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Vocabulary knowledge is one of the most important elements of reading comprehension. Text coverage is the proportion of known words in a given text. We hypothesize that text comprehension increases exponentially with text coverage due to network effects and activation of prior knowledge. In addition, the lexical threshold hypothesis states that text comprehension increases faster above a certain amount of text coverage. The exponential relationship between text coverage and text comprehension, as well as the lexical threshold, are at the heart of text comprehension theory and are of great interest for optimizing language instruction. In this study, we first used vocabulary knowledge to estimate text coverage based on test scores from N = 924 German fourth graders. Second, we compared linear with non-linear models of text coverage and vocabulary knowledge to explain text comprehension. Third, we used a broken-line regression to estimate a lexical threshold. The results showed an exponential relationship between text coverage and text comprehension. Moreover, text coverage explained text comprehension better than vocabulary knowledge, and text comprehension increased more quickly above 56% text coverage. From an instructional perspective, the results suggest that reading activities with text coverage below 56% are too difficult for readers and likely inappropriate for instructional purposes. Further applications of the results, such as for standard setting and readability analyses, are discussed.

Well-informed decisions on curricular materials can be an efficient way to boost student achievement. Prior studies have employed experimental and quasi-experimental designs to investigate the effects of textbooks on mathematics achievement. This is the first study to consider textbook effects in social studies education. Within the context of a textbook adoption cycle in Texas, we use a difference-in-differences approach with district-level administrative data and estimate the effects of adopting a state-approved textbook on social studies achievement. We find no evidence of a practically meaningful adoption effect. We conclude by highlighting the need for further high-quality research in this often-overlooked school subject area.

When translating large-scale policy changes into educational practice, classroom-level implementation is crucial and dependent on teachers' capabilities. Most research underscores the importance of professional development (PD) as a proximal factor to achieve educational change. Connected to the recent Advanced Placement (AP) science examination reform, this large-scale quantitative study (N[subscript Teachers] = 9,096; N[subscript Students] = 197,589) investigates teacher participation in different PD formats (i.e., face-to-face, online, materials) and their associations with teacher- and student-level outcomes. We found that teacher characteristics (e.g., gender and administrative support) were associated with the choice of the PD format. We also found AP subscore differences between students of teachers who participated in different PD formats. Furthermore, teachers who participated in formal PD activities tended to report higher challenges with the AP reform. Thus, this study encourages PD providers to implement changes alongside a variety of PD programs to account for the potential differential benefits for teachers.

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.

Students' academic achievement is a central predictor of a long list of important educational outcomes, such as access to higher education and socioeconomic success. Prior studies have extensively focused on identifying variables that are related to academic achievement and an important variable in this context appears to be students' personality. Notably, although findings from more recent studies suggested that the association between student achievement and personality varies by the subject domain (language vs. STEM) and the type of achievement measure (grades vs. test scores), systematic meta-analytical evidence is still lacking. To address this gap in the educational research literature, we conducted a meta-analysis based on 78 studies, with 1491 effect sizes representing data from 500,218 students and 110 samples from elementary to high school. We used a random-effects model with robust variance estimation to calculate mean effect sizes and standard deviations. We found moderating effects of measure or domain for all five personality traits, with differences in the direction of the effects. Our results highlight the importance of the domain and measure when examining how personality traits relate to academic achievement in school. The combination of subject domain and achievement was also found to be relevant for some of the traits. These findings emphasize that subject domains and types of achievement measures should be explicitly considered when investigating the personality saturation of student achievement. We discuss implications for future research, highlighting that there is no "best" or "more objective" achievement measure but, instead, that achievement measures should be chosen based on the research question of interest.

