This pilot study examined the feasibility of using worked examples as a mechanism to improve verbal explanations of fractions concepts among Grade 5 students with mathematics difficulties in a small-scale randomized controlled trial (RCT) before scaling up to a large-scale RCT. Students (N = 49) were randomly assigned to a business-as-usual (BAU) comparison group and to two variants of intervention. One intervention condition received both correct and incorrect worked example solutions, the other received correct solutions only. On a measure of verbal explanations, intervention students significantly outperformed students in BAU. Furthermore, students who received both correct and incorrect solutions significantly outperformed students who received correct solutions only on verbal explanations. On fractions proficiency outcomes, results were not significant between intervention and BAU or between the two intervention groups; however, positive findings demonstrate promise for using worked examples to elicit and develop students' verbal explanations of fractions concepts.

Collaboration is key to STEM, where multidisciplinary team research can solve complex problems. However, inequality in STEM fields hinders their full potential, due to persistent psychological barriers in underrepresented students’ experience. This paper documents teamwork in STEM and explores the transformative potential of computational modeling and generative AI in promoting STEM-team diversity and inclusion. Leveraging generative AI, this paper outlines two primary areas for advancing diversity, equity, and inclusion. First, formalizing collaboration assessment with inclusive analytics can capture fine-grained learner behavior. Second, adaptive, personalized AI systems can support diversity and inclusion in STEM teams. Four policy recommendations highlight AI's capacity: formalized collaborative skill assessment, inclusive analytics, funding for socio-cognitive research, human-AI teaming for inclusion training. Researchers, educators, and policymakers can build an equitable STEM ecosystem. This roadmap advances AI-enhanced collaboration, offering a vision for the future of STEM where diverse voices are actively encouraged and heard within collaborative scientific endeavors. © The Author(s) 2024.

Argumentation, a key scientific practice presented in the Framework for K-12 Science Education, requires students to construct and critique arguments, but timely evaluation of arguments in large-scale classrooms is challenging. Recent work has shown the potential of automated scoring systems for open response assessments, leveraging machine learning (ML) and artificial intelligence (AI) to aid the scoring of written arguments in complex assessments. Moreover, research has amplified that the features (i.e., complexity, diversity, and structure) of assessment construct are critical to ML scoring accuracy, yet how the assessment construct may be associated with machine scoring accuracy remains unknown. This study investigated how the features associated with the assessment construct of a scientific argumentation assessment item affected machine scoring performance. Specifically, we conceptualized the construct in three dimensions: complexity, diversity, and structure. We employed human experts to code characteristics of the assessment tasks and score middle school student responses to 17 argumentation tasks aligned to three levels of a validated learning progression of scientific argumentation. We randomly selected 361 responses to use as training sets to build machine-learning scoring models for each item. The scoring models yielded a range of agreements with human consensus scores, measured by Cohen’s kappa (mean = 0.60; range 0.38 − 0.89), indicating good to almost perfect performance. We found that higher levels of Complexity and Diversity of the assessment task were associated with decreased model performance, similarly the relationship between levels of Structure and model performance showed a somewhat negative linear trend. These findings highlight the importance of considering these construct characteristics when developing ML models for scoring assessments, particularly for higher complexity items and multidimensional assessments. © The Author(s) 2023.

Block construction is ubiquitous in early development, yet is surprisingly complex, involving stepby-step sequenced actions to create specific structures. Here, we use novel analytic methods to characterize these action sequences in detail, including which individual parts of the structure ('states') are built and how these structures are combined, creating a fully specified build path towards the final structure. We find that, like adults tested in a previous study, 4- to 8-year-olds build by creating a small subset of possible individual states and full build paths, and that they prioritize building layer-by-layer. The individual states and build paths that children produce are strikingly similar to those of adults, resulting in structures that are more stable than other possible (but not attested) states and paths. Our approach serves as a lens into the cognitive processes underlying block building and suggests that children's building is guided by significant cognitive constraints consistent with computational thinking.

Prior literature has documented the importance of faculty advisors in the doctoral student socialization process, with a few studies describing negative advising relationships characterized by disengagement, disinterest, unsupportive behavior, and interpersonal conflict. We extend this research by exploring how negative advising relationships emerge and develop over time. Examining longitudinal interviews over four years with 15 doctoral students in biological sciences in the USA who experienced negative relationships with their advisors, we illuminate how negative advising relationships unfold over the course of graduate studies. We find two primary patterns in challenging relationships: some students show a gradual decline in relationship health over time, while others point to a single event altering their relationship trajectory. We also identify specific factors that shape each of these negative relationship types. By revealing the different social processes that underlie the emergence of negative advising relationships, our findings provide a valuable contribution to understanding the complex social landscape of doctoral education. The findings further the dialogue on how faculty advisors can craft successful pathways through graduate education, thereby supporting the academic and professional success of doctoral students. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

A major focus of innovation in higher education today is to improve faculty teaching, especially their focus on students' career readiness and acquisition of workplace-relevant communication and teamwork competencies (i.e., transferable skills). Some contend that such instruction is best achieved through hiring faculty with prior work experience in industry, where the "culture" is preferable to academia where practical skills and career guidance are undervalued. However, little research exists on the topic and in this study we draw on person-centered views of culture to conceptualize industry experience as a form of cultural knowledge (i.e., cultural scripts) that can travel with a person (or not) over time and space. Using a mixed methods design where we gathered survey (n = 1,140) and interview (n = 89) data from STEMM faculty, we used thematic and HLM techniques to explore the relationships among industry experience, various situational factors, and transferable skills instruction. Results show that while most had industry experience (76.2%), transferable skills are rarely emphasized, a variety of individual (e.g., race) and institutional (e.g., discipline) factors are associated with transferable skills instruction, and that industry experience provides both generalized and specific cultural scripts for career- and skills-oriented teaching. We conclude that instead of promoting skills-focused instructional innovations via hiring policies that assume the value of one institutional culture over another, it is more useful and respectful (to faculty) to teach industry-based cultural knowledge via faculty development programming in a way similar to work-integrated learning (WIL) and communication in the disciplines (CID) initiatives.

