This study explored the role of learner-generated and instructor-provided visuals in learning from scientific text. 134 college students studied a lesson on the human circulatory system and then completed recall and transfer tests. Across two consecutive study periods, students were randomly assigned to either view a provided illustration twice (provided-provided), generate a drawing from the text and then revise their drawing (generated-revised), view a provided illustration and then generate a drawing (provided-generated), or generate a drawing and then view a provided illustration (generated-provided). Results indicated a group by learning outcome interaction: the generated-provided and provided-generated groups performed higher on the transfer test and lower on the recall test compared to the provided-provided group. Furthermore, spatial ability was positively associated with learning outcomes among students who generated drawings but not among students in the provided-provided group. Finally, the relationship between spatial ability and learning outcomes among students who generated drawings was mediated by drawing quality. These findings suggest that provided and generated visuals have unique effects on different learning outcomes, and spatial ability plays an important role in supporting learner-generated visuals.

Metacomprehension is key to successful learning of complex topics when using multimedia materials. The goal of this study was to determine if eye-movement dyads could be: (1) identified by sequence mining techniques, and (2) aligned with self-reported metacognitive judgments during learning with multimedia materials that contain conceptual discrepancies designed to interfere with participants' metacomprehension. Thirty-two undergraduate students' metacognitive judgments were examined with RM-MANOVAs, and sequential pattern mining and differential sequence mining were conducted on their eye movements as they learned with complex multimedia materials. Additionally, we distinguished between event- (i.e., if participants looked at specific areas of the content) and duration-based (i.e., if participants looked at areas of interest [AOIs] for a medium or long amount of time) eye-movement dyads to assess if qualitative and quantitative differences existed in their eye-movement behaviors. For content with text and graph discrepancies, results indicated participants' metacognitive judgments were lower and less accurate, and more fixation dyads were found between the text and graph. Furthermore, specific dyads of different length (i.e., long fixations on the graph to medium fixations on the text) fixations may align with lowered and inaccurate metacognitive judgments for content with text and graph discrepancies. This study begins to address how to identify behavioral indices of metacomprehension processes during multimedia learning.

Analyzing multimodal multichannel data about self-regulated learning (SRL) obtained during the use of advanced learning technologies such as intelligent tutoring systems, serious games, hypermedia, and immersive virtual learning environments is key to understanding the interplay among cognitive, affective, metacognitive, and social processes and their impact on learning, problem solving, reasoning, and conceptual understanding in learners of all ages and contexts. In this special issue of Computers in Human Behavior, we report six studies conducted by interdisciplinary teams' use of various trace methodologies such as eye tracking, log-files, physiological data, facial expressions of emotions, screen recordings, concurrent think-alouds, and linguistic analyses of discourse. The research studies focus on how these data were analyzed using a combination of traditional statistical techniques as well as educational data-mining procedures to detect, measure, and infer cognitive, metacognitive, and social processes related to regulating the self and others across several tasks, domains, ages, and contexts. The results of these studies point to future work necessitating interdisciplinary researchers' collaboration to use theoretically based and empirically derived approaches to collecting, measuring, and modeling multimodal multichannel SRL data to extend our current models, frameworks, and theories by making them more predictive by elucidating the nature, complexity, and temporality of underlying processes. Lastly, analyses of multimodal multichannel SRL process data can significantly augment advanced learning technologies by providing real-time, intelligent, adaptive, individualized scaffolding and feedback to address learners' self-regulatory needs.

The aim of this study was to investigate how teachers interact with students in order to prepare them to conduct research with multiple online texts as part of the process of scientific inquiry in the classroom. The specific focus of this work was on understanding how teachers used classroom dialogue to create an environment that supports the use of multiple online text-based resources as part of the process of doing science. Data collection for this study occurred in the 6th grade classrooms of two teachers in a Midwestern school district. Each of the teachers taught three science classes for a total of 150 students. A test of students' content knowledge of physics was used in order to evaluate students' understanding of the physics concepts targeted in the curriculum. An analysis of covariance (ANCOVA) revealed that the students from one teacher's classes performed significantly better on the physics test than the students of the other teacher (p<.05). To qualitatively investigate the differences between the whole class dialogue used by the two teachers, teachers' interactions with students as they prepared them to engage in research with the multiple texts were coded. Coding of the dialogue revealed that the teacher whose students exhibited higher learning outcomes engaged in more deep level facilitation strategies during whole class discussion, including setting learning goals for text interactions, connecting to prior knowledge, and discussing the use of multiple texts as part of doing science.

We investigated whether and to what extent deficits in executive functions (EF) increase kindergarten children's risk for repeated academic difficulties across elementary school. We did so by using growth mixture modeling to analyze the first- through third-grade achievement growth trajectories in mathematics, reading, and science of a large (N = 11,010) sample of children participating in the nationally representative Early Childhood Longitudinal Study-Kindergarten Cohort of 2011 (ECLS-K: 2011). The modeling yielded four growth trajectory classes in mathematics, reading, and science. We observed an at-risk trajectory class in each academic domain using a standardized scale. Children in the at-risk class initially averaged very low levels of achievement (i.e., about two standard deviations below the mean) in first grade. Their trajectories remained very low or declined further by third grade. Trajectories for other classes were also generally flat but started and remained at higher levels of standardized achievement. Deficits in EF, particularly in working memory, increased kindergarten children's risk of experiencing repeated mathematics, reading, and science difficulties across elementary school. These predictive relations replicated across three academic domains following statistical control for domain-specific and-general autoregressors as well as socio-demographic characteristics. (C) 2018 Elsevier Inc. All rights reserved.