Students' academic achievement is a key predictor of various life outcomes and is commonly used for selection as well as for educational monitoring and accountability. With regard to achievement indicators, a differentiation has traditionally been drawn between grades and standardized tests. There is initial, albeit inconclusive, evidence that these indicators might differentially reflect students' personality as encapsulated in the Big Five personality traits as well as measures of cognitive abilities. In this article, we propose the personality-achievement saturation hypothesis (PASH) as an overarching model that describes the association between achievement indicators and personality. The PASH suggests that the differentiation between grades and tests is too simplistic and that associations between personality and achievement instead vary across five main features of the achievement measures that are used: level of standardization, relevance for the student, curricular validity, instructional sensitivity, and cognitive ability saturation. On the basis of findings from prior studies, we focused in particular on conscientiousness and openness to test the PASH. We used data from three large-scale studies (total N = 14,953) and aggregated our findings across these studies. In line with the PASH, the Big Five trait of conscientiousness was most strongly related to measures that were less standardized and less saturated with cognitive ability but higher on curriculum validity, relevance, and instructional sensitivity. In addition, openness was most strongly related to measures that were higher on standardization and cognitive ability saturation but lower on relevance, curriculum validity, and instructional sensitivity in English.

Understanding how early reading and mathematics co-develop is important from both theoretical and pedagogical standpoints. Previous research has provided mixed results. This paper investigates the development of reading and mathematics in a longitudinal sample of N = 355,883 students from the United Kingdom (2005-2019) aged 5 to 12 (49% girls). Results indicate a positive relation between the development of the two domains. In addition, a substantial statistically significant positive association between prior reading scores and subsequent changes in achievement in mathematics was found, whereas changes in reading were smaller for students with a higher prior performance in mathematics. The findings suggest that acquiring good reading skills is highly relevant for developing mathematics skills. Implications for theory and practice are discussed.

Equally able students have lower academic self-concept in high achieving schools or classes, a phenomenon known as the big fish little pond effect (BFLPE). The class (more so than the school) has been shown to be the pivotal frame-of-reference for academic self-concept formation--a local dominance effect. However, many school systems worldwide employ forms of course-by-course tracking, thus exposing students to multiple class environments. Due to the high correlation between multiple student environments, the frame-of-reference used for academic self-concept formation in course-by-course tracked systems is unclear to date. We addressed this unresolved issue by using data from a comprehensive survey that measured the entire population of Austrian eighth-grade students in the domain of mathematics in 2012. General secondary school students were in the core subjects (i.e., mathematics, German, and English) grouped according to ability, whereas regular class composition was the same in all other subjects. Using cross-classified multilevel models, we regressed math self-concept on average math achievement of students' school, math class, and regular class. Consistent with the local dominance effect, we found the BFLPE on the school level to be weak after controlling for the class levels. We found a stronger BFLPE on the regular class level and the strongest BFLPE on the math class level. Our study demonstrates the importance of multiple class environments as frames-of-reference for academic self-concept formation.

Empirically rigorous research on teacher education entry is extremely limited. Evidence on who enters teacher education and why is crucial for identifying undesired gaps in entry and can assist with teacher recruitment. Grounded in prior research, we identified four primary groups of relevant individual-level variables: student/family background, achievement/abilities, personality, and motivation. We empirically tested this conceptual model using rich data from a national cohort of German ninth-graders followed to college/university and their parents. Our study is the first to predict eventual teacher education entry early in secondary school. Although those students who eventually entered teacher education were significantly different from their counterparts across most variables, fewer were conditionally predictive. Social interests, desired working conditions, desire to have children, and students' parents were particularly influential. Implications for research and teacher recruitment are discussed.

Prior research suggests that teachers' positive perceptions of a reform are key when it comes to its successful implementation. The importance of teachers as enactors of change efforts results from their close tie to what is happening in the classroom. This suggests that without persuaded change agents, innovations are unlikely to be implemented coherently, and in turn, likely to fail. In this study, we investigated the relationships between different sets of predictors (specific to the teacher, the school, the reform, or support structures) and teachers' overall rating of a curricular reform. We found that teachers' perceived added value and their perceptions of specific reform aspects predicted overall reform ratings. Furthermore, we identified heterogeneity regarding the importance of specific predictors between different school tracks, which calls for increased attention to school context when considering teachers' perceptions of reforms.

