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See detailWhat Primary Schools Are Doing Right: Educational Value-Added in Luxembourg
Emslander, Valentin UL; Levy, Jessica UL; Fischbach, Antoine UL

Poster (2022, November 10)

In such a diverse context as Luxembourg, educational inequalities can arise from diverse languages spoken at home, a migration background, or a family’s socioeconomic status. This diversity leads to ... [more ▼]

In such a diverse context as Luxembourg, educational inequalities can arise from diverse languages spoken at home, a migration background, or a family’s socioeconomic status. This diversity leads to different preconditions for learning math and languages (e.g. the language of instruction) and thus shapes the school careers of students (Hadjar & Backes, 2021). The aim of the project Systematic Identification of High Value-Added in Educational Contexts (SIVA) was to answer the questions (1) what highly effective schools are doing “right” or differently and (2) what other schools can learn from them in alleviating inequalities. In collaboration with the Observatoire National de la Qualité Scolaire, we investigated the differences of schools with stable high value-added (VA) scores to those with stable medium or low VA scores from multiple perspectives. VA is a statistical regression method usually used to fairly estimate schools’ effectiveness considering diverse student backgrounds. First, we identified 16 schools which had a stable high, medium, or low VA scores over two years. Second, we collected data on their pedagogical strategies, student background, and school climate through questionnaires and classroom observations. Third, we matched our data to results from the Luxembourg School Monitoring Programme ÉpStan (LUCET, 2021). We selected the variables based on learning models focusing on aspects such as school organization or classroom management (e.g., Hattie, 2008; Helmke et al., 2008; Klieme et al., 2001). We further investigated specificities about the Luxembourgish school system, which are not represented in international school learning models (such as the division into two-year learning cycles, the multilingual school setting, or the diverse student population). We will discuss the SIVA-project, its goals, and its data collection leading to data from observations in 49 classroom and questionnaires with over 500 second graders, their parents, their teachers, as well as school presidents and regional directors. Literature Hadjar, A., & Backes, S. (2021). Bildungsungleichheiten am Übergang in die Sekundarschule in Luxemburg. https://doi.org/10.48746/BB2021LU-DE-21A Hattie, J. (2008). Visible Learning: A synthesis of over 800 meta-analyses relating to achievement (0 ed.). Routledge. https://doi.org/10.4324/9780203887332 Helmke, A., Rindermann, H., & Schrader, F.-W. (2008). Wirkfaktoren akademischer Leistungen in Schule und Hochschule [Determinants of academic achievement in school and university]. In M. Schneider & M. Hasselhorn (Eds.), Handbuch der pädagogischen Psychologie (Vol. 10, pp. 145–155). Hogrefe. Klieme, E., Schümer, G., & Knoll, S. (2001). Mathematikunterricht in der Sekundarstufe I: “Aufgabenkultur” und Unterrichtsgestaltung. TIMSS - Impulse für Schule und Unterricht, 43–57. LUCET. (2021). Épreuves Standardisées (ÉpStan). https://epstan.lu [less ▲]

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See detailTeacher-Student-Relationships and Student Outcomes in Heterogeneous Educational Settings: A Systematic Review of Meta-Analyses
Emslander, Valentin UL; Holzberger, Doris; Fischbach, Antoine UL et al

Scientific Conference (2022, November 09)

Especially in diverse educational settings, positive relationships between students and their teachers can foster students’ learning and help alleviate systematic inequalities. Characterized by emotional ... [more ▼]

Especially in diverse educational settings, positive relationships between students and their teachers can foster students’ learning and help alleviate systematic inequalities. Characterized by emotional warmth or closeness, positive teacher-student relationships (TSR) can improve several student outcomes. For instance, existing meta-analyses suggest significant links between TSR and students’ peer relations, school engagement, academic achievement, emotions, executive functions, general well-being, and reductions in aggressive or disruptive behaviors. However, the evidence on these links is scattered, and a comprehensive overview of the associations with TSR integrating academic, behavioral, socio-emotional, motivational, and general cognitive outcomes is lacking. Furthermore, researchers have been unequivocal about possible moderators, such as how these relationships vary with student age or gender. In light of these research gaps, we systematically reviewed the meta-analytic literature and examined (a) the extent to which academic, behavioral, socio-emotional, motivational, and general cognitive student outcomes are related to TSR in the meta-analytic literature; (b) which moderators influence this association; and (c) the methodological quality of the included meta-analyses. We included meta-analyses with preschool or K-12 samples in our dataset which reported some measure of the relation between TSR and student outcomes. With this dataset, we systematically mapped the evidence on (a) the TSR-outcome relationship; (b) the moderators; and (c) the methodological quality of the meta-analyses. We will present our core findings and discuss future research with this second-order, meta-analytic dataset and the impact of positive TSR in diverse and heterogeneous settings. [less ▲]

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See detailValue-Added Scores Show Limited Stability over Time in Primary School
Emslander, Valentin UL; Levy, Jessica; Scherer, Ronny et al

