Factorial trials can be conducted when statistical interactions between two or more treatment factors are not anticipated, but also when they are. A k-in-1 factorial trial answers k single-factor questions with the same number of units as one parallel-group trial if there are no interactions. Literature on k-in-1 factorial trials has dominated trialists understanding of factorial trials in the UK. However, factorial experiments originated from the Design of Experiments field to enable interactions to be robustly estimated. Seemingly conflicting guidance from these literatures poses a source of confusion and misunderstanding for trialists. We bring these literatures together to provide clarity on the arguments that have been used to recommend use of factorial trials in the presence and absence of interactions. We outline motivating examples. We summarise the rationales for using factorial trials, the treatment contrasts of interest, and the properties of their estimators, for the two schools of thought. We describe the debate, going back to 1935, and use an empirical example to illustrate the impact of different analysis approaches. We conclude that it is vital that trialists carefu
Natural disasters threaten the resilience of power systems, causing widespread power outages that disrupt critical loads (e.g., hospitals) and endanger public safety. Compared to the conventional restoration methods that often have long response times, leveraging government-controlled electric school buses (ESBs) with large battery capacity and deployment readiness offers a promising solution for faster power restoration to critical loads during disasters while traditional maintenance is underway. Therefore, we study the problem of routing and scheduling a heterogeneous fleet of ESBs to satisfy the energy demand of critical isolated loads around disasters addressing the following practical aspects: combined transportation and energy scheduling of ESBs, multiple back-and-forth trips of ESBs between isolated loads and charging stations, and spatial-wise coupling among multiple ESB routes. We propose an efficient mixed-integer programming model for routing and scheduling ESBs, accounting for the practical aspects, to minimize the total restoration cost over a planning horizon. We develop an efficient exact branch-and-price (B&P) algorithm and a customized heuristic B&P algorit
Administrative registry data can be used to construct population-scale networks whose ties reflect shared social contexts between persons. With machine learning, such networks can be encoded into numerical representations -- embeddings -- that automatically capture an individual's position within the network. We created embeddings for all persons in the Dutch population from a population-scale network that represents five shared contexts: neighborhood, work, family, household, and school. To assess the informativeness of these embeddings, we used them to predict right-wing populist voting. Embeddings alone predicted right-wing populist voting above chance-level but performed worse than individual characteristics. Combining the best subset of embeddings with individual characteristics only slightly improved predictions. After transforming the embeddings to make their dimensions more sparse and orthogonal, we found that one embedding dimension was strongly associated with the outcome. Mapping this dimension back to the population network revealed that differences in educational ties and attainment corresponded to distinct network structures associated with right-wing populist voting.
As large language models (LLMs) become more common in educational tools and programming environments, questions arise about how these systems should interact with users. This study investigates how different interaction styles with ChatGPT-4o (passive, proactive, and collaborative) affect user performance on simple programming tasks. I conducted a within-subjects experiment where fifteen high school students participated, completing three problems under three distinct versions of the model. Each version was designed to represent a specific style of AI support: responding only when asked, offering suggestions automatically, or engaging the user in back-and-forth dialogue.Quantitative analysis revealed that the collaborative interaction style significantly improved task completion time compared to the passive and proactive conditions. Participants also reported higher satisfaction and perceived helpfulness when working with the collaborative version. These findings suggest that the way an LLM communicates, how it guides, prompts, and responds, can meaningfully impact learning and performance. This research highlights the importance of designing LLMs that go beyond functional correctn
The rapid development of Large Language Models (LLMs) opens up the possibility of using them as personal tutors. This has led to the development of several intelligent tutoring systems and learning assistants that use LLMs as back-ends with various degrees of engineering. In this study, we seek to compare human tutors with LLM tutors in terms of engagement, empathy, scaffolding, and conciseness. We ask human tutors to annotate and compare the performance of an LLM tutor with that of a human tutor in teaching grade-school math word problems on these qualities. We find that annotators with teaching experience perceive LLMs as showing higher performance than human tutors in all 4 metrics. The biggest advantage is in empathy, where 80% of our annotators prefer the LLM tutor more often than the human tutors. Our study paints a positive picture of LLMs as tutors and indicates that these models can be used to reduce the load on human teachers in the future.