Rapid advancements in artificial intelligence (AI) are driving transformative changes in many areas, with significant environmental implications. Yet, environmental assessments for specific applications are scarce. This study presents an in‐depth life cycle assessment of “Foodforecast,” a machine learning (ML) cloud service designed to reduce food waste in bakeries by optimizing sales forecasting. It covers four impact categories: global warming, abiotic resource depletion, cumulative energy demand, and freshwater eutrophication. The assessment includes both the direct environmental impacts of the ML model and the underlying system hardware, as well as the indirect benefits of avoided bakery returns compared to traditional ordering methods, using real‐world case study data. In 2022, “Foodforecast” led to an average 30% reduction in bakery returns, primarily bread and rolls, according to sales reports. The associated environmental benefits significantly outweighed the system's direct impacts by one to three orders of magnitude across impact categories and return utilization scenarios. The study identifies support activities such as service maintenance during deployment as major direct impact factors, surpassing those from cloud compute for ML operations. Data processing and inference dominate the latter, while the much‐discussed ML training plays a minor role. The environmental consequences of AI are complex and dual sided. This case study demonstrates that AI might provide environmental benefits in certain contexts, yet results are constrained by methodological challenges and data uncertainties. There remains a need for further holistic LCAs across different ML applications to inform decision‐making processes and ultimately guide the responsible use of AI. [ABSTRACT FROM AUTHOR]

Recent research indicated the advantages of diversified teacher professional development participation patterns (PDPP) to foster teachers’ professionalisation. This study investigated teachers’ PDPP from both cross-sectional and longitudinal perspectives using data from the National Educational Panel Study (NEPS) utilising information on teachers’ professional development enrolment across three years (N = 3,539). Results from latent class and multiple group analyses indicated that teachers showing higher professional engagement and teachers who were granted paid temporary leave from teaching demonstrated more diversified PDPP. Multiple logistic regression models revealed that the same variables seemed to prevent changes to less diversified PDPP over time. If our study results are confirmed in replications and studies that allow to detect cause-effect relationships, implications for educational policy and practice could be to raise teachers’ professional engagement and grant engaged teachers a paid temporary leave from teaching to participate in professional development activities. [ABSTRACT FROM AUTHOR]

Teacher-assigned grades provide important information that is used by universities and colleges to make admission decisions and by employers to make hiring decisions. Besides grades, the results of standardized achievement tests are frequently used for student selection and allocation. However, correlations between the two achievement measures are far from perfect, and researchers have argued that this discrepancy can be at least partially attributed to norm-referenced grading, which is based on the composition of performance in a class. In this study, we investigated the results of a curricular intensification reform, which introduced changes in the distribution of student performance by making enrollment in certain courses mandatory. We investigated whether the reform resulted in changes in the relationship between standardized achievement-test results and teacher-assigned grades. We analyzed cohort control design data from two large representative samples of students from two German states (Baden-Württemberg: N = 5,574; Thuringia: N = 2,202) before and after the reform. Results indicated that students who received a certain grade before the reform (e.g., a grade of A, B, C, or D) differed in their standardized test performance from students who received the same grade after the reform. Furthermore, in math, course-level-specific reform effects on the association between grades and standardized test performance were found to vary between groups of students receiving good and poor grades. Implications for educational policy and school reforms and suggestions for grading are discussed.

In recent decades, several countries have made an effort to increase the enrollment rates and performance of students in science and mathematics by means of mandatory, rigorous course work, which is often referred to as curricular intensification (CI). However, there is a lack of research on intended and unintended effects of CI reforms on achievement and motivation. Using representative data from the National Educational Panel Study, we examined effects of a prototypical CI reform in 1 German state. We compared data from the last student cohort before and the first student cohort after the reform at the end of upper secondary school. There was no statistically significant effect on average achievement. However, we found first evidence for differential effects on English reading and a higher English self-concept of young men after the reform, whereas the reform had a negative effect on young women's math self-concept.

