Market Overview:
The global machine learning in education market is expected to grow at a CAGR of over 25% during the forecast period from 2018 to 2030. The growth of this market can be attributed to the increasing demand for intelligent tutoring systems, virtual facilitators, content delivery systems, and interactive websites across the globe. Additionally, the growing adoption of cloud-based machine learning in education solutions is also contributing significantly to the growth of this market. North America dominates the global machine learning in education market and is expected to maintain its dominance throughout the forecast period.
Product Definition:
Machine learning is a method of teaching computers to learn from data, without being explicitly programmed. It has many applications in education, including personalized recommendations (such as for course choices or textbooks), adaptive tutoring, and identification of student needs. Machine learning can also help educators detect cheating and plagiarism.
Cloud-Based:
Cloud-based solutions are software or a service that is delivered over the internet and used on a device without any additional hardware. The cloud-based solutions offer various advantages such as easy accessibility from any location, flexibility of using the solution on different devices, low cost of maintenance and installation, high scalability based on demand etc. These factors have increased its acceptance among enterprises and education institutions for data storage & processing in recent years.
On-Premise:
On-premise solutions are software and hardware systems that are installed on the end user's premises. The end-users can access these solutions directly, without relying on a third party for their operations. These types of solutions have been around for decades; however, they have gained significant traction in recent years due to high demand from customers seeking greater control over data security and privacy protection as well as lower latency compared to cloud based services.
Application Insights:
The others segment dominated the market in 2017 and is expected to witness significant growth over the forecast period. Other applications of machine learning include fraud detection, predictive asset management, risk assessment and management, clinical trials monitoring & evaluation, etc. Intelligent tutoring systems are one of the most popular uses of machine learning in education as it helps teachers deliver effective lessons by analyzing student data from their past experiences. Virtual facilitators are also gaining traction as they help improve communication between students and teachers by delivering personalized content based on student performance. The virtual facilitator uses natural language processing techniques to understand what has been said previously and repeat similar patterns during discussions with students for better understanding among both parties involved in a discussion or lesson plan formulation process.
Regional Analysis:
North America dominated the market in terms of revenue share in 2017. This can be attributed to the presence of a large number of players operating in this region, along with increased adoption machine learning techniques by companies for enhancing their products and services. Moreover, increasing investments by governments for leveraging ML-based technologies are also expected to drive regional growth over the forecast period.
Asia Pacific is anticipated to witness significant growth over the forecast period owing to increasing government initiatives aimed at improving education standards across countries such as China and India.
Growth Factors:
- Increasing demand for skilled professionals in the field of machine learning
- Growing popularity of online courses and Massive Open Online Courses (MOOCs)
- Rising number of research papers and journals on machine learning
- Advent of new technologies such as artificial intelligence (AI) and deep learning
- Emergence of startups focused on machine learning education
Scope Of The Report
Report Attributes
Report Details
Report Title
Machine Learning in Education Market Research Report
By Type
Cloud-Based, On-Premise
By Application
Intelligent Tutoring Systems, Virtual Facilitators, Content Delivery Systems, Interactive Websites, Others
By Companies
IBM, Microsoft, Google, Amazon, Cognizan, Pearson, Bridge-U, DreamBox Learning, Fishtree, Jellynote, Quantum Adaptive Learning
Regions Covered
North America, Europe, APAC, Latin America, MEA
Base Year
2021
Historical Year
2019 to 2020 (Data from 2010 can be provided as per availability)
Forecast Year
2030
Number of Pages
153
Number of Tables & Figures
108
Customization Available
Yes, the report can be customized as per your need.
Global Machine Learning in Education Market Report Segments:
The global Machine Learning in Education market is segmented on the basis of:
Types
Cloud-Based, On-Premise
The product segment provides information about the market share of each product and the respective CAGR during the forecast period. It lays out information about the product pricing parameters, trends, and profits that provides in-depth insights of the market. Furthermore, it discusses latest product developments & innovation in the market.
Applications
Intelligent Tutoring Systems, Virtual Facilitators, Content Delivery Systems, Interactive Websites, Others
The application segment fragments various applications of the product and provides information on the market share and growth rate of each application segment. It discusses the potential future applications of the products and driving and restraining factors of each application segment.
