How To Use Machine Learning In Corporate eLearning
Machine Learning is not a new concept. However, modern tech has brought it to a whole new level, making it possible to predict and adapt to learning behaviors. As a matter of fact, you can now use algorithms and analytics in your LMS to analyze your data more effectively, then provide more targeted eLearning resources to your audience. But what role does Machine Learning play in the future of eLearning? And how you can use it with your current LMS and HR systems?
1. Machine Learning Algorithms For Learning Management Systems
The most significant role that Machine Learning plays in eLearning is personalization. This is achieved through more effective data analysis and automation. An LMS that uses Machine Learning is able to access user data and use it to improve the eLearning experience. However, it can also be fully integrated with your HR systems to analyze learner data and pinpoint trends with greater efficiency. This allows you to identify areas for improvement based on analytical patterns and pre-set algorithms. For example, the LMS uses algorithms to analyze an online learner’s past performance and combine it with new information to predict the outcome. As such, you’re able to set online learners on the right path based on skill, performance, or knowledge gaps. This is all based on a variety of pre-determined criteria, such as current organizational objectives, goals, and job duties.
Here are 3 examples of the algorithm formats that Machine Learning engineers use for Learning Management Systems:
a. Decision Trees
Online learners' decisions or actions lead them down different paths, much like a branching scenario map. The paths may pertain to consequences, outcomes, resource recommendations, or even online training risks or expenses.
b. Ordinary Least Squares Regression
A more statistical approach that involves “linear regression” and point charts. Machine Learning engineers must draw a line through multiple points on the graph, then calculate the vertical distance between the line and each point, and determine the sum. The shortest distance indicates the final data line.
c. Ensemble Methods
A series of algorithms that involve classifiers and “weighted voted” predictions. Model averaging and Bayesian production are examples of ensemble methods in Machine Learning.
2. Machine Learning In The Corporate Sector
There’s also another Machine Learning application to consider in the corporate sector, which is to improve employee retention and satisfaction. This involves a combination of the HR and Learning Management Systems. Predictive analytics and iterative evaluations allow you to pinpoint patterns, such as a significant spike in course drop-outs or certification lapses. Thus, you can intervene before it’s too late to retain your top talent and ensure that everyone is in compliance, saving you the expense of having to vet job candidates and retrain new hires. There are also a variety of other uses for Machine Learning in corporate eLearning, such as:
a. Automated Online Training Paths
Machine Learning enables you to provide automated personalized online training paths for each corporate learner. For example, data from the HR system and LMS is used to create an individualized plan for customer service employees. This is based on their past performance, current job responsibilities, and required skills. The system also analyzes patterns and trends to automatically adjust the corporate learners’ coursework to meet their training needs.
b. Adaptable Online Training Resources
Your company must be able to evolve, and it all hinges on an adaptable online training system. Machine Learning that integrates with your LMS and HR systems gives you the power to quickly amend your current online training strategy. Simply update the algorithm or criteria to deploy updated online training content on a larger scale. For example, automatically deliver current product knowledge online training resources to your remote sales team, but not your IT staff.
c. Targeted Recommendations
The challenge that many organizations face is lack of data. Specifically, data they can use to create actionable goals and detect patterns that hinder employee productivity. Machine Learning provides you with a wealth of Big Data that you can use to offer targeted online training recommendations. Every employee has access to online training materials that cater to their training needs and learning goals, rather than one-size-fits-all online training resources that don’t factor in predictive analytics and past performance.
d. Improve Resource Allocation
Machine Learning automates a significant amount of the data mining process. As such, your employees are able to focus on other aspects of the L&D process. For instance, developing online training modules or activities that focus on real-world application, thereby enabling employees to build practical experience and fill knowledge gaps that were revealed by in-depth data analysis.
e. More Efficient Online Assessment Strategy
You can also use Machine Learning in your LMS to create more effective online assessments for corporate learners. Some automated systems generate test questions autonomously, or deliver specific online assessments to individual corporate learners based on their performance or work tasks. For example, user reports and online assessment results reveal that an employee lacks communication skills, which is a requirement based on their HR profile. The system will automatically deliver the necessary online training resources they need to meet your organization’s standards.
f. Intuitive Planning And Development
Big Data is a valuable asset that helps your L&D team develop goal-centered online training resources. Machine Learning offers them the information they need to design more intuitive online training materials and streamline the planning process. They can use learning behaviors, performance indicators, and emerging patterns to personalize online training.
Machine Learning gives you the opportunity to effectively analyze Big Data and identify patterns that stand in the way of success. It also helps you create more learner-centered corporate eLearning courses that rely on statistical data and predictive analytics. Thus, you can provide every member of your team with the online training resources they need to achieve organizational objectives.
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