Insecure Attachment,Maladaptive Perfectionism,Self-Esteem,Depression, and Bulimic Behaviors for College Women: A Cross-Cultural Comparison

International Journal for the Advancement of Counselling - This cross-cultural comparison study examined the direct and indirect effects (via several cognitive-affective pathways) of insecure...     

Factors impacting the rapid transition of anatomy curricula to an online environment in response to Covid-19

Due to the Covid-19 pandemic, many academic institutions had to rapidly transition education to a remote online environment. While a hurdle for most educators, this transition posed an even greater challenge for anatomy educators, many of whom were forced to depart from the traditional cadaver-based laboratory to a virtual format. Recent publications have discussed the rapid transition to online formats necessitated by Covid-19 and the accompanying difficulties, but none have identified specific factors that influenced the difficulty of this transition. Anatomy educators were surveyed to examine how this transition was accomplished and perceived. Of the 165 educators who responded, the majority utilized cadaver-based laboratory instruction. Educators felt that transitioning the laboratory portion of their courses was significantly more difficult and required more time than converting lecture materials. Factors that impacted the difficulty of the transition included a number of pedagogical aspects of the pre-Covid-19 curricula, including the delivery format of prior content, availability of pre-existing electronic materials, and the laboratory technique previously used. Additionally, the length of time an educator had been teaching prior to Covid-19 impacted their perception of difficulty, with newer and more senior educators finding this much more challenging than mid-tenure educators. Ease of transition may be related to previous exposure to curricular reform, experience with multiple anatomy pedagogies, and educator adaptability. While not surprising that converting a cadaver-based laboratory to an online format was challenging, knowledge of the alignment of this difficulty with prior educator pedagogy can help guide future innovations to anatomy education.     

Social goals as predictors of children's in vivo emotional responses to social challenges

This study examined the role of social goals in shaping children's in vivo emotional responses during a challenging dyadic peer interaction. In all, 132 children (M_age = 9.46 years, SD = 0.33; 50% girls; 72% White) participated in a dyadic social challenge (conflict-of-interest situation) and reported their social goals and emotions during the task, and observers coded child emotions and dyad negativity. Mastery goals predicted more positive emotions unless interactions were highly negative. Performance-avoidance goals predicted more negative emotions, particularly in the context of negative interactions and disappointing outcomes. Performance-approach goals predicted less negative displayed emotions but more negative self-reported emotions. Findings provide novel insights into how context-specific social goals contribute to affective social competence during peer interactions.     

Cheating among elementary school children: A machine learning approach

Academic cheating is common, but little is known about its early emergence. It was examined among Chinese second to sixth graders (N = 2094; 53% boys, collected between 2018 and 2019) using a machine learning approach. Overall, 25.74% reported having cheated, which was predicted by the best machine learning algorithm (Random Forest) at a mean accuracy of 81.43%. Cheating was most strongly predicted by children's beliefs about the acceptability of cheating and the observed prevalence and frequency of peer cheating at school. These findings provide important insights about the early development of academic cheating, and how to promote academic integrity and limit cheating before it becomes entrenched. The present research demonstrates that machine learning can be effectively used to analyze developmental data.     

Developmental trajectories of behavioral inhibition from infancy to age seven: The role of genetic and environmental risk for psychopathology

The present study leveraged data from a longitudinal adoption study of 361 families recruited between 2003 and 2010 in the United States. We investigated how psychopathology symptoms in birth parents (BP; M_age = 24.1 years; 50.5–62.9% completed high school) and adoptive parents (AP; M_age = 37.8 years; 80.9% completed college; 94% mother–father couples) influenced children's behavioral inhibition (BI) trajectories. We used latent growth models of observed BI at 18 and 27 months, and 4.5 and 7 years in a sample of adopted children (Female = 42%, White = 57%, Black = 11%, Multi-racial = 21%, Latinx = 9%). BI generally decreased over time, yet there was substantial variability in these trajectories. Neither BP nor AP psychopathology symptoms independently predicted systematic differences in BI trajectories. Instead, we found that AP internalizing symptoms moderated the effects of BP psychopathology on trajectories of BI, indicating a gene by environment interaction.     

Code-switching 101: Black women counselor educators’ personal and professional identity development

The perceptions of personal and professional identity development from four Black women counselor educators were examined through a collaborative autoethnography methodology. Themes identified include (a) intentionality of code-switching, (b) inextricable identities, (c) fight for and with authenticity, and (d) mutual development of personal and professional identities.     

The 2011 Benjamin Franklin Medal in computer and cognitive science presented to John R. Anderson

John R. Anderson is an international leader in the computational representation and simulation of human cognition. He is credited as having developed the first “cognitive architecture”—a computational framework for specifying both the abilities and limitations of human behavior. Anderson has also made fundamental contributions in applying cognitive architectures to practical problems, most notably in the development of intelligent tutoring systems: computer-based tutors that continually infer the cognitive state of the student, striving to determine what the student knows and does not know, and targeting further instruction accordingly. For these achievements, Anderson was awarded the 2011 Benjamin Franklin Medal in Computer and Cognitive Science.