Some of the companies that are profiled in this report are:
- IBM
- Microsoft
- Google
- Amazon
- Cognizan
- Pearson
- Bridge-U
- DreamBox Learning
- Fishtree
- Jellynote
- Quantum Adaptive Learning
Highlights of The Machine Learning in Education Market Report:
- The market structure and projections for the coming years.
- Drivers, restraints, opportunities, and current trends of market.
- Historical data and forecast.
- Estimations for the forecast period 2030.
- Developments and trends in the market.
- By Type:
- Cloud-Based
- On-Premise
- By Application:
- Intelligent Tutoring Systems
- Virtual Facilitators
- Content Delivery Systems
- Interactive Websites
- Others
- Market scenario by region, sub-region, and country.
- Market share of the market players, company profiles, product specifications, SWOT analysis, and competitive landscape.
- Analysis regarding upstream raw materials, downstream demand, and current market dynamics.
- Government Policies, Macro & Micro economic factors are also included in the report.
We have studied the Machine Learning in Education Market in 360 degrees via. both primary & secondary research methodologies. This helped us in building an understanding of the current market dynamics, supply-demand gap, pricing trends, product preferences, consumer patterns & so on. The findings were further validated through primary research with industry experts & opinion leaders across countries. The data is further compiled & validated through various market estimation & data validation methodologies. Further, we also have our in-house data forecasting model to predict market growth up to 2030.
Regional Analysis
- North America
- Europe
- Asia Pacific
- Middle East & Africa
- Latin America
Note: A country of choice can be added in the report at no extra cost. If more than one country needs to be added, the research quote will vary accordingly.
The geographical analysis part of the report provides information about the product sales in terms of volume and revenue in regions. It lays out potential opportunities for the new entrants, emerging players, and major players in the region. The regional analysis is done after considering the socio-economic factors and government regulations of the countries in the regions.
How you may use our products:
- Correctly Positioning New Products
- Market Entry Strategies
- Business Expansion Strategies
- Consumer Insights
- Understanding Competition Scenario
- Product & Brand Management
- Channel & Customer Management
- Identifying Appropriate Advertising Appeals
8 Reasons to Buy This Report
- Includes a Chapter on the Impact of COVID-19 Pandemic On the Market
- Report Prepared After Conducting Interviews with Industry Experts & Top Designates of the Companies in the Market
- Implemented Robust Methodology to Prepare the Report
- Includes Graphs, Statistics, Flowcharts, and Infographics to Save Time
- Industry Growth Insights Provides 24/5 Assistance Regarding the Doubts in the Report
- Provides Information About the Top-winning Strategies Implemented by Industry Players.
- In-depth Insights On the Market Drivers, Restraints, Opportunities, and Threats
- Customization of the Report Available
Frequently Asked Questions?
Machine learning is a subset of artificial intelligence that allows computers to learn from data without being explicitly programmed. This means that machine learning algorithms can automatically improve over time, based on experience.
Some of the major players in the machine learning in education market are IBM, Microsoft, Google, Amazon, Cognizan, Pearson, Bridge-U, DreamBox Learning, Fishtree, Jellynote, Quantum Adaptive Learning.
The machine learning in education market is expected to grow at a compound annual growth rate of 25%.
