Making the most of human memory:Studies on personalized fact-learning and visual working memory

Rote learning of facts is boring. Here, we present a way to make it more efficient so facts are learned faster. Learners usually need to repeat facts multiple times to learn them well. The optimal timing of these repetitions depends on the facts that are studied and the characteristics of the learner. Learning software that takes such differences into account is called adaptive. Here, we show that the adaptive system developed in our lab outperforms a flashcard system often utilized by students: on average, students learn more and make fewer errors during learning if they use the adaptive system. This improvement is possible because the system simulates a very simplified version of each learner’s memory and can predict when individual facts are forgotten. We show that parameters estimated during learning can predict test performance but are not related to intelligence or working memory capacity (in the tested sample). We also demonstrate that the parameters estimated for one learner are stable over time but differ slightly over materials. Finally, future developments and improvements of the system are discussed. For example, the adaptive system’s capabilities should be extended to multi-session learning. Gaining access to large amounts of data collected in realistic (educational) settings will be the key to these developments. Collaborations with Noordhoff and HoeGekIsNL? and a RUG-funded project called Rugged Learning will provide such data and will enable us to verify and test additional assumptions that should further improve the system