E-print/Working paper (2022)

Value-added (VA) models are used for accountability purposes and quantify the value a teacher or a school adds to their students’ achievement. If VA scores lack stability over time and vary across outcome ... [more ▼]

Value-added (VA) models are used for accountability purposes and quantify the value a teacher or a school adds to their students’ achievement. If VA scores lack stability over time and vary across outcome domains (e.g., mathematics and language learning), their use for high-stakes decision making is in question and could have detrimental real-life implications: teachers could lose their jobs, or a school might receive less funding. However, school-level stability over time and variation across domains have rarely been studied together. In the present study, we examined the stability of VA scores over time for mathematics and language learning, drawing on representative, large-scale, and longitudinal data from two cohorts of standardized achievement tests in Luxembourg (N = 7,016 students in 151 schools). We found that only 34-38% of the schools showed stable VA scores over time with moderate rank correlations of VA scores from 2017 to 2019 of r = .34 for mathematics and r = .37 for language learning. Although they showed insufficient stability over time for high- stakes decision making, school VA scores could be employed to identify teaching or school practices that are genuinely effective—especially in heterogeneous student populations. [less ▲]

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See detailQuality Assessment in Meta-Analyses (QuAMA)
Scherer, Ronny; Emslander, Valentin UL

Report (2022)

This project develops methods and procedures to (a) quantify the quality of primary studies in meta-analyses; and (b) account for primary-study quality in moderator analyses. As part of the project, we ... [more ▼]

This project develops methods and procedures to (a) quantify the quality of primary studies in meta-analyses; and (b) account for primary-study quality in moderator analyses. As part of the project, we develop an analytic procedure to create study quality indicators and incorporate them in the meta-analysis. We will present this procedure in a step-by-step tutorial with illustrative examples. [less ▲]

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See detailThe Associations Between Teacher-Student-Relationships and Student Outcomes: A Systematic Review of Meta-Analyses (ReMA-TSR)
Emslander, Valentin UL; Holzberger, Doris; Fischbach, Antoine UL et al

Report (2022)

The relationships between students and their teachers can impact students’ learning and development. Characterized by emotional warmth or closeness, positive teacher-student-relationships (TSR) can ... [more ▼]

The relationships between students and their teachers can impact students’ learning and development. Characterized by emotional warmth or closeness, positive teacher-student-relationships (TSR) can improve a variety of student outcomes. Existing meta-analyses suggest strong links between TSR and students’ peer relations, school engagement, academic achievement, emotions, executive functions, general well-being, and reductions in aggressive or disruptive behaviors. However, this evidence base is scattered, and a comprehensive overview of the TSR-outcome associations integrating academic, behavioral, socio-emotional, and general cognitive outcomes is lacking. Further, researchers have been unequivocal about possible moderators, such as how these relationships change with student age as their relationship to family, peers, and teachers change. Considering these research gaps, we aim to systematically review the meta-analytic literature and examine the following two research questions: Research Question 1: To what extent do existing meta-analyses provide evidence supporting significant relations between TSR and children’s academic, behavioral, socioemotional, motivational, and general cognitive outcomes? (Overall relationship) Research Question 2: To what extent do these relationships vary by the characteristics of the meta-analyses, such as student samples, measurement characteristics, and the quality of the meta-analyses? To address these research questions, we conduct a systematic review of existing meta-analyses, integrating the findings of eligible studies. We will include quantitative meta-analyses with preschool or K-12 samples who have no diagnosed disorder or disability. [less ▲]

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See detailSystematic Identification of High "Value-Added" in Educational Contexts (SIVA)
Emslander, Valentin UL; Levy, Jessica; Fischbach, Antoine UL

Report (2022)

The aim of the SIVA project is to investigate differences between schools with stable high value-added (VA) scores to those with low or medium VA scores to learn about their effective pedagogical ... [more ▼]

The aim of the SIVA project is to investigate differences between schools with stable high value-added (VA) scores to those with low or medium VA scores to learn about their effective pedagogical strategies. We attempt to achieve this goal through classroom observations and questionnaires for students in grade 2, their parents, their teachers, as well as school presidents. More specifically, with the present study we want to learn from target schools with stable positive VA scores – a statistical method usually used to estimate schools' effectiveness. We will use VA modelling constructively to compare those schools identified as highly effective (i.e., with high VA scores) to schools with medium or low VA scores on variables such as pedagogical strategies, student background, and school climate. To this end, a mixed-methods design based on questionnaires, observations, and results from the Luxembourg School Monitoring Programme ÉpStan (LUCET, 2021) will be applied. The content of the investigation is based on a synthesis of models of school learning and quality, focusing on aspects such as school organization or classroom management (e.g., Hattie, 2008; Helmke et al., 2008; Klieme et al., 2001) and is extended by specificities about the Luxembourgish school system, which are not represented in international school learning models (such as the division into two-year learning cycles, the multilingual school setting, and the diverse student population). With the aim to obtain a preferably broad picture, students, parents, teachers, school presidents and regional directors will be investigated. While parents, teachers, school presidents and regional directors can—as adults—fill out questionnaires individually, obtaining the opinion from children at such a young age can be challenging. The SIVA project tackles this issue by choosing item formats that are appealing and understandable for young children (see, e.g.,Lehnert, 2019), as well as by including classroom observations conducted by neutral educational experts (please, find both the questionnaires and observation sheets in the attachments). [less ▲]