Chapter 1 Executive Summary
Chapter 2 Assumptions and Acronyms Used
Chapter 3 Research Methodology
Chapter 4 Machine Learning in Education Market Overview
4.1 Introduction
4.1.1 Market Taxonomy
4.1.2 Market Definition
4.1.3 Macro-Economic Factors Impacting the Market Growth
4.2 Machine Learning in Education Market Dynamics
4.2.1 Market Drivers
4.2.2 Market Restraints
4.2.3 Market Opportunity
4.3 Machine Learning in Education Market - Supply Chain Analysis
4.3.1 List of Key Suppliers
4.3.2 List of Key Distributors
4.3.3 List of Key Consumers
4.4 Key Forces Shaping the Machine Learning in Education Market
4.4.1 Bargaining Power of Suppliers
4.4.2 Bargaining Power of Buyers
4.4.3 Threat of Substitution
4.4.4 Threat of New Entrants
4.4.5 Competitive Rivalry
4.5 Global Machine Learning in Education Market Size & Forecast, 2018-2028
4.5.1 Machine Learning in Education Market Size and Y-o-Y Growth
4.5.2 Machine Learning in Education Market Absolute $ Opportunity
Chapter 5 Global Machine Learning in Education Market Analysis and Forecast by Type
5.1 Introduction
5.1.1 Key Market Trends & Growth Opportunities by Type
5.1.2 Basis Point Share (BPS) Analysis by Type
5.1.3 Absolute $ Opportunity Assessment by Type
5.2 Machine Learning in Education Market Size Forecast by Type
5.2.1 Cloud-Based
5.2.2 On-Premise
5.3 Market Attractiveness Analysis by Type
Chapter 6 Global Machine Learning in Education Market Analysis and Forecast by Applications
6.1 Introduction
6.1.1 Key Market Trends & Growth Opportunities by Applications
6.1.2 Basis Point Share (BPS) Analysis by Applications
6.1.3 Absolute $ Opportunity Assessment by Applications
6.2 Machine Learning in Education Market Size Forecast by Applications
6.2.1 Intelligent Tutoring Systems
6.2.2 Virtual Facilitators
6.2.3 Content Delivery Systems
6.2.4 Interactive Websites
6.2.5 Others
6.3 Market Attractiveness Analysis by Applications
Chapter 7 Global Machine Learning in Education Market Analysis and Forecast by Region