Autism Spectrum Disorder (ASD) diagnosis systems in school environments increasingly relies on IoT-enabled cameras, yet pure cloud processing raises privacy and latency concerns while pure edge inference suffers from limited accuracy. We propose Confidence-Constrained Cloud-Edge Knowledge Distillation (C3EKD), a hierarchical framework that performs most inference at the edge and selectively uploads only low-confidence samples to the cloud. The cloud produces temperature-scaled soft labels and distils them back to edge models via a global loss aggregated across participating schools, improving generalization without centralizing raw data. On two public ASD facial-image datasets, the proposed framework achieves a superior accuracy of 87.4\%, demonstrating its potential for scalable deployment in real-world applications.
Socioeconomic segregation often arises in school districting and other contexts, causing some groups to be over- or under-represented within a particular district. This phenomenon is closely linked with disparities in opportunities and outcomes. We formulate a new class of geographical partitioning problems in which the population is heterogeneous, and it is necessary to ensure fair representation for each group at each facility. We prove that the optimal solution is a novel generalization of the additively weighted Voronoi diagram, and we propose a simple and efficient algorithm to compute it, thus resolving an open question dating back to Dvoretzky et al. (1951). The efficacy and potential for practical insight of the approach are demonstrated in a realistic case study involving seven demographic groups and $78$ district offices.
Research on children and youth's participation in different roles in the design of technologies is one of the core contributions in child-computer interaction studies. Building on this work, we situate youth as advisors to a group of high school computer science teacher- and researcher-designers creating learning activities in the context of emerging technologies. Specifically, we explore algorithm auditing as a potential entry point for youth and adults to critically evaluate generative AI algorithmic systems, with the goal of designing classroom lessons. Through a two-hour session where three teenagers (16-18 years) served as advisors, we (1) examine the types of expertise the teens shared and (2) identify back stage design elements that fostered their agency and voice in this advisory role. Our discussion considers opportunities and challenges in situating youth as advisors, providing recommendations for actions that researchers, facilitators, and teachers can take to make this unusual arrangement feasible and productive.
Machine translation systems for high resource languages perform exceptionally well and produce high quality translations. Unfortunately, the vast majority of languages are not considered high resource and lack the quantity of parallel sentences needed to train such systems. These under-represented languages are not without resources, however, and bilingual dictionaries and grammar books are available as linguistic reference material. With current large language models (LLMs) supporting near book-length contexts, we can begin to use the available material to ensure advancements are shared among all of the world's languages. In this paper, we demonstrate incorporating grammar books in the prompt of GPT-4 to improve machine translation and evaluate the performance on 16 topologically diverse low-resource languages, using a combination of reference material to show that the machine translation performance of LLMs can be improved using this method.
This study employs cutting-edge wearable monitoring technology to conduct high-precision, high-temporal-resolution (1-second interval) cognitive load assessment on electroencephalogram (EEG) data from the FP1 channel and heart rate variability (HRV) data of secondary vocational students. By jointly analyzing these two critical physiological indicators, the research delves into their application value in assessing cognitive load among secondary vocational students and their utility across various tasks. The study designed two experiments to validate the efficacy of the proposed approach: Initially, a random forest classification model, developed using the N-BACK task, enabled the precise decoding of physiological signal characteristics in secondary vocational students under different levels of cognitive load, achieving a classification accuracy of 97%. Subsequently, this classification model was applied in a cross-task experiment involving the National Computer Rank Examination (Level-1), demonstrating the method's significant applicability and cross-task transferability in diverse learning contexts. Conducted with high portability, this research holds substantial theoretical and pr
Project TAIPAN has been carried out jointly by Trinity Research Lab and the Frequency and Quantum Metrology Research Group located at the School of Physics, Mathematics and Computing of the University of Western Australia (UWA). Lockheed Martin Corporation (USA) has also been a partner in this joint collaboration providing financial backing to the project and other support including advanced modelling, assessment of laboratory tests and data analysis. The project aim was to develop a miniaturised gravity gradiometer to measure horizontal mixed gradient components of the Earth gravity in a small, lightweight package that can be deployed in a fixed 4D mode, in a borehole, or on moving exploration platforms including ground-based, airborne and submersible. The gradiometer design has evolved through a few prototypes combining the design of its sensing element with ultra low noise microwave and capacitive read out. The most recent prototype of the gradiometer using novel ultra sensitive capacitive pick off metrology has been trialled in the harsh environment of Outback Western Australia over a known gravity anomaly displaying steep gradients. Despite adverse weather conditions, results
We provide an historical overview of how advances in technology influenced high school and university mathematical competitions in the United States and at the International Mathematical Olympiad. While students are not allowed the usage of technological aids during mathematical competitions, the developments in technology (especially graphing technology) throughout the past century and the increasing employment of such aids in the classroom have affected both the nature of the proposed problems and their expected solutions. We examine several interesting examples from competitions going back several decades.
