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Instructional support can be implemented in learning environments to pseudo-modify the difficulty or time intensity of items presented to persons. This support can affect both the response accuracy of persons towards items as well as the time persons require to complete items. This study proposes a framework to model response time in learning environments as a function of instructional support. Moreover, it explores the effect of instructional support on response time in assembly tasks training using Virtual Reality. Three models are fitted with real-life data collected by a project that involves both industry and academic partners from Belgium. A Bayesian approach is followed to implement the models, where the Bayes factor is used to select the best fitting model.

Over the last decade, Higher Education has focused more of its attention toward soft skills compared to traditional technical skills. Nevertheless, there are not many studies concerning the relation between the courses followed within an academic program and the development of soft skills. This work presents a practical approach to model the effects of courses on soft skills proficiency. Multiple Membership Ordinal Logistic Regression models are trained with real data from students of the 2021, 2022, and 2023 cohorts from the general engineering program in a French Higher Education institution. The results show that attending a postgraduate course in average increases the odds of being more proficient in terms of soft skills. Nonetheless, there is considerable variability in the individual effect of courses, which suggest there can be huge differences between courses. Moreover, the data also suggest great dispersion in the students' initial soft skill proficiency.

Rural preschool teachers are increasingly experiencing job burnout, which could lead to their intention to leave and negatively impact education quality. This research explored the prevalence of job burnout among preschool teachers in rural China. It further investigated the potential influence of job-related characteristics on their levels of burnout. This study surveyed 10,581 preschool teachers across 34 counties in 18 provinces in China, utilizing multilevel structural equation models to analyze the situation and factors influencing job burnout. The findings indicate that the situation regarding job burnout among preschool teachers is not encouraging, particularly in the western areas and independent public kindergartens. Job resources were found to be associated with a reduction in burnout, while job demands had the opposite effect. The findings also revealed that job demands served as a mediating variable between job resources and job burnout. Moreover, the results also showed that reduced job burnout among preschool teachers was related to teacher cooperation, decision making, kindergarten resources and salary. On the other hand, role commitments, business issues, and classroom management were associated with increased burnout among preschool teachers. Furthermore, the impact of demands and resources on burnout was found to be intensified by kindergarten variables. To address the issue of burnout, it is essential to recognize the diversity and heterogeneity of kindergartens and take specific measures to reduce work demands while providing adequate and specific resources. Attention should be given to diversity and integration to ensure a positive work environment that can effectively prevent job burnout among preschool teachers.

Abstract In cognitive diagnosis assessment (CDA), the impact of misspecified item-attribute relations (or “Q-matrix”) designed by subject-matter experts has been a great challenge to real-world applications. This study examined parameter estimation of the CDA with the expert-designed Q-matrix and two refined Q-matrices for international large-scale data. Specifically, the G-DINA model was used to analyze TIMSS data for Grade 8 for five selected countries separately; and the need of a refined Q-matrix specific to the country was investigated. The results suggested that the two refined Q-matrices fitted the data better than the expert-designed Q-matrix, and the stepwise validation method performed better than the nonparametric classification method, resulting in a substantively different classification of students in attribute mastery patterns and different item parameter estimates. The results confirmed that the use of country-specific Q-matrices based on the G-DINA model led to a better fit compared to a universal expert-designed Q-matrix.

The aim of the present study was to examine whether loneliness and friendship quality are bidirectionally associated with one another over time. Based on the Evolutionary Theory of Loneliness (Cacioppo & Cacioppo, 2018) and the classical definition of loneliness (Peplau & Perlman, 1982), such a bidirectional association would be expected, but empirical research in this regard is limited. We used data from 615 adolescents (Mage = 10.77 years at Wave 1; 53.5% girls) in a three-wave longitudinal study with one-year intervals between successive measurement waves. Loneliness was measured using the peer-related loneliness subscale of the Loneliness and Aloneness Scale for Children and Adolescents. Friendship quality was measured using the help and closeness subscales of the Friendship Qualities Scale. To test our hypothesis we estimated a cross-lagged panel model. Higher friendship quality was associated with lower levels of concurrent loneliness, which is in line with findings of previous research. However, no significant across-time predictions were found between the two constructs. Hence, using a sample of early adolescents, we could not provide evidence supporting important aspects of the aforementioned loneliness theories.

