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Effects of social and individual school self-concepts on school engagement during adolescence
(2020)
While school self-concept is an important facilitator of a student's school engagement, previous studies rarely investigated whether it may also explain the change in students' school engagement during secondary school. Moreover, as social relations play an increasingly important role in adolescence, the current research distinguishes between the social and individual school self-concepts of a student. Whereas individual school self-concept uses the perception of a student's own ability in the past in order to estimate perceived current ability, social school self-concept refers to the comparison of a student's own perceived current ability with the current perceived abilities of others. We examined the role of students' individual and social school self-concepts in the development of behavioral and emotional school engagement during the period from grade 8 to grade 9. The sample consisted of 1088 German adolescents at the first measurement time (M-age = 13.70, SD = 0.53; 53.9% girls). The findings suggested a significant decline in both emotional and behavioral school engagement over the span of 1.5 years. In addition, social-but not individual-school self-concept was associated with the change in both dimensions of school engagement over time, such as it may intensify a student's decline in school engagement levels. This might be due to the fact that students with a high social school self-concept tend to increasingly emphasize competition and comparison and strive for high grades, which lowers students' school participation and identification in the long term.
Differentiation of intelligence refers to changes in the structure of intelligence that depend on individuals' level of general cognitive ability (ability differentiation hypothesis) or age (developmental differentiation hypothesis). The present article aimed to investigate ability differentiation, developmental differentiation, and their interaction with nonlinear factor analytic models in 2 studies. Study 1 was comprised of a nationally representative sample of 7,127 U.S. students (49.4% female; M-age = 14.51, SD = 1.42, range = 12.08-17.00) who completed the computerized adaptive version of the Armed Service Vocational Aptitude Battery. Study 2 analyzed the norming sample of the Berlin Intelligence Structure Test with 1,506 German students (44% female; M-age = 14.54, SD = 1.35, range = 10.00-18.42). Results of Study 1 supported the ability differentiation hypothesis but not the developmental differentiation hypothesis. Rather, the findings pointed to age-dedifferentiation (i.e., higher correlations between different abilities with increasing age). There was evidence for an interaction between age and ability differentiation, with greater ability differentiation found for older adolescents. Study 2 provided little evidence for ability differentiation but largely replicated the findings for age dedifferentiation and the interaction between age and ability differentiation. The present results provide insight into the complex dynamics underlying the development of intelligence structure during adolescence. Implications for the assessment of intelligence are discussed.
Germany historically responded to student diversity by tracking students into different schools beginning with grade 5. In the last decades, sociopolitical changes, such as an increase in "German-as-a-second-language" speaking students (GSL), have increased diversity in all tracks and have forced schools to consider forms of individualization. This has opened up the scientific debate in Germany on merits and limitations of individualization for different student groups within a tracked system and heterogeneous classes. The aim of the present exploratory study was to examine how individualized teaching (i.e., teacher self-reported individualized teaching practices and individual reference norm orientation) is related to student-perceived teaching quality. Additionally, we considered moderation effects of classroom composition in relation to achievement and proportion of GSL students. Longitudinal data came from 35 mathematics classes with 659 9th and 10th grade students. Results showed significant relation between teacher self-reported individualized teaching practices and individual reference norm orientation and monitoring. Regarding the composition effects, the proportion of GSL students in class moderated the relation between teacher self-reported individual reference norm orientation and cognitive activation. Our findings contribute to the growing body of evidence that classroom composition can differentially impact the relation between teachers' behaviors and students' perceptions of teaching quality.
Migration is not a new phenomenon. However, recent data indicate that unprecedented numbers of people have experienced forced migration around the world with 51% under the age of 18 years. How can educational policies and practices respond sensitively to increasing cultural and migration-based diversity? The purpose of this special section that includes eight studies is to consider these issues more deeply. As a frame for the special section, we address the main question: What are promotive or protective factors for positive development of children and youth attending culturally diverse school contexts? In the collection of papers, these promotive and protective factors range from peers and families, to teachers, to organisational context and climate. With continued disruptions in children's lives due to a pandemic, climate change, war, conflict and poverty, migration will remain a pressing concern and will continue to transform the student populations in our classrooms and schools for the foreseeable future. The need to address how we can best provide students from diverse backgrounds equitable and supportive education, continues.
