Starting with early research on multiple source comprehension that primarily emerged from work in history, researchers have explored several types of instructional manipulations including altering the features of the inquiry task that is given (such as being asked to write a narrative or an argument); changing features of the task environment (such as the format of the source documents or features of the document set); and varying the instructional context (such as having students engage in a particular activity or training prior to engaging in a multiple-source inquiry task). These three broad categories continue to represent the main types of manipulations that have been studied as this literature has expanded to also include explorations into the comprehension of scientific phenomena from multiple-source inquiry tasks. This chapter provides an overview discussing the kinds of processes that are theoretically involved in multiple-source comprehension; articulates the challenges that readers face when they attempt to engage in multiple-source inquiry tasks; and summarizes the empirical research that has attempted to explore multiple source comprehension processes using manipulations of the inquiry task, the task environment, and the instructional context. [This paper was published in: J. L. G. Braasch, I. Bråten, & M. T. McCrudden (Eds.) "Handbook of Multiple Source Use" (p341-361). New York, NY: Routledge.]

Self-regulated learning (SRL) involves learners’ ability to monitor and regulate their cognitive, affective, metacognitive, and motivational (CAMM2) processes and plays a critical role in learning about challenging domains while using advanced learning technologies (ALTs). Additionally, emerging empirical evidence indicates that CAM processes play an important role in learning and problem solving as well as self-regulation with ALTs. However, capturing CAM processes during learning with ALTs poses several major conceptual, theoretical, methodological, and analytical challenges. For example, researchers currently measure CAM SRL processes using several online trace methodologies, such as concurrent think-alouds, eye tracking, log files, physiological sensors, and so forth. While these methods have the potential to advance current SRL frameworks, models, and theories, they still pose serious challenges (e.g., temporal alignment of data channels, lack of analytical techniques, and accuracy of inferences made from individual channels and across data channels) that currently plague the field. Our chapter focuses on understanding and reasoning about real-time CAM processes to foster self-regulation with ALTs. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

The number of women studying STEM careers and pursuing graduate degrees has not changed in the last decade (National Student Clearinghouse Research Center, 2015; Science & Engineering Degree Attainment: 2004-2014). Most prior research to explain this problem has focused on the topics of identity, access, pedagogy, and choice (Brotman & Moore, 2008; Journal of Research in Science Teaching, 45, 971-1002). Additional research is needed on how internal and external factors interact with one another to demotivate girls and young women from pursuing science careers. Here, we show how girls' competency beliefs are an essential foundation for science content learning during middle school and how these effects of competency beliefs are mediated by in and out-of-school factors. We recruited over 2,900 6th and 8th grade students from two different regions in the United States. At two different time points, students completed surveys asking about their stance toward science such as competency beliefs in science, willingness to engage in argumentation, and choice preferences toward optional science experiences. We also collected a reasoning ability measure, and pre- and post-tests on science content knowledge. Moreover, students also reported on their cognitive behavioral engagement during a sampled science class on two separate occasions. Multiple regression and mediation analyses show that as boys grow older, their willingness to engage in argumentation and to participate in science experiences suppresses the role of competency beliefs on their learning science content. By contrast, as girls grew older, they showed an increasing need to have high competency beliefs to achieve strong content learning gains. Our results demonstrate that despite girls' willingness to participate in scientific argumentation and to take part in science experiences, they probably do not receive enough support in their environment to access the benefits of these experiences, and hence they have a stronger need to have high competency beliefs in order to achieve significant growth in science learning. (c) 2017 Wiley Periodicals, Inc. J Res Sci Teach 54:790-822, 2017

Why would Marcus, a high school volleyball player, spend countless hours practicing his serve in his backyard, until his arms were so tired that he could hardly move? Does the value he sees in practicing help him overcome his fatigue and maintain his passion toward volleyball? Why would Erica, a high school student, spend her weekends attending math competitions, often travelling many hours each way? Why does she enjoy math and see the importance of math to her future when so many other students do not? In the parlance of interest research, these two individuals exhibit well-developed interests in volleyball and math, respectively. In this chapter we consider how finding value and meaning in activities and topics leads to the development of interest. We also highlight a program of research that we have pursued over the last decade designed to promote interest development. Finally, we discuss how other people in our lives influence value, both directly and indirectly, and as a result, the development of interest. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

In this paper, we investigate the relationship between students' (N = 28) individual differences and visual attention to pedagogical agents (PAs) during learning with MetaTutor, a hypermedia-based intelligent tutoring systems. We used eye tracking to capture visual attention to the PAs, and our results reveal specific visual attention-related metrics (e.g., fixation rate, longest fixations) that are significantly influenced by learning depending on student achievement goals. Specifically, performance-oriented students learned more with a long longest fixation and a high fixation rate on the PAs, whereas mastery-oriented students learned less with a high fixation rate on the PAs. Our findings contribute to understanding how to design PAs that can better adapt to student achievement goals and visual attention to the PA.

