Neuromyths and knowledge about intellectual giftedness in a highly educated multilingual country.

Introduction: Understanding brain functioning and intellectual giftedness can be challenging and give rise to various misconceptions. Nonetheless, there seems to be a widespread fascination and appetite for these subjects among the lay public and diverse professionals. The present study is the first to investigate general knowledge about the brain, neuromyths and knowledge about giftedness in a highly multilingual and educated country. Methods: Starting from and extending two seminal studies on neuromyths, several novel statements on intellectual giftedness have been included in order to explore knowledge and misconceptions concerning giftedness. Our sample (N = 200) was composed of Luxembourgish education professionals, including students in educational science and cognitive psychology, thus allowing to analyze responses in general and according to training and professional profiles. Specifically, Group 1 consisted of teachers and futures teachers (n = 152). Group 2 consisted of other education professionals and psychology students (n = 48). Results: Despite the size and the unbalanced distribution of the sample, our findings indicate a good level of general knowledge about the brain and learning (71.3% of correct responses in average) which does, however, not preclude the presence of the typically observed original neuromyths. Thus, we replicate the classical finding that misconceptions on Learning Styles (70% of error rate) and the Multiple Intelligence Theory (71.5% of error rate) are the most represented, both in (future and in-service) teachers and other education professionals. Moreover, the present sample also revealed a high presence of misconceptions on intellectual giftedness. Discussion: Limitations and future directions are discussed.

The relation between Self-Control, Need for Cognition and Action Orientation in secondary school students: A conceptual replication study.

Self-Control can be defined as the self-initiated effortful process that enables individuals to resist temptation impulses. It is relevant for conducting a healthy and successful life. For university students, Grass et al. found that Need for Cognition as the tendency to engage in and enjoy thinking, and Action Orientation as the flexible recruitment of control resources in cognitively demanding situations, predict Self-Control. Further, Action Orientation partially mediated the relation between Need for Cognition and Self-Control. In the present conceptual replication study, we investigated the relations between Self-Control, Need for Cognition, and Action Orientation in adolescence (N = 892 9th graders) as a pivotal period for the development of self-control. We replicated the findings that Need for Cognition and Action Orientation predict Self-Control and that Action Orientation partially mediates the relation between Need for Cognition and Self-Control. In addition, Action Orientation moderates the relation between Need for Cognition and Self-Control. This result implies that in more action-oriented students Need for Cognition more strongly predicted Self-Control than in less action-oriented students. Our findings strengthen theoretical assumptions that Need for Cognition and Action Orientation are important cognitive and behavioral mechanisms that contribute to the successful exertion of Self-Control.

How sensitive are the evaluations of a school's effectiveness to the selection of covariates in the applied value-added model?

There is no final consensus regarding which covariates should be used (in addition to prior achievement) when estimating value-added (VA) scores to evaluate a school's effectiveness. Therefore, we examined the sensitivity of evaluations of schools' effectiveness in math and language achievement to covariate selection in the applied VA model. Four covariate sets were systematically combined, including prior achievement from the same or different domain, sociodemographic and sociocultural background characteristics, and domain-specific achievement motivation. School VA scores were estimated using longitudinal data from the Luxembourg School Monitoring Programme with some 3600 students attending 153 primary schools in Grades 1 and 3. VA scores varied considerably, despite high correlations between VA scores based on the different sets of covariates (.66 < r < 1.00). The explained variance and consistency of school VA scores substantially improved when including prior math and prior language achievement in VA models for math and prior language achievement with sociodemographic and sociocultural background characteristics in VA models for language. These findings suggest that prior achievement in the same subject, the most commonly used covariate to date, may be insufficient to control for between-school differences in student intake when estimating school VA scores. We thus recommend using VA models with caution and applying VA scores for informative purposes rather than as a mean to base accountability decisions upon. Supplementary Information: The online version contains supplementary material available at 10.1007/s11092-022-09386-y.

Value-added scores show limited stability over time in primary school.

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.

Creating positive learning experiences with technology: A field study on the effects of user experience for digital concept mapping.

Learning and assessment are increasingly mediated by digital technologies. Thus, learners' experiences with these digital technologies are growing in importance, as they might affect learning and assessment. The present paper explores the impact of user experience on digital concept mapping. It builds on user experience theory to explain variance in the intention to use digital concept mapping tools and in concept map-based assessment scores. Furthermore, it identifies fulfillment of psychological needs as an important driver of positive experiences. In a field study in three schools and a university (N = 71), we tested two concept mapping prototypes on computers and tablets. We found that user experience is a significant factor explaining variance in intention to use. User experience also explained variance in three out of four concept mapping scores on tablets, potentially related to the lower pragmatic quality of the tablet prototypes. Fulfillment of psychological needs strongly affected perceptions of different qualities of user experience with digital concept mapping. These results indicate that user experience needs to be considered in digital concept mapping to provide a positive and successful environment for learning and assessment. Finally, we discuss implications for designers of digital learning and assessment tools.

Developing and Validating a Short-Form Questionnaire for the Assessment of Seven Facets of Conscientiousness in Large-Scale Assessments.

Conscientiousness is the most important personality predictor of academic achievement. It consists of several lower order facets with differential relations to academic achievement. There is currently no short instrument assessing facets of conscientiousness in the educational context. Therefore, in the present multi-study report, we develop and validate a short-form questionnaire for the assessment of seven Conscientiousness facets, namely Industriousness, Perfectionism, Tidiness, Procrastination Refrainment, Control, Caution, and Task Planning. To this end, we examined multiple representative samples totaling N = 14,604 Grade 9 and 10 students from Luxembourg. The questionnaire was developed by adapting and shortening an existing scale using an exhaustive search algorithm. The algorithm was specified to select the best item combination based on model fit, reliability, and measurement invariance across the German and French language versions. The resulting instrument showed the expected factorial structure. The relations of the facets with personality constructs and academic achievement were in line with theoretical assumptions. Reliability was acceptable for all facets. Measurement invariance across language versions, gender, immigration status and cohort was established. We conclude that the presented questionnaire provides a short measurement of seven facets of Conscientiousness with valid and reliable scores.

Contrasting Classical and Machine Learning Approaches in the Estimation of Value-Added Scores in Large-Scale Educational Data.

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.