Enhancing learning and retention through the distribution of practice repetitions across multiple sessions.

The acquisition and retention of knowledge is affected by a multitude of factors including amount of practice, elapsed time since practice occurred, and the temporal distribution of practice. The third factor, temporal distribution of practice, is at the heart of research on the spacing effect. This research has consistently shown that separating practice repetitions by a delay slows acquisition but enhances retention. The current study addresses an empirical gap in the spacing effects literature. Namely, how does the allocation of a fixed number of practice repetitions among multiple sessions impact learning and retention? To address this question, we examined participants' acquisition and retention of declarative knowledge given different study schedules in which the number of practice repetitions increased, decreased, or remained constant across multiple acquisition sessions. The primary result was that retention depended strongly on the total number of sessions in which an item appeared, but not on how practice repetitions were distributed among those sessions. This outcome was consistent with predictions from a computational cognitive model of skill acquisition and retention called the Predictive Performance Equation (PPE). The success of the model in accounting for the patterns of performance across a large set of study schedules suggests that it can be used to tame the complexity of the design space and to identify schedules to enhance knowledge acquisition and retention.

Personalized Training Schedules for Retention and Sustainment of Cardiopulmonary Resuscitation Skills.

INTRODUCTION: The study examined how the spacing of training during initial acquisition of cardiopulmonary resuscitation (CPR) skill affects longer-term retention and sustainment of these skills. METHODS: This was a multiphased, longitudinal study. Nursing students were randomly assigned to 2 initial acquisition conditions in which they completed 4 consecutive CPR training sessions spaced by shorter (1 or 7 days) or longer (30 or 90 days) training intervals. Students were additionally randomized to refresh skills for 1 year every 3 months, 6 months, or at a personalized interval prescribed by the Predictive Performance Optimizer (PPO), a cognitive tool that predicts learning and decay over time. RESULTS: At the end of the acquisition period, performance was better if training intervals were shorter. At 3 or 6 months after acquisition, performance was better if initial training intervals were longer. At 1 year after acquisition, compression and ventilation scores did not differ by initial training interval nor by 3-month or PPO-prescribed sustainment interval refreshers. However, 6-month interval refreshers were worse than the PPO for compressions and worse than 3 months for ventilations. At the final test session, participants in the personalized PPO condition had less variability in compression scores than either the 3- or 6-month groups. CONCLUSIONS: Results suggest that CPR learning trajectories may be accelerated by first spacing training sessions by days and then expanding to longer intervals. Personalized scheduling may improve performance, minimize performance variability, and reduce overall training time.

Cognition-Enhanced Machine Learning for Better Predictions with Limited Data.

The fields of machine learning (ML) and cognitive science have developed complementary approaches to computationally modeling human behavior. ML's primary concern is maximizing prediction accuracy; cognitive science's primary concern is explaining the underlying mechanisms. Cross-talk between these disciplines is limited, likely because the tasks and goals usually differ. The domain of e-learning and knowledge acquisition constitutes a fruitful intersection for the two fields' methodologies to be integrated because accurately tracking learning and forgetting over time and predicting future performance based on learning histories are central to developing effective, personalized learning tools. Here, we show how a state-of-the-art ML model can be enhanced by incorporating insights from a cognitive model of human memory. This was done by exploiting the predictive performance equation's (PPE) narrow but highly specialized domain knowledge with regard to the temporal dynamics of learning and forgetting. Specifically, the PPE was used to engineer timing-related input features for a gradient-boosted decision trees (GBDT) model. The resulting PPE-enhanced GBDT outperformed the default GBDT, especially under conditions in which limited data were available for training. Results suggest that integrating cognitive and ML models could be particularly productive if the available data are too high-dimensional to be explained by a cognitive model but not sufficiently large to effectively train a modern ML algorithm. Here, the cognitive model's insights pertaining to only one aspect of the data were enough to jump-start the ML model's ability to make predictions-a finding that holds promise for future explorations.

Aircrew Actual vs. Prescriptive Sleep Schedules and Resulting Fatigue Estimates.

