Are We Designing Learning For Humans-Or For Algorithms?

"Today's eLearning solutions use algorithms for many things, including recommendations for courses, tags for skills, scores for completions, heat maps, and metrics for engagement levels. Anyone interested in eLearning sees learning in new ways; all of those ways are measurable, sortable, and optimizable. We seem to have come a long way in terms of learning. Through data-driven learning, one can increase efficiency, personalize learning, and scale it up."

"The hard question for L&D teams to consider is whether they still design learning for people or whether they design learning for algorithms. Learning design has been optimized based on what the system will reward (i.e., the system incentives), leading to larger numbers of shorter learning modules, greater numbers of assessments (which are easier to measure, track, and report via an LMS), as well as smaller, bite-sized content (which is what we refer to as microlearning)."

eLearning platforms apply algorithms for recommendations, skill tagging, completion scores, heat maps, and engagement metrics. Data-driven learning can increase efficiency, personalize content, and enable scaling. System incentives push designers toward more short modules, frequent assessments, and microlearning because these elements are easy to measure, track, and report. Many learners equate completion with success rather than building durable capability. Genuine learning involves making mistakes, reflecting, and learning from them, processes that resist algorithmic measurement. Recommendation engines often use limited signals such as clicks, view duration, and descriptive words, capturing only observable interactions.

Read at eLearning Industry