This quantitative study describes how teachers responded to the large-scale, top-down, mandated curriculum and examination reform of the Advanced Placement (AP) program in chemistry. This study analyzed data from a nationwide sample of teachers (N = 1,062) teaching redesigned AP Chemistry courses in the first two years of the curriculum reform. Repeated measures analysis of covariance (RM-ANCOVA) indicated that teachers' perceived challenges about the AP redesign substantially decreased once they gained more experience teaching redesigned AP Chemistry courses. Teachers reported that inquiry laboratory investigations and changes in the AP examinations were the most challenging aspect of the curriculum reform, whereas the use of new textbooks and chemistry content was perceived as the least challenging. With respect to classroom practices, teachers more frequently enacted instructional elements directly related to the AP examination instead of incorporating references to core elements of the curriculum reform in their instruction. Similarly, though teachers reported frequently conducting laboratory investigations in their classrooms, only about 25% of the laboratory investigations included elements of student-generated inquiry, an important component of the redesigned AP Chemistry program. Surprisingly, teachers' self-reported classroom instruction remained similar in both the first year and the second year of the AP redesign. In general, this study suggests that school leaders and administrators should not be discouraged when teachers experience challenges during early phases of curriculum reforms. Adaptation to reform takes time. However, teachers may need special encouragement to adopt more in-depth aspects of curriculum reforms.

Empirical evidence suggests children's socio-emotional skills—an important determinant of school achievement—vary according to socioeconomic family background. This study assesses the degree to which differences in socio-emotional skills contribute to the achievement gap between socioeconomically advantaged and disadvantaged children. We used data on 74 countries from the 2018 Programme for International Student Assessment, which contains an extensive set of psychological measures, including growth mindset, self-efficacy, and work mastery. We developed three conceptual scenarios to analyze the role of socio-emotional skills in learning inequality: simple accumulation, multiplicative accumulation, and compensatory accumulation. Our findings are in line with the simple accumulation scenario: Socioeconomically advantaged children have somewhat higher levels of socio-emotional skills than their disadvantaged peers, but the effect of these skills on academic performance is largely similar in both groups. Using a counterfactual decomposition method, we show that the measured socio-emotional skills explain no more than 8.8 percent of the socioeconomic achievement gap. Based on these findings, we argue that initiatives to promote social and emotional learning are unlikely to substantially reduce educational inequality. [ABSTRACT FROM AUTHOR]

Math achievement, math self-concept, and vocational interests are critical predictors of STEM careers and are closely linked to high school coursework. Young women are less likely to choose advanced math courses in high school, and encouraging young women to enroll in advanced math courses may therefore bring more women into STEM careers. We looked at a German statewide educational reform that required all students to take advanced math courses and examined differential effects of the reform on young men and women's math achievement, math self-concept, vocational interests, and field of study at university. We compared data from 4,730 students before the reform and 4,715 students after the reform. We specified multiple regression models and tested main effects of gender and cohort as well as the effect of the Cohort × Gender interaction on all outcomes. All outcomes showed clear gender differences favoring young men before the reform. However, the reform was associated with different effects for young men and women: Whereas gender differences in math achievement were smaller after the reform, differences between young men and women in math self-concept and realistic and investigative vocational interests were larger after the reform than before. Gender differences in the field of study at university did not differ between before and after the reform. Results suggest that reducing course choice options in high school does not automatically increase gender equality in STEM fields.

Objective: Although intelligence and personality traits have long been recognized as key predictors of students' academic achievement, little is known about their longitudinal and reciprocal associations. Here, we charted the developmental interplay of intelligence, personality (Big Five) and academic achievement in 3880 German secondary school students, who were assessed four times between the ages 11 and 14 years (i.e., in grades 5, 6, 7, and 8). Method: We fitted random intercept cross‐lagged panel models (RI‐CLPs) to investigate reciprocal within‐person associations between (a) academic achievement and intelligence, (b) academic achievement and personality, as well as (c) intelligence and personality. Results: The results revealed negative within‐person associations between Conscientiousness and Extraversion assessed at the first wave of measurement and intelligence assessed at the second wave. None of the reciprocal personality–achievement associations attained statistical significance. Academic achievement and intelligence showed reciprocal within‐person relations, with the strongest coefficients found for achievement longitudinally predicting intelligence. Conclusions: Our work contributes to developmental theorizing on interrelations between personality, intelligence, and academic achievement, as well as to within‐person conceptualizations in personality research. [ABSTRACT FROM AUTHOR]