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Is Your LMS Missing the Big Picture? Putting Context Engines in Perspective

Written By: Raluca Druta
Published On: October 4 2013

Contextual engines have become popular in many areas of enterprise software. They initially emerged to support the customer-centric approach to business development and success. Contextual engines in any software are based on continuously collecting data about an individual and matching that one profile to other similar profiles to recommend potentially interesting and relevant shopping items for that individual.

For example, if you’re interested in Leonard Cohen’s music, the application you are using to search for this music will certainly not recommend Miley Cyrus, as you and others like you have probably never browsed to any items related of the latter due to the huge aesthetic gap between the two singers. However, you may get recommendations like Madeleine Peyroux or k.d. lang, as both artists are well known for their covers to some Leonard Cohen songs. Contextual engines are able to infer what you may be interested in by mapping your interests based on what you and other individuals like you have clicked on, commented on, and so on.

More recently, context-specific recommendations have made their way into learning management, as learning is gradually moving from brick and mortar shops to virtual or online "spaces." Also, talent management specialists are now looking at ways to unleash the talent from within organizations and are hoping to take advantage of the benefits of contextual engines to simplify their work.

Contextual recommendations introduce automated learning advising, as the capabilities of this type of functionality can be extended not only toward what courses may be suitable for a learner’s profile, but also what career paths would be a good match for the learner as well as the groups of people that may support the learner in his/her career decisions. To come up with a set of recommendations, contextual engines look at learners’ overall learning engagement and outcomes (be it test results, activities that they’ve been involved in, etc.).

From a corporate learning perspective, contextual engines look at things such as overall business goals or skill sets to unveil learning paths so that employees become better equipped to solve day-to-day challenges. For example, a sales rep attempting to close a deal with a government agency may need to take a specific course in order to have the necessary knowledge and skills to achieve that goal. What I am trying to underline here is that contextual engines can be configured to aid the context in which they are being employed. But, looking at today’s learning context, I would like to raise a few concerns with respect to using contextual engines across the board without continuous criticism of the results that they yield.

Education these days justifies itself primarily through the lenses of what is relevant for the job market. In other words, the success of schools is measured by how many students found paying jobs or opened viable businesses. I am not saying this is not a valid indicator. Quite the contrary, I believe this is a critical indicator when assessing the overall success of learning, as most of us would like to be productive members of our communities. However, if we limit learning success to what the existing job market demands and rewards the most, then aren’t we are running the risk of not being able to recognize other societal and economic needs. For instance, many societies place great emphasis on the necessity for young people to start their families, yet daycare workers—who are responsible for keeping children safe and intellectually and emotionally stimulated—get paid very small wages. I would venture to say that it is simply inefficient for the job market to identify valuable qualities in people and nurture them through learning only if these qualities drive immediate revenue.

In fact, I believe learning experiences should help individuals not only develop their job-related skills, but also expand their experiences to enhance various viewpoints and perspectives regarding the scientific, mythological, cultural, economic, artistic, etc., realms. And our experiences should help us understand the frameworks within which we have been growing up and living, as well as what influences our decisions. Most importantly, I think learning should be about developing individuals into critical thinkers—to continuously evolve and avoid complacency. It is difficult to unleash talent if individuals do not get out of comfort zones when working on projects or trying to solve work-related issues.

I think that for contextual engines to be an effective part of learning management, criteria such as critical analysis of texts, legislations, or decisions needs to be factored into the configuration of the software. The results that they produce also need to be thoroughly analyzed not only at the organizational level, but also at a larger-scale societal level.

In today’s global economy, we embrace our differences partly because they help us better accomplish tasks at work. For example, recognizing certain cultural traits when working with someone from across the globe may lead to smoother exchanges, and working with someone with a drastically different value set may promote richer experiences and more interesting ways to problem solving. But without a learning approach that nurtures knowledge that goes beyond solving day-to-day practical tasks, learning may be viewed as just training and a very limited tool to tackling business tasks and problems. And if we limit ourselves to learning for the sake of tackling the necessary work-related tasks, we are missing an opportunity to develop our potential and become critical thinkers who are responsible not only for the growth of our organization, but also for the well-being of our culture, society, and planet.
 
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