural convolutional filters are designed to reduce the parameter space and save storage/computation

Knowledge Distillation

• A distilled model is used to train a more compact neural network to reproduce the output of a larger network

Now let’s take a look at a few papers that introduced novel compression models:

1.Deep Neural Network Compression with Single and Multiple Level Quantization

In this paper, the authors propose two novel network quantization approaches single-level network quantization (SLQ) for high-bit quantization and multi-level network quantization (MLQ).

The network quantization is considered from both width and depth level.

2.Efficient Neural Network Compression

In this paper the authors proposed an efficient method for obtaining the rank configuration of the whole network. Unlike previous methods which consider each layer separately, this method considers the whole network to choose the right rank configuration.

3.3LC: Lightweight and Effective Traffic Compression

3LC is a lossy compression scheme developed by the Google researchers that can be used for state change traffic in distributed machine learning (ML) that strikes a balance between multiple goals: traffic reduction, accuracy, computation overhead, and generality. It combines three techniques — value quantization with sparsity multiplication, base encoding, and zero-run encoding.SEE ALSO

DEVELOPERS CORNER

WHAT IS DATAOPS? THINGS AROUND IT THAT YOU NEED TO KNOW

4.Universal Deep Neural Network Compression

This work for the first time, introduces universal DNN compression by universal vector quantization and universal source coding. In particular, this paper examines universal randomised lattice quantization of DNNs, which randomises DNN weights by uniform random dithering before lattice quantization and can perform near-optimally on any source without relying on knowledge of its probability distribution.

5.Compression using Transform Coding and Clustering

The compression (encoding) approach consists of transform and clustering with great encoding efficiency, which is expected to fulfill the requirements towards the future deep model communication and transmission standard. Overall, the framework works towards light weight model encoding pipeline with uniform quantization and clustering has yielded great compression performance, which can be further combined with existing deep model compression approaches towards light-weight models.

6.Weightless: Lossy Weight Encoding

The encoding is based on the Bloomier filter, a probabilistic data structure that saves space at the cost of introducing random errors. The results show that this technique can compress DNN weights by up to 496x; with the same model accuracy, this results in up to a 1.51x improvement over the state-of-the-art.

7.Adaptive Estimators Show Information Compression

The authors developed more robust mutual information estimation techniques, that adapt to hidden activity of neural networks and produce more sensitive measurements of activations from all functions, especially unbounded functions. Using these adaptive estimation techniques, they explored compression in networks with a range of different activation functions. 

8.MLPrune: Multi-Layer Pruning For Neural Network Compression

It is computationally expensive to manually set the compression ratio of each layer to find the sweet spot between size and accuracy of the model. So,in this paper, the authors propose a Multi-Layer Pruning method (MLPrune), which can automatically decide appropriate compression ratios for all layers.

Large number of weights in deep neural networks make the models difficult to be deployed in low memory environments. The above-discussed techniques achieve not only higher model compression but also reduce the compute resources required during inferencing. This enables model deployment in mobile phones, IoT edge devices as well as “inferencing as a service” environments on the cloud.

The post 8 NEURAL NETWORK COMPRESSION TECHNIQUES FOR ML DEVELOPERS appeared first on Artificial Intelligence.

Futurist Arthur C. Clarke wrote, “Any sufficiently advanced technology is indistinguishable from magic.” The magic of software (giving data and rules to get answers) is often confused with the magic of machine learning (giving data and answers to get rules) but it is machine learning, not software that is transforming the world of computer chess.

So far, computer chess programs codified the actions of the best human players and inevitably pivoted around the strategy of “material”, wherein the number and value of pieces mattered most. But recently, AlphaZero playing Stockfish counter-intuitively sacrificed a bishop for a pawn. Reports suggest AlphaZero taught itself chess from scratch in just four hours by playing against itself and rejected human rules developed over centuries.

As it started with only the basic rules, researchers suggest that its lack of knowledge of human chess history may have enabled AlphaZero to see the game in a fresh way. We’d like to make the case that machine learning is transforming online education, but Indian online education is held back by regulatory cholesterol.

Before diving into online education, let’s reflect on challenges in education. Knowing must shift to learning because Google knows everything. Metrics need shifting from inputs to outcomes because only money is not working. Differentiation and personalization are not about making things easier for children but making learning accessible by tapping into motivations and abilities. Assessment needs to shift from annual exams to regular feedback. Teachers knowing content is not the same as their ability to create learning.

There is an element of eat your spinach in education, but schools largely work for front-row students. Lifelong learning needs a continuum between prepare, repair and upgrade. Employability is an objective. Timetables are an industrial-era model of one size fits all that blunt choices and learner agency. Most importantly, if you think formal education is everything, then just look at the president of the US.

Many educators agree online learning can transform education, but they don’t know how. Textbook and PowerPoint repackaged e-learning—the digital equivalent of paving the cow path rather than building a highway—mean that, so far, online offerings have not been able to blunt the obvious downsides of physical classrooms (one size fits all, huge costs, uneven teacher quality, etc.) despite obvious advantages (teaching with different speeds to people with different backgrounds and different starting points, class of one, cost, on-the-go, on-demand, crowdsourced, gamified, etc.).

We believe that the massification of machine learning could be the missing ingredient—enabling personalization, flip classrooms, rethinking assessments, enabling non-conventional credentialing, etc. Personalization via intelligent tutor systems that track “mental steps” and modify feedback, exercises, explanations and intervention to promote self-regulation, self-monitoring and self-explanation would revolutionize engagement.

A recursive and real-time meta-analysis of learning outcomes across students, cohorts, schools would considerably improve the efficacy of flip classrooms (where classrooms are used for discussions and students finish the lecture and learning in advance). Natural language, computer vision, and deep learning could answer student questions.

These systems are infrastructure to improve the signalling value of non-conventional or micro-credentialing, which in turn would discover the cognitive, behavioural and affective preferences for each learner. The biggest impact would be in assessment by moving it from an event to a process and reducing its labour intensity; for instance, tools like Sochobots, Lingolens and Gradescope use computer vision and machine learning to grade students’ work (even stuff like essays).

However, Indian online education is held back by regulatory cholesterol that distinguishes between distance and online education. E-commerce would never have happened if financial regulators had insisted on separating the offline and online. UPI/BHIM have gone from 0.1 million transactions in the month before demonetization to 140 million last month; they will reach a billion in a year. Payments for Indian consumers are almost free (marginal cost), while in the US regulations have protected margins for private platforms.

India’s regulatory issues include hubris (the ability of regulators to anticipate all situations), micromanaging (including defining the type of web links on your website) and continuous lobbying because of poor state capacity to effectively regulate, supervise and enforce. It is too late for evolution; we need a revolution under which universities do not require permission to launch any online courses.

Regulators can prescribe broad guidelines with a policy objective of creating biodiversity and innovation in business and operating models that would tackle the difficult trade-off between cost, quality and scale. Like with most treatment of regulatory cholesterol, this revamped regulation would be accompanied by improved supervision and strengthened consumer protection. But drunk-driving is not an argument against cars and regulations that ban or make online education difficult are silly.

Einstein once said that if you judge a fish by its ability to climb a tree, it will live its life believing it is stupid. Physical classrooms—because of the limitations of time and space—often make this error. India needs a massification and vocationalization of higher education at a cost that only online learning can do. This needs machine learning. But before that we need changes to our regulatory cholesterol.

The post Machine Learning can transform education appeared first on Artificial Intelligence.