Although gaming the system, a behavior in which students attempt to solve problems by exploiting help functionalities of digital learning environments, has been studied across multiple learning environments, little research has been done to study how (and whether) gaming manifests differently across populations of students and learning environments. In this paper, we study the differences in usage of 13 different patterns of actions associated with gaming the system by comparing their distribution across different populations of students using Cognitive Tutor Algebra and across students using one of three learning environments: Cognitive Tutor Algebra, Cognitive Tutor Middle School and ASSISTments. Results suggest that differences in gaming behavior are more strongly associated to the learning environments than to student populations and reveal different trends in how students use fast actions, similar answers and help request in different systems.

Affect detection has become a prominent area in student modeling in the last decade and considerable progress has been made in developing effective models. Many of the most successful models have leveraged physical and physiological sensors to accomplish this. While successful, such systems are difficult to deploy at scale due to economic and political constraints, limiting the utility of their application. Examples of sensor-free affect detectors that assess students based solely using data on the interaction between students and computer-based learning platforms exist, but these detectors generally have not reached high enough levels of quality to justify their use in real-time interventions. However, the classification algorithms used in these previous sensor-free detectors have not taken full advantage of the newest methods emerging in the field. The use of deep learning algorithms, such as recurrent neural networks (RNNs), have been applied to a range of other domains including pattern recognition and natural language processing with success, but have only recently been attempted in educational contexts. In this work, we construct new deep sensor-free affect detectors and report significant improvements over previously reported models.

This article describes several approaches to assessing student understanding using written explanations that students generate as part of a multiple-document inquiry activity on a scientific topic (global warming). The current work attempts to capture the causal structure of student explanations as a way to detect the quality of the students' mental models and understanding of the topic by combining approaches from Cognitive Science and Artificial Intelligence, and applying them to Education. First, several attributes of the explanations are explored by hand coding and leveraging existing technologies (LSA and Coh-Metrix). Then, we describe an approach for inferring the quality of the explanations using a novel, two-phase machine-learning approach for detecting causal relations and the causal chains that are present within student essays. The results demonstrate the benefits of using a machine-learning approach for detecting content, but also highlight the promise of hybrid methods that combine ML, LSA and Coh-Metrix approaches for detecting student understanding. Opportunities to use automated approaches as part of Intelligent Tutoring Systems that provide feedback toward improving student explanations and understanding are discussed.

Purpose: Researchers who study mortality among survey participants have multiple options for obtaining information about which participants died (and when and how they died). Some use public record and commercial databases; others use the National Death Index; some use the Social Security Death Master File; and still others triangulate sources and use Internet searches and genealogic methods. We ask how inferences about mortality rates and disparities depend on the choice of source of mortality information. Methods: Using data on a large, nationally representative cohort of people who were first interviewed as high school sophomores in 1980 and for whom we have extensive identifying information, we describe mortality rates and disparities through about age 50 using four separate sources of mortality data. We rely on cross-tabular and multivariate logistic regression models. Results: These sources of mortality information often disagree about which of our panelists died by about age 50 and also about overall mortality rates. However, differences in mortality rates (i.e., by sex, race/ethnicity, education) are similar across of sources of mortality data. Conclusion: Researchers' source of mortality information affects estimates of overall mortality rates but not estimates of differential mortality by sex, race and/or ethnicity, or education. (C) 2016 Elsevier Inc. All rights reserved.

Whether executive functioning deficits result in children experiencing learning difficulties is presently unclear. Yet evidence for these hypothesized causal relations has many implications for early intervention design and delivery. We used a multi-year panel design, multiple criterion and predictor variable measures, extensive statistical control for potential confounds including autoregressive prior histories of both reading and mathematics difficulties, and additional epidemiological methods to preliminarily examine these hypothesized relations. Results from multivariate logistic regression analyses of a nationally representative and longitudinal sample of 18,080 children (i.e., the Early Childhood Longitudinal Study – Kindergarten Cohort of 2011, or ECLS-K: 2011) indicated that working memory and, separately, cognitive flexibility deficits uniquely increased kindergarten children’s risk of experiencing reading as well as mathematics difficulties in first grade. The risks associated with working memory deficits were particularly strong. Experimentally-evaluated, multi-component interventions designed to help young children with reading or mathematics difficulties may also need to remediate early deficits in executive function, particularly in working memory.