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The ability to navigate scientific obstacles is widely recognized as a hallmark of a scientific disposition and is one predictor of science, technology, engineering, and mathematics persistence for early-career scientists. However, the development of this competency in undergraduate research has been largely underexplored. This study addresses this gap by examining introductory students' emotional and behavioral responses to research-related challenges and failures that occur in two sequential research-based courses. We describe commonly reported emotions, coping responses, and perceived outcomes and examine relationships between these themes, student demographics, and course enrollment. Students commonly experience frustration, confusion, and disappointment when coping with challenges and failures. Yet the predominance of students report coping responses likely to be adaptive in academic contexts despite experiencing negative emotions. Being enrolled in the second course of a research-based course sequence was related to several shifts in response to challenges during data collection, including less reporting of confusion and fewer reports of learning to be cautious from students. Overall, students in both the first and second courses reported many positive outcomes indicating improvements in their ability to cope with challenge and failure. We assert that educators can improve research-based educational courses by scaffolding students' research trials, failures, and iterations to support students' perseverance.

This study describes an effective protocol for naturalistic archaeological weapons experiments that improves cross-validation with controlled experiments and allows testing of multiple hypotheses. Stone-tipped atlatl darts and arrows were launched by skilled users against fresh carcasses, with high-speed cameras and radar guns capturing details of ballistic performance, impacts to bone and stone armatures, and other variables. The results pertaining to terminal ballistics in soft tissues are presented, with implications for what made ancient hunting projectiles effective and can be observed archaeologically. Fine-grained knappable stones seem to produce sharper armatures that can dramatically improve penetration, and presumably, lethality. Two commonly used metrics by archaeologists for estimating armature efficacy, tip cross-sectional area (TCSA), and perimeter (TCSP), are not among the significant variables for capturing penetration depth in soft tissues. However, armatures with larger TCSAs tend to be fitted to larger shafts that carry more energy and penetrate more deeply, providing one method for predicting wounding potential. The variability within weapon systems means that isolating efficacy to individual variables, such as tip cross-sectional size of stone armatures, can lead to erroneous interpretations.

Statisticians and data scientists have been called upon to increase the impact they have through their collaborative projects. Statistics and data science practitioners and their educators can achieve and enable greater impact by learning how to create shared understanding with their collaborators as well as teaching this concept to their students, colleagues, and mentees. In this article, we explore and explain the concepts of common knowledge and shared understanding, which is the basis for action to accomplish greater impacts. We also explore related concepts of misunderstanding and doubtful understanding. We describe a process for teaching oneself and others how to create shared understanding. We conclude that incorporating the concept of shared understanding into one's practice of statistics or data science and following the steps described will result in having more impact on projects and throughout one's career.

Despite growing calls to develop data science students' ethical awareness and expand human-centered approaches to data science education, introductory courses in the field remain largely technical. A new interdisciplinary data science program aims to merge STEM and humanities perspectives starting at the very beginning of the data science curriculum. Existing literature suggests that humanities integration can make STEM courses more appealing to a wider range of students, including women and students of color, and enhance student learning of essential concepts and foundational reasoning skills, such as those collectively known as data acumen. Cultivating students' data acumen requires a more inclusive vision of how the knowledge and insights generated through computational methods and statistical analysis relates to other ways of knowing.

Pair-bond formation depends vitally on neuromodulatory signaling within the nucleus accumbens, but the neuronal dynamics underlying this behavior remain unclear. Using 1-photon in vivo Ca2+ imaging in monogamous prairie voles, we found that pair bonding does not elicit differences in overall nucleus accumbens Ca2+ activity. Instead, we identified distinct ensembles of neurons in this region that are recruited during approach to either a partner or a novel vole. The partner-approach neuronal ensemble increased in size following bond formation, and differences in the size of approach ensembles for partner and novel voles predict bond strength. In contrast, neurons comprising departure ensembles do not change over time and are not correlated with bond strength, indicating that ensemble plasticity is specific to partner approach. Furthermore, the neurons comprising partner and novel-approach ensembles are nonoverlapping while departure ensembles are more overlapping than chance, which may reflect another key feature of approach ensembles. We posit that the features of the partner-approach ensemble and its expansion upon bond formation potentially make it a key neuronal substrate associated with bond formation and maturation.

Data science is collaborative and its students should learn teamwork and collaboration. Yet it can be a challenge to fit the teaching of such skills into the data science curriculum. Team-Based Learning (TBL) is a pedagogical strategy that can help educators teach data science better by flipping the classroom to employ small-group collaborative learning to actively engage students in doing data science. A consequence of this teaching method is helping students achieve the workforce-relevant data science learning goals of effective communication, teamwork, and collaboration. We describe the essential elements of TBL: accountability structures and feedback mechanisms to support students collaborating within permanent teams on well-designed application exercises to do data science. The results of our case study of using TBL to teach a modern, introductory data science course indicate that the course effectively taught reproducible data science workflows, beginning R programming, and communication and collaboration. Students also reported much room for improvement in their learning of statistical thinking and advanced R concepts. To help the data science education community adopt this appealing pedagogical strategy, we outline steps for deciding on using TBL, preparing and planning for it, and overcoming potential pitfalls when using TBL to teach data science.

