Search

The relationship between students' subject-specific academic self-concept and their academic achievement is one of the most widely researched topics in educational psychology. A large proportion of this research has considered cross-lagged panel models (CLPMs), oftentimes synonymously referred to as reciprocal effects models (REMs), as the gold standard for investigating the causal relationships between the two variables and has reported evidence of a reciprocal relationship between self-concept and achievement. However, more recent methodological research has questioned the plausibility of assumptions that need to be satisfied in order to interpret results from traditional CLPMs causally. In this substantive-methodological synergy, we aimed to contrast traditional and more recently developed methods to investigate reciprocal effects of students' academic self-concept and achievement. Specifically, we compared results from CLPMs, full-forward CLPMs (FF-CLPMs), and random intercept CLPMs (RI-CLPMs) with two weighting approaches developed to study causal effects of continuous treatment variables. To estimate these different models, we used rich longitudinal data of N = 3757 students from lower secondary schools in Germany. Results from CLPMs, FF-CLPMs, and weighting methods supported the reciprocal effects model, particularly when math self-concept and grades were considered. Results from the RI-CLPMs were less consistent. Implications from our study for the interpretation of effects from the different models and methods as well as for school motivation theory are discussed.

A central question in educational research is how classroom climate variables, such as teaching quality, goal structures, or interpersonal teacher behavior, are related to critical student outcomes, such as students' achievement and motivation. Student ratings are frequently used to measure classroom climate. When using student ratings to assess classroom climate, researchers first ask students to rate classroom climate characteristics and then aggregate the ratings on the class level. Multilevel latent variable modeling is then used to determine whether class-mean ratings of classroom climate are predictive of student outcomes and to correct for unreliability so that the relations can be estimated without bias. In this article, we adopt an optimal design perspective on this specific strategy. Specifically, after briefly recapping a prominent model in climate research, we show and explain (a) how statistical power can be maximized by choosing optimal numbers of classes and students per class given a fixed budget for conducting a study and (b) how the budget required to achieve a prespecified level of power can be minimized. Moreover, we present an example from research on teaching quality to illustrate the procedures and to provide guidance to researchers who are interested in studying the role of classroom climate. Also, we present a Shiny App that can be used to help find optimal designs for classroom climate studies. The app can be accessed at https://psychtools.shinyapps.io/optimalDesignsClassroomClimate

A central goal of physical education (PE) is to empower students to be physically active in a health-enhancing way. Therefore, using a competence-based approach, the objective of the health- and fitness-related PE intervention 'Promotion of physical activity-related health competence in PE' (GEKOS) was to address practical and theoretical content regarding training, fitness, and health. The aim of this person-oriented study was to examine differential effects of the GEKOS intervention on control competence and related knowledge, skills, abilities, and motivation. A total of 860 ninth graders were randomly assigned to an intervention or control group. Intervention group students received six PE lessons that combined practical and theoretical content regarding training, fitness, and health. Measurements were taken before, directly after, and with a follow-up after 8-12 weeks. Students completed a knowledge test and a fitness test, and filled out scales considering motivation and perceived control competence. Latent profile analysis and multinomial logistic regression were estimated. Five patterns of outcome measures were found. Intervention group students transitioned significantly more often to patterns with improved outcome values. The intervention was especially effective for a subgroup of students who initially had rather low outcome values. For a small proportion of students, the intervention entailed a loss of health-related motivation. The study indicated that students with low control competence and related knowledge, skills, abilities, and motivation can benefit from the GEKOS intervention. Future studies should investigate modes of actions and interventions that explicitly address motivation and vary the content and methods used in PE.

Epigenetic clocks comprise a set of CpG sites whose DNA methylation levels measure subject age. These clocks are acknowledged as a highly accurate molecular correlate of chronological age in humans and other vertebrates. Also, extensive research is aimed at their potential to quantify biological aging rates and test longevity or rejuvenating interventions. Here, we discuss key challenges to understand clock mechanisms and biomarker utility. This requires dissecting the drivers and regulators of age-related changes in single-cell, tissue- and disease-specific models, as well as exploring other epigenomic marks, longitudinal and diverse population studies, and non-human models. We also highlight important ethical issues in forensic age determination and predicting the trajectory of biological aging in an individual.

Teachers must be able to monitor students' behavior and identify valid cues in order to draw conclusions about students' actual engagement in learning activities. Teacher training can support (inexperienced) teachers in developing these skills by using videotaped teaching to highlight which indicators should be considered. However, this supposes that: (1) valid indicators of students' engagement in learning are known; and (2) work with videos is designed as effectively as possible to reduce the effort involved in manual coding procedures and in examining videos. One avenue for addressing these issues is to utilize the technological advances made in recent years in fields such as machine learning to improve the analysis of classroom videos. Assessing students' attention-related processes through visible indicators of (dis)engagement in learning might become more effective if automated analyses can be employed. Thus, in the present study, we validated a new manual rating approach and provided a proof of concept for a machine vision-based approach evaluated on pilot classroom recordings of three lessons with university students. The manual rating system was significantly correlated with self-reported cognitive engagement, involvement, and situational interest and predicted performance on a subsequent knowledge test. The machine vision-based approach, which was based on gaze, head pose, and facial expressions, provided good estimations of the manual ratings. Adding a synchrony feature to the automated analysis improved correlations with the manual ratings as well as the prediction of posttest variables. The discussion focuses on challenges and important next steps in bringing the automated analysis of engagement to the classroom.