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See detailAre Value-Added Scores Stable Enough for High-Stakes Decisions?
Emslander, Valentin UL; Levy, Jessica UL; Scherer, Ronny et al

Scientific Conference (2022, March)

Theoretical Background: Can we quantify the effectiveness of a teacher or a school with a single number? Researchers in the field of value-added (VA) models may argue just that (e.g., Chetty et al., 2014 ... [more ▼]

Theoretical Background: Can we quantify the effectiveness of a teacher or a school with a single number? Researchers in the field of value-added (VA) models may argue just that (e.g., Chetty et al., 2014; Kane et al., 2013). VA models are widely used for accountability purposes in education and quantify the value a teacher or a school adds to their students’ achievement. For this purpose, these models predict achievement over time and attempt to control for factors that cannot be influenced by schools or teachers (i.e., sociodemographic & sociocultural background). Following this logic, what is left must be due to teacher or school differences (see, e.g., Braun, 2005). To utilize VA models for high-stakes decision-making (e.g., teachers’ tenure, the allocation of funding), these models would need to be highly stable over time. School-level stability over time, however, has hardly been researched at all and the resulting findings are mixed, with some studies indicating high stability of school VA scores over time (Ferrão, 2012; Thomas et al., 2007) and others reporting a lack of stability (e.g., Gorard et al., 2013; Perry, 2016). Furthermore, as there is no consensus on which variables to use as independent or dependent variables in VA models (Everson, 2017; Levy et al., 2019), the stability of VA could vary between different outcome measures (e.g., language or mathematics). If VA models lack stability over time and across outcome measures, their use as the primary information for high-stakes decision-making is in question, and the inferences drawn from them could be compromised. Questions: With these uncertainties in mind, we examine the stability of school VA model scores over time and investigate the differences between language and mathematics achievement as outcome variables. Additionally, we demonstrate the real-life implications of (in)stable VA scores for single schools and point out an alternative, more constructive use of school VA models in educational research. Method: To study the stability of VA scores on school level over time and across outcomes, we drew on a sample of 146 primary schools, using representative longitudinal data from the standardized achievement tests of the Luxembourg School Monitoring Programme (LUCET, 2021). These schools included a heterogeneous and multilingual sample of 7016 students. To determine the stability of VA scores in the subject of mathematics and in languages over time, we based our analysis on two longitudinal datasets (from 2015 to 2017 and from 2017 to 2019, respectively) and generated two VA scores per dataset, one for language and one for mathematics achievement. We further analyzed how many schools displayed stable VA scores in the respective outcomes over two years, and compared the rank correlations of VA scores between language and mathematics achievement as an outcome variable. Results and Their Significance: Only 34-38 % of the schools showed stable VA scores from grade 1 to 3 with moderate rank correlations of r = .37 with language and r = .34 with mathematics achievement. We therefore discourage using VA models as the only information for high-stakes educational decisions. Nonetheless, we argue that VA models could be employed to find genuinely effective teaching or school practices—especially in heterogeneous student populations, such as Luxembourg, in which educational disparities are an important topic already in primary school (Hoffmann et al., 2018). Consequently, we contrast the school climate and instructional quality, which might be a driver of the differences between schools with stable high vs. low VA scores. Literature Braun, H. (2005). Using student progress to evaluate teachers: A primer on value-added models. Educational Testing Service. Chetty, R., Friedman, J. N., & Rockoff, J. E. (2014). Measuring the impacts of teachers I: Evaluating bias in teacher value-added estimates. American Economic Review, 104(9), 2593–2632. https://doi.org/10.1257/aer.104.9.2593 Everson, K. C. (2017). Value-added modeling and educational accountability: Are we answering the real questions? Review of Educational Research, 87(1), 35–70. https://doi.org/10.3102/0034654316637199 Ferrão, M. E. (2012). On the stability of value added indicators. Quality & Quantity, 46(2), 627–637. https://doi.org/10.1007/s11135-010-9417-6 Gorard, S., Hordosy, R., & Siddiqui, N. (2013). How unstable are “school effects” assessed by a value-added technique? International Education Studies, 6(1), 1–9. https://doi.org/10.5539/ies.v6n1p1 Kane, T. J., McCaffrey, D. F., Miller, T., & Staiger, D. O. (2013). Have We Identified Effective Teachers? Validating Measures of Effective Teaching Using Random Assignment. Research Paper. MET Project. Bill & Melinda Gates Foundation. https://files.eric.ed.gov/fulltext/ED540959.pdf Levy, J., Brunner, M., Keller, U., & Fischbach, A. (2019). Methodological issues in value-added modeling: An international review from 26 countries. Educational Assessment, Evaluation and Accountability, 31(3), 257–287. https://doi.org/10.1007/s11092-019-09303-w LUCET. (2021). Épreuves Standardisées (ÉpStan). https://epstan.lu Perry, T. (2016). English value-added measures: Examining the limitations of school performance measurement. British Educational Research Journal, 42(6), 1056–1080. https://doi.org/10.1002/berj.3247 Thomas, S., Peng, W. J., & Gray, J. (2007). Modelling patterns of improvement over time: Value added trends in English secondary school performance across ten cohorts. Oxford Review of Education, 33(3), 261–295. https://doi.org/10.1080/03054980701366116 [less ▲]