The transition from primary to secondary school is an impactful life event for early adolescents. This life event is hypothesized to be associated with increased feelings of loneliness, as it is often associated with changes in adolescents’ social network. However, this hypothesis has not been empirically tested so far. The present study aimed to fill this gap in the literature. A sample of 330 early adolescents (MageW1 = 10.73, SD = 0.46) were followed for three years. Between the second and the third wave, the secondary school transition occurred for most of the adolescents in the sample. Multi-level models with random intercepts were used to model the change in loneliness. A cross-level interaction was included to examine whether these changes in loneliness were associated with the secondary school transition. The results showed that loneliness decreased across the three waves for all adolescents. This decreasing developmental trend was not moderated by the secondary school transition. Our findings do not show that the secondary school transition is associated with increases in adolescent loneliness. That is, the decreasing developmental trend of loneliness across the three waves was the same for all adolescents. (189 words)

Due to swift technological changes in society, programming tasks are proliferating in formal and informal education around the globe. However, challenges arise regarding the acquisition of programming skills. Many students are unequipped to develop programming skills due to limited instruction or background and therefore feel insecure when encountering programming in higher education. Some after-school initiatives focus on teaching younger students programming skills, however, not all students have the opportunity to attend. It can also be very challenging for teachers to teach programming--even more so due to significant differences in students' knowledge and interests. To alleviate these challenges, we designed and developed a digital personalized learning (DPL) track for programming in the first grade of secondary education (12-14 year-old students) with a threefold purpose: (a) to encourage students bridging the gap between visual and more general-purpose textual programming languages (b) to meet differences in students' programming knowledge by challenging them, albeit on their own pace, and subsequently (c) to support teachers in the delivery of programming education with relevant supportive learning materials. The design was tested by students and teachers, both of varying technical abilities. Assessments of the DPL-track were positive, with students identifying the tasks as challenging and the tools as motivating. Teachers praised the adaptivity, as well as the gradual transition from visual to textual programming. We present several suggestions for design improvement and dilemmas while reflecting on our design case.

Comparative judgments permit the assessment of open-ended student works by constructing a latent quality scale through repeated pairwise comparisons (i.e., which works “win” or “lose”). Adaptive comparative judgments speed up the judgment process by maximizing the Fisher information of the next comparison. However, at the start of a judgment process, such an adaptive algorithm will not perform well. In order to reliably approximate the Fisher Information of possible pairs well, multiple comparisons are needed. In addition, adaptive comparative judgments have been shown to inflate the scale separation coefficient, which is a reliability estimator for the quality estimates. Current methods to solve the inflation issue increase the number of required comparisons. The goal of this study is to alleviate the cold-start problem of adaptive comparative judgments for essays or other textual assignments, but also to minimize the bias of the scale separation coefficient. By using text-mining techniques, which can be performed before the first judgment, essays can be adaptively compared from the start. More specifically, we propose a selection rule that is based both on a high (1) cosine similarity of the vector representations and (2) Fisher Information of essay pairs. At the start of the judgment process, the cosine similarity has the highest weight in the selection rule. With more judgments, this weight decreases progressively, whereas the weight of the Fisher Information increases. Using simulated data, the proposed strategy is compared with existing approaches. The results indicate that the proposed selection rule can mitigate both the cold-start. That is, fewer judgments are needed to obtain accurate and reliable quality estimates. In addition, the selection rule was found to reduce the inflation of the scale separation reliability.

