Psychologists are emphasizing the importance of predictive conclusions. Machine learning methods, such as supervised neural networks, have been used in psychological studies as they naturally fit prediction tasks. However, we are concerned about whether neural networks fitted with random datasets (i.e., datasets where there is no relationship…

The recently proposed "continuous-time latent curve model with structured residuals" (CT-LCM-SR) addresses several challenges associated with longitudinal data analysis in the behavioral sciences. First, it provides information about process trends and dynamics. Second, using the continuous-time framework, the CT-LCM-SR can handle…

The present large-scale quantitative study investigated the predictive effects of learning strategies and styles on Chinese undergraduate students' English achievement and their mediating effects on each other's relation to English achievement. A total of 439 students from different universities answered the 30-item Perceptual Learning Style…

Knowledge of results (KR), particularly its informational role, has often been regarded as redundant for learning interception-like tasks, such as coincidence-anticipation timing tasks. However, it is possible that the KR's guiding effect might be detrimental to motor adaptation, instead of only redundant, leading to a dependency on KR and…

Turnover in the teacher workforce imposes significant costs to schools, both in terms of student achievement and the time and expense required to recruit and train new staff. This paper examines the potential for structured ratings of teacher applicants, solicited from their professional references, to inform hiring decisions through the selection…

Growth-to-standard models evaluate student growth against the growth needed to reach a future standard or target of interest, such as proficiency. A common growth-to-standard model involves comparing the popular Student Growth Percentile (SGP) to Adequate Growth Percentiles (AGPs). AGPs follow from an involved process based on fitting a series of…

This study highlights the role and importance of advanced, machine learning-driven predictive models in enhancing the accuracy and timeliness of identifying students at-risk of negative academic outcomes in data-driven Early Warning Systems (EWS). K-12 school districts have, at best, 13 years to prepare students for adulthood and success. They…

This study evaluated the forecasting accuracy of trend estimation methods applied to time-series data from computer adaptive tests (CATs). Data were collected roughly once a month over the course of a school year. We evaluated the forecasting accuracy of two regression-based growth estimation methods (ordinary least squares and Theil-Sen). The…

In order to make appropriate educational recommendations, psychologists must understand how cognitive test scores influence specific academic outcomes for students of different ability levels. We used data from the WISC-V and WIAT-III (N = 181) to examine which WISC-V Index scores predicted children's specific and broad academic skills and if…

The Elicited Imitation Test (EIT) is widely recognized for its reliability in research settings as a proficiency assessment tool. However, there exists a need to examine its predictive validity in English as a Foreign Language (EFL) classrooms. This study investigates the extent to which the EIT, alongside the Oxford Placement Test (OPT), can…

Recent methods have sought to improve precision in randomized controlled trials (RCTs) by utilizing data from large observational datasets for covariate adjustment. For example, consider an RCT aimed at evaluating a new algebra curriculum, in which a few dozen schools are randomly assigned to treatment (new curriculum) or control (standard…