Adaptive fact learning systems have been developed to makeoptimal use of testing and spacing effects by taking intoaccount individual differences in learning efficiency.Measures derived from these systems, capturing theindividual differences, predict later performance in similarand different fact learning tasks. Additionally, there is a richbody of literature showing that individual differences ingeneral cognitive ability or working memory capacity canpredict scores on achievement tests. If these measures alsoinfluence fact learning, incorporating them might furtherenhance adaptive systems. However, here we provideevidence that performance during fact learning is neitherrelated to working memory capacity nor general cognitiveability. This means that the individual differences captured byour adaptive learning system encapsulate characteristics oflearners that are independent of their general cognitive ability.Consequently, adaptive learning methods should focusprimarily on memory-related processes.

© 2015, Routledge. All rights reserved. In structural equation modeling (SEM), researchers need to evaluate whether item response data, which are often multidimensional, can be modeled with a unidimensional measurement model without seriously biasing the parameter estimates. This issue is commonly addressed through testing the fit of a unidimensional model specification, a strategy previously determined to be problematic. As an alternative to the use of fit indexes, we considered the utility of a statistical tool that was expressly designed to assess the degree of departure from unidimensionality in a data set. Specifically, we evaluated the ability of the DETECT “essential unidimensionality” index to predict the bias in parameter estimates that results from misspecifying a unidimensional model when the data are multidimensional. We generated multidimensional data from bifactor structures that varied in general factor strength, number of group factors, and items per group factor; a unidimensional measurement model was then fit and parameter bias recorded. Although DETECT index values were generally predictive of parameter bias, in many cases, the degree of bias was small even though DETECT indicated significant multidimensionality. Thus we do not recommend the stand-alone use of DETECT benchmark values to either accept or reject a unidimensional measurement model. However, when DETECT was used in combination with additional indexes of general factor strength and group factor structure, parameter bias was highly predictable. Recommendations for judging the severity of potential model misspecifications in practice are provided.