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See detailThe Relation Between Executive Functions and Math Intelligence in Preschool Children: A Systematic Review and Meta-Analysis
Emslander, Valentin UL; Scherer, Ronny

in Psychological Bulletin (2022), 148(5-6), 337-369

Executive functions (EFs) are key skills underlying other cognitive skills that are relevant to learning and everyday life. Although a plethora of evidence suggests a positive relation between the three ... [more ▼]

Executive functions (EFs) are key skills underlying other cognitive skills that are relevant to learning and everyday life. Although a plethora of evidence suggests a positive relation between the three EF subdimensions inhibition, shifting, and updating, and math skills for schoolchildren and adults, the findings on the magnitude of and possible variations in this relation are inconclusive for preschool children and several narrow math skills (i.e., math intelligence). Therefore, the present meta-analysis aimed to (a) synthesize the relation between EFs and math intelligence (an aggregate of math skills) in preschool children; (b) examine which study, sample, and measurement characteristics moderate this relation; and (c) test the joint effects of EFs on math intelligence. Utilizing data extracted from 47 studies (363 effect sizes, 30,481 participants) from 2000 to 2021, we found that, overall, EFs are significantly related to math intelligence (r = .34, 95% CI [.31, .37]), as are inhibition (r = .30, 95% CI [.25, .35]), shifting (r = .32, 95% CI [.25, .38]), and updating (r = .36, 95% CI [.31, .40]). Key measurement characteristics of EFs, but neither children’s age nor gender, moderated this relation. These findings suggest a positive link between EFs and math intelligence in preschool children and emphasize the importance of measurement characteristics. We further examined the joint relations between EFs and math intelligence via meta-analytic structural equation modeling. Evaluating different models and representations of EFs, we did not find support for the expectation that the three EF subdimensions are differentially related to math intelligence. [less ▲]

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See detailStability of Primary School Value-Added Scores over Time: A Comparison Between Math and Language Achievement as Outcome Variables
Emslander, Valentin UL; Levy, Jessica UL; Scherer, Ronny et al

Scientific Conference (2021, November)

Value-added (VA) models are widely used for accountability purposes in education. Tracking a teacher’s or a school’s VA score over time forms oftentimes the basis for high-stakes decision-making and can ... [more ▼]

Value-added (VA) models are widely used for accountability purposes in education. Tracking a teacher’s or a school’s VA score over time forms oftentimes the basis for high-stakes decision-making and can determine whether teachers can keep their jobs or schools may receive certain funding. Despite their high-stakes application, the stability of VA scores over time has not yet been investigated for primary schools. Moreover, it is unclear whether different outcome measures (e.g., language and mathematics) may differ in their stability over time. In the present study, we aimed to clarify the stability of VA scores over time and investigate the differences across outcome variables. Furthermore, we wanted to showcase the real-life implications of (in)stable VA scores for single schools, with a focus on an informative use of VA scores rather than an evaluative way. The exploration of school VA scores in primary schools is especially relevant for heterogeneous student populations, for instance, in Luxembourg. Thus, we drew on representative longitudinal data from the standardized achievement tests of the Luxembourg School Monitoring Programme and examined the stability of school VA scores over two years in 146 schools (N = 7016 students). The overall stability, as measured by correlation coefficients, was moderate with r = .37 for VA scores in language and r = .34 for VA scores in mathematics from grade one to grade three. Real-life implications for schools will be discussed. [less ▲]

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See detailStability of Value-Added Models: Comparing Classical and Machine Learning Approaches
Emslander, Valentin UL; Levy, Jessica UL; Scherer, Ronny et al

Scientific Conference (2021, September)

Background: What is the value that teachers or schools add to the evolution of students’ performance? Value-added (VA) modeling aims to answer this question by quantifying the effect of pedagogical ... [more ▼]

