This study investigates the presence of dynamical patterns of interpersonal coordination in extended deceptive conversations across multimodal channels of behavior. Using a novel "devil’s advocate" paradigm, we experimentally elicited deception and truth across topics in which conversational partners either agreed or disagreed, and where one partner was surreptitiously asked to argue an opinion opposite of what he or she really believed. We focus on interpersonal coordination as an emergent behavioral signal that captures interdependencies between conversational partners, both as the coupling of head movements over the span of milliseconds, measured via a windowed lagged cross correlation (WLCC) technique, and more global temporal dependencies across speech rate, using cross recurrence quantification analysis (CRQA). Moreover, we considered how interpersonal coordination might be shaped by strategic, adaptive conversational goals associated with deception. We found that deceptive conversations displayed more structured speech rate and higher head movement coordination, the latter with a peak in deceptive disagreement conversations. Together the results allow us to posit an adaptive account, whereby interpersonal coordination is not beholden to any single functional explanation, but can strategically adapt to diverse conversational demands.

Subjects learned to classify images of rocks into the categories igneous, metamorphic, and sedimentary. In accord with the real-world structure of these categories, the to-be-classified rocks in the experiments had a dispersed similarity structure. Our central hypothesis was that learning of these complex categories would be improved through observational study of organized, simultaneous displays of the multiple rock tokens. In support of this hypothesis, a technique that included the presentation of the simultaneous displays during phases of the learning process yielded improved acquisition (Experiment 1) and generalization (Experiment 2) compared to methods that relied solely on sequential forms of study and testing. The technique appears to provide a good starting point for application of cognitive-psychology principles of effective category learning to the science classroom.

In this chapter, we address several questions. How do the prior beliefs of students influence the way in which they process this information? Will students comprehend information with more or less success if it matches or does not match their prior beliefs? Under what circumstances might students change their beliefs? Do students change beliefs to make them consistent with data reported in scientific studies, or are other factors at play? We also argue that the traditional methods and theories from text comprehension are well suited to examine the influences of beliefs on the processing students do and the mental representations they form when reading belief-related texts. [This chapter was published in M. F. Schober, D. N. Rapp, & M. A. Britt (Eds). "Handbook of Discourse Processes, 2nd Edition." (pp. 295-314). New York, NY: Taylor & Francis.]

Teaching introductory statistics using curricula focused on modeling and simulation is becoming increasingly common in introductory statistics courses and touted as a more beneficial approach for fostering students' statistical thinking. Yet, surprisingly little research has been conducted to study the impact of modeling and simulation curricula on student thinking, nor is there much research on how students make sense of the computer models they construct. The work presented here utilizes a framework developed by Biehler, Frischemeier, and Podworny (2015) for comparing two groups problems via a modeling and simulation approach using TinkerPlotsTM. Our work makes a contribution to the field by delving deeper into student reasoning as students create TinkerPlotsTM models to solve a comparing two groups problem. © International Association for Statistical Education (IASE/ISI), November, 2017.

The challenge of connecting employers and educators to collaboratively plan for training future workers is an enduring one -- particularly for jobs that are rapidly changing because of technological advancements. This report addresses this challenge as it pertains to employers and educators in the oil and natural gas industry located in and around the Utica and Marcellus shales. The combination of horizontal drilling and hydraulic fracturing to tap natural gas has resulted in the Utica and Marcellus shales becoming major sources of natural gas supply within the United States and are predicted to bring significant long-term economic benefits to the tristate region of Ohio, Pennsylvania, and West Virginia. To inform policy decisions on how best to expand and sustain the pool of workers with knowledge and skills needed by oil and natural gas employers in the tristate region, this report summarizes the findings from surveys administered to the region's oil and gas employers and education providers. We found that basic cross-cutting skills -- such as time management, speaking, and writing -- and knowledge of business operations (including sales and marketing) are reported by employers as essential for their workers to competently perform in high-priority occupations. However, these basic skills tend not to be emphasized in local postsecondary degree programs that support the oil and natural gas industry. We also found a clear lack of collaboration and partnerships between oil and gas companies and education providers across the region, with colleges and employers each pointing to the other's unwillingness as the source for lack of partnerships or collaboration. [To view the brief, "How Educators and Employers Can Align Efforts to Fill Middle-Skills STEM Jobs," see ED594813.]

