Published in Summer 2022
Since the introduction of the first teaching machine in 1924, learning technologies have come a long way. Specifically, learning management systems (LMS) have evolved over the past decades with the introduction of SCORM, xAPI, cloud technology, artificial intelligence, predictive analytics and blockchain. Furthermore, the uptake of LMSs has been accelerated by the shift to remote work and learning.
On one hand, an LMS is useful in coordinating the learning workflow; on the other hand, the way organizations make use of their LMSs can be very limiting, with the focus largely on management and tracking. There is much more to an LMS, and it is time we shift our paradigm when configuring and implementing the system from a technology-centered to a learner-centered perspective.
Consider the Entire User Experience (UX)
The perceived quality of an LMS is often based on how usable the system is. For an effective UX, we must consider the entire learner journey from logging in for the first time and browsing the course catalog to interacting with other users in the system and revisiting learning material. A coherent and positive experience is particularly important, as LMSs are increasingly integrated with the larger talent development ecosystem.
For example, when setting up error-handling messages, ensure that the language is consistent across systems and jargon-free — aim at the right level for the target users, take cultural context into account and describe how the users can fix the error.
Make Better Use of Learning Analytics
While most LMSs offer functions for tracking and reporting, there is room for making better use of learning analytics. In addition to pre-built reports, deep learning analytics capabilities can help inform better learning design decisions and support learners better. Some possibilities to look for include data visualization and personalized dashboards, API access for data integration, and access to log files.
Moreover, the LMS is often the first data source we look to extract data from to work with an external analytics tool, so look for richer sets of data. Data sources such as learning data generated by xAPI courses and SCORM-based courses, learner search data, survey results, learner-to-learner interactions (e.g., discussion forum, chat or comments) and content engagement rates could potentially provide greater insights and help identify areas of improvement.
Security, Privacy and Inclusivity
Considerations of system security, data privacy and inclusivity should be in the forefront of any LMS implementation. Modern LMSs have improved significantly in terms of security features — but a lot of work still needs to be done around learner privacy and bias in learning data.
For example, there are some LMSs that apply biometric technology such as facial recognition to monitor attendance or detect engagement. Yet, facial recognition technology is known to have a much higher error rate in identifying women of color, transgender and Black people in general. To mitigate the misuse of data and data bias, issues about the handling and storing of biometric data need to be properly addressed, and consent must be obtained from users. You can start by asking LMS vendors what type of data safeguarding best practices are in place to ensure user privacy, as well as advocating data policy and governance within your organization.
While LMSs are designed primarily for the purpose of hosting, managing and tracking learning, it will serve us well to look beyond the management functions and consider learner and learning-driven experience and support.