Background: What is the value that teachers or schools add to the evolution of students’ performance? Value-added (VA) modeling aims to answer this question by quantifying the effect of pedagogical actions on students’ achievement, independent of students’ backgrounds (e.g., Braun, 2005). A plethora of VA models exist, and several outcome measures are in use to estimate VA scores, yet without consensus on the model specification (Everson, 2017; Levy et al., 2019). Furthermore, it is unclear whether the most frequently used VA models (i.e., multi-level, linear regression, and random forest models) and outcome measures (i.e., language and mathematics achievement) indicate a similar stability of VA scores over time. Objectives: Drawing from the data of a highly diverse and multilingual school setting, where leveling out the influence of students’ backgrounds is of special interest, we aim to (a) clarify the stability of school VA scores over time; (b) shed light on the sensitivity toward different statistical models and outcome variables; and (c) evaluate the practical implications of (in)stable VA scores for individual schools. Method: Utilizing the representative, longitudinal data from the Luxembourg School Monitoring Programme (LUCET, 2021), we examined the stability of school VA scores. We drew on two longitudinal data sets of students who participated in the standardized achievement tests in Grade 1 in 2014 or 2016 and then again in Grade 3 two years later (i.e., 2016 and 2018, respectively), with a total of 5875 students in 146 schools. School VA scores were calculated using classical approaches (i.e., linear regression and multilevel models) and one of the most commonly used machine learning approaches in educational research (i.e., random forests). Results and Discussion: The overall stability over time across the VA models was moderate, with multilevel models showing greater stability than linear regression models and random forests. Stability differed across outcome measures and was higher for VA models with language achievement as an outcome variable as compared to those with mathematics achievement. Practical implications for schools and teachers will be discussed. [less ▲]

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See detailMeta-Analytic Structural Equation Models of Executive Functions and Math Intelligence in Preschool Children
Emslander, Valentin UL; Scherer, Ronny

Scientific Conference (2021, September)

BACKGROUND: Response inhibition, attention shifting, and working memory updating are the three core executive functions (EFs; Miyake et al., 2000) underlying other cognitive skills that are relevant for ... [more ▼]

BACKGROUND: Response inhibition, attention shifting, and working memory updating are the three core executive functions (EFs; Miyake et al., 2000) underlying other cognitive skills that are relevant for learning and everyday life. For example, they have shown to be differentially related to the mathematical component of intelligence (i.e., math intelligence) in school students and adults. While researchers suppose these three EFs to become more differentiated from early childhood to adulthood, neither the link of these constructs nor their structure has been conclusively established in preschool children yet. Primary studies on path models connecting EFs and math intelligence diverge in the exact relation of EFs and math intelligence. It remains unclear whether inhibition, shifting, and updating exhibit distinct but correlated constructs with respect to their relation to math intelligence. OBJECTIVES: With our meta-analysis, we aimed to (a) synthesize the relation between the three EFs and math intelligence in preschool children; and (b) compare plausible models of the effects of EFs on math intelligence. METHODS/RESULTS: Synthesizing data from 47 studies (363 effect sizes, 30,481 participants) from the last two decades via novel multilevel and multivariate meta-analytic models (Pustejovsky & Tipton, 2020), we found the three core EFs to be significantly related to math intelligence: Inhibition ("r" ̅ = .30, 95 % CI [.25, .35]), shifting ("r" ̅ = .32, 95 % CI [.25, .38]), and updating ("r" ̅ = .36, 95 % CI [.31, .40]). Looking at the three core EFs as one construct, the correlation was "r" ̅ = .34, 95 % CI [.31, .37]. Utilizing correlation-based, meta-analytic structural equation modeling (Jak & Cheung, 2020), our results exhibited significant relations of all EFs to math intelligence. These relations did not differ between the three core EFs. DISCUSSION: Our findings corroborate the positive link between EFs and math intelligence in preschool children and are similar to other age groups. From the model testing, we learned that representing EFs by a latent variable, thus capturing the covariance among the three core EFs, explained substantially more variation in math intelligence than representing them as distinct constructs. [less ▲]

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See detailMeasuring Executive Functions and their Relation to Math Intelligence in Preschool Children: A Meta-Analysis
Emslander, Valentin UL; Scherer, Ronny

Scientific Conference (2021, July)

Introduction: Executive functions (inhibition, attention shifting, updating) are linked to math intelligence in school students and adults. This link is particularly important because performance in ... [more ▼]

