E - Learning Platform Architecture

Introduction 

 
An e-learning Platform typically has layers: Functional, Technical, and Infrastructural services. Functional services are related to e-learning and domain-related services. Technical services consist of a presentation layer, portal, web application framework, web layout management, process layer, rules engine, business activity monitoring, search, document management, image management, persistence, data access, auditing, logging, scheduling, caching, alert management, configuration management, and exceptional handling. The integration layer will have an API gateway that will provide integration, communications, service interaction, message processing, web request processing, and file handling services. Data Services will be related to reporting, business intelligence, backup, archiving, etc...
 
E - Learning Platform Architecture
 
Infrastructural services are typically database, application server, web server, operating system, network. Ecommerce functional components will be catalog management, order management, customer relationship management, customer management, customer service, customer self-service, and reporting, etc., Typically the e-learning platform has typical e-commerce, shopping cart, and payment steps. e-learning and other learning portals have the personalization, user management, course management, secured content, content tracking inbuilt.
 
The following modules are required to reward loyal and bulk purchasing customers:
 
Customer management, Customer information management, loyalty points management, Campaign management, and Promotions are some of the modules which are required. Job Management, Applicant Management, Faculty Management, Job Application Management, Interview Schedule Management, Interview Process Management, and Selection Process Management are the modules required for handling recruiting.
 
In critical use cases of an e-learning platform, non-functional requirements such as the number of users, the concurrent number of users, peaks of load, etc. The activities which are related to sizing are system sizing, layer-wise performance testing, and statistics gathering. The scaling and performance improvement plan of the existing e-learning platform will be understanding the application environment, categorizing workload, determining the most affected, applying the scaling and performance techniques.
 
AI, Blockchain, AR/VR, Video content management technologies are transforming the e-learning platforms. Customer-focused learning systems are evolving in enterprises. Enterprises are structuring their training courses to help solve the high-value requirements. Adaptive Learning Management system can be customized for better educational experience by choosing courses based on the learner's style. The courses are becoming more focussed and helping the learners grasp the content easily. e-learning platforms are differentiating by having faster ways to search the relevant content based on the need. The modern adaptive learning management platform is an end-to-end digital solution with features such as create, distribute, edit, and manage digital course from start to finish independent of the content.
 
Blockchain is being utilized for learning user’s on-boarding, payments, and rewards. New monetization models are emerging which are based on tokens from virtual currency offerings (ICO). The learning management solution provides a platform for educating professionals, learners, and publishers for smart contracts, token security, token mechanics, token allocation, white paper, and token deployment.
 
Video-centered training is gaining popularity and evolving into modern cloud-based learning platforms, high-speed wireless networks, connected mobile devices, social media channels and live video streaming standards. Video is winning the debate. With the right learning management system software, you can create courses for each of these learning types. Videos, MP3 files, written documents, and recorded webinars are some of the options which flexible LMS systems offer. The best e-learning software has live webinar tools, podcasts, and news feeds, as well as microlearning and blended learning options.
 
E - Learning Platform Architecture
 
VR creates a virtual environment for individuals. AR adds real pictures to the existing environment. VR and AR enable technologies for e-learning applications to support those assumptions. Virtual reality momentum has reached the state of saturation from the plateau. Learners are of different types such as visual learners, auditory learners, written learners, and kinesthetic learners. E-learning methodologies are categorized as live streaming learning, peer-to-peer learning, advanced self-learning, project learning, mentorship based learning, gamified learning, and group-based learning. The e-learning platform can be customized based on the educational experience by choosing the courses with the content which suits the learning style and ensure effective learning experience. E-learning platforms will provide learners to select the educational content from various choices and provide high value to instructors and students using recommendation algorithms.
 
Educational success and fulfillment are possible through personalization and optimization of the learner’s path which has courses and gaining of competencies. This new class of learning technology vendors is created to add their systems with cloud-based applications that are integrable with an enterprise-scale technology ecosystem. Enterprises are monitoring and analyzing learning methods & experiences.
 
Experiences with higher detail can be used to achieve an ongoing program and business outcomes. Tracking and reporting features of learner-oriented dashboards are used by the enterprise staff. Machine Learning is showing how AI algorithms can be applied to monitoring and capturing the teacher’s course instructions and directions to the students. Mapping e-learning experiences to learner’s needs, interests, and behaviors are enabled by adaptive e-learning management systems.
 
