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This study aimed to determine the dimensionality of morphological knowledge by examining different sources of variance. According to the Morphological Pathways Framework ("Levesque et al., Journal of Research in Reading," 44, 10-26, 2021), morphological awareness, morphological analysis and morphological decoding are related, but distinct dimensions of morphological knowledge. However, multidimensionality might also stem from construct-irrelevant variance due to methodological artifacts. We assessed 612 Norwegian third graders on five measures of morphological knowledge and one measure of general vocabulary. Fitting a series of confirmatory factor analysis (CFA) models, we evaluated the dimensionality of morphological knowledge both within and across the five tests. Furthermore, we fitted three structural equation models (SEMs) to explore how different conceptualizations affect the relationship between morphological knowledge and general vocabulary: a five-factor model, a bifactor model, and a higher-order model representing morphological awareness, morphological analysis and morphological decoding. CFAs supported a multidimensional view of morphological knowledge and highlighted the need to account for construct-irrelevant variance. SEM analyses further illustrated that construct-irrelevant variance introduces a confounding element to the relations between morphological knowledge and vocabulary in the test-specific five-factor model, as only the bifactor and higher-order models separate between construct-relevant variance and variance due to methodological artifacts. The bifactor model is useful for separating sources of variance, especially during test development. For research purposes, however, we recommend conceptualizing morphological knowledge in line with "Levesque et al., Journal of Research in Reading," 44, 10-26, 2021, to increase knowledge of morphological dimensions and their relations to other areas of literacy.

Process data from educational assessments enhance the understanding of how students answer cognitive items. However, effectively making use of these data is challenging. We propose an approach to identify solution patterns from operation sequences and response times by generating networks from process data and defining network features that extract essential information from them. With these features, we group respondents to a problem-solving task from PISA 2012 using Gaussian mixture models. The results indicate the presence of two and four clusters for groups defined by failure and success on the task, respectively. We interpret the clusters as less-able, low-effort, adaptable, back-and-forth, deliberate, and trial-and-error clusters by considering the cluster-specific feature statistics. The proposed approach sheds light on students' problem-solving mental processes, which can aid item development and facilitate individualized feedback to students. The method is applicable to many computer-based problems, but a limitation is that the feature definitions can be task-dependent.

The certainty of response index (CRI) measures respondents' confidence level when answering an item. In conjunction with the answers to the items, previous studies have used descriptive statistics and arbitrary thresholds to identify student knowledge profiles with the CRIs. Whereas this approach overlooked the measurement error of the observed item responses and indices, we address this by proposing a factor mixture model that integrates a latent class model to detect student subgroups and a measurement model to control for student ability and confidence level. Applying the model to 773 seventh graders' responses to an algebra test, where some items were related to new material that had not been taught in class, we found two subgroups: (1) students who had high confidence in answering items involving the new material; and (2) students who had low confidence in answering items involving the new material but higher general self-confidence than the first group. We regressed the posterior probability of the group membership on gender, prior achievement, and preview behavior and found preview behavior a significant factor associated with the membership. Finally, we discussed the implications of the current study for teaching practices and future research.

Enabling comparable scores across grades is of interest for policymakers to evaluate educational systems, for researchers to investigate substantive questions, and for teachers to infer student growth. This study implemented a vertical scaling design to numeracy tests given in grades 5 and 8 as part of the Norwegian national testing system. Our design bridges the gap between grades 5 and 8 with a linking test tailored for grades 6 and 7, without the need for new item development. The design combines the existing administration for all grade 5 and 8 students with additional tests for samples of grade 6 and 7 students. The findings indicate that vertically scaling existing tests is possible through a cost-effective design and that numeracy, as measured by the Norwegian national tests, is comparable across four grades. We discuss the implications of our study for creating vertical scales in the context of national assessment systems.

The estimation of high-dimensional latent regression item response theory (IRT) models is difficult because of the need to approximate integrals in the likelihood function. Proposed solutions in the literature include using stochastic approximations, adaptive quadrature, and Laplace approximations. We propose using a second-order Laplace approximation of the likelihood to estimate IRT latent regression models with categorical observed variables and fixed covariates where all parameters are estimated simultaneously. The method applies when the IRT model has a simple structure, meaning that each observed variable loads on only one latent variable. Through simulations using a latent regression model with binary and ordinal observed variables, we show that the proposed method is a substantial improvement over the first-order Laplace approximation with respect to the bias. In addition, the approach is equally or more precise to alternative methods for estimation of multidimensional IRT models when the number of items per dimension is moderately high. Simultaneously, the method is highly computationally efficient in the high-dimensional settings investigated. The results imply that estimation of simple-structure IRT models with very high dimensions is feasible in practice and that the direct estimation of high-dimensional latent regression IRT models is tractable even with large sample sizes and large numbers of items.

Background Purpose Methods Results Conclusions Robotic radiosurgery treatments allow for precise non‐coplanar beam delivery by utilizing a robot equipped with a linac that traverses through a set of predetermined nodes. High quality treatment plans can be produced but treatment times can grow large, with one substantial component being the robot traversal time.The aim of this study is to reduce the treatment time for robotic radiosurgery treatments by introducing algorithms for reducing the robot traversal time. The algorithms are integrated into a commercial treatment planning system.First, an optimization framework for robotic radiosurgery planning is detailed, including a heuristic optimization method for node selection. Second, two methods aimed at reducing the traversal time are introduced. One utilizes a centrality measure focusing on the structure of the node network, while the other is based on the direct computation of traversal times during optimization. A comparison between plans with and without the time‐reducing algorithms is made for three brain cases and one liver case with basis in treatment time, plan quality, monitor units, and network structure of the selected nodes.Large decreases in traversal times are obtained by the traversal time reducing algorithms, with reductions of up to 49 % in the brain cases and 31 % in the liver case. The resulting reductions in treatment times are up to 30 % and 13 %, respectively. Small differences in plan quality are observed, with similar dose‐volume histograms, dose distributions, and conformity/gradient indices.The total treatment time of the robotic radiosurgery treatments can be reduced by selecting nodes with more efficient robot traversal paths, while maintaining plan quality. [ABSTRACT FROM AUTHOR]

We applied latent class analysis and the rule space model to verify the cumulative characteristic of conceptual change by developing a learning progression for buoyancy. For this study, we first abstracted seven attributes of buoyancy and then developed a hypothesized learning progression for buoyancy. A 14-item buoyancy instrument was administered to 1089 8th grade students to verify and refine the learning progression. The results suggest four levels of progression during conceptual change when 8th grade students understand buoyancy. Students at level 0 can only master "Density." When students progress to level 1, they can grasp "Direction," "Identification," "Submerged volume," and "Relative density" on the basis of the prior level. Then, students gradually master "Archimedes' theory" as they reach level 2. The most advanced students can further grasp "Relation with motion" and arrive at level 3. In addition, this four-level learning progression can be accounted for by the Qualitative-Quantitative-Integrative explanatory model.