Meta-analytic datasets can be large, especially when in primary studies multiple effect sizes are reported. The visualization of meta-analytic data is therefore useful to summarize data and understand information reported in primary studies. The gold standard figures in meta-analysis are forest and funnel plots. However, none of these plots can yet account for the existence of multiple effect sizes within primary studies. This manuscript describes extensions to the funnel plot, forest plot and caterpillar plot to adapt them to three-level meta-analyses. For forest plots, we propose to plot the study-specific effects and their precision, and to add additional confidence intervals that reflect the sampling variance of individual effect sizes. For caterpillar plots and funnel plots, we recommend to plot individual effect sizes and averaged study-effect sizes in two separate graphs. For the funnel plot, plotting separate graphs might improve the detection of both publication bias and/or selective outcome reporting bias.

Abstract In the Trends in Mathematics and Science Study (TIMSS) 2015, the educational systems from the Dutch speaking part of Belgium (Flanders), Germany, and Norway included scales capturing three dimensions of instructional quality (INQUA): classroom management, supportive climate, and cognitive activation. With the inclusion of these extra scales, a unique opportunity was created to investigate the various dimensions of INQUA and their relation to educational outcomes. In this study, multilevel structural equational modelling analyses are conducted to answer three research questions: (a) ‘Do the items reliably measure the three dimensions of INQUA as classroom constructs? And if not, can we build reliable scales with the items, capturing the dimensions of INQUA as classroom constructs?’ (b) ‘To what extent can INQUA contribute to achievement?’, and (c) ‘To what extent can INQUA contribute to (social and ethnic) equity?’. Results indicate that INQUA might serve as a catalyst in increasing achievement in education systems. Furthermore, results indicate that INQUA does not relate to equity and consequently that all students benefit from the same educational practices. However, issues in the cross-cultural conceptualization and measurement of INQUA are raised, questioning the extent to which the three well-known dimensions of INQUA (a) are well-defined and might be sufficiently differentiated from each other, (b) sufficiently capture the diverse set of educational practices relating to students’ educational outcomes, and (c) can be established across countries in a unified manner. The results of this study give direction to educational practitioners and policy makers in creating and providing effective learning environments.

A teaching workforce with good quality is a key factor in the process of China's rapid development. Although 76% of Chinese pupils are studying at schools within county areas, a general portray of the corresponding teaching workforce is still not clear. This study presents data from a nationally representative survey of primary education teachers in 35 counties of 18 provinces in China. Findings presented include demographic and professional characteristics, living conditions as well as attitudes towards work. Besides, variations among school locations and geographical regions are also examined. The key findings are the followings: 1) Quality of primary school teachers in county areas has been improved regarding education background; 2) Teaching force in village primary schools has an unbalanced age and gender composition; 3) Out-of-field teaching practice is widespread, especially for minor subjects. 4) Primary school teachers perceived relative low salary and low social status.

Massive open online courses (MOOCs) generate learners’ performance data that can be used to understand learners’ proficiency and to improve their efficiency. However, the approaches currently used, such as assessing the proportion of correct responses in assessments, are oversimplified and may lead to poor conclusions and decisions because they do not account for additional information on learner, content, and context. There is a need for theoretically grounded data-driven explainable educational measurement approaches for MOOCs. In this conceptual paper, we try to establish a connection between psychometrics, a scientific discipline concerned with techniques for educational and psychological measurement, and MOOCs. First, we describe general principles of traditional measurement of learners’ proficiency in education. Second, we discuss qualities of MOOCs which hamper direct application of approaches based on these general principles. Third, we discuss recent developments in measuring proficiency that may be relevant for analyzing MOOC data. Finally, we draw directions in psychometric modeling that might be interesting for future MOOC research.

