The LXP Is No Longer the New Kid on the Block

From the moment they were first introduced in 2014 and 2015, learning experience platforms (LXPs) began changing the game of digital learning by offering a more personalized learner experience. Acting as true content delivery systems, their ability to curate content from a company’s internal digital learning assets, external content found on the internet and user-generated content engages learners in an interactive environment.

This approach to digital learning creates a learner-centric ecosystem within the organization and increases learners’ personal motivation to hone their skills and apply them to real-world scenarios. Dynamic social settings where users can share and rate content, leave comments, and receive recommendations only add to the appeal.

The Original Draw: LXPs Drive Business Results

Companies invest in LXPs to help them stay competitive. These high-tech platforms are a great way to manage performance across the company and assess employee skill levels. Organizations can use them to address each employee’s specific learning needs and identify knowledge gaps in order to align skills development with business goals. Furthermore, they help reduce turnover, because employees feel more satisfied when they are free to develop their skills and knowledge as they please. Lastly, they unify teams and departments by increasing collaboration, leading to higher performance and positively impacting business results.

Dramatic Evolutions Using Data, Data and More Data

We predicted back in a 2018 article that our “always-on” workplaces would drive the need for increased speed and quality of learning with cutting-edge technology, such as artificial intelligence (AI) and machine learning, augmented reality, chatbots, and conversational interfaces. As anticipated, LXPs have advanced dramatically in the past few years, and these high-tech advancements have created a renewed urgency in the market. The next step is to tap into a valuable resource that already exists in every organization’s digital learning platform: data.

Data is driving smarter learning by delivering more efficient recommendations based on job profiles, interests and levels of expertise. Skills-based learning is improved by mapping skills to content, tagging certain programs with related skills, and then recommending them based on the skills necessary for a learner’s career path or job profile. Usage-based recommendations are another way to personalize skills development, enabling organizations to benefit further from troves of data points that machine learning algorithms pull and correlate in order to recognize trends and interests based on learner activity.

AI-based content categorization, using levels of expertise or self-assessments, is also data-centric, using auto-tagging and metadata to match content to the competencies of each learner and help them advance in their chosen fields. This feature effectively eliminates the one-size-fits-all approach to learning, which saw experienced employees suffer through introductory courses in their areas of expertise just to satisfy training requirements. It also ensures that less experienced employees are well-trained in the basics before they move on to more advanced courses.

LMS Integration Capabilities

Far from being replaced by LXPs, the trusty learning management system (LMS) has become the foundation for these new technologies, raising their value as a digital learning commodity. Integration is key to seamless interaction between the two systems. The technology can combine data from each employee’s human resources (HR) file, including his or her current role, career progression and level of expertise, with learning activity data on the LXP to guide personalized learning. Once it’s integrated into an LMS, an LXP can create an engaging environment to offer on-demand learning based on specific topics, skills or learning objectives.

Where LXPs Need to Improve

With all this focus on data-driven recommendations and content consumption, where is the follow-through? Measuring the impact and efficacy of actual skills acquisition and on-the-job application risks taking second fiddle to pre-training skill assessments and recommendations. But outcomes are a critical part of skills training, and organizations must measure and apply them toward continued skills development. Outcomes are also important in determining return on investment (ROI) for L&D teams. In addition, maintaining high quality and using a proven pedagogy when designing learning content remains foundational to a robust L&D strategy, and these basics could become lost in the race toward machine learning.

User experience (UX) design and learner marketing are other areas that directly touch learner experience and engagement. They both require a hands-on approach based on engagement metrics and platform interaction. Direct communication with learners is necessary in order to understand how they are using and benefiting from (or struggling with!) the platform. This communication can occur using chatbots, feedback questionnaires, heatmap tools and honest discussions with managers.

Another concern is the end goal of personalized training. Self-assessments and behavior can be subjective, and despite its value to each employee, personalized learning may not always serve the overall objectives of the company or even individual departments. While personal and career development on an individual level has many benefits, stakeholder expectations must be integrated into the overall L&D strategy in order to create symbiosis between the two.

Potential Challenges

Data management also poses security challenges, especially if an organization contracts with application programming interfaces (APIs) and third-party micro-service providers for the consolidation and aggregation of information. This flash flood of data activity also requires a new information technology (IT) architecture to ensure that there are no performance issues on the platform.

Integration capabilities must be up to par to seamlessly coexist with a company’s LMS. Regarding data, there is preparatory work involved in gathering, analyzing and configuring skills and profiles to recommendations. It also requires millions of data points to tag content and give helpful recommendations.

Looking Ahead

According to Josh Bersin, another technological evolution will likely occur with the combining of LXP and LMS into one seamless platform. However, this evolution will first require the resolution of issues related to data storage. Another concern is factoring in how to measure outcomes. If LXPs can’t demonstrate their value on skills acquisition and application to real workplace scenarios, they will quickly lose their shine.

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