The impact of an automated learning component against a traditional lecturing environment

This experimental study investigates the effect on the examination performance of a cohort of first‐year undergraduate learners undertaking a Unified Modelling Language (UML) course using an adaptive learning system against a control group of learners undertaking the same UML course through a traditional lecturing environment. The adaptive learning system uses two components for the creation of suitable content for individual learners: a content analyser that automatically generates metadata describing cognitive resources within instructional content and a selection model that utilizes a genetic algorithm to select and construct a course suited to the cognitive ability and pedagogic preference of an individual learner, defined by a digital profile. Using the Kruskal–Wallis H test, it was determined that there was a statistically significant difference between the control group of learners and the learners that participated in the UML course using the adaptive learning system following an examination once the UML course concluded, with p = 0.005, scoring on average 15.71% higher using the adaptive system. However, this observed statistically significant difference observed a small effect size of 20%.