AbstractBackground.Social anxiety symptoms (SAS) are among the most common mental health problems during adolescence, and it has been shown that parenting influences the adolescent’s level of social anxiety. In addition, it is now widely assumed that most mental health problems, including social anxiety, originate from a complex interplay between genes and environment. However, to date, gene–environment (G × E) interactions studies in the field of social anxiety remain limited. In this study, we have examined how 274 genes involved in different neurotransmission pathways interact with five aspects of perceived parenting as environmental exposure (i.e., support, proactive control, psychological control, punitive control, and harsh punitive control) to affect SAS during adolescence.Methods.We have applied an analytical technique that allows studying genetic information at the gene level, by aggregating data from multiple single-nucleotide-polymorphisms within the same gene and by taking into account the linkage disequilibrium structure of the gene. All participants were part of the STRATEGIES cohort of 948 Flemish adolescents (mean age = 13.7), a population-based study on the development of problem behaviors in adolescence. Relevant genes were preselected based on prior findings and neurotransmitter-related functional protein networks.Results.The results suggest that genes involved in glutamate (SLC1A1), glutathione neurotransmission (GSTZ1), and oxidative stress (CALCRL), in association with harsh punitive parenting, may contribute to social anxiety in adolescence. Isolated polymorphisms in these genes have been related to anxiety and related disorders in earlier work.Conclusions: Taken together, these findings provide new insights into possible biological pathways and environmental risk factors involved in the etiology of social anxiety symptoms’ development.Conclusions.Taken together, these findings provide new insights into possible biological pathways and environmental risk factors involved in the etiology of social anxiety symptoms’ development.

Recommender systems are increasingly being used in university or online education. However, recommender systems still have not found major usage in K12 education. This may be because of the unique challenges that recommender systems face when used by a young and diverse population. However, recommender systems for K12 education could provide many benefits for students and teachers, such as the simplification of personalized learning. Some of the issues with K12 educational recommender systems may be solved by the use of deep learning and implicit feedback. As such, we investigated the use of deep recommendation algorithms and implicit feedback for K12 educational recommender systems. To do this, we compared metrics for highly cited traditional and deep recommendation algorithms trained on explicit and implicit data. We found that recommendation algorithms using deep learning as a group do not differ in performance compared to traditional recommendation algorithms. We also found that the use of implicit feedback led to higher performance than using explicit feedback. The best performing algorithm used both deep learning and implicit feedback. We conclude that deep learning can be a benefit for K12 recommender systems, particularly when the ordered sequence of items is carefully accounted for. We also conclude that researchers and developers must carefully consider which feedback contributes the most information for learning.

Background: Augmented reality (AR) is receiving increasing interest as a tool to create an interactive and motivating learning environment. Yet, it is unclear how instructional support affects performance in AR. Objectives: This study sought to explore how varying the instructional support in AR can affect performance-related behaviours of students with low cognitive abilities during assembly work. Methods: A total of 90 Belgian secondary school students repeatedly executed four different realistic assembly tasks. Three levels of instructional support (low, medium, and high) in AR as well as a control condition with paper instructions with a high level of detail were systematically varied across tasks and participants. Results and Conclusions: Multilevel regression analyses showed that AR instructions yielded lower assembly times and a lower perceived physical effort than paper instructions. Additionally, participants perceived tasks as less complex when given AR instructions with a high or a medium level of detail than when given a low level of detail. No effects of instructional support were established for other performance-related behaviours, namely necessary assistance, error-making, cognitive load, competence frustration, and stress. Effect sizes were small, at least among the instructional support conditions studied, yielding a limited base for adaptivity. Presumably, tailoring the instructional support in AR is only beneficial for highly complex tasks. The results might be useful for the design and implementation of AR in educational settings.

