In psychology, researchers are often interested in the predictive classification of individuals. Various models exist for such a purpose, but which model is considered a best practice is conditional on attributes of the data. Under certain conditions, linear discriminant analysis (LDA) has been shown to perform better than other predictive methods, such as logistic regression, multinomial logistic regression, random forests, support-vector machines, and the K-nearest neighbor algorithm. The purpose of this Tutorial is to provide researchers who already have a basic level of statistical training with a general overview of LDA and an example of its implementation and interpretation. Decisions that must be made when conducting an LDA (e.g., prior specification, choice of cross-validation procedures) and methods of evaluating case classification (posterior probability, typicality probability) and overall classification (hit rate, Huberty’s I index) are discussed. LDA for prediction is described from a modern Bayesian perspective, as opposed to its original derivation. A step-by-step example of implementing and interpreting LDA results is provided. All analyses were conducted in R, and the script is provided; the data are available online.

The purposes of this study included conducting a meta-analysis and reviewing the study reporting quality of math interventions implemented in informal learning environments (e.g., the home) by children’s caregivers. This meta-analysis included 25 preschool to third-grade math interventions with 83 effect sizes that yielded a statistically significant summary effect (g = 0.26, 95% CI [0.07, 0.45) on children’s math achievement. Significant moderators of the treatment effect included the intensity of caregiver training and type of outcome measure. There were larger average effects for interventions with caregiver training that included follow-up support and for outcomes that were comprehensive early numeracy measures. Studies met 58.0% of reporting quality indicators, and analyses revealed that quality of reporting has improved in recent years. The results of this study offer several recommendations for researchers and practitioners, particularly given the growing evidence base of math interventions conducted in informal learning environments.

In many states, writing is tested only in selected grades causing writing instruction often to be over-emphasized some years and neglected in others. The present study examines how students’ motivation for writing differs during the middle school years. We administered the Self-Beliefs, Writing Beliefs, and Attitude Survey (SWAS) to a cross-sectional sample of students at two middle schools shortly after the 7th graders completed a high-stakes writing exam. Results indicate that 6th and 8th graders had higher average scores of writing motivation compared to their 7th-grade peers, with 6th graders possessing higher average levels of attitude toward writing and beliefs about writing compared to 8th graders. The varying pressure and absence of writing may be influencing students’ motivation to write and views of writing.

Purpose: The evidence base for active learning in medical education is based largely on trials with suspect internal validity. We empirically compared the learning and feeling of learning of participants when in large-group interactive sessions and passive lectures using a rigorous trial capable of providing an unambiguous assessment of effect. Further, we evaluated if there was a differential effect based on prior achievement. Materials and Method: We conducted a two-day randomized cross-over trial with 146 s-year medical students. Passive learning involved lecture-based case presentation with minimal interaction; in the large-group interactive session, students worked in teams on the same cases. Participants completed a test of learning and a feeling of learning survey. Effects were estimated using linear mixed-effects models. Results: Participants in the large-group interactive session scored 0.27 standard deviations higher on the test of learning (p = 0.010) than when in the passive lecture. Learners in the lower 50% of prior achievement benefited most from active learning. The feeling of learning was 0.56 standard deviations higher in the large-group interactive session (p < 0.001). Conclusions: Transformation of passive lectures to interactive learning sessions is feasible, has the potential to close achievement gaps by benefiting the lowest achieving learners the most, and provides students a greater sense of learning than passive lectures.

The intentional integration of physical activity in elementary school classrooms—including brief instructional breaks for activity, or integration into lessons—can benefit children’s physical activity and education outcomes. Teachers are key implementation agents, but despite physical activity in the classroom being an evidence-informed practice, many teachers do not regularly implement it. The aim of this study was to obtain updated nationally representative prevalence estimates in United States public elementary schools, regarding four key outcomes: (1) school adoption of physically active lessons (PA lessons); (2) school adoption of physical-activity breaks (PA breaks); (3) penetration in the classroom, defined as ≥50% of teachers using PA breaks; and (4) dose, defined as an average of at least 50 min per week of PA breaks. We examined variations in outcomes by school demographic characteristics, and by three factors hypothesized to be implementation facilitators (administrative support, financial resources, and presence of a wellness champion at the school). In the 2019–20 school year, surveys were distributed to a nationally representative sample of 1010 public elementary schools in the US; responses were obtained from 559 (55.3%). The weighted prevalence of schools reporting adoption of PA lessons was 77.9% (95% CI = 73.9% to 81.9%), and adoption of PA breaks was nearly universal at 91.2% (95% CI = 88.4% to 94.1%). Few demographic differences emerged, although adoption of PA lessons was less prevalent at higher-poverty schools (73.9%) and medium-poverty schools (77.0%) as compared to schools with lower poverty levels (87.1%; p < 0.01). Across all four outcomes, associations emerged with facilitators in multivariable logistic regression models. The prevalence of adoption of PA lessons, adoption of PA breaks, and dose of PA breaks were all significantly higher at schools where administrative encouragement occurred more frequently. Financial support was associated with implementation outcomes, including adoption of PA lessons, and penetration and dose of PA breaks. Presence of a champion was associated with higher prevalence of reporting adoption of PA lessons. School leaders can play a crucial role in supporting teachers’ implementation of PA breaks and lessons in the classroom, through providing financial resources, encouragement, and supporting champions. Effective school-leadership practices have the potential to positively impact students at a large-scale population level by supporting implementation of PA lessons and breaks.