An e-learning course enables people to learn material at their own pace, with no instructor present. Machine learning programs optimize learning with supervised, unsupervised, semi-unsupervised, and reinforcement methods.
While individuals use these courses the most, the highest-paying customers include higher education institutions and corporations.
Of note, although the term is often used interchangeably with “distance learning” or “virtual classroom,” these terms typically refer to a learning environment overseen by a professional educator. E-learning is self-directed.
E-learning courses provide personalized education to people at low cost and with no commute. Indeed, there are very few restrictions on access beyond having internet.
Artificial intelligence programs identify student learning styles and particular material within the course that they struggle with understanding or retaining. The programs use the information to present the material in the best way for that individual to learn. In fact, good courses can even adjust lesson plans immediately, for the benefit of the user.
Other AI assistance includes chatbots, voice recognition, and translation or transcription programs on video material.
An effective e-learning course requires a course strategy and assessment methods, first and foremost. Then user behavior,(especially the amount of time spent studying), completion rates, and progress rates direct developers in improving the course.
Natural Language Processing (NLP) programs are also very good for e-learning courses. These programs support chatbots, voice recognition, transcription services, and more.
A good e-learning program also includes user interactions with the e-learning environment (website, app, forums) and feedback, like surveys and reviews. This information not only provides user psychographics but also information on user difficulties. While the psychographic information can help marketers, the information on user struggles show developers how to improve their lesson plans.
Other external data include phone or computer usage data, so that the course can time push reminders to enter the course or use the app. Additionally, industry news (in education or whatever is relevant to the course topic) and psychological studies can provide valuable information on how to reach potential students and keep them studying until successful completion of the course.
Student challenges for this use case may, depending on the circumstances of the user, include inconsistent access to the internet and a lack of external motivation to complete the course. Even the most talented and interested students can struggle with this. For this reason, many developers incorporate gamification strategies into their courses, allowing them to retain many more students than traditional instruction would.
eLearning Industry: 5 Types of Big Data to Extract from Your LMS and How to Use It
ReserchGate: Artificial intelligence in e-learning
Academic E-learning Market size was valued at USD 103.8 Billion in 2019 and is expected to grow CAGR 11.23% by 2025.
Major factors driving the growth of Academic E-Learning Market size are increasing higher education e-Learning enrollments, increasing adoption of paid LMS, and the launch of new online degrees.
Academic eLearning is shifting toward more inclusive and collaborative approaches. Present developments in digital education are also heading towards more enticing formats, depending heavily on AR/VR and gamification to attract and sustain learners’ attention.
Cision PR Newswire: Academic E-Learning Market CAGR 11.23% by 2025 | Valuates Reports
Applicants to higher vocational education by sex, educational orientation, the student’s prior level of education, the student’s upper secondary education, national background, observations and year
Graduates, attendees and dropouts of higher vocational education by sex, educational orientation, national background, form of study of the programme, observations and year
Innovation co-operation with private sector by industrial classification NACE Rev. 2, size class, type of value, observations and period
Innovation co-operation with public sector and non-profits by industrial classification NACE Rev. 2, size class, type of value, observations and period
Median age of students and graduates of higher vocational education by sex, educational orientation, national background, observations and year