Executive function (EF) is critical to students' academic behaviors and well-being. Environmental influences, particularly teacher-student interaction (TSI), play a key role in enhancing EF development. Previous studies have linked TSI quality to children's EF, yet the relationships between subdimensions of TSI--such as closeness, conflict, dependency, emotional support, classroom management, and instructional support--and EF outcomes remain unclear. Moreover, it is unclear whether these relations hold true across different cultural contexts. From an initial pool of 14,915 articles, in this meta-analysis, we considered 84 empirical studies across 18 cultural regions, revealing that TSI subdimensions are consistently related to EF with small-to-medium effect sizes. The analysis also identified statistically significant moderating factors, such as individualism, power distance, EF type, EF instrument, and socioeconomic status. Notably, stronger TSI-EF correlations were found among children from cultures with low-individualism and high-power-distance, as well as among children from higher socioeconomic backgrounds. These patterns were particularly pronounced in studies measuring hot EF through adult-reported data. This comprehensive meta-analysis bridges knowledge gaps in TSI-EF dynamics, and supporting theories, such as attachment theory, social learning theory, and socio-cultural theory within educational settings. Crucially, it provides cross-cultural perspectives showing how cultural and contextual factors may intertwine with TSI-EF interactions.

Pre-school teachers' competence to see and decide to use learning opportunities in informal play-based situations (i.e. situation-specific skills) is key for high-quality pre-school instruction. Current insights into pre-school teachers' situation-specific skills and their association with underlying teacher disposition and instructional materials are limited. We aimed to complement existing findings by systematically analysing pre-service and in-service pre-school teachers' situation-specific skills in association with their underlying disposition and the type of instructional material in the domain of early mathematics. We assessed 274 pre-service and in-service pre-school teachers' mathematical content and pedagogical content knowledge (MCK, MPCK), beliefs and situation-specific skills, operationalised as the mathematical questions they formulate during simulated shared picture book reading activities. Logistic multilevel regression analyses revealed a complex interplay between pre-service and in-service pre-school teachers' disposition, the type of instructional material and situation-specific skills, pointing to the need for future studies on the topic.

Background: Case management is a client-centered approach to improve the coordination and continuity of service delivery, especially for persons with substance use disorders (SUD) and multiple and complex support needs. This intervention supports individuals by helping them identify needed services, facilitate linkage with services, and promote participation and retention in services. However, it is questionable whether case management is equally effective in promoting recovery and aspects of personal functioning. The objective was to conduct an updated meta-analysis and to assess whether case management was more effective than treatment as usual (TAU) among persons with SUD for improving treatment-related (e.g., successful linkage with and retention in treatment) as well as personal functioning outcomes (e.g., substance use).Methods: This meta-analysis focuses on randomized controlled trials (RCTs) that included persons with alcohol or drug use disorders and compared case management with TAU. To be eligible, interventions had to meet core case management functions as defined in the literature. We conducted searches of the following databases to May 2017: the Cochrane Drugs and Alcohol Specialized Register, CENTRAL, PubMed, Embase, CINAHL, and Web of Science. Also, reference lists of retrieved publications were scanned for relevant (un)published studies.Results: The overall effect size for case management compared to TAU across all outcome categories and moments was small and positive (SMD = 0.18, 95% CI 0.07–0.28), but statistically significant. Effects were considerably larger for treatment tasks (SMD = 0.33, 95% CI 0.18–0.48) than for personal functioning outcomes (SMD = 0.06, 95% CI −0.02 to 0.15). The largest effect sizes were found for retention in substance abuse treatment and linkage with substance abuse services. Moderator effects of case management models and conditions were assessed, but no significant differences were observed.Conclusions: The primary results from earlier meta-analyses were supported: case management is more effective than TAU conditions for improving outcomes, but this effect is significantly larger for treatment-related tasks than for personal functioning outcomes. Case management can be an important supplement to available services for improving linkage and retention, although further research is needed to assess its potential for supporting recovery from a longitudinal perspective.

