Human behavior is fundamentally generative: People create pictures, write stories, compose music, and engage in conversation. Traditional approaches in psychology and cognitive science have not focused on this open-endedness, instead favoring more constrained task settings that admit a limited set of outcomes. Although those approaches have been fruitful, new approaches might be needed to develop a unified understanding of the generative, open-ended behaviors that are so emblematic of human cognition. This article demonstrates the value of generative behaviors as targets for cognitive modeling by providing rich behavioral data that reveal how multiple cognitive processes coordinate. Drawing production serves as a case study illustrating this approach, showing how perception, memory, social inference, and motor control coordinate flexibly on the basis of communicative context. Recent advances in generative artificial intelligence offer both new tools for modeling open-ended human behavior and new comparative targets for understanding similarities and differences between human and machine intelligence. However, applying these tools effectively might require new experimental paradigms, larger data sets, and careful consideration of what mechanistic correspondence between models and human cognition is necessary for scientific progress. Embracing the open-ended nature of human thought and behavior poses methodological challenges but offers a promising path toward understanding the most distinctive aspects of human intelligence.

Gender parity in academia remains elusive, even in fields, such as biological sciences, that award approximately half of their PhDs to women. Based on a sample of graduate students in biological sciences followed over 5 years during their PhD programs, we find that women publish in journals with lower impact factors and are less likely to publish in top journals. Notably, gender inequality in journal impact factors holds only for total publications but not for first-authored publications, which implies that differential opportunities for collaborations leading to high-impact publications may be one factor hindering gender equity.

Despite a tremendous increase in the use of video for conducting research in classrooms as well as preparing and evaluating teachers, there remain notable challenges to using classroom videos at scale, including time and financial costs. Recent advances in artificial intelligence could make the process of analyzing, scoring, and cataloguing videos more efficient. These advances include natural language processing, automated speech recognition, and deep neural networks. To train artificial intelligence to accurately classify activities in classroom videos, humans must first annotate a set of videos in a consistent way. This paper describes our investigation of the degree of inter-annotator reliability regarding identification of and duration of activities among annotators with and without experience analyzing classroom videos. Validity of human annotations is crucial for research involving temporal analysis within classroom video research. The study reported here represents an important step towards applying methods developed in other fields to validate temporal analytics within learning analytics research for classifying time- and event-based activities in classroom videos.

Abstract Background STEM minority participation programs have been widely implemented in higher education with the goal of diversifying the global STEM workforce. Informed by research highlighting the potential of targeted exploration of STEM roles and reflection on the self in relation to STEM (identity exploration), this work examines how engagement in a government funded STEM minority participation program shaped these processes in current students and program alumni. Results Epistemic network analysis (ENA) was used to visualize conceptual connections between identity themes that emerged from interviews with present and past program participants. Network models were developed for current students and alumni for cross-group comparisons. Differences were found in how participants at different stages of their careers enact and describe their identity exploration processes. Summative network models highlighted how students discussed action-taking (sometimes through participation in STEM minority program initiatives) as they explored less-certain possible future STEM roles, while alumni integrated more diverse and holistic facets of their identities when conceptualizing their futures. To close the interpretive loop, a qualitative interpretation of interview discourse was used to give context to network patterns. Conclusions Results highlight the differences between novices' and professionals' conceptualizations of their future selves and illustrate how minoritized individuals describe their long-term patterns of identity exploration related to STEM majors and careers. Implications for future STEM identity research and practice, including higher education programming as a tool to support students' STEM identity exploration processes, are discussed.

Funding of research is critically important because it affects the flow of new, doctorally qualified scientists into the workforce. This paper provides new insights into how survey data can be combined with administrative records to examine the ways in which funding affects workforce decisions. We show that NSF supports more graduate students per dollar spent than other federal agencies. Not surprisingly, NIH heavily supports biology, health, and psychology PhDs, while NSF heavily supports PhDs in engineering, the physical sciences, mathematics, and computer science.

Conceptual knowledge is only one part of what students need to learn to solve problems. Knowing how to reason through a problem is also an important skill. In this paper, we study the effects of a computer-based training on students' reasoning on introductory physics problems. Participants solved three sets of conceptual problems, each of them containing three training problems with solutions followed by one near transfer problem and one far transfer problem. We analyzed students' verbal answers to elucidate the reasoning resources that they activated to construct the different explanations to the problems. We found that the students changed their reasoning when presented with solutions to training problems and later posed a transfer problem. Our results provide insights into students' activation of their resources and the procedure they used to construct their reasoning in response to the training problems.