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.

The authors estimate weighted mean values for nine information processing parameters for older adults using the Card, Moran, and Newell (1983) Model Human Processor model. The authors validate a subset of these parameters by modeling two mobile phone tasks using two different phones and comparing model predictions to a sample of younger (N = 20; M age = 20) and older (N = 20; M age = 69) adults. Older adult models fit keystroke-level performance at the aggregate grain of analysis extremely well (R = 0.99) and produced equivalent fits to previously validated younger adult models. Critical path analyses highlighted points of poor design as a function of cognitive workload, hardware/software design, and user characteristics. The findings demonstrate that estimated older adult information processing parameters are valid for modeling purposes, can help designers understand age-related performance using existing interfaces, and may support the development of age-sensitive technologies. Keywordsage; modeling; GOMS; human engineering; design; mobile phone A hallmark of good design is represented by the human factors injunction to "know the user" (Nielson, 1994, p. 73) before design specifications are made. It is essential to account for basic human processing requirements in order to optimize performance with respect to time, accuracy, and satisfaction, in order to produce "user-friendly" systems, interfaces, or devices. Research in human factors and human-computer interaction has a legacy of designing products around the needs of the younger user, in part because many designers are young themselves (the largest population density of workers are between the ages of 30-34; National Science Foundation, 2006), and, as a result, older adults often find that they have difficulties interacting with existing technology and report feelings of frustration and confusion when trying to adapt (e.g., Fisk & Rogers, 1997;Rogers, Fisk, Mead, Walker, & Cabrera, 1996;.As it currently stands, older adult competencies and limitations are not usually accounted for in these earliest stages of design and engineering. Some strides have been made to help accommodate older adults to existing systems through modification of interfaces and Correspondence concerning this article should be addressed to: Tiffany S. Jastrzembski, Department of Psychology, Florida State University, 1107 W Call Street, Tallahassee, FL 32306-4301. tiffany.jastrzembski@mesa.afmc.af.mil. Tiffany S. Jastrzembski and Neil Charness, Department of Psychology, Florida State University. HHS Public Access Author Manuscript Author ManuscriptAuthor ManuscriptAuthor Manuscript systems (e.g., changing acceleration of a mouse, enlarging font size, enhancing contrast), or through modification of the individual (e.g., adapting the user to the product with training and practice); yet a disconnect exists between the fields of cognitive aging and human engineering design. Human engineering design refers to testing systems with cognitive models that simulate human performance, so ...

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AimAlthough 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.

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