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It is well known that repeatedly studying facts results in decreased reaction times. This finding is typically explained by an increased strength of the memory trace leading to a reduction of the duration of the memory retrieval. However, direct evidence for this hypothesis has been scarce. Here, we present such evidence from a vocabulary learning task in an electroencephalography (EEG) and a magnetoencephalography (MEG) experiment. To estimate the duration of the memory retrievals directly, we applied hidden semi-Markov model multivariate pattern analysis (HsMM-MVPA). HsMM-MVPA results identified eight cognitive processing stages, seven of which remained the same irrespective of number of repetitions and difficulty of the items, but one stage showed different durations across conditions. We found that the duration of this stage – which we labelled retrieval – correlated with the number of repetitions and item difficulty. During this retrieval stage, MEG source localization results revealed extensive activation of prefrontal and temporal regions. We conclude that decreased reaction times due to repeatedly studying facts are indeed caused by a single cognitive stage decreasing in duration. This stage was defined by strong co-activation of temporal and prefrontal brain regions, related to memory retrieval and representation.

Memorizing vocabulary is a crucial aspect of learning a new language. Although computerized adaptive learning systems have successfully improved the process of vocabulary learning by tailoring learning procedures to the needs of individual learners, the majority of such systems are limited to typing-based learning and do not allow for speech practice. Here, we aim to examine the possibilities for personalized speech-based learning. We combined a response-time based algorithm for adaptive item scheduling that was originally developed for typing-based learning with automatic speech recognition technology and tested the system with 50 participants. We show that typing- and speech-based learning result in similar learning outcomes and that using an adaptive scheduling algorithm improves learning performance relative to traditional learning in both modalities. These results can inform the development of language learning systems that—unlike traditional systems—focus on one of the most important aspects of learning a language: to practice speech.

Over a century of scientific research into learning and forgetting has revealed a number of laws describing human memory processes which are the principal constituents of mathematical models that capture forgetting processes and how they unfold over time. Recently, these models have been applied in computerized learning systems that adapt to individual learners. This is typically done by having the model predict the accuracy of a response to an item, and then updating the model based on the observed behavior to improve the fidelity of predictions on subsequent repetitions. Using accuracy works well in extensive learning sessions that provide enough time for students to make sufficient mistakes to discriminate easy from difficult items (and/or good from poor learners). However, real-life learning sessions tend to be relatively short, and requiring errors to update the model is therefore suboptimal. Here we show that a memory model-based system can be calibrated to a learner’s proficiency in relatively short learning sessions by taking accuracy and response latency into account. We conducted two experiments, testing the system in a real-life secondary education classroom setting as well as in the laboratory with university students, with and without prior knowledge of the material, respectively. We find an advantage of model-based systems over simple control conditions and highlight the benefit of using response latencies along with accuracy.

The COVID-19 pandemic caused lockdowns and sudden school closures around the world in spring 2020, significantly impacting the education of students. Here, we investigate how the switch to distance learning affected study activity and performance in an online retrieval practice tool used for language learning in Dutch secondary education. We report insights from a rich data set consisting of over 116 million retrieval practice trials completed by more than 133 thousand students over the course of two consecutive school years. Our findings show that usage of the tool increased substantially at the start of lockdown, with the bulk of study activity occurring on weekday mornings. In general, students' progress through the material was largely unaffected by lockdown, although students from the highest educational track were somewhat more likely to be on or ahead of schedule than students from lower tracks, compared to the previous year. Performance on individual study trials was generally stable, but accuracy and response time on open answer questions went up, perhaps as a result of students being more focused at home. These encouraging findings contribute to a growing literature on the educational ramifications of distance learning during lockdown.