Do children think of genetic inheritance as deterministic or probabilistic? In two novel tasks, children viewed the eye colors of animal parents and judged and selected possible phenotypes of offspring. Across three studies (N = 353, 162 girls, 172 boys, 2 non-binary; 17 did not report gender) with predominantly White U.S. participants collected in 2019-2021, 4- to 12-year-old children showed a probabilistic understanding of genetic inheritance, and they accepted and expected variability in the genetic inheritance of eye color. Children did not show a mother bias but they did show two novel biases: perceptual similarity and sex-matching. These results held for unfamiliar animals and several physical traits (e.g., eye color, ear size, and fin type), and persisted after a lesson.

BackgroundLearning assistants (LAs) in undergraduate STEM lectures facilitate discussions between students in small groups. In this research study, we investigate the impact of LA facilitation on student learning as it occurs during LA-student interactions. To do so, our work builds on two sociocultural frameworks focused on LA facilitation and student in-the-moment learning. We conceptualize LA facilitation as either authoritative if it centers one perspective or dialogic if it centers multiple perspectives. Student in-the-moment learning is understood as the progression of student needs and the filling of those needs with LA and student ideas.ResultsOur analysis of 78 video recordings of LA-student interactions from 37 different chemistry and physics LAs revealed that LA facilitation had four major impacts on student in-the-moment learning: increasing grappling, reaching closure, sharing ideas and reasoning, and revisiting an earlier need. Rather than these impacts differing upon the use of authoritative and dialogic facilitation, all four impacts sometimes resulted from authoritative and sometimes from dialogic facilitation. However, authoritative facilitation was more often correlated with LA-centered manifestation of these impacts, while dialogic facilitation was more often correlated with student-centered manifestation. In addition to these conceptual impacts, we also found five socioemotional impacts: less participation, dominance continues, fostering participation, students choose not to participate, and lighthearted conversation. LAs added socioemotional components to both authoritative and dialogic facilitation, and actions aimed at bringing more students into the conversation indeed had this impact, while actions addressing specific students often continued to privilege the participation of the same students.ConclusionOur study expands theory on authoritativeness and dialogicity as it empirically validates that authoritative facilitation is more often correlated with LA-centered learning and dialogic facilitation is more often correlated with student-centered learning. Further, our work is the first to explore the socioemotional impact of LAs in the moment of interaction. Our findings can be used in LA trainings to teach LAs how to intentionally use authoritative and dialogic facilitation, how to incorporate socioemotional components to their facilitation, and how to adjust their practice to align with learning goals for students in their context.

Teachers' knowledge of the subject matter is considered an important component of their expertise in teaching mathematics. Yet how teachers' understanding of one content area is related to other content areas has not been investigated in depth. We explored this question by investigating teachers' knowledge of two theoretically related areas: (1) fractions and (2) ratios and proportional relationships. We also investigated the extent to which teachers' educational backgrounds are related to their understanding of these concepts. Based on the results obtained from structural equation modeling and path analysis, we found that teachers' knowledge of these two concepts is highly interdependent, forming a single construct. Furthermore, holding a credential in teaching mathematics, the route teachers took to enter teaching, and their undergraduate majors were associated with their knowledge of these concepts. This study illustrates the importance of attending to the theoretical relationships among different content areas when assessing teachers' subject matter knowledge and provides initial evidence that teachers' subject matter knowledge may be unidimensional for theoretically related domains.

Having a robust understanding of viruses is critical for children to understand the COVID-19 pandemic and the protective measures recommended to promote their safety. However, viral transmission is not part of current educational standards in the United States, so children likely must learn about it through informal means, such as media and conversations with caregivers-contexts that often animate and anthropomorphize viruses. In this registered report, we developed an at-home educational intervention to teach children about viruses by creating a picture storybook about COVID-19. We tested children ages 5-8 on their understanding of viruses before and after reading the book at home with their caregivers. Critically, we manipulated which of three books children received: realistic (that detailed the microscopic processes involved in COVID-19 transmission), anthropomorphic (that depicted all the same information but using anthropomorphic language and images for COVID-19), or control (that only showed the visible aspects of illness). Bayesian analyses revealed that children learned about COVID-19 by reading the picture books with their parents at home and extended this knowledge to other viruses and that learning was substantially higher for those reading the realistic and anthropomorphic books than the control books. We also found that learning did not differ as a function of whether the book used anthropomorphic depictions or not although children reading the anthropomorphic book reported being less afraid of viruses. Altogether, these results demonstrate that carefully constructed picture books can help children learn about complex scientific topics at home.