Multiple-choice questions (MCQs) are popular among learners, but are often criticized for emphasizing recognition over active recall, which makes them less effective for long-term memory retention. Well-designed competitive distractors that are similar to the correct answer in meaning (semantically related) or wordform (orthographically related) can prompt deeper cognitive processing and overcome this deficit. However, creating high-quality distractors traditionally requires a significant time investment and domain expertise. Recent advances in artificial intelligence enable the generation of distractors at scale, but are insufficient when it comes to creating distractors that specifically target learners' misconceptions about word forms. In this study, we present a scalable, data-driven method for automatically generating orthographically related distractors, based on common incorrect responses from open-answer retrieval practice, supplemented with rule-based generation of common misspellings where necessary. We apply this method to a large dataset of learners' errors in vocabulary learning, demonstrating that it is feasible to create distractors that align with learners' shared misconceptions in a real-world setting. This work contributes to making MCQs a more effective pedagogical tool.

Abstract Adaptive learning systems offer a personalised digital environment that continually adjusts to the learner and the material, with the goal of maximising learning gains. Whenever such a system encounters a new learner, or when a returning learner starts studying new material, the system first has to determine the difficulty of the material for that specific learner. Failing to address this “cold-start” problem leads to suboptimal learning and potential disengagement from the system, as the system may present problems of an inappropriate difficulty or provide unhelpful feedback. In a simulation study conducted on a large educational data set from an adaptive fact learning system (about 100 million trials from almost 140 thousand learners), we predicted individual learning parameters from response data. Using these predicted parameters as starting estimates for the adaptive learning system yielded a more accurate model of learners’ memory performance than using default values. We found that predictions based on the difficulty of the fact (“what”) generally outperformed predictions based on the ability of the learner (“who”), though both contributed to better model estimates. This work extends a previous smaller-scale laboratory-based experiment in which using fact-specific predictions in a cold-start scenario improved learning outcomes. The current findings suggest that similar cold-start alleviation may be possible in real-world educational settings. The improved predictions can be harnessed to increase the efficiency of the learning system, mitigate the negative effects of a cold start, and potentially improve learning outcomes.

Memorising vocabulary is an important aspect of formal foreign language learning. Advances in cognitive psychology have led to the development of adaptive learning systems that make vocabulary learning more efficient. These computer-based systems measure learning performance in real time to create optimal study strategies for individual learners. While such adaptive learning systems have been successfully applied to written word learning, they have thus far seen little application in spoken word learning. Here we present a system for adaptive, speech-based word learning. We show that it is possible to improve the efficiency of speech-based learning systems by applying a modified adaptive model that was originally developed for typing-based word learning. This finding contributes to a better understanding of the memory processes involved in speech-based word learning. Furthermore, our work provides a basis for the development of language learning applications that use real-time pronunciation assessment software to score the accuracy of the learner’s pronunciations. Speech-based learning applications are educationally relevant because they focus on what may be the most important aspect of language learning: to practice speech.