SECA: A Feedback Rules Model in a Ubiquitous Microlearning Context

Abstract

Understanding learner behavior is the key to the success of any learning process. The more we know about learners, the more likely we are to personalize learning experiences and provide successful feedback. This paper presents a feedback rules model called SECA: (i) Scenario, that defines the context behavior in a microlearning environment, (ii) Event, provided by a predictive model, (iii) Condition, that evaluates the events, and (iv) Action, that provides the learner’s feedback. The proposal is achieved through a controlled experiment in which a microlearning environment is available to collect data from a ubiquitous context, and predictive analytics are applied to guide the definition of a set of feedback rules intended to support the learner’s learning process. In the end, we presented an exemplified set of feedback rules, which could be used to provide automatic recommendations and improve the learner experience. Thus, the experiment allows us to analyze the learner behavior in a ubiquitous microlearning context from a feedback perspective.