搜索结果：New directions for student leadership

Over the years, the concept of leadership has experienced a paradigm shift - from solitary leader (centralized leadership) to de-centralized leadership or distributed leadership. This paper explores the idea that centralized leadership, as earlier suggested, negatively impacts team performance. I applied the hypothesis to cricket, a sport in which leaders play an important role in team's success. I generated batting partnership network and evaluated the central-most player in the team, applying tools of social network analysis. Analyzing 3420 matches in one day international cricket and 1979 Test matches involving 10 teams, I examined the impact of centralized leadership in outcome of a contest. I observed that the odds for winning a one day international match under centralized leadership is 30% higher than the odds for winning under de-centralized leadership. In both forms of cricket (Test and one day international ), I failed to find evidence that distributed leadership is associated with higher team performance. These results suggest important implications for cricket administrators in development and management of working teams.

Artificial Intelligence is currently and rapidly changing the way organizations and businesses operate. Ethical leadership has become significantly important since organizations and businesses across various sectors are evolving with AI. Organizations and businesses may be facing several challenges and potential opportunities when using AI. Ethical leadership plays a central role in guiding organizations in facing those challenges and maximizing on those opportunities. This article explores the essence of ethical leadership in the age of AI, starting with a simplified introduction of ethical leadership and AI, then dives into an understanding of ethical leadership, its characteristics and importance, the ethical challenges AI causes including bias in AI algorithms. The opportunities for ethical leadership in the age of AI answers the question: What actionable strategies can leaders employ to address the challenges and leverage opportunities? and describes the benefits for organizations through these opportunities. A proposed framework for ethical leadership is presented in this article, incorporating the core components: fairness, transparency, sustainability etc. Through the impor

Feedback is a critical aspect of improvement. Unfortunately, when there is a lot of feedback from multiple sources, it can be difficult to distill the information into actionable insights. Consider student evaluations of teaching (SETs), which are important sources of feedback for educators. They can give instructors insights into what worked during a semester. A collection of SETs can also be useful to administrators as signals for courses or entire programs. However, on a large scale as in high-enrollment courses or administrative records over several years, the volume of SETs can render them difficult to analyze. In this paper, we discuss a novel method for analyzing SETs using natural language processing (NLP) and large language models (LLMs). We demonstrate the method by applying it to a corpus of 5,000 SETs from a large public university. We show that the method can be used to extract, embed, cluster, and summarize the SETs to identify the themes they express. More generally, this work illustrates how to use the combination of NLP techniques and LLMs to generate a codebook for SETs. We conclude by discussing the implications of this method for analyzing SETs and other types o

It is clear, from the major press coverage that Virtual Reality (VR) development is garnering, that there is a huge amount of development interest in VR across multiple industries, including video streaming, gaming and simulated learning. Even though PC, web, and mobile are still the top platforms for software development, it is important for university computer science (CS) programs to expose students to VR as a development platform. Additionally, it is important for CS students to learn how to learn about new technologies, since change is constant in the CS field. CS curriculum changes happen much slower than the pace of technology adoption. As new technologies are introduced, CS faculty and students often learn together, especially in smaller CS programs. This paper describes how student-led VR projects are used, across the CS curriculum, as basic CS concepts are covered. The student-led VR projects are engaging, and promote learning and creativity. Additionally, each student project inspires more students to try their hand at VR development as well.

Student mental health is a sensitive issue that necessitates special attention. A primary concern is the student-to-counselor ratio, which surpasses the recommended standard of 250:1 in most universities. This imbalance results in extended waiting periods for in-person consultations, which cause suboptimal treatment. Significant efforts have been directed toward developing mental health dialogue systems utilizing the existing open-source mental health-related datasets. However, currently available datasets either discuss general topics or various strategies that may not be viable for direct application due to numerous ethical constraints inherent in this research domain. To address this issue, this paper introduces a specialized mental health dataset that emphasizes the active listening strategy employed in conversation for counseling, also named as ConvCounsel. This dataset comprises both speech and text data, which can facilitate the development of a reliable pipeline for mental health dialogue systems. To demonstrate the utility of the proposed dataset, this paper also presents the NYCUKA, a spoken mental health dialogue system that is designed by using the ConvCounsel dataset.

Leadership in social groups is often a dynamic characteristic that emerges from interactions and opinion exchange. Empirical evidence suggests that individuals with strong opinions tend to gain influence, at the same time maintaining alignment with the social context is crucial for sustained leadership. Motivated by the social psychology literature that supports these empirical observations, we propose a novel dynamical system in which opinions and leadership co-evolve within a social network. Our model extends the Friedkin-Johnsen framework by making susceptibility to peer influence time-dependent, turning it into the leadership variable. Leadership strengthens when an agent holds strong yet socially aligned opinions, and declines when such alignment is lost, capturing the trade-off between conviction and social acceptance. After illustrating the emergent behavior of this complex system, we formally analyze the coupled dynamics, establishing sufficient conditions for convergence to a non-trivial equilibrium, and examining two time-scale separation regimes reflecting scenarios where opinion and leadership evolve at different speeds.

