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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.
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.
German orthography systematically marks all nouns (even other nominalized word classes) by capitalizing their first letter. It is often claimed that readers benefit from the uppercase-letter syntactic and semantic information, which makes the processing of sentences easier (e.g., Bock et al., 1985, 1989). In order to test this hypothesis, we asked 54 German readers to read single sentences systematically manipulated by a target word (N). In the experimental condition (EXP), we used semantic priming (in the following example: sick -> cold) in order to build up a strong expectation of a noun, which was actually an attribute for the following noun (N+1) (translated to English e.g., "The sick writer had a cold (N) nose (N+1) ..."). The sentences in the control condition were built analogously, but word N was purposefully altered (keeping word length and frequency constant) to make its interpretation as a noun extremely unlikely (e.g., "The sick writer had a blue (N) nose (N+1) ..."). In both conditions, the sentences were presented either following German standard orthography (Cap) or in lowercase spelling (NoCap). The capitalized nouns in the EXP/Cap condition should then prevent garden-path parsing, as capital letters can be recognized parafoveally. However, in the EXP/NoCap condition, we expected a garden-path effect on word N+1 affecting first-pass fixations and the number of regressions, as the reader realizes that word N is instead an adjective. As the control condition does not include a garden-path, we expected to find (small) effects of the violation of the orthographic rule in the CON/NoCap condition, but no garden-path effect. As a global result, it can be stated that reading sentences in which nouns are not marked by a majuscule slows a native German reader down significantly, but from an absolute point of view, the effect is small. Compared with other manipulations (e.g., transpositions or substitutions), a lowercase letter still represents the correct allograph in the correct position without affecting phonology. Furthermore, most German readers do have experience with other alphabetic writing systems that lack consistent noun capitalization, and in (private) digital communication lowercase nouns are quite common. Although our garden-path sentences did not show the desired effect, we found an indication of grammatical pre-processing enabled by the majuscule in the regularly spelled sentences: In the case of high noun frequency, we post hoc located parafovea-on-fovea effects, i.e., longer fixation durations, on the attributive adjective (word N). These benefits of capitalization could only be detected under specific circumstances. In other cases, we conclude that longer reading durations are mainly the result of disturbance in readers' habituation when the expected capitalization is missing.
The aim of educational policy should be to provide a good education to all students. Thus, a key question arises regarding the extent to which key characteristics of school composition (proportion of students with migration background, socioeconomic status [SES], prior school achievement, and achievement heterogeneity), instructional quality, school quality, and later school achievement are interrelated. The present study addressed this research question by examining school inspection data, official school statistics, and large-scale achievement data from all primary schools in Berlin, Germany (N = 343). The results of correlation and path analyses showed that school composition (average SES, average prior school achievement) predicted components of instructional quality (SES: classroom management, cognitive activation; achievement: cognitive activation, individual learning support). The relation between school composition characteristics and most components of school quality was close to zero. Contrary to our expectations, only the effect of school SES on later achievement was mediated by instructional quality.
Becoming Canadian
(2020)
We investigate how economic immigrants in Canada negotiate their identity in the process of "becoming Canadian" through an analysis of public texts. Drawing on the master narrative framework, we examine the interplay between individual and societal narratives as immigrants grapple with the tension between notions of "desirable" immigrants as those that are well integrated professionally and the reality of facing career related barriers. Among those whose success stories align with the master narrative of professional attainment there was little questioning of this expectation, thereby allowing it to remain invisible. Among those who had not (yet) achieved work related success in the receiving country, they tended to engage alternative narratives elaborating on the antecedents, outcomes, and barriers to labor market participation. Despite the countering nature of these alternative narratives, they strengthen the societal expectation of professional success as a key pathway to inclusion, thereby reinforcing the rigidity of this narrative. We contribute to literature on the social construction of national identity by examining the process of becoming national and the role of labor market participation in immigrants' perceptions of inclusion in their new society. Our study highlights the importance of including immigrants' voices in the construction of a more inclusive society, which may aid in breaking down exclusionary narratives of national identity.
Reflecting in written form on one's teaching enactments has been considered a facilitator for teachers' professional growth in university-based preservice teacher education. Writing a structured reflection can be facilitated through external feedback. However, researchers noted that feedback in preservice teacher education often relies on holistic, rather than more content-based, analytic feedback because educators oftentimes lack resources (e.g., time) to provide more analytic feedback. To overcome this impediment to feedback for written reflection, advances in computer technology can be of use. Hence, this study sought to utilize techniques of natural language processing and machine learning to train a computer-based classifier that classifies preservice physics teachers' written reflections on their teaching enactments in a German university teacher education program. To do so, a reflection model was adapted to physics education. It was then tested to what extent the computer-based classifier could accurately classify the elements of the reflection model in segments of preservice physics teachers' written reflections. Multinomial logistic regression using word count as a predictor was found to yield acceptable average human-computer agreement (F1-score on held-out test dataset of 0.56) so that it might fuel further development towards an automated feedback tool that supplements existing holistic feedback for written reflections with data-based, analytic feedback.
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.
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.