An Fragen wachsen
(2020)
Four topics were investigated in this longitudinal person-centered study: (a) profiles of subjective task values and ability self-concepts of adolescents in the domain of mathematics, (b) the stability of and changes to the profiles of motivational beliefs from Grade 7 to 12, (c) the relation of changes to student-perceived classroom characteristics, and (d) the extent to which profile membership in early adolescence predicted mathematics achievement and career plans in late adolescence and the choice of math-related college majors and occupations in adulthood. Data were drawn from the Michigan Study of Adolescent and Adult Life Transitions Study. We focused on students who participated in the following 4 waves of data collection (N = 867): at the beginning of Grade 7 (Wave 3), at the end of Grade 7, in Grade 10 (Wave 5), and in Grade 12 (Wave 6). Four profiles that were stable across Grades 7 to 12 were identified using Latent Profile Analysis. Student-reported fairness and friendliness and competition in class predicted changes in profile membership. Profile membership in Grade 7 predicted math-related career plans in Grade 12. Profile membership in Grade 12 predicted the choice of math-related college major after finishing school and of math-related occupations in adulthood.
Background: Students' self-concept of ability is an important predictor of their achievement emotions. However, little is known about how learning environments affect these interrelations.
Aims: Referring to Pekrun's control-value theory, this study investigated whether teacher-reported teaching quality at the classroom level would moderate the relation between student-level mathematics self-concept at the beginning of the school year and students' achievement emotions at the middle of the school year.
Sample: Data of 807 ninth and tenth graders (53.4% girls) and their mathematics teachers (58.1% male) were analysed.
Method: Students and teachers completed questionnaires at the beginning of the school year and at the middle of the school year. Multi-level modelling and cross-level interaction analyses were used to examine the longitudinal relations between self-concept, teacher-perceived teaching quality, and achievement emotions as well as potential interaction effects.
Results: Mathematics self-concept significantly and positively related to enjoyment in mathematics and negatively related to anxiety. Teacher-reported structuredness decreased students' anxiety. Mathematics self-concept only had a significant and positive effect on students' enjoyment at high levels of teacher-reported cognitive activation and at high levels of structuredness.
Conclusions: High teaching quality can be seen as a resource that strengthens the positive relations between academic self-concept and positive achievement emotions.
Teacher self-efficacy for classroom management is an important component of teachers' identity with implications for their teaching quality. Theoretically, it has been described that contextual variables play an important role for self-efficacy development and its consequences. However, little is known about the interrelationships of job resources and demands with teacher self-efficacy, and consequences for teachers' professional behaviors. We extend teacher self-efficacy research by drawing on the Job Demands-Resources model in examining contextual influences on developmental dynamics between classroom management self-efficacy and teacher-reported classroom management, from prior to qualifying as a teacher until mid-career. Participants were 395 primary and secondary Australian school teachers. Longitudinal structural equation models showed teachers' classroom management self-efficacy positively related to aspects of their perceived classroom management, particularly during early career. Between early and mid-career, the positive relationship between self-efficacy and classroom management was moderated by early career excessive demands. Implications are outlined for teacher education and school administration.
There is no consensus on which statistical model estimates school value-added (VA) most accurately. To date, the two most common statistical models used for the calculation of VA scores are two classical methods: linear regression and multilevel models. These models have the advantage of being relatively transparent and thus understandable for most researchers and practitioners. However, these statistical models are bound to certain assumptions (e.g., linearity) that might limit their prediction accuracy. Machine learning methods, which have yielded spectacular results in numerous fields, may be a valuable alternative to these classical models. Although big data is not new in general, it is relatively new in the realm of social sciences and education. New types of data require new data analytical approaches. Such techniques have already evolved in fields with a long tradition in crunching big data (e.g., gene technology). The objective of the present paper is to competently apply these "imported" techniques to education data, more precisely VA scores, and assess when and how they can extend or replace the classical psychometrics toolbox. The different models include linear and non-linear methods and extend classical models with the most commonly used machine learning methods (i.e., random forest, neural networks, support vector machines, and boosting). We used representative data of 3,026 students in 153 schools who took part in the standardized achievement tests of the Luxembourg School Monitoring Program in grades 1 and 3. Multilevel models outperformed classical linear and polynomial regressions, as well as different machine learning models. However, it could be observed that across all schools, school VA scores from different model types correlated highly. Yet, the percentage of disagreements as compared to multilevel models was not trivial and real-life implications for individual schools may still be dramatic depending on the model type used. Implications of these results and possible ethical concerns regarding the use of machine learning methods for decision-making in education are discussed.
Geht das unter die Haut?
(2020)