Publications

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The testing effect refers to the benefits to retention that result from structuring learning activities in the form of a test. As educators consider implementing testenhanced learning paradigms in real classroom environments, we think it is critical to consider how an array of factors affecting test-enhanced learning in laboratory studies bear on test-enhanced learning in real-world classroom environments. This review discusses the degree to which test feedback, test format (of formative tests), number of tests, level of the test questions, timing of tests (relative to initial learning), and retention duration have import for testing effects in ecologically valid contexts (e.g., classroom studies). Attention is also devoted to characteristics of much laboratory testing-effect research that may limit translation to classroom environments, such as the complexity of the material being learned, the value of the testing effect relative to other generative learning activities in classrooms, an educational orientation that favors criterial tests focused on transfer of learning, and online instructional modalities. We consider how student-centric variables present in the classroom (e.g., cognitive abilities, motivation) may have bearing on the effects of testing-effect techniques implemented in the classroom. We conclude that the testing effect is a robust phenomenon that benefits a wide variety of learners in a broad array of learning domains. Still, studies are needed to compare the benefit of testing to other learning strategies, to further characterize how individual differences relate to testing benefits, and to examine whether testing benefits learners at advanced levels.

Studies of bilingual language processing typically assign participants to groups based on their language proficiency and average across participants in order to compare the two groups. This approach loses much of the nuance and individual differences that could be important for furthering theories of bilingual language comprehension. In this study, we present a novel use of machine learning (ML) to develop a predictive model of language proficiency based on behavioral data collected in a priming task. The model achieved 75% accuracy in predicting which participants were proficient in both Spanish and English. Our results indicate that ML can be a useful tool for characterizing and studying individual differences.

As the ability to collect and store data grows, so does the need to efficiently analyze that data. As human-machine teams that use machine learning (ML) algorithms as a way to inform human decision-making grow in popularity it becomes increasingly critical to understand the optimal methods of implementing algorithm assisted search. In order to better understand how algorithm confidence values associated with object identification can influence participant accuracy and response times during a visual search task, we compared models that provided appropriate confidence, random confidence, and no confidence, as well as a model biased toward over confidence and a model biased toward under confidence. Results indicate that randomized confidence is likely harmful to performance while non-random confidence values are likely better than no confidence value for maintaining accuracy over time. Providing participants with appropriate confidence values did not seem to benefit performance any more than providing participants with under or over confident models.

Expert advice and conventional wisdom say that important information within a statistical graph should be more salient than the other components. If readers are able to find relevant information quickly, in theory, they should perform better on corresponding response tasks. To our knowledge, this premise has not been thoroughly tested. We designed two types of salient cues to draw attention to task-relevant information within statistical graphs. One type primarily relied on text labels and the other on color highlights. The utility of these manipulations was assessed with groups of questions that varied from easy to hard. We found main effects from the use of our salient cues. Error and response time were reduced, and the portion of eye fixations near the key information increased. An interaction between the cues and the difficulty of the questions was also observed. In addition, participants were given a baseline skills test, and we report the corresponding effects. We discuss our experimental design, our results, and implications for future work with salience in statistical graphs.

The Sandia Matrices are a free alternative to the Raven’s Progressive Matrices (RPMs). This study offers a psychometric review of Sandia Matrices items focused on two of the most commonly investigated issues regarding the RPMs: (a) dimensionality and (b) sex differences. Model-data fit of three alternative factor structures are compared using confirmatory multidimensional item response theory (IRT) analyses, and measurement equivalence analyses are conducted to evaluate potential sex bias. Although results are somewhat inconclusive regarding factor structure, results do not show evidence of bias or mean differences by sex. Finally, although the Sandia Matrices software can generate infinite items, editing and validating items may be infeasible for many researchers. Further, to aide implementation of the Sandia Matrices, we provide scoring materials for two brief static tests and a computer adaptive test. Implications and suggestions for future research using the Sandia Matrices are discussed.

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The analysis of seismic data for evidence of possible nuclear explosion testing is a critical global security mission that relies heavily on human expertise to identify and mark seismic signals embedded in background noise. To assist analysts in making these determinations, we adapted two compression distance metrics for use with seismic data. First, we demonstrated that the Normalized Compression Distance (NCD) metric can be adapted for use with waveform data and can identify the arrival times of seismic signals. Then we tested an approximation for the NCD called Sliding Information Distance (SLID), which can be computed much faster than NCD. We assessed the accuracy of the SLID output by comparing it to both the Akaike Information Criterion (AIC) and the judgments of expert seismic analysts. Our results indicate that SLID effectively identifies arrival times and provides analysts with useful information that can aid their analysis process.

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