As inclusion becomes a growing standard for universities in the United States, data extracted from OCLC suggest that university libraries in the United States build their Spanish-language collections with books purchased primarily in Spain and Mexico. This overlooks presses from 19 other Spanish-speaking countries in the Americas. This research is concerned with the representation of books published in these Hispano-American countries and examines the holdings of 88 university libraries. The article discusses the drawbacks in neglecting purchase of literature published in Hispano-American countries and the benefits of changing collection practices into a more inclusive design, including recommendations for best practices.

Statistics and data science are especially collaborative disciplines that typically require practitioners to interact with many different people or groups. Consequently, interdisciplinary collaboration skills are part of the personal and professional skills essential for success as an applied statistician or data scientist. These skills are learnable and teachable, and learning and improving collaboration skills provides a way to enhance one's practice of statistics and data science. To help individuals learn these skills and organizations to teach them, we have developed a framework covering five essential components of statistical collaboration: Attitude, Structure, Content, Communication, and Relationship. We call this the ASCCR Frame. This framework can be incorporated into formal training programs in the classroom or on the job and can also be used by individuals through self-study. We show how this framework can be applied specifically to statisticians and data scientists to improve their collaboration skills and their interdisciplinary impact. We believe that the ASCCR Frame can help organize and stimulate research and teaching in interdisciplinary collaboration and call on individuals and organizations to begin generating evidence regarding its effectiveness.

Training complex skills is typically accomplished by means of a trainer or mediator who tailors instruction to the individual trainee. However, facilitated training is costly and labor intensive, and the use of a mediator is infeasible in remote or extreme environments. Imparting complex skills in applications like longduration human spaceflight, military field operations, or remote medicine may require automated training algorithms. Virtual reality (VR) is an effective, easily programmable, immersive training medium that has been used widely across fields. However, there remain open questions in the search for the most effective algorithms for guiding automated training progression. This study investigates the effects of responsiveness, personalization, and subtask independence on the efficacy of automated training algorithms in VR for training complex, operationally relevant tasks. Thirty-two subjects (16M/16F, 18-54 years) were trained to pilot and land a spacecraft on Mars within a VR simulation using four different automated training algorithms. Performance was assessed in a physical cockpit mock-up. We found that personalization results in faster skill acquisition on average when compared with a standardized progression built for a median subject (p = 0.0050). The standardized progression may be preferable when consistent results are desired across all subjects. Independence of the difficulty adjustments between subtasks may lead to increased skill acquisition, while lockstep in the progression of each subtask increases self-reported flow experience (p = 0.01), fluency (p = 0.02), and absorption (p = 0.01) on the Flow Short Scale. Data visualization suggests that highly responsive algorithms may lead to faster learning progressions and higher skill acquisition for some subjects. Improving transfer of skills from training to testing may require either high responsiveness or a standardized training progression. Optimizing the design of automated, individually adaptive algorithms around the training needs of a group may be useful to increase skill acquisition for complex operational tasks. [ABSTRACT FROM AUTHOR]

Data science is inherently collaborative as individuals across fields and sectors use quantitative data to answer relevant questions. As a result, there is a growing body of research regarding how to teach interdisciplinary collaboration skills. However, much of the work evaluating methods of teaching statistics and data science collaboration relies primarily on self-reflection data. Additionally, prior research lacks detailed methods for assessing the quality of collaboration skills. In this case study, we present a method for teaching statistics and data science collaboration, a framework for identifying elements of effective collaboration, and a comparative case study to evaluate the collaboration skills of both a team of students and an experienced collaborator on two components of effective data science collaboration: structuring a collaboration meeting and communicating with a domain expert. Results show that the students could facilitate meetings and communicate comparably well to the experienced collaborator, but that the experienced collaborator was better able to facilitate meetings and communicate to develop strong relationships, an important element for high-quality and long-term collaboration. Further work is needed to generalize these findings to a larger population, but these results begin to inform the field regarding effective ways to teach specific data science collaboration skills. [ABSTRACT FROM AUTHOR]

During spring 2020, emergency remote teaching became the norm for hundreds of higher education institutions in the United States due to the COVID-19 pandemic. Librarians were suddenly tasked with moving in-person services and resources online. For librarians with instruction responsibilities, this online mandate meant deciding between synchronous and asynchronous sessions, learning new technologies and tools for active learning, and vetting these same tools for security issues and ADA compliance. In an effort to understand our shared and unique experiences with emergency remote teaching, the authors surveyed 202 academic instruction librarians in order to answer the following questions: (1) What technology tools are academic librarians using to deliver content and engage student participation in emergency remote library sessions during COVID-19? (2) What do instruction librarians perceive as the strengths and weaknesses of these tools? (3) What digital literacy gaps are instruction librarians identifying right now that may prevent access to equitable information literacy instruction online? This study will deliver and discuss findings from the survey as well as make recommendations toward best practices for utilizing technology tools and assessing them for equity and student engagement., INTRODUCTION The worldwide COVID-19 pandemic has had important repercussions for university libraries. All library services, including information literacy instruction, moved online in a matter of days, creating a wave of [...]