Exploration of the Rate of Forgetting as a Domain-Specific Individual Differences Measure

Learners differ in their learning aptitude. Modern computerized fact-learning systems take these individual differences into account by adapting repetition schedules to the learner's characteristics. Adaptation is based on monitoring responses during learning and using these responses to inform the model's decisions about when to introduce and repeat material by updating the model's internal parameters. Typically, adaptive systems start a learning session with a set of default parameters, with these parameters being updated and adapted to the learner's characteristics when responses are collected. Here we explore whether domain-general individual differences such as working-memory capacity or measures of general intelligence, which can be assessed prior to learning sessions, can inform the choice of initial model parameters. Such an approach is viable if the domain-general individual differences are related to the model parameters estimated during learning. In the current study, we asked participants to learn factual information, and assessed whether their learning performance, operationalized as (1) a model-parameter that captures the rate of forgetting, and (2) the results on an immediate and delayed post-test, was related to two common measures of individual differences: working memory capacity (WMC) and general cognitive ability (GCA). We failed to find evidence in favor for such relations, suggesting that, at least in this relatively small and homogeneous sample, executive functioning and attentional control did not play important roles in predicting delayed recall. The model parameters estimated during learning, on the other hand, are highly correlated with delayed recall of the studied material

Training modulates memory-driven capture

Attention is captured by information matching the contents of working memory. Though many factors modulate the amount of capture, there is surprising resistance to cognitive control. Capture occurs even when participants are instructed either that an item would never be a target or to drop that item from memory. Does the persistence of capture under these conditions reflect a rigidity in capture, or can properly motivated participants learn to completely suppress distractors and/or completely drop items from memory? Surprisingly, no studies have looked at the influence of extensive training of involuntary capture from working memory items. Here, we addressed whether training leads to a reduction or even elimination of memory-driven capture. After memorizing a single object, participants were cued to remember or to forget this object. Subsequently, they were asked to execute a search task. To measure capture, we compared search performances in displays that did and did not contain a distractor matching the earlier memorized object. Participants completed multiple experimental sessions over four days. The results showed that attentional capture by to-be-remembered distractors was reduced, but not eliminated in subsequent sessions compared with the first session. Training did not impact capture by to-be-forgotten objects. The results suggest observable, but limited, cognitive control over memory-driven capture

Within-Subject Performance on a Real-Life, Complex Task and Traditional Lab Experiments:Measures of Word Learning, Raven Matrices, Tapping, and CPR

In this data report, we describe a three-session experiment spanning six months. Several well-controlled laboratory tasks (Word Learning, Raven Matrices, and Tapping) and Cardiopulmonary Resuscitation (CPR), a complex but well-defined real-world task, were administered. Data are reported from 50 participants for the first session, 40 for the second, and 34 for the third. CPR is a useful domain for studying real-world performance inside the laboratory because clear performance standards can be applied to quantifying learners’ proficiency covering both the first steps that need to be taken prior to the initiation of CPR (declarative knowledge) as well as the compressions and ventilations themselves (procedural skill). This research resulted in a rich dataset with a range of different measures for all participants. For all tasks, the complete set of raw data are made available along with relevant aggregate performance scores (see https://osf.io/m8bxe/). The raw data in particular will enable other researchers to explore potential analyses and modeling beyond the scope of our own. The details of the data collection protocol and available data are documented here to facilitate this process

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