This study addressed the cognitive impacts of providing correct and incorrect machine learning (ML) outputs in support of an object detection task. The study consisted of five experiments that manipulated the accuracy and importance of mock ML outputs. In each of the experiments, participants were given the T and L task with T-shaped targets and…

There is an increasing need in eyewitness identification research to identify factors that not only influence identification accuracy but may also impact the confidence--accuracy (CA) relationship. One such variable that has a notable impact on memory for faces is viewing distance, with faces encoded from a shorter distance remembered better than…

In experimental tasks, preschool children sometimes mistakenly attribute memories to themselves rather than external sources, with decreasing source errors between 4- and 5-years of age. In this research, we tested whether these developmental changes are also observed in spontaneously generated memories of personal life events. 4- and 5-year olds…

Rapid reviews have been widely employed to support timely decision-making, and limiting the search date is the most popular approach in published rapid reviews. We assessed the accuracy and workload of search date limits on the meta-analytical results to determine the best rapid strategy. The meta-analyses data were collected from the Cochrane…

In this article, we argue that automated scoring engines should be transparent and construct relevant--that is, as much as is currently feasible. Many current automated scoring engines cannot achieve high degrees of scoring accuracy without allowing in some features that may not be easily explained and understood and may not be obviously and…

Predictive modeling is a core technique used in tackling various tasks in learning analytics research, e.g., classifying educational forum posts, predicting learning performance, and identifying at-risk students. When applying a predictive model, it is often treated as the first priority to improve its prediction accuracy as much as possible.…

Dynamic Item Response Models extend the standard Item Response Theory (IRT) to capture temporal dynamics in learner ability. While these models have the potential to allow instructional systems to actively monitor the evolution of learner proficiency in real time, existing dynamic item response models rely on expensive inference algorithms that…

The identification procedure can greatly affect eyewitness performance, but this may be contingent upon a relatively weak memory for the perpetrator. In a large preregistered experiment (N = 13,728), we manipulated memory strength and tested participants with a target-present or -absent showup or lineup (size 3 or 6). All fillers were…

The beneficial effect of eye-closure during retrieval was demonstrated in many studies addressing eyewitness memory or memory of episodic events. Fewer studies examined the effect concerning the intentional learning of verbal information. Furthermore, the question of whether the eye-closure effect is modality-specific, boosting visual memory only,…

There has been a growing interest in approaches based on machine learning (ML) for detecting test collusion as an alternative to the traditional methods. Clustering analysis under an unsupervised learning technique appears especially promising to detect group collusion. In this study, the effectiveness of hierarchical agglomerative clustering…

When scores are used to make decisions about respondents, it is of interest to estimate classification accuracy (CA), the probability of making a correct decision, and classification consistency (CC), the probability of making the same decision across two parallel administrations of the measure. Model-based estimates of CA and CC computed from the…

Previous work on indices of error-monitoring strongly supports that errors are distracting and can deplete attentional resources. In this study, we use an ecologically valid multitasking paradigm to test post-error behavior. It was predicted that after failing an initial task, a subject re-presented with that task in conflict with another…

Sociologists increasingly face choices among competing algorithms that represent reasonable approaches to the same task, with little guidance in choosing among them. We develop a strategy that uses simulated data to identify the conditions under which different methods perform well and applies what is learned from the simulations to predict which…

This quantitative, non-experimental study explored the differences between principals' perceived level of influence evaluation barriers have on the accuracy of teacher evaluations. A self-administered questionnaire was used to collect data from Montana principals regarding their perceptions of: (a) teacher performance combinations (b) the strength…

In order to study the big data of college students' employment, this paper takes the big data of college students' employment as the premise, analyzes the current employment data by establishing a DBN model, and puts forward relevant management measures, aiming to provide scientific basis for the management of graduates' employment data. The…