Academic leadership is essential for research innovation and impact. Until now, there has been no dedicated measure of leadership by bibliometrics. Popular bibliometric indices are mainly based on academic output, such as the journal impact factor and the number of citations. Here we develop an academic leadership index based on readily available bibliometric data that is sensitive to not only academic output but also research efficiency. Our leadership index was tested in two studies on peer-reviewed journal papers by extramurally-funded principal investigators in the field of life sciences from China and the USA, respectively. The leadership performance of these principal investigators was quantified and compared relative to university rank and other factors. As a validation measure, we show that the highest average leadership index was achieved by principal investigators at top national universities in both countries. More interestingly, our results also indicate that on an individual basis, strong leadership and high efficiency are not necessarily associated with those at top-tier universities nor with the most funding. This leadership index may become the basis of a comprehens

Effectively predicting intent and behavior requires inferring leadership in multi-agent interactions. Dynamic games provide an expressive theoretical framework for modeling these interactions. Employing this framework, we propose a novel method to infer the leader in a two-agent game by observing the agents' behavior in complex, long-horizon interactions. We make two contributions. First, we introduce an iterative algorithm that solves dynamic two-agent Stackelberg games with nonlinear dynamics and nonquadratic costs, and demonstrate that it consistently converges. Second, we propose the Stackelberg Leadership Filter (SLF), an online method for identifying the leading agent in interactive scenarios based on observations of the game interactions. We validate the leadership filter's efficacy on simulated driving scenarios to demonstrate that the SLF can draw conclusions about leadership that match right-of-way expectations.

Since the COVID-19 pandemic, online lectures have spread rapidly and many students are satisfied with them. However, one challenge remains the loss of concentration due to the lack of students' copresence. Our previous work suggests that presenting 3D characters with appropriate actions has the potential to improve concentration in online lectures. Nevertheless, an effective combination of actions has not yet been identified. In this study, we developed a lecture watching system that presents a 3D virtual classroom using a naked-eye 3D display. The system includes student characters that show copresence with various actions such as nodding, notetaking, and sleeping. An evaluation experiment was conducted with two conditions; (1) student characters perform only positive actions and (2) both positive and negative actions. The results, analyzed using posture and notetaking behavior as key indicators, suggest that the system can help to maintain concentration when the student characters perform both positive and negative actions, rather than only positive ones. These findings provide promising strategies for maintaining student focus in on-demand lectures and contribute to the developm

With the growing popularity of blockchains, modern chained BFT protocols combining chaining and leader rotation to obtain better efficiency and leadership democracy have received increasing interest. Although the efficiency provisions of chained BFT protocols have been thoroughly analyzed, the leadership democracy has received little attention in prior work. In this paper, we scrutinize the leadership democracy of four representative chained BFT protocols, especially under attack. To this end, we propose a unified framework with two evaluation metrics, i.e., chain quality and censorship resilience, and quantitatively analyze chosen protocols through the Markov Decision Process (MDP). With this framework, we further examine the impact of two key components, i.e., voting pattern and leader rotation on leadership democracy. Our results indicate that leader rotation is not enough to provide the leadership democracy guarantee; an adversary could utilize the design, e.g., voting pattern, to deteriorate the leadership democracy significantly. Based on the analysis results, we propose customized countermeasures for three evaluated protocols to improve their leadership democracy with only s

The vision of 2030STEM is to address systemic barriers in institutional structures and funding mechanisms required to achieve full inclusion in Science, Technology, Engineering, and Mathematics (STEM) and accelerate leadership pathways for individuals from underrepresented populations across STEM sectors. 2030STEM takes a systems-level approach to create a community of practice that can test, learn and promote programs and policies that affirm and value cultural identities in STEM. To achieve parity and full representation in the STEM workforce, a variety of changes are needed across academia and STEM professional industries (e.g., business, finance, biotech, government) to accelerate underrepresented groups into positions of leadership throughout the STEM ecosystem. Through a series of subject matter interviews, roundtables, and curated analysis four major themes have surfaced, which, if implemented, could exponentially accelerate the creation of critical pathways to leadership, break down pre-existing barriers and biases, intentionally elevate the voices, value, and research of underrepresented groups in STEM, and implement new structural strategies at scale. This white paper pro

Scientific machine learning (SciML) methods such as physics-informed neural networks (PINNs) are used to estimate parameters of interest from governing equations and small quantities of data. However, there has been little work in assessing how well PINNs perform for inverse problems across wide ranges of governing equations across the mathematical sciences. We present a new and challenging benchmark problem for inverse PINNs based on a parametric sweep of the 2D Burgers' equation with rotational flow. We show that a novel strategy that alternates between first- and second-order optimization proves superior to typical first-order strategies for estimating parameters. In addition, we propose a novel data-driven method to characterize PINN effectiveness in the inverse setting. PINNs' physics-informed regularization enables them to leverage small quantities of data more efficiently than the data-driven baseline. However, both PINNs and the baseline can fail to recover parameters for highly inviscid flows, motivating the need for further development of PINN methods.

