Positive interpersonal relationships require shared understanding along with a sense of rapport. A key facet of rapport is mirroring and convergence of facial expression and body language, known as nonverbal synchrony. We examined nonverbal synchrony in a study of 29 heterosexual romantic couples, in which audio, video, and bracelet accelerometer were recorded during three conversations. We extracted facial expression, body movement, and acoustic-prosodic features to train neural network models that predicted the nonverbal behaviors of one partner from those of the other. Recurrent models (LSTMs) outperformed feed-forward neural networks and other chance baselines. The models learned behaviors encompassing facial responses, speech-related facial movements, and head movement. However, they did not capture fleeting or periodic behaviors, such as nodding, head turning, and hand gestures. Notably, a preliminary analysis of clinical measures showed greater association with our model outputs than correlation of raw signals. We discuss potential uses of these generative models as a research tool to complement current analytical methods along with real world applications (e.g., as a tool in therapy).

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.

This paper examines the growing field of computational thinking (CT) in education. A review of the relevant literature shows a diversity in definitions, interventions, assessments, and models. After synthesizing various approaches used to develop the construct in K-16 settings, we have created the following working definition of CT: The conceptual foundation required to solve problems effectively and efficiently (i.e., algorithmically, with or without the assistance of computers) with solutions that are reusable in different contexts. This definition highlights that CT is primarily a way of thinking and acting, which can be exhibited through the use particular skills, which then can become the basis for performance-based assessments of CT skills. Based on the literature, we categorized CT into six main facets: decomposition, abstraction, algorithm design, debugging, iteration, and generalization. This paper shows examples of CT definitions, interventions, assessments, and models across a variety of disciplines, with a call for more extensive research in this area.

In this paper, we review computer-based assessment for learning (CBAfL), in elementary and secondary education, as a viable way to merge instruction and assessment of students' developing proficiencies. We begin by contextualizing our topic relative to summative and formative assessment before presenting the current literature, which we categorized into the following: (a) supplementary use in classrooms, (b) web-based, and (c) data-driven, continuous CBAfL. Examples of research studies per category are provided. Findings show that using CBAfL in the classroom, via the Internet, or embedded in a game, generally enhances learning and other outcomes across a range of content areas (e.g. biology, math, and programming). One conclusion is that feedback, to be most beneficial to learning, should not be overly complex and must be used to be effective. Findings also showed that the quality of the assessment (i.e. validity, reliability, and efficiency) is unimpaired by the inclusion of feedback. The possibilities created by advances in the learning sciences, measurement, and technology have paved the way toward new assessment approaches that will support personalized learning and that can accurately measure and support complex competencies. The next steps involve evaluating the new assessments regarding their psychometric properties and support of learning. Lay Description: What is currently known about computer-based assessment for learning (CBAfL)? Early CBAfL systems were divided into linear and branching programs with no diagnostics and evolved into systems possessing more personalized/adaptive remediation with AI. Current CBAfL can support a range of competencies in various digital environments. Advanced learning analytic methods include learning analytics and stealth assessment. What our paper adds to what is already known about CBA for learning? Trends in our review suggest CBAs will improve in personalizing learning in a variety of contexts. Innovative CBAfL techniques will move beyond the laboratory and into the mainstream. Boundaries between instruction, learning and assessment will eventually become blurred, thus removing the need for high-stake tests of learning. What are the implications of our topic for practitioners? With CBAfL advances, teachers will have more time to provide targeted support to learners. Students would not need to worry about taking exams if CBAfL is continuous and formative. Educators will be able to provide personalized learning experiences for diverse students. Students will be equipped with the knowledge and skills needed to succeed in the 21st century. © 2017 John Wiley & Sons Ltd