Introduction: Executive functions (inhibition, attention shifting, updating) are linked to math intelligence in school students and adults. This link is particularly important because performance in school mathematics is predictive of various competencies later in life. While some researchers argue that tests of executive functions and math intelligence measure the same underlying construct, others argue that they measure distinct but correlated constructs. Also, evidence on the differentiation of cognitive skills over time exists. Clarifying the relation between executive functions and math intelligence is, however, challenging, especially because preschoolers cannot fill in commonly used questionnaires that require them to read. As a consequence, researchers have to resort to behavioral, verbal, apparatus-, or computer-based assessments of executive functions. Objectives/Methodology: With this meta-analysis of 29 studies containing 268 effect sizes, we examined the link between executive functions and math intelligence for a total sample of 25,510 preschool children. Specifically, we synthesized the corresponding correlations and sought to clarify which executive function assessments were used for preschool children and how the assessment characteristics may moderate the correlation between executive functions and mathematical skills. Results: Utilizing three-level random-effects meta-analysis, we found a moderate correlation between executive functions and mathematical skills in preschool children, r = 0.35. The type of assessment (behavioral, verbal, apparatus-, or computer-based assessments) did not moderate this relation. Differentiating between the three executive functions revealed average correlations of r = 0.30 between math and inhibition, r = 0.38 between math and attention shifting, and r = 0.36 between math and updating. These analyses will be supplemented by further moderator analyses. Conclusions: Our findings support the significant link between executive functions and mathematical skills in preschoolers—yet, the average correlations do not suggest that both measures are identical. Results will be discussed against the background of deployed assessments and testing environments. [less ▲]

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See detailLinking Executive Functions and Math Intelligence in Preschool Children: A Meta-Analysis
Emslander, Valentin UL; Scherer, Ronny

Scientific Conference (2021, May 20)

Background: Executive functions (i.e., response inhibition, attention shifting, working memory updating) have shown to be related to the mathematical component of intelligence, which, in turn, is ... [more ▼]

Background: Executive functions (i.e., response inhibition, attention shifting, working memory updating) have shown to be related to the mathematical component of intelligence, which, in turn, is predictive of various competences later in life. While this relation has already been thoroughly researched in school students and adults, a comprehensive research synthesis on preschool children—a group for which the assessment of these constructs is more challenging—is still missing. Evidence on the differentiation of cognitive skills over time suggests a differential relation of the three executive functions with math intelligence in older but not in younger children. It remains unclear, however, whether and which one of the three executive functions is more closely related to math intelligence in preschool children. Further research gaps concern the measurement of both executive functions and math intelligence in preschool children, as they cannot complete reading- and writing-based questionnaires. Addressing this measurement challenge, a plethora of inventive measurements has been used to assess both cognitive skills. These measurement differences might also have an influence on the relation between executive functions and math intelligence. Objectives: With our meta-analysis, we aimed to clarify the relation between executive functions and math intelligence in preschool children. Further, we wanted to investigate the influence of different measurement methods on this relation and look into the specific links of inhibition, shifting, and updating with math intelligence more closely. Research questions: 1. To what extent are inhibition, shifting, and updating (as a composite and separately) related to math intelligence in preschool children? (Overall correlations) 2. Which sample, study, and measurement characteristics moderate this relation? (Heterogeneity and moderators) 3. How much variation in math intelligence do inhibition, shifting, and updating explain jointly? (Model testing) Methods: We examined the relation between executive functions and math intelligence for 268 effect sizes from 29 studies for a total sample of 25,510 preschool children. Specifically, we synthesized the corresponding correlations by means of three-level random-effects meta-analyses (RQ 1) and examined the study, sample, and measurement characteristics as possible moderators of this relation between EFs and math intelligence via mixed-effects modeling (RQ 2). Further, we performed meta-analytic structural equation modeling to investigate the joint and differential effects inhibition, shifting, and updating on math intelligence (RQ 3). Results: We found executive functions and math intelligence to correlate moderately in preschool children (r = .35). Investigating inhibition, shifting, and updating separately also revealed moderate average correlations to math intelligence (r = .30, r = .38 , and r = .36, respectively). While we did not find age to explain significant amounts of heterogeneity, four measurement characteristics moderated the relation between executive function and math intelligence. When considered jointly through meta-analytic structural equation modeling, the relations of inhibition, shifting, and updating to math intelligence were similar. Conclusions and Implications: By presenting evidence for a significant relation between executive functions and math intelligence also in preschool children, our findings contribute to the discussion on the differentiation of cognitive skills. They highlight the importance of considering measurement characteristics when researching executive functions and math intelligence. Further, we could not confirm that inhibition, shifting, and updating are differentially related to math intelligence. Further research is needed to clarify the impact of age on the relation between executive functions and math intelligence. [less ▲]

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See detailMeasuring Executive Functions and their Relations to Mathematical Skills in Preschool Children: A Meta-Analysis
Emslander, Valentin UL; Scherer, Ronny

Scientific Conference (2020, July)

Introduction: Executive functions (inhibition, attention shifting, working memory) are linked to mathematical skills in school students and adults. This link is particularly important because performance ... [more ▼]