Despite increasing emphasis in the United States on promoting student engagement and achievement in science, technology, engineering, and mathematics (STEM) fields, the origins of scientific literacy remain poorly understood. We begin to address this limitation by considering the potential contributions of two distinct domain-general skills to early scientific literacy. Given their relevance to making predictions and evaluating evidence, we consider the degree to which causal reasoning skills relate to scientific literacy (as measured by an adaptive standardized test specifically designed for preschoolers). We also consider executive function (EF) as a potentially more fundamental contributor. While previous research has demonstrated that EF is predictive of achievement in other core academic domains like reading and math, its relationship to scientific literacy, particularly in early childhood, has received little attention. To examine how causal reasoning and EF together potentially relate to the development of scientific literacy in young children, we recruited 125 3-year-olds to complete three causal reasoning tasks, three EF tasks, and the aforementioned measure of scientific literacy. Results from a series of hierarchical regressions revealed that EF, and one measure of causal reasoning (causal inferencing) were related to scientific literacy, even after controlling for age, ethnicity, maternal education, and vocabulary knowledge. Moreover, causal inferencing ability was a significant partial mediator between EF and scientific literacy. Although additional research will be required to further specify the nature of these relationships, the current work suggests that EF has the potential to support scientific literacy, perhaps in part, by scaffolding causal reasoning skills. (C) 2018 Elsevier Inc. All rights reserved.

What do numerical estimates tell us about developing an understanding of number? One theory is that bounded number line estimation (NLE) tasks reveal a representational shift from logarithmically to linearly organized mental representations of number over development. According to a different theoretical framework, developmental change in estimation reflects changes in children's numerical knowledge and their ability to make appropriate relative judgments. Empirical support for this proportion estimation framework includes the fact that quantitative models of proportion estimation describe signature patterns of estimation bias. A recent study argued against this latter theory by suggesting that patterns of curvature in number line placements are simply artifacts of a task procedure in which participants receive explicit information about the location of the numerical midpoint. We tested this claim in two experiments with children aged 6-8 years (Experiment 1: N = 47; Experiment 2: N = 104). Results demonstrated that the proportion estimation framework provides a good explanation of children's number line placement in the absence of explicit midpoint cues, that explicit cues to the midpoint are associated with more frequent use of middle reference points in young children, and that children can use a middle reference point spontaneously in the absence of explicit cues (with this tendency increasing with age). These findings provide novel support for the idea that psychophysical models of proportion estimation successfully account for numerical estimates across development regardless of whether spatial and numerical midpoint cues are provided as part of the NLE task. (C) 2019 Elsevier Inc. All rights reserved.

Children's ability to estimate fractions on a number line is strongly related to algebra and overall high school math achievement, and number line training leads to better fraction magnitude comparisons compared with area model training. Here, we asked whether unidimensionality is necessary for the number line to promote fraction magnitude concepts and whether left-to-right orientation and labeled endpoints are sufficient. We randomly assigned second and third-graders (N = 148) to one of four 15-min one-on-one, experimenter-led trainings. Three number line trainings had identical scripts, where the experimenter taught children to segment and shade the number line along the horizontal dimension. The number line conditions varied only in the vertical dimension of the training number line: pure unidimensional number line (17.5 cm horizontal line), hybrid unidimensional number line (17.5 x 0.6 cm rectangle), and square number line (17.5 x 17.5 cm). In the area model condition, children were taught to segment and shade a square (17.5 x 17.5 cm) along both dimensions. The conditions significantly differed in posttest fraction magnitude comparison accuracy (a transfer task), controlling for pretest accuracy, reading achievement, and age. In preregistered analyses, the hybrid unidimensional number line condition significantly outperformed the square area model condition and the square number line condition. In exploratory analyses accounting for training protocol fidelity, these results held and the pure unidimensional number line also outperformed the area model condition on fraction magnitude comparisons. We argue that unidimensionality is a critical feature of the number line for promoting fraction magnitude concepts because it aligns with a key concept that real numbers, including fractions, can be ordered along a single dimension. (C) 2019 Elsevier Inc. All rights reserved.

Integrated STEAM (science, technology, engineering, arts, and math) making activities have become increasingly popular in recent years. Many tout their benefits for STEAM interest development. However, we know relatively little about how these activities cultivate STEAM interests or about the relation between interest development and learning. This paper examines these issues in the context of one set of in-school, choice-based, STEAM making and learning environments, FUSE Studios. Drawing on sociocultural approaches to interest development, we present the case of one student's interest pathway through FUSE. By the end of the schoolyear, this student had developed an interest in and was recognized as a relative expert at 3D printing. She also connected this interest in 3D printing to a career aspiration to help cancer kids and become a doctor for them. Drawing on ethnographic observations and microanalysis of video-recordings, we trace her year-long interest pathway through FUSE to understand how her interests, in interaction with the socio-material context of FUSE, shaped her learning. We argue that the choice-based nature of FUSE allowed her to pursue her interests, organize her own learning, and consequently, cultivate STEAM interests and learning.

This technical note describes the results of a pilot approach to link administrative and survey data to better describe the richness and complexity of the research enterprise. In particular, we demonstrate how multiple funding channels can be studied by bringing together two disparate datasets: UMETRICS, which is based on university payroll and financial records, and the Survey of Earned Doctorates (SED), which is one of the most important US survey datasets about the doctoral workforce. We show how it is possible to link data on research funding and the doctorally qualified workforce to describe how many individuals are supported in different disciplines and by different agencies. We outline the potential for more work as the UMETRICS data expands to incorporate more linkages and more access is provided.