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The market for learning management systems (LMS) has expanded at an exponential rate.
FREMONT, CA: Content and learners are the two most critical aspects of your LMS to manage. Specifically, learning professionals organise content and then decide which content to assign to which learner groups. More precisely, as a best practice, learning professionals group a series of courses or modules into a learning path or curriculum format and enroll a group of users into a specific path curriculum using smart course registration. Some important ways to leverage LMS successfully are as follows:
Matching Learners to Content Using a Training Matrix
Employee development and change management are popular role-based training uses. A training matrix is created by setting one or more learning paths for separate employee groups, depending on the organization’s size, an individual's goal.
Sales training, for example, differs from training for an operations support team. The learning path of a new sales representative would include product training, personal skills training, such as customer service, and training on the business tools they will use to manage sales. Additionally, a junior sales representative may require content other than an experienced sales representative. Suppose a business undergoes a significant change, such as implementing a new platform for enterprise resource planning (ERP). In that case, companies must identify and group employees according to their job roles or tasks with the new ERP system.
External training involves instructing customers and partners to use a product. Enterprises will need to develop a customised learning curriculum for the various features and functions of their products. Numerous technology firms train their customers using a certification model, which is advantageous because roles and knowledge organise it.
When LMS Is Incorporated with Big Data
Another critical component of successfully leveraging the LMS is collecting learner feedback and gaining access to learning data and people analytics. Using statistical and artificial intelligence (AI) tools, data mining is used to analyse large amounts of data readily available. The objective is to identify behavioural patterns and predictive models. Learning analytics is the process of quantifying, collecting, analysing, and reporting learner data to understand better and optimise learning and its environments. With the collection of new types of digital data and the application of data science and artificial intelligence (AI), learning analytics provides actionable insights for learning and development (L&D) programmes. Big data and machine learning analytics can also help users understand their behaviour patterns and usage patterns.
Developing a Data Scientist's Mindset
Learning professionals must assume the role of a data scientist and make data-driven decisions. Business owners may need to incorporate new values and metrics into their LMS, such as tenure, location, and educational level, to track the most pertinent data to their business and hypothesis.
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