Introduction: Executive functions (inhibition, attention shifting, working memory) are linked to mathematical skills in school students and adults. This link is particularly important because performance in school mathematics is predictive of various competencies later in life. While some researchers argue that tests of executive functions and mathematical skills measure the same underlying construct, others argue that they measure distinct but correlated constructs. Also, evidence on the differentiation of cognitive skills over time exists. Clarifying the relation between executive functions and mathematical skills is, however, challenging, especially because preschoolers cannot fill in commonly used questionnaires that require them to read. As a consequence, researchers have to resort to behavioral, verbal, apparatus-, or computer-based assessments of executive functions. Objectives/Methodology: With this meta-analysis of 26 studies containing 238 effect sizes, we examined the link between executive functions and early mathematical skills for a total sample of 24,256 preschool children. Specifically, we synthesized the corresponding correlations and sought to clarify which executive function assessments were used for preschool children and how the assessment characteristics may moderate the correlation between executive functions and mathematical skills. Results: Utilizing three-level random-effects meta-analysis, we found a moderate correlation between executive functions and mathematical skills in preschool children, r = 0.35. The type of assessment (behavioral, verbal, apparatus-, or computer-based assessments) did not moderate this relation. Differentiating between the three executive functions revealed average correlations of r = 0.31 between math and inhibition, r = 0.38 between math and attention shifting, and r = 0.36 between math and updating. These analyses will be supplemented by further moderator analyses. Conclusions: Our findings support the significant link between executive functions and mathematical skills in preschoolers—yet, the average correlations do not suggest that both measures are identical. Results will be discussed against the background of deployed assessments and testing environments. [less ▲]

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See detailThe coronavirus (COVID‐19) fatality risk perception of US adult residents in March and April 2020
Niepel, Christoph UL; Kranz, Dirk; Borgonovi, Francesca et al

in British Journal of Health Psychology (2020)

The study compares empirical results on the coronavirus SARS‐CoV‐2 (causing COVID‐19) fatality risk perception of US adult residents stratified for age, gender, and race in mid‐March 2020 (N1 = 1,182) and ... [more ▼]

The study compares empirical results on the coronavirus SARS‐CoV‐2 (causing COVID‐19) fatality risk perception of US adult residents stratified for age, gender, and race in mid‐March 2020 (N1 = 1,182) and mid‐April 2020 (N2 = 953). While the fatality risk perception has increased from March 2020 to April 2020, our findings suggest that many US adult residents severely underestimated their absolute and relative fatality risk (i.e., differentiated for subgroups defined by pre‐existing medical conditions and age) at both time points compared to current epidemiological figures. These results are worrying because risk perception, as our study indicates, relates to actual or intended health‐protective behaviour that can reduce SARS‐CoV‐2 transmission rates. [less ▲]

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See detailMeasures of Executive Functions and Mathematical Skills are Distinct Even at a Young Age: A Meta-Analysis with Preschool Children
Emslander, Valentin UL; Scherer, Ronny

Scientific Conference (2020)

Measures of executive functions (inhibition, attention shifting, working memory) are linked to measures of mathematical skills in school students and adults. However, the magnitude of this relation in ... [more ▼]

Measures of executive functions (inhibition, attention shifting, working memory) are linked to measures of mathematical skills in school students and adults. However, the magnitude of this relation in preschool children is unclear. Following the literature on the differentiation of cognitive skills over time, some researchers suggest that tests of executive functions and mathematical skills measure the same underlying construct, while others suggest that they measure correlated but distinct constructs. This dispute does not only tap the question of how the constructs can be understood but also the question of cost and test efficiency (i.e., assessments of single vs. multiple constructs). Clarifying the relation between measures of the two constructs can be especially challenging because preschoolers cannot fill in commonly used questionnaires that require them to read. Thus, researchers have to resort to behavioral, verbal, apparatus-, or computer-based assessments of executive functions. As a result, executive functions may vary in their relation to mathematical skills as a consequence of their measurement. We examined the link between executive functions and early mathematical skills measures, conducting a meta-analysis of 26 studies containing 238 effect sizes for a total sample of 24,256 preschool children. Specifically, we synthesized the corresponding correlations and aimed to clarify which executive function assessments were used for preschool children and how assessment characteristics may moderate the correlation between executive functions and mathematical skills. Three-level random-effects meta-analysis revealed a small to moderate average correlation between executive functions and mathematical skills measures of preschool children, r = 0.35. The type of assessment (behavioral, verbal, apparatus-, or computer-based assessments) did not moderate this relation. Investigating the three executive functions separately, we found average correlations of r = 0.31 between mathematical skills and inhibition, r = 0.38 between mathematical skills and attention shifting, and r = 0.36 between mathematical skills and updating. These analyses will be supplemented by further moderator and sensitivity analyses. These findings emphasize the significant link between executive functions and mathematical skills measures in preschoolers—hereby, supporting that the measures of both constructs are distinct. In addition, under-researched areas around the assessment of executive functions and mathematical abilities will be discussed. [less ▲]

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See detailImplicit motives and children's salivary cortisol reactivity to an adapted version of the Trier Social Stress Test for Children (TSST-C)
Spengler, Benedikt; Hofer, Jan; Busch, Holger et al

in Personality and Individual Differences (2020), 162

This research addresses the interplay between two implicit motives, that are power (nPow) and affiliation (nAff), and the cortisol reactivity (CR) to a psychosocial stressor (an adaption of the Trier ... [more ▼]

