Predicting University Students’ Exam Performance Using a Model-Based Adaptive Fact-Learning System

Modern educational technology has the potential to support students to use their study time more effectively. Learning analytics can indicate relevant individual differences between learners, which adaptive learning systems can use to tailor the learning experience to individual learners. For fact learning, cognitive models of human memory are well suited to tracing learners’ acquisition and forgetting of knowledge over time. Such models have shown great promise in controlled laboratory studies. To work in realistic educational settings, however, they need to be easy to deploy and their adaptive components should be based on individual differences relevant to the educational context and outcomes. Here, we focus on predicting university students’ exam performance using a model-based adaptive fact-learning system. The data presented here indicate that the system provides tangible benefits to students in naturalistic settings. The model’s estimate of a learner’s rate of forgetting predicts overall grades and performance on individual exam questions. This encouraging case study highlights the value of model-based adaptive fact-learning systems in classrooms

Prior Knowledge Norms for Naming Country Outlines:An Open Stimulus Set

Paired-associate stimuli are an important tool in learning and memory research. In cognitive psychology, many studies use materials of which the learners are expected to have little to no prior knowledge. Despite their theoretical usefulness, conclusions from these studies are difficult to generalize to real-world learning contexts, where learners can be expected to have varying degrees of prior knowledge. Here, we present an ecologically valid stimulus set with 112 country outline-name pairs, and report response times and prior knowledge for these items in 285 largely Western European participants. Prior knowledge per item ranged from very high (94.4%) to zero (0.3%), thereby allowing researchers to select materials of which participants can be expected to have any given amount of prior knowledge. As such, this database provides a useful tool for research on real-world learning. The database can be accessed at: https://osf.io/q25rd/

Translating a Typing-Based Adaptive Learning Model to Speech-Based L2 Vocabulary Learning

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

Lockdown Learning:Changes in Online Foreign-Language Study Activity and Performance of Dutch Secondary School Students During the COVID-19 Pandemic

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 115 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

Capturing Dynamic Performance in a Cognitive Model:Estimating ACT-R Memory Parameters With the Linear Ballistic Accumulator

The parameters governing our behavior are in constant flux. Accurately capturing these dynamics in cognitive models poses a challenge to modelers. Here, we demonstrate a mapping of ACT-R's declarative memory onto the linear ballistic accumulator (LBA), a mathematical model describing a competition between evidence accumulation processes. We show that this mapping provides a method for inferring individual ACT-R parameters without requiring the modeler to build and fit an entire ACT-R model. Existing parameter estimation methods for the LBA can be used, instead of the computationally expensive parameter sweeps that are traditionally done. We conduct a parameter recovery study to confirm that the LBA can recover ACT-R parameters from simulated data. Then, as a proof of concept, we use the LBA to estimate ACT-R parameters from an empirical dataset. The resulting parameter estimates provide a cognitively meaningful explanation for observed differences in behavior over time and between individuals. In addition, we find that the mapping between ACT-R and LBA lends a more concrete interpretation to ACT-R's latency factor parameter, namely as a measure of response caution. This work contributes to a growing movement towards integrating formal modeling approaches in cognitive science

Benefits of Adaptive Learning Transfer From Typing-Based Learning to Speech-Based Learning

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. One way these computer-based systems optimize learning is by measuring learning performance in real time to create optimal repetition schedules for individual learners. While such adaptive learning systems have been successfully applied to word learning using keyboard-based input, they have thus far seen little application in word learning where spoken instead of typed input is used. Here we present a framework for speech-based word learning using an adaptive model that was developed for and tested with typing-based word learning. We show that typing- and speech-based learning result in similar behavioral patterns that can be used to reliably estimate individual memory processes. We extend earlier findings demonstrating that a response-time based adaptive learning approach outperforms an accuracy-based, Leitner flashcard approach in learning efficiency (demonstrated by higher average accuracy and lower response times after a learning session). In short, we show that adaptive learning benefits transfer from typing-based learning, to speech based learning. 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. We discuss the implications for our approach for the development of educationally relevant, adaptive speech-based learning applications