BACKGROUND: Fatigue is an insidious and costly occurrence in the aviation community, commonly a consequence of insufficient sleep. Some organizations use scheduling tools to generate prescriptive sleep schedules to help aircrew manage their fatigue. It is important to examine whether aircrew follow these prescriptive schedules, especially in very dynamic environments. The current study compares aircrew sleep during missions to prescriptive sleep schedules generated by a mission scheduling tool. METHODS: Participating in the study were 44 volunteers (Mage= 28.23, SDage= 4.23; Proportionmale= 77.27%) from a C-17 mobility squadron providing 25 instances of sleep and mission data (80 flights total). Aircrew wore actigraph watches to measure sleep during missions and prescriptive sleep schedules were collected. Actual and prescriptive sleep was compared with calculated performance effectiveness values per minute across mission flights. RESULTS: Prescriptive schedules generally overestimated effectiveness during missions relative to estimated actual sleep, potentially causing shifts in effectiveness to ranges of increased risk requiring elevated fatigue mitigation efforts. Actual and prescriptive effectiveness estimates tended to increasingly diverge over the course of missions, which magnifies differences on longer missions. DISCUSSION: The current study suggests that aircrew sleep during missions often does not align with prescriptive sleep schedules generated by mission planning software, resulting in effectiveness estimates that are generally lower than predicted. This might discourage aircrew from using mission effectiveness graphs as a fatigue mitigation tool. Additionally, because fatigue estimates factor into overall operational risk management processes, these schedules might underestimate risks to safety, performance, and health. Morris MB, Veksler BZ, Krusmark MA, Gaines AR, Jantscher HL, Gunzelmann G. Aircrew actual vs. prescriptive sleep schedules and resulting fatigue estimates. Aerosp Med Hum Perform. 2021; 92(10):806814.

Baseline Cardiopulmonary Resuscitation Skill Performance of Nursing Students Is Improved After One Resuscitation Quality Improvement Skill Refresher.

This article reports the results of baseline cardiopulmonary resuscitation (CPR) skills performance measurements from 467 nursing students. All participants had completed a CPR course. Baseline measurements were compared to performance after one 10-minute refresher training session on the Resuscitation Quality Improvement system. Significant improvements were made after the computer- and practice-based refresher. Findings suggest that staff developers should evaluate the use of audio and visual feedback devices to improve the quality of CPR provided by clinical staff.

Training interval in cardiopulmonary resuscitation.

AIM: Although evidence supports brief, frequent CPR training, optimal training intervals have not been established. The purpose of this study was to compare nursing students' CPR skills (compressions and ventilations) with 4 different spaced training intervals: daily, weekly, monthly, and quarterly, each for 4 times in a row. METHODS: Participants were nursing students (n = 475) in the first year of their prelicensure program in 10 schools of nursing across the United States. They were randomly assigned into the 4 training intervals in each of the schools. Students were trained in CPR on a Laerdal Resusci Anne adult manikin on the Resuscitation Quality Improvement (RQI) mobile simulation station. The outcome measures were quality of compressions and ventilations as measured by the RQI program. RESULTS: Although students were all certified in Basic Life Support prior to the study, they were not able to adequately perform compressions and ventilations at pretest. Overall compression scores improved from sessions 1 to 4 in all training intervals (all p < .001), but shorter intervals (daily training) resulted in larger increases in compression scores by session 4. There were similar findings for ventilation skills, but at session 4, both daily and weekly intervals led to better skill performance. CONCLUSION: For students and other novices learning to perform CPR, the opportunity to train on consecutive days or weeks may be beneficial: if learners are aware of specific errors in performance, it may be easier for them to correct performance and refine skills when there is less time in between practice sessions.

Mechanisms underlying the spacing effect in learning: A comparison of three computational models.

The spacing effect is one of the most widely replicated results in experimental psychology: Separating practice repetitions by a delay slows learning but enhances retention. The current study tested the suitability of the underlying, explanatory mechanism in three computational models of the spacing effect. The relearning of forgotten material was measured, as the models differ in their predictions of how the initial study conditions should affect relearning. Participants learned Japanese-English paired associates presented in a massed or spaced manner during an acquisition phase. They were tested on the pairs after retention intervals ranging from 1 to 21 days. Corrective feedback was given during retention tests to enable relearning. The results of 2 experiments showed that spacing slowed learning during the acquisition phase, increased retention at the start of tests, and accelerated relearning during tests. Of the 3 models, only 1, the predictive performance equation (PPE), was consistent with the finding of spacing-accelerated relearning. The implications of these results for learning theory and educational practice are discussed. (PsycINFO Database Record

Evaluating the Theoretic Adequacy and Applied Potential of Computational Models of the Spacing Effect.

The spacing effect is among the most widely replicated empirical phenomena in the learning sciences, and its relevance to education and training is readily apparent. Yet successful applications of spacing effect research to education and training is rare. Computational modeling can provide the crucial link between a century of accumulated experimental data on the spacing effect and the emerging interest in using that research to enable adaptive instruction. In this paper, we review relevant literature and identify 10 criteria for rigorously evaluating computational models of the spacing effect. Five relate to evaluating the theoretic adequacy of a model, and five relate to evaluating its application potential. We use these criteria to evaluate a novel computational model of the spacing effect called the Predictive Performance Equation (PPE). Predictive Performance Equation combines elements of earlier models of learning and memory including the General Performance Equation, Adaptive Control of Thought-Rational, and the New Theory of Disuse, giving rise to a novel computational account of the spacing effect that performs favorably across the complete sets of theoretic and applied criteria. We implemented two other previously published computational models of the spacing effect and compare them to PPE using the theoretic and applied criteria as guides.