This research addresses the interplay between two implicit motives, that are power (nPow) and affiliation (nAff), and the cortisol reactivity (CR) to a psychosocial stressor (an adaption of the Trier Social Stress Test for Children; TSST-C; Buske-Kirschbaum et al., 1997) in 89 healthy children (45 female; Mage = 7.74, SDAge = 0.46). We assessed implicit motives by a 6-image Picture Story Exercise (PSE) and cortisol by 6 saliva samples. As hypothesized, the procedure triggered a significant cortisol reaction, F(1.44, 127.05Greenhouse-Geisser) = 8.22, p = .002, η²part = 0.09. Contrary to our hypothesis, children high in nPow showed no significant increase in CR (β = 0.06, p = .60). However, our results were in line with the findings of Wegner, Schüler, and Budde (2014) that a high implicit affiliation motive is associated with an attenuated CR (β = −0.21, p = .05). Perspectives for future research on implicit motives and children's CR to psychosocial stress are discussed. [less ▲]

Detailed reference viewed: 47 (3 UL)
See detailQuid pro quo: Führt milde Benotung zu besseren Ergebnissen bei der Lehrveranstaltungsevaluation?
Emslander, Valentin UL

in Ellwart, Thomas; Peiffer, Henrike (Eds.) Forschungspakete aus dem Seminarraum (2019)

See download: https://www.uni-trier.de/fileadmin/fb1/prof/PSY/WIP/Sonstiges/Forschungspakete/1901_Forschungspaket.pdf !!! In Aus-, Fort- und Weiterbildungen in Hochschule und Beruf werden berufsrelevante ... [more ▼]

See download: https://www.uni-trier.de/fileadmin/fb1/prof/PSY/WIP/Sonstiges/Forschungspakete/1901_Forschungspaket.pdf !!! In Aus-, Fort- und Weiterbildungen in Hochschule und Beruf werden berufsrelevante Qualifikationen gelehrt. Um die Qualität entsprechender Lehrveranstaltungen zu sichern, führen Bildungsstätten Lehrveranstaltungsevaluationen (LVE) durch. Positive Ergebnisse bei diesen LVE stellen nicht nur ein Maß für die Qualitätssicherung der Lehre dar, sondern dienen auch als Aushängeschild für die Bildungsstätten und als Feedbackinstrument für die Lehrenden. Es stellt sich jedoch die Frage, ob Lehrende durch die Vergabe zu guter/milder Noten/Bewertungen die Evaluationsergebnisse der eigenen Lehrveranstaltung positiv beeinflussen können und es somit zu einer Verzerrung in den Evaluationsergebnissen kommt. Dies kann die Gültigkeit von LVE und deren Nutzen als aussagekräftiges Feedbackinstrument einschränken. Emslander, V. (2019). Quid pro quo: Führt milde Benotung zu besseren Ergebnissen bei der Lehrveranstaltungsevaluation? [Quid pro quo: Does grading leniency lead to better results in the course evaluation?] In T. Ellwart & H. Peiffer (Hrsg.) Forschungspakete aus dem Seminarraum, (01/2019), Download: https://www.uni-trier.de/index.php?id=64878 [less ▲]

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See detailSozialpsychologische Mechanismen im AfD-Wahlprogramm erkennen: Ein Workshop gegen den Rechts-Populismus
Emslander, Valentin UL; Leistikow, Helen; Bolli, Swantje

in Walther, Eva; Isemann, Simon D. (Eds.) Die AfD – psychologisch betrachtet (2019)

Seit ihrer Gründung zieht die Partei Alternative für Deutschland eine immer größere Wählerschaft an. Sie stellt sich selbst als Protestpartei dar, die für die wahren Bedürfnisse der Bürger*innen eintrete ... [more ▼]

Seit ihrer Gründung zieht die Partei Alternative für Deutschland eine immer größere Wählerschaft an. Sie stellt sich selbst als Protestpartei dar, die für die wahren Bedürfnisse der Bürger*innen eintrete und diese gegen das Establishment und Minderheiten verteidige. Doch was steht hinter den rechtspopulistischen Inhalten, die die Partei vermittelt? Welche Strategien verwendet die AfD um Bürger*innen anzuziehen? Und wie kann man sich diesen erwehren und auch andere zum reflektierten Umgang mit den Parteiinhalten bewegen? Um diese Fragen zu beantworten, wird in diesem Kapitel nach einer kurzen Einleitung zum Thema Populismus ein Workshop vorgestellt, der Menschen für die sozialpsychologischen Mechanismen, die im Wahlprogramm der Alternative für Deutschland wirken, sensibilisieren soll. [less ▲]

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See detailSozialpsychologie und die AfD – Wieviel Psychologie steckt im Wahlprogramm?
Emslander, Valentin UL; Bolli, Swantje; Schellen, Guido

Speeches/Talks (2018)

Detailed reference viewed: 73 (0 UL)