Leadership in social groups emerges dynamically through interaction and opinion exchange. Empirical evidence indicates that individuals expressing strong opinions tend to gain influence, while sustained leadership critically depends on maintaining alignment with the surrounding social context. Motivated by these observations, we introduce a coupled dynamical model describing the simultaneous evolution of opinions and leadership in a networked population. Extending the Friedkin-Johnsen framework, we represent leadership as a time-varying susceptibility to social influence, which evolves according to a game-theoretic mechanism, consistent with social psychology evidence. Within this setting, agents strengthen their leadership by expressing decisive yet socially coherent opinions, whereas misalignment with the collective state results in a loss of influence. We analyze the coupled dynamics and establish sufficient conditions to identify which agents necessarily emerge as leaders and which act as followers in the social network.

Letters of recommendation (LORs) provide valuable insights into candidates' capabilities and experiences beyond standardized test scores. However, reviewing these text-heavy materials is time-consuming and labor-intensive. To address this challenge and support the admission committee in providing feedback for students' professional growth, our study introduces LORI: LOR Insights, a novel AI-based detection tool for assessing leadership skills in LORs submitted by online master's program applicants. By employing natural language processing and leveraging large language models using RoBERTa and LLAMA, we seek to identify leadership attributes such as teamwork, communication, and innovation. Our latest RoBERTa model achieves a weighted F1 score of 91.6%, a precision of 92.4%, and a recall of 91.6%, showing a strong level of consistency in our test data. With the growing importance of leadership skills in the STEM sector, integrating LORI into the graduate admissions process is crucial for accurately assessing applicants' leadership capabilities. This approach not only streamlines the admissions process but also automates and ensures a more comprehensive evaluation of candidates' capab

This volume represents the proceedings of the CHI 2019 Workshop on New Directions for the IoT: Automate, Share, Build, and Care.

In this work, we explain the underlying interaction mechanisms which govern students' influence on each other in Massive Open Online Courses (MOOCs). Specifically, we outline different ways in which students can be negatively exposed to their peers on MOOC forums and discuss a simple formulation of learning network diffusion, which formalizes the essence of how such an influence spreads and can potentially lead to student attrition over time. We also view the limitations of our student modeling in the light of real world MOOC behavior and consequently suggest ways of extending the diffusion model to handle more complex assumptions. Such an understanding is very beneficial for MOOC designers and instructors to create a conducive learning environment that supports students' growth and increases their engagement in the course.

Delegation and leadership are critical components of software management, as they play a crucial role in determining the success of the software development process. This study examined the relationship between delegation and leadership in software management and the impact of these factors on project outcomes. Results showed that effective delegation and transformational leadership styles can improve workflow, enhance team motivation and productivity, and ultimately lead to successful software development projects. The findings of this study have important implications for software management practices, as they suggest that organizations and software managers should prioritize the development of effective delegation and leadership practices to ensure the success of their software development initiatives. Further research is needed to explore the complex interplay between delegation and leadership in software management and to identify best practices for improving these processes.

Addressing female underrepresentation in leadership positions has become a key policy objective. However, little is known about the extent to which leadership appeals differently to women. Collecting new data from a large firm, I document that women are substantially less likely to apply for early-career promotions. Realized application patterns and large-scale surveys reveal the role of an understudied feature of promotions -- having to assume responsibility over a team -- which is less appealing to women. This gender difference is not accounted for by standard explanations, such as success likelihood or confidence, but is rather a product of common design features of leadership positions.

Using the New Horizons LORRI camera, we searched for satellites near five Kuiper belt objects (KBOs): four cold classicals (CCs: 2011 JY31, 2014 OS393, 2014 PN70, 2011 HZ102) and one scattered disk object (SD: 2011 HK103). These objects were observed at distances of 0.092-0.290 au from the New Horizons spacecraft, achieving spatial resolutions of 136-430 km (resolution is ~2 camera pixels), much higher than possible from any other facilities. Here we report that CC 2011 JY31 is a binary system with roughly equal brightness components, CC 2014 OS393 is likely an equal brightness binary system, while the three other KBOs did not show any evidence of binarity. The 2011 JY31 binary has a semi-major axis of 198.6 +/- 2.9 km, an orbital inclination of 61.34 +/- 1.34 deg, and an orbital period of 1.940 +/- 0.002 d. The 2014 OS393 binary objects have an apparent separation of ~150 km, making 2011 JY31 and 2014 OS393 the tightest KBO binary systems ever resolved. Both 2011 HK103 and 2011 HZ102 were detected with SNR~10, and our observations rule out equal brightness binaries with separations larger than ~430 km and ~260 km, respectively. The spatial resolution for 2014 PN70 was ~200 km, but