Ethnic minorities, such as Latinx people of Hispanic or Latino origin, and women earn fewer engineering degrees than Caucasians and men. With shifting population dynamics and high demands for a technically qualified workforce, it is important to achieve broad participation in the engineering workforce by all ethnicities and both genders. Previous research has examined the knowledge of and interest in engineering among students in grades five and higher. In contrast, the present study examined elementary school students in grades K-5. The study found that older students in grades 4 and 5 had both greater knowledge of engineering occupational activities and greater interest in engineering than younger students in grades K-3. Moreover, Caucasian students had greater knowledge and interest levels than Latinx students. There were no significant differences between boys and girls, nor any significant interactions among gender, grade level, and ethnicity. A significant positive correlation between knowledge of engineering occupational activities and interest in engineering was also found. The findings suggest that early engineering outreach interventions are important. Such early interventions could potentially contribute to preserving the equivalent interest levels of males and females for engineering as students grow older. Also, ethnic disparities in engineering knowledge and interest could potentially be mitigated through early interventions. © 2017, Purdue University Press. All rights reserved.

How is a child's successful participation in science learning shaped by their family's support? We focus on the critical time period of early adolescents, testing (i) whether the child's perception of family support is important for both choice preferences to participate in optional learning experiences and engagement during science learning, and (ii) whether the effects on choice preferences and engagement are mediated through effects on child interest and self-efficacy in science. Structural equation modeling is applied to data from two different contexts, one examining engagement during a science and technology center visit and the other examining engagement and learning during classroom instruction. Models from both datasets suggest that early adolescents' perceived family support for learning is associated with their choices for and engagement in science learning, and that these effects are mediated by effects on child interest and self-efficacy in science. Further, children's family physical resources (e.g., available learning spaces and materials) predicts their perceived family support, but is not separately connected to either interest or self-efficacy. (c) 2015 Wiley Periodicals, Inc. J Res Sci Teach 53: 450-472, 2016

In this study we tested whether external regulation provided by artificial pedagogical agents (PAs) was effective in facilitating learners' self-regulated learning (SRL) and can therefore foster complex learning with a hypermedia-based intelligent tutoring system. One hundred twenty (N = 120) college students learned about the human circulatory system with MetaTutor during a 2-hour session under one of two conditions: adaptive scaffolding (AS) or a control (C) condition. The AS condition received timely prompts from four PAs to deploy various cognitive and metacognitive SRL processes, and received immediate directive feedback concerning the deployment of the processes. By contrast, the C condition learned without assistance from the PAs. Results indicated that those in the AS condition gained significantly more knowledge about the science topic than those in the C condition. In addition, log-file data provided evidence of the effectiveness of the PAs' scaffolding and feedback in facilitating learners' (in the AS condition) metacognitive monitoring and regulation during learning. We discuss implications for the design of external regulation by PAs necessary to accurately detect, track, model, and foster learners' SRL by providing more accurate and intelligent prompting, scaffolding, and feedback regarding SRL processes.

This study examined the proportional learning gains attained by 165 college students as they learned about the human circulatory system over two sessions with the intelligent tutoring system, MetaTutor. Results indicated that learners in the prompt and feedback condition, which were afforded the full capabilities of the four pedagogical agents (PAs), attained significantly greater proportional learning gains than learners in the control condition who did not receive the same scaffolding. In addition, we also found that the amount of time spent with each PA produced different types of impacts on the learners, with Sam the Strategizer having the most influence on proportional learning gains. Lastly, results from the revised Agent Persona Inventory (API), administered following the learning session with MetaTutor, revealed key findings regarding learners’ overall retrospective affective reactions towards each individual PA. These results have implications for the design of future PAs capable of offering real-time and adaptive pedagogical instruction within Intelligent Tutoring Systems (ITSs). © Springer International Publishing Switzerland 2016.

Recent research on self-regulated learning (SRL) includes multichannel data, such as eye-tracking, to measure the deployment of key cognitive and metacognitive SRL processes during learning with adaptive hypermedia systems. In this study we investigated how 147 college students’ proportional learning gains (PLGs), proportion of time spent on areas of interest (AOIs), and frequency of fixations on AOI-pairs, differed based on their prior knowledge of the overall science content, and of specific content related to sub-goals, as they learned with MetaTutor. Results indicated that students with low prior sub-goal knowledge had significantly higher PLGs, and spent a significantly larger proportion of time fixating on diagrams compared to students with high prior sub-goal knowledge. In addition, students with low prior knowledge had significantly higher frequencies of fixations on some AOI-pairs, compared to students with high prior knowledge. The results have implications for using eye-tracking (and other process data) to understand the behavioral patterns associated with underlying cognitive and metacognitive SRL processes and provide real-time adaptive instruction, to ensure effective learning. © Springer International Publishing Switzerland 2016.