E - Learning Platform Architecture
 

Opensource Frameworks

 
Slack NLP
 
Artificial intelligence-based Natural Language Parsing framework scans and parses the messages from various communication channels like Slack, Social Media, email, and messaging tools. Slack is generally used for communicating new courses, mentor conversations, cohort groups, general discussions, and other postings from students. Natural Language Parsing framework has the features of identifying threads and conversations from different communication channels.
 
E - Learning Platform Architecture
 
This will help to identify and analyze the entities, sentiments, no speech, tone, mood, emotions, utterances, and other speech parts from the student’s conversations. This will help in using the history analysis about the students and their choices to adapt to the e-learning path and course content. The adaptive Learning framework is used to modify the progressing content of materials and assessments based on the educational goals of the learners. Natural Language Parsing framework helps in capturing time to mastery, completion rates, material reading complexity, language complexity, and complexity of content topics. These topics are important for historical analysis which will be used for adaptive learning.
 
Adapto Lernado
 
Adaptive learning is a learning method in which every learner who is learning can be better provided giving guidance to find correct answers themselves. When learners successfully understand a concept, you can help those students and offer high skill content to stretch their knowledge at a different pace. Adaptive Feedback to learners helps to provide the right information exactly when the learner wants it. The information can be a hint, a video, a graph, or additional course material. The feedback is typically triggered by a question response, the time spent on a screen, or the number of question attempts.
 
E - Learning Platform Architecture
 
Combining an adaptive learning framework with predictive analytics has a huge potential for improving the way learners learn and resulting in positive learning outcomes. AI-powered learning frameworks can gather and process big data from learners’ learning artifacts, like the amount of time spent on completing each activity, response latency, and assessment results. The data can be used to find patterns and create predictive models that help find individual learner needs and sharpen the content delivered to every student.
 
Algorithms analyze data much faster than human beings. So, learners get the content, prompts, and interventions—all of which change in real-time based on their individual needs and abilities. Although many teachers can see the benefits of adaptive learning, the challenge is finding a way to implement it and to do so in a cost-effective way.
 
Adaptive Learning - Use Cases 
  1. Recommend personalized learning paths based on learners’ pace, skills, learning goals, and preferences.
  2. Recommendation framework to predict the exam questions to be presented based on a person’s profile and past performance.
  3. Prediction techniques regarding learner's progress and success/failure.
  4. Content presentation based on learner's learning capability.

Courses - Learning Path

  • Recommend practice questions for exam preparation
  • Matching exam questions to learning objectives
  • Student Analysis - Analysis performance data of learners
  • Grouping of learners by the similarity of their profile
  • Probability of learning a course
The next-generation e-learning frameworks vary by offering students quicker methods to search relevant course content when they want it. Customization, Personalization, Multimedia support, learner’s experience, and omnichannel access are important features of the next-gen e-learning frameworks. Assessment is a specialized science. A quality assessment with real-time feedback is integrated with these e-learning frameworks. These e-learning frameworks have the features to create, distribute, edit, and manage entire courses with various media content types.
 
Services related to managing activities such as learning courses, registering courses, managing assets, managing jobs, managing processes, learning outcomes, and others are the key elements of the e-learning framework. Assessment management, content management, teaching outcome management, course management are the key modules that provide good learning experiences. These experiences can be authored, managed, and delivered. Authorization, Authentication, Single Sign-on, Workflow, Configuration, Calendaring, Messaging, Notifications, Alerts, Collaboration tools are the e-learning framework features.
 
Adaptive e-learning is the future of education. Learners everywhere are going to benefit from selecting courses, using assets, and modules that are more closely customized based on how learners prefer and need to learn. Online School platforms and college e-learning platforms that offer adaptive courses—with the software to deliver them—will gain the advantage over those that don’t.
 

What’s Next?

 
Social learning capabilities are evolving with the Learning Management System on different levels across the e-learning framework, within classes, and in online content. At the framework level, each student typically has a unique profile that can follow others and see the course content they post or recommend, find teachers, mentors, or experts, post questions, like, comment and chat. Within a cohort or class, course-private communication is available to support. Course assignment submissions, workgroup discussions, peer and instructor grading, and other interactions are the key features of the next-gen e-learning framework. Within online content, students can comment on any course page or video and link to other resources. The e-learning framework is integrated into the useable workspace and not far off on a “Collaboration” tab somewhere.