5G for Education

Nurturing Future-Ready Minds

5G for Energy
5G for Energy

Schools and Classrooms Transformed

Level up the learning experience with 5G and instil students with a tech-savvy approach to problem-solving.

Smart classrooms e.g. IoT-enabled attendance
Smart classrooms e.g.
IoT-enabled attendance
5G-powered tools take the hassle out of menial tasks like roll call and give educators more time to focus on the needs of their students.
AR/VR immersive lessons
AR/VR immersive lessons
AR/VR technology captures students’ imaginations and lets them explore everything the world has to offer without leaving the classroom.
3D holographic telepresence
3D holographic telepresence
Ultra-high bandwidth and low latency allow for the projection of 3D holographic images into the classroom to make learning more immersive and engaging.
Cloud-based learning materials storage for learning on demand
Cloud-based learning materials
storage for learning on demand
Students have access to learning materials anytime, anywhere with cloud-based storage.

Pain points in the Education industry

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Pain Point 1

Repetitive tasks by lecturers

Explore Solutions
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Pain Point 2

Improving remote learning interactions between teachers and learners

Explore Solutions
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Pain Point 3

Students dropping out unexpectedly

Explore Solutions
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How 5G-enabled digital solutions can help

Make an inventory of tasks and determine what data to be collected to take out many of these tasks using AI.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • We will know what type of sensors to focus on once we know the tasks at hand

Step 2: Connectivity

Step 2: Connectivity More Information
  • Data sent to Enterprise store

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • Not required for real-time intervention, but this is expected to change

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • Equipment data stored to train AI algorithms
  • All data in Enterprise store

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • Type of models still to be determined
  • Maybe NLP gets information out of large documentation sets.

Application logic

Scenario 1

  • Create an overview of the main tasks of a lecturer.
  • Look at all tasks and determine how ML / MV / NLP can help execute or simplify these tasks. Start tasks where AI can make most of the impact.
  • Based on this list, determine if/what data is needed to be able to use this as input for these models.
  • We need SMEs and data scientists to develop and enhance these models.
  • Reach out to a number of universities to see if / what tooling they are using in this space.
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Expected benefits

  • More efficient use of lecturers
  • Higher success rate of students in completing their academic qualifications

Note: 

iconAI – Artificial Intelligence

iconML – Machine Learning

iconMV – Machine Vision​

iconSME – Subject Matter Expert

iconP&ID - Piping and Instruments Diagram

iconGHG - Greenhouse Gas

iconIT - Information Technology

iconOPAF - Open Process Automation Forum

iconOT - Operational Technology

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Key value created

  • Improved productivity of the lecturer
  • Reduced costs since more work can be done in shorter periods

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

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How 5G-enabled digital solutions can help

Looking at various types of data (results; environment; personal; etc.) and determine how XR (VR/AR) can improve the student experience.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • Look at the type of data that is important to collect to know if/what sensors to use
  • If / what streaming analysis can be used

Step 2: Connectivity

Step 2: Connectivity More Information
  • Sensor data transport
  • Other relevant data

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • Not required for real-time, but this is expected to change

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • Equipment data stored to train AI algorithms
  • All data in Enterprise store

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • OpenXR as the standard platform to be used
  • Type of models to be curated based on applications and services

Application logic

Scenario 1

  • Using the OpenXR XR platform we can support a broad spectrum of these devices: Separate contents from direct device choice.
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Expected benefits

  • Learning to use XR as a prime user interface

Note: 

iconAI – Artificial Intelligence

iconML – Machine Learning

iconMV – Machine Vision​

iconSME – Subject Matter Expert

iconP&ID - Piping and Instruments Diagram

iconGHG - Greenhouse Gas

iconIT - Information Technology

iconOPAF - Open Process Automation Forum

iconOT - Operational Technology

icon

Key value created

  • Better student results

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

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How 5G-enabled digital solutions can help

Using 5G and AI against data collected to predict early issues that could potentially lead to students dropping out.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • List the data sources/types of importance to follow student progress
  • Based on that, determine if/what sensors to be used

Step 2: Connectivity

Step 2: Connectivity More Information
  • Sensor-initiated data to Enterprise store

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • Not required for real-time intervention, but this is expected to change

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • All edge data collected to be loaded in Enterprise store
  • All data in Enterprise store

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • Types of models to be curated based on applications and services
  • However, they will most likely be ML-driven
  • First applications are coming to the market to manage this. 

Application logic

Scenario 1

  • Make all data about students available:
  • What is relevant data?
  • What other info will give info about this? Is there data external to the school, etc.

Scenario 2

  • Based on the answers in the previous column we can decide if/what the AI (ML / MV) models will be.
  • Identify the signs (in data terms) which could identify students going to drop out.
  • Using SMEs and data scientists to get to the appropriate models for this; This will take several steps due to the new nature of this.
  • Therefore, do implement but early results will be poor.

Scenario 3

  • As stated, we need to be ready for each type of model.
  • Given that we will collect data about results, etc. the focus will be ML driven.
  • However if we develop data about student faces whilst studying, then it will be MV-based
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Expected benefits

  • Higher success rate of students in completing their academic qualification.

Note: 

iconAI – Artificial Intelligence

iconML – Machine Learning

iconMV – Machine Vision​

iconSME – Subject Matter Expert

iconP&ID - Piping and Instruments Diagram

iconGHG - Greenhouse Gas

iconIT - Information Technology

iconOPAF - Open Process Automation Forum

iconOT - Operational Technology

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

Cross-industry solutions

Autonomous robots and aerial drones can traverse harsh and dangerous environments and reduce hazard risks for human workers. Such technology will provide enhanced monitoring and safety upkeep for facilities. They can be fitted with various sensors geared towards the purpose they are meant for and by using 5G, we can deploy them in real-time whereby the data collected can be sent directly to an AI application for investigation and immediate further action.

Wearable technology allows security and medical personnel to conduct background and health checks efficiently. Of course, they have a much broader focus and can be used in many different industries where clothing has been enriched with wearable technologies. Wearables are also able to collect extra information for a 5G-enabled digital service.

Sensors are crucial for capturing real-time data and insights and pave the way for more informed decision-making. Sensors will be the data collection point for every 5G-enabled digital service and therefore will have to cover a broad spectrum of types of information to be collected and the way they are connected to the DNB 5G network.

XR is set to take work and play to the next level with cutting edge technology never seen before. These devices have the potential to boost productivity and enhance entertainment, changing the way we experience our reality through digital transformation. There is an increasing broad spectrum of devices available for both consumer and enterprise markets, making it increasingly important for each user experience.

DNB Ecosystem

Disclaimer of Endorsement

All information provided on potential 5G-enabled solutions and potential vendors is provided for information purposes only and based on our awareness of the available 5G-enabled solutions and potential vendors in the market. It does not constitute endorsement, recommendation or favouring by DNB. It is your responsibility to verify and evaluate such 5G-enabled solutions and potential vendors. DNB is not an agent or legal representative of any of such 5G-enabled solutions and potential vendors, is not associated or endorsed by such potential vendors, has no authority to act on behalf of any such potential vendors, and will be an independent party if you enter a business relationship with such potential vendors. Third party links included are for convenience only and are not under DNB’s control. DNB does not assume any responsibility or liability for your use of such third-party links.