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A Guided Ungrading Example for Independent Research Projects in an Upper-Division Ecosystem Ecology STEM Course
No full textUngrading is a pedagogical approach that, in some iterations, emphasizes formative feedback and student self-assessment over traditional point-based grading. While increasingly popular in the humanities and social sciences, ungrading remains uncommon in STEM courses, particularly those that are content-intensive. This article presents a structured example of ungrading implemented in an upper-division ecosystem ecology course through a 7-week independent research project. Students used public or previously collected ecological data to investigate a scientific question, culminating in a short-format, manuscript-style research paper and a 10 min oral presentation. The project was scaffolded with multiple checkpoints, including structured reflections, peer review, and instructor feedback. Students were invited to define personal growth goals, determine how they wished to be evaluated, and reflect on their progress throughout the project. While the rest of the course retained traditionally graded assessments, this ungraded project accounted for 40% of the final grade. Students reported high levels of engagement and ownership, with ~86% of students opting to receive both an instructor grade and self-grade, and a further ~9% of students opting to entirely self-grade, with only ~5% of students preferring a traditional grading schema. The model was particularly effective in a small, seminar-style course with students specializing in environmental science and may require adaptation for larger courses, lower-division settings, or to accommodate generative artificial intelligence usage guidelines. This case study offers a replicable framework for integrating ungrading into STEM curricula and highlights the importance of in-class support and iterative feedback when using an ungrading approach. By emphasizing student agency and identity “as a scientist,” this approach aligns with the goals of authentic research experiences and provides a flexible alternative to traditional grading in content-heavy STEM courses that could include independent projects
Quantifying Single, Compound and Cascading Climate Extremes: Implications for Agricultural Resilience in California
No full textAs climate change intensifies, extreme weather increasingly threatens California’s Central Valley (CV), a vital agricultural region exposed to rising risks from heatwaves (HW), droughts (DR), and compound extremes. These events disrupt crop productivity and broader processes like water demand, pest dynamics, and soil stability, posing systemic risks. This study examines the spatiotemporal dynamics of HW, coldwaves (CW), DR, excessive rainfall (ER), and their compound (e.g., HWDR) and cascading forms from 1951 to 2025, using NOAA nClimGrid-Daily data. We assessed trends in frequency, intensity, and duration over long-term (1951–2025) and mid-term (1981–2025) periods. Results show increasing HW and DR in the southern CV, with 35 % and 60 % of the region exhibiting strong upward trends in HW frequency and intensity (mid-term), respectively, linked to warming (+0.234 °C/decade) and precipitation variability. Northern CV, especially Tehama to Sacramento, shows greater vulnerability to ER, CWDR, and CWER, reflecting heightened climate variability. Hotspots of extremes are shifting northward, with HWDR migrating at 20.94 km/decade. June–July HW and DR peaks coincide with critical crop stages, while February–March rainfall supports early vegetation growth. Almonds showed the highest vulnerability to drought, with severe kNDVI anomaly drops in 2014, while grapes responded moderately to heat and benefited from early rain, reaching a kNDVI anomaly peak of 0.03 in 2005. Intra-annual analysis reveals that 2014′s extreme HW and DR, combined with low rainfall and groundwater decline, intensified stress during hull split and fruit set. Targeted strategies like precision irrigation and drought-tolerant cultivars are essential to enhance agricultural resilience
Phi Beta Kappa, Psi of California Chapter, Induction Ceremony 2025
No full texthttps://digitalcommons.chapman.edu/pbk_induction_ceremony_2025/1001/thumbnail.jp
Phi Beta Kappa, Psi of California Chapter, Induction Ceremony 2025
No full texthttps://digitalcommons.chapman.edu/pbk_induction_ceremony_2025/1029/thumbnail.jp
Phi Beta Kappa, Psi of California Chapter, Induction Ceremony 2025
No full texthttps://digitalcommons.chapman.edu/pbk_induction_ceremony_2025/1042/thumbnail.jp
Ectopic Adipose Tissue in Subsistence Populations with Minimal Coronary Disease, Large Left Atria, and Very Low Rates of Atrial Fibrillation
