Context-awareness and adaptation are complementary areas that seek to promote personalized experiences in technology-enhanced learning environments. However, their contribution provides different perspectives related to microlearning proposals' growth and guide actions from data scenarios to understand the learner's behavior by applying learning analytics processes and techniques. This paper presents a context-aware adaptive microlearning design defined from a learning-conceptual model and deployed by a progressive web application supporting learners' interactions that provides features, data to develop a learning analytics strategy to understand learner behavior. We used Technical Action Research to guide our approach's conception, design, and implementation; it is complemented with the ASUM-DM to conduct the data analytics activities. Among the most relevant achieved results was identifying three learning styles: reflexive, active, and sensitive; three behavioral patterns: assimilation, browsing, and distraction; at the same time, outcomes based on the assimilation time and the average grade. Finally, we conclude that combining knowledge resources (content), context-awareness, and the omnichannel allowed us to guide a feedback process through LA-based adaptation scenarios.