7.1 Introduction
7.1.1 Key Market Trends & Growth Opportunities by Region
7.1.2 Basis Point Share (BPS) Analysis by Region
7.1.3 Absolute $ Opportunity Assessment by Region
7.2 Machine Learning in Education Market Size Forecast by Region
7.2.1 North America
7.2.2 Europe
7.2.3 Asia Pacific
7.2.4 Latin America
7.2.5 Middle East & Africa (MEA)
7.3 Market Attractiveness Analysis by Region
Chapter 8 Coronavirus Disease (COVID-19) Impact
8.1 Introduction
8.2 Current & Future Impact Analysis
8.3 Economic Impact Analysis
8.4 Government Policies
8.5 Investment Scenario
Chapter 9 North America Machine Learning in Education Analysis and Forecast
9.1 Introduction
9.2 North America Machine Learning in Education Market Size Forecast by Country
9.2.1 U.S.
9.2.2 Canada
9.3 Basis Point Share (BPS) Analysis by Country
9.4 Absolute $ Opportunity Assessment by Country
9.5 Market Attractiveness Analysis by Country
9.6 North America Machine Learning in Education Market Size Forecast by Type
9.6.1 Cloud-Based
9.6.2 On-Premise
9.7 Basis Point Share (BPS) Analysis by Type
9.8 Absolute $ Opportunity Assessment by Type
9.9 Market Attractiveness Analysis by Type
9.10 North America Machine Learning in Education Market Size Forecast by Applications
9.10.1 Intelligent Tutoring Systems
9.10.2 Virtual Facilitators
9.10.3 Content Delivery Systems
9.10.4 Interactive Websites
9.10.5 Others
9.11 Basis Point Share (BPS) Analysis by Applications
9.12 Absolute $ Opportunity Assessment by Applications
9.13 Market Attractiveness Analysis by Applications
Chapter 10 Europe Machine Learning in Education Analysis and Forecast
10.1 Introduction
10.2 Europe Machine Learning in Education Market Size Forecast by Country
10.2.1 Germany
10.2.2 France
10.2.3 Italy
10.2.4 U.K.
10.2.5 Spain
10.2.6 Russia
10.2.7 Rest of Europe
10.3 Basis Point Share (BPS) Analysis by Country
10.4 Absolute $ Opportunity Assessment by Country
10.5 Market Attractiveness Analysis by Country
10.6 Europe Machine Learning in Education Market Size Forecast by Type
10.6.1 Cloud-Based
10.6.2 On-Premise
10.7 Basis Point Share (BPS) Analysis by Type
10.8 Absolute $ Opportunity Assessment by Type
10.9 Market Attractiveness Analysis by Type
10.10 Europe Machine Learning in Education Market Size Forecast by Applications
10.10.1 Intelligent Tutoring Systems
10.10.2 Virtual Facilitators
10.10.3 Content Delivery Systems
10.10.4 Interactive Websites
10.10.5 Others
10.11 Basis Point Share (BPS) Analysis by Applications
10.12 Absolute $ Opportunity Assessment by Applications
10.13 Market Attractiveness Analysis by Applications
Chapter 11 Asia Pacific Machine Learning in Education Analysis and Forecast
11.1 Introduction
11.2 Asia Pacific Machine Learning in Education Market Size Forecast by Country
11.2.1 China
11.2.2 Japan
11.2.3 South Korea
11.2.4 India
11.2.5 Australia
11.2.6 South East Asia (SEA)
11.2.7 Rest of Asia Pacific (APAC)
11.3 Basis Point Share (BPS) Analysis by Country
11.4 Absolute $ Opportunity Assessment by Country
11.5 Market Attractiveness Analysis by Country
11.6 Asia Pacific Machine Learning in Education Market Size Forecast by Type
11.6.1 Cloud-Based
11.6.2 On-Premise
11.7 Basis Point Share (BPS) Analysis by Type
11.8 Absolute $ Opportunity Assessment by Type
11.9 Market Attractiveness Analysis by Type
11.10 Asia Pacific Machine Learning in Education Market Size Forecast by Applications
11.10.1 Intelligent Tutoring Systems
11.10.2 Virtual Facilitators
11.10.3 Content Delivery Systems
11.10.4 Interactive Websites
11.10.5 Others
11.11 Basis Point Share (BPS) Analysis by Applications
11.12 Absolute $ Opportunity Assessment by Applications
11.13 Market Attractiveness Analysis by Applications
Chapter 12 Latin America Machine Learning in Education Analysis and Forecast
12.1 Introduction
12.2 Latin America Machine Learning in Education Market Size Forecast by Country
12.2.1 Brazil
12.2.2 Mexico
12.2.3 Rest of Latin America (LATAM)
12.3 Basis Point Share (BPS) Analysis by Country
12.4 Absolute $ Opportunity Assessment by Country
12.5 Market Attractiveness Analysis by Country
12.6 Latin America Machine Learning in Education Market Size Forecast by Type
12.6.1 Cloud-Based
12.6.2 On-Premise
12.7 Basis Point Share (BPS) Analysis by Type
12.8 Absolute $ Opportunity Assessment by Type
12.9 Market Attractiveness Analysis by Type
12.10 Latin America Machine Learning in Education Market Size Forecast by Applications
12.10.1 Intelligent Tutoring Systems
12.10.2 Virtual Facilitators
12.10.3 Content Delivery Systems
12.10.4 Interactive Websites
12.10.5 Others
12.11 Basis Point Share (BPS) Analysis by Applications
12.12 Absolute $ Opportunity Assessment by Applications
12.13 Market Attractiveness Analysis by Applications
Chapter 13 Middle East & Africa (MEA) Machine Learning in Education Analysis and Forecast
13.1 Introduction
13.2 Middle East & Africa (MEA) Machine Learning in Education Market Size Forecast by Country
13.2.1 Saudi Arabia
13.2.2 South Africa
13.2.3 UAE
13.2.4 Rest of Middle East & Africa (MEA)
13.3 Basis Point Share (BPS) Analysis by Country
13.4 Absolute $ Opportunity Assessment by Country
13.5 Market Attractiveness Analysis by Country
13.6 Middle East & Africa (MEA) Machine Learning in Education Market Size Forecast by Type
13.6.1 Cloud-Based
13.6.2 On-Premise
13.7 Basis Point Share (BPS) Analysis by Type
13.8 Absolute $ Opportunity Assessment by Type
13.9 Market Attractiveness Analysis by Type
13.10 Middle East & Africa (MEA) Machine Learning in Education Market Size Forecast by Applications
13.10.1 Intelligent Tutoring Systems
13.10.2 Virtual Facilitators
13.10.3 Content Delivery Systems
13.10.4 Interactive Websites
13.10.5 Others
13.11 Basis Point Share (BPS) Analysis by Applications
13.12 Absolute $ Opportunity Assessment by Applications
13.13 Market Attractiveness Analysis by Applications
Chapter 14 Competition Landscape
14.1 Machine Learning in Education Market: Competitive Dashboard
14.2 Global Machine Learning in Education Market: Market Share Analysis, 2019
14.3 Company Profiles (Details – Overview, Financials, Developments, Strategy)
14.3.1 IBM
14.3.2 Microsoft
14.3.3 Google
14.3.4 Amazon
14.3.5 Cognizan
14.3.6 Pearson
14.3.7 Bridge-U
14.3.8 DreamBox Learning
14.3.9 Fishtree
14.3.10 Jellynote
14.3.11 Quantum Adaptive Learning