No full textBackground Greater deposits of epicardial adipose tissue are associated with atrial fibrillation and coronary disease, but have not been studied in subsistence populations. Methods We performed CT imaging to measure coronary artery and thoracic aortic calcium (CAC, TAC), epicardial fat thickness (EFT), liver density, and left atrial (LA) anteroposterior diameter and, using a deep learning-enabled software program, epicardial and thoracic fat volume (EFV, TFV), in two remote Amerindian subsistence populations with minimal coronary artery calcification and virtually no atrial fibrillation. We compared 893 adult Tsimane (mean age 58.3±10.5 y, 51.6% male), 440 Moseten (55.9±10.4 y, 53.6% male) to 955 U.S. (56.8±10.8 y, 51.6% male) subjects. Results Tsimane and Moseten had 43%-52% lower EFT, EFV, TFV, and 48-92% less CAC and TAC, respectively than the U.S. cohort. Mean liver measurements were 14-22% denser and LA diameters 10-14% larger (≈ 40% larger by volume). For EFV, Tsimane, Moseten, and U.S. cohorts averaged 54.2±25.6, 60.3±35.1, and 106±53.5 cc, respectively (p\u3c 0.05 for all comparisons). EFV remained significantly smaller after adjustment for age, BMI, and other characteristics. For all CT metrics, the more acculturated Moseten measures were intermediate between Tsimane and the U.S. cohort. Conclusions Tsimane mean EFV was the lowest of any population ever reported in the literature, achieving a new population standard. The low levels of EFV in the Tsimane and Moseten add to the body of evidence linking ectopic fat and atherosclerosis and further confirm (in the negative) the association, and likely causative role, of epicardial fat and atrial fibrillation
Revisiting Kin and Ethnic Favoritism in the Bribery Experiment
No full textWe report a conceptual replication of Akbari et al. (2020), who study the impact of co-ethnicity and kinship on behavior in an experimental “bribery game”. In the game, player A can offer a bribe to B, who can help A by inefficiently transferring resources from passive third-party C. We replicate the finding that by varying the relatedness of A, B and C, we can substantially modulate the willingness of A to offer the bribe and the willingness of B to reciprocate the bribe by harming C. The findings are consistent with theories of kin altruism and ethnic favoritism
Multidimensional Arbitration
No full textMost analyses of arbitration consider disputes over a single dimensional issue; however, in many situations more than one issue can be in dispute. In a labor context, the parties may disagree on the hourly wage, number of paid holidays, and fringe benefits as well as other conditions of employment. In addition to conventional arbitration (CA), two variants of multidimensional final-offer arbitration (FOA) are employed in practice. Under one version of FOA, the arbitrator must pick the entire package submitted by one of the disputants. Under the other version of FOA, the arbitrator can select elements from each disputant’s final offer on an issue-by-issue basis. Theoretically, we show that when disputants have different relative values between dimensions, package FOA leads to expected welfare improvements for both parties as compared to CA and issue by issue FOA. In a controlled laboratory experiment, we examine how asymmetry in relative values between issues impacts proposals and welfare under both package FOA and issue-by-issue FOA. For package FOA, we observe that asymmetry leads to proposals that are more aggressive in the more valuable dimension and more generous in the less valuable dimension, as predicted by theory. As a result, payoffs are higher under package FOA than issue-by-issue FOA or CA, even though observed treatment effects are not as large as predicted
Developing an Intuitive Gesture-Based Interface and AI for Precise Object Positioning in Extended Reality
No full textHuman-object interactions, such as translation and rotation, are fundamental to extended reality (XR) applications, which blend physical and virtual environments. XR is increasingly used in immersive design, virtual prototyping, and digital twins, where high-precision object manipulation is crucial. Gesture-based 3D interaction offers a more intuitive and immersive experience than keyboard-and-mouse interfaces but lacks precision. This project introduces Context AI for Object Positioning (CAOP), a gesture-based interface that leverages artificial intelligence (AI) to infer user intent, integrate high-level design principles, and enable precise object manipulation. The system has been integrated into VRMoVi, a virtual reality (VR) environment to support immersive, real-time 3D interaction. Automatically generating aesthetically pleasing and functional layouts for a room in VR. CAOP uses a Markov Chain Monte Carlo model to iteratively refine room layouts according to 11 high-level interior design principles, such as alignment, symmetry, and balance. The layout is optimized by minimizing a weighted cost function. Each term evaluates how well the layout adheres to a specific design rule. A lower cost indicates better overall compliance with the combined guidelines. The weights of each design principle can be adjusted to prioritize different layout aesthetics. The algorithm iteratively adjusts each object’s position and rotation, then recalculates the layout cost after each one, keeping the configuration with the lowest cost. CAOP was developed in C# within Unity to support real-time, interactive 3D layout manipulation. By combining AI-driven layout synthesis with intuitive gesture-based interaction, CAOP allows users to rapidly transform messy furniture arrangements into organized, visually appealing layouts while maintaining precise object control. After the system organizes the furniture, the gesture recognition, which is currently part of VRMoVi, will be integrated so that users can adjust furniture with hand gestures, and the optimal layout will be recalculated