Adaptive learning systems have received an increasing attention as they enable to provide personalized instructions tailored to the behaviors and needs of individual learners. In order to reach this goal, it is desired to have an assessment system, monitoring each learner's ability change in real time. The Elo Rating System (ERS), a popular scoring algorithm for paired competitions, has recently been considered as a fast and flexible method that can assess learning progress in online learning environments. However, it has been argued that a standard ERS may be problematic due to the multidimensional nature of the abilities embedded in learning materials. In order to handle this issue, we propose a system that incorporates a multidimensional item response theory model (MIRT) in the ERS. The basic idea is that instead of updating a single ability parameter from the Rasch model, our method allows a simultaneous update of multiple ability parameters based on a compensatory MIRT model, resulting in a multidimensional extension of the ERS (“M-ERS”). To evaluate the approach, three simulation studies were conducted. Results suggest that the ERS that incorrectly assumes unidimensionality has a seriously lower prediction accuracy compared to the M-ERS. Accounting for both speed and accuracy in M-ERS is shown to perform better than using accuracy data only. An application further illustrates the method using real-life data from a popular educational platform for exercising math skills.

Popularity of online courses with open access and unlimited student participation, the so-called massive open online courses (MOOCs), has been growing intensively. Students, professors, and universities have an interest in accurate measures of students' proficiency in MOOCs. However, these measurements face several challenges: (a) assessments are dynamic: items can be added, removed or replaced by a course author at any time; (b) students may be allowed to make several attempts within one assessment; (c) assessments may include an insufficient number of items for accurate individual-level conclusions. Therefore, common psychometric models and techniques of Classical Test Theory (CTT) and Item Response Theory (IRT) do not serve perfectly to measure proficiency. In this study we try to cover this gap and propose cross-classification multilevel logistic extensions of the common IRT model, the Rasch model, aimed at improving the assessment of the student's proficiency by modeling the effect of attempts and by involving non-assessment data such as student's interaction with video lectures and practical tasks. We illustrate these extensions on the logged data from one MOOC and check the quality using a cross-validation procedure on three MOOCs. We found that (a) the performance changes over attempts depend on the student: whereas for some students performance ameliorates, for other students, the performance might deteriorate; (b) similarly, the change over attempts varies over items; (c) student's activity with video lectures and practical tasks are significant predictors of response correctness in a sense of higher activity leads to higher chances of a correct response; (d) overall accuracy of prediction of student's item responses using the extensions is 6% higher than using the traditional Rasch model. In sum, our results show that the approach is an improvement in assessment procedures in MOOCs and could serve as an additional source for accurate conclusions on student's proficiency.

Computer vision has shown great accomplishments in a wide variety of classification, segmentation and object recognition tasks, but tends to encounter more difficulties when tasks require more contextual assessment. Measuring the engagement of students is an example of such a complex task, as it requires a strong interpretative component. This research describes a methodology to measure students’ engagement, taking both an individual (student-level) and a collective (classroom) approach. Results show that students’ individual behaviour, such as note-taking or hand-raising, is challenging to recognise, and does not correlate with students’ self-reported engagement. Interestingly, students’ collective behaviour can be quantified in a more generic way using measures for students’ symmetry, reaction times and eye-gaze intersections. Nonetheless, the evidence for a connection between these collective measures and engagement is rather weak. Although this study does not succeed in providing a proxy of students’ self-reported engagement, our approach sheds light on the needs for future research. More concretely, we suggest that not only the behavioural, but also the emotional and cognitive component of engagement should be captured.

Theory and research both point at epigenetic processes affecting both parenting behavior and child functioning. However, little is known about the convergence of mother and child’s epigenetic patterns in families. Therefore, the current study investigated epigenetic covariance in mother–child dyads’ methylation levels regarding four stress-regulation related genes (5HTT, NR3C1, FKBP5, and BDNF). Covariance was tested in a general population sample, consisting of early adolescents (Mage = 11.63, SDage = 2.3) and mothers (N = 160 dyads). Results showed that mother and offspring 5HTT and NR3C1 methylation patterns correlated. Furthermore, when averaged across genes, methylation levels strongly correlated. These findings partially supported that child and parent methylation levels covary. It might be important to consider this covariance to understand maladaptive parent–child relationships.

Este estudio tiene como objetivo investigar las propiedades psicométricas de la Lista de afecto positivo y negativo para la Infancia (PANAS-C) en Perú. Después de traducir la escala en español, su validez y fiabilidad se investigaron en los datos obtenidos de un estudio longitudinal de dos ondas con una muestra de estudiantes peruanos preadolescentes (N = 170). La estructura factorial de la escala fue investigada por técnicas de análisis factorial de confirmación, mientras que la validez convergente y discriminante se analizaron utilizando la Escala de Soledad Lovaina para Niños y Adolescentes y el Inventario estresantes eventos para preadolescentes. Los resultados apoyan la idoneidad de un modelo de dos factores (afecto positivo y afecto negativo) en todos los casos. Los resultados también mostraron una buena validez convergente y discriminante, así como una buena fiabilidad. Los presentes hallazgos sugieren que los cosntructos de PA y NA son menos distinguibles en los niños más pequeños y que dichos conceptos se presentan más claramente definidos a medida que los niños crecen, posiblemente debido a una creciente capacidad para categorizar la experiencia emocional.

This study explores the sources of variability in Mathematics achievement of Ugandan students at the student, classroom and school level. The Mathematics score and questionnaire responses of 4,819 first-year secondary school students (Grade Seven, about 14-15 years old) from 78 classrooms of 49 schools were analysed. A three-level linear model was used. The results indicate that out of the total variance in Mathematics achievement 68.8%, 14.2% and 17.0% are situated at student, classroom and school level, respectively. Of all the considered explanatory variables at the three levels, i.e. socio-economic status, gender, prior Mathematics achievement, parental support, peer influence, class mean of prior Mathematics achievement and of students' perception of good classroom assessment, school mean of class climate (class mean of attitude toward mathematics) and of parental support were significant predictors of Mathematics achievement. The relevant factors could explain 7.6%, 73.1% and 84.3%, respectively, of student-, classroom- and school-level differences. Implications of our study are considered.

In this article, we present PCovR, an R package for performing principal covariates regression (PCovR; De Jong and Kiers 1992). PCovR was developed for analyzing regression data with many and/or highly collinear predictor variables. The method simultaneously reduces the predictor variables to a limited number of components and regresses the criterion variables on these components. The flexibility, interpretational advantages, and computational simplicity of PCovR make the method stand out between many other regression methods. The PCovR package offers data preprocessing options, new model selection procedures, and several component rotation strategies, some of which were not available in R up till now. The use and usefulness of the package is illustrated with a real dataset, called psychiatrists.

Gene-by-environment interaction (GxEs) studies have gained popularity over the last decade, but the robustness of such observed interactions has been questioned. The current study contributes to this debate by replicating the only study on the interaction between the serotonin transporter gene (5-HTTLPR) and perceived parental support on adolescents' peer-related loneliness. A total of 1,111 adolescents (51% boys) with an average age of 13.70 years (SD = 0.93) participated and three annual waves of data were collected. At baseline, adolescent-reported parental support and peer-related loneliness were assessed and genetic information was collected. Assessment of peer-related loneliness was repeated at Waves 2 and 3. Using a cohort-sequential design, a Latent Growth Curve Model was estimated. Overall, a slight increase of loneliness over time was found. However, the development of loneliness over time was found to be different for boys and girls: girls' levels of loneliness increased over time, whereas boys' levels of loneliness decreased. Parental support was inversely related to baseline levels of loneliness, but unrelated to change of loneliness over time. We were unable to replicate the main effect of 5-HTTLPR or the 5-HTTLPR x Support interaction effect. In the Discussion, we examine the implications of our non-replication.

The research on electronic learning environments has evolved towards creating adaptive learning environments. In this study, the focus is on adaptive curriculum sequencing, in particular, the efficacy of an adaptive curriculum sequencing algorithm based on matching the item difficulty level to the learner’s proficiency level. We therefore explored the effect of the relative difficulty level on learning outcome and motivation. Results indicate that, for learning environments consisting of questions focusing on just one dimension and with knowledge of correct response, it does not matter whether we present easy, moderate or difficult items or whether we present the items with a random mix of difficulty levels, regarding both learning and motivation.