Although it has a history that goes back about three decades, Metaverse has grown to be one of the most talked-about subjects today. Metaverse gradually increased its influence in the realm of business discourse after initially being restricted to discussions about entertainment. Before getting deep into the Metaverse, it should be noted that failure and deviating from the business path are highly likely for an enterprise that relies heavily on information technology (IT) because of improper use and thinking about IT. The idea of enterprise architecture (EA) emerged as a management strategy to address this issue. As the first school of thought of EA, it sought to transform IT from an unnecessary burden in an enterprise to a guiding and supporting force. Then an extended EA model is suggested as a result of the attempt made in this paper to use the idea of EA to steer virtual enterprises on Metaverse-based platforms. Finally, to evaluate the conceptual model and demonstrate that the Metaverse can support businesses, three case studies Decentraland, Battle Infinity, and Rooom were utilized.
In this paper, we discuss at The 8th International Workshop on Application of Big Data for Computational Social Science, October 26-29, 2023, Venice, Italy. To achieve the realization of the Global and Innovation Gateway for All (GIGA) initiative (2019), proposed in December 2019 by the Primary and Secondary Education Planning Division of the Elementary and Secondary Education Bureau of the Ministry of Education, Culture, Sports, Science and Technology, a movement has emerged to utilize information and communication technology (ICT) in the field of education. The history of ICT education in Japan dates back to the 100 Schools Project (1994), which aimed to provide network access environments, and the New 100 Schools Project (1997), which marked the beginning of full-scale ICT education in Japan. In this paper, we discuss the usage dynamics of smartphone-based learning applications among young people (analyzing data from January to September 2020) and their current status. Further, the results are summarized and future research topics and issues are discussed. The results show that there are situations in which ICT learning environments can be effectively utilized and others in whic
It has been a long-standing problem how schooling fish optimize their motion by exploiting the vortices shed by the others. A recent experimental study showed that a pair of fish reduce energy consumption by matching the phases of their tailbeat according to their distance. In order to elucidate the dynamical mechanism by which fish control the motion of caudal fins via vortex-mediated hydrodynamic interactions, we introduce a new model of a self-propelled swimmer with an active flapping plate. The model incorporates the role of the central pattern generator network that generates rhythmic but noisy activity of the caudal muscle, in addition to hydrodynamic and elastic torques on the fin. For a solitary fish, the model reproduces a linear relation between the swimming speed and tailbeat frequency, as well as the distributions of the speed, tailbeat amplitude, and frequency. For a pair of fish, both the distribution function and energy dissipation rate exhibit periodic patterns as functions of the front-back distance and phase difference of the flapping motion. We show that a pair of fish spontaneously adjust their distance and phase difference via hydrodynamic interaction to reduce
You might've heard about various mathematical properties of scattering amplitudes such as analyticity, sheets, branch cuts, discontinuities, etc. What does it all mean? In these lectures, we'll take a guided tour through simple scattering problems that will allow us to directly trace such properties back to physics. We'll learn how different analytic features of the S-matrix are really consequences of causality, locality of interactions, unitary propagation, and so on. These notes are based on a series of lectures given in Spring 2023 at the Institute for Advanced Study in Princeton and the Higgs Centre School of Theoretical Physics in Edinburgh.
In order to ease the process of library management many technologies have been adopted but most of them focus on inventory management. There has hardly been any progress of automation in the field of issuing and returning books to the library on time. In colleges and schools, hostellers often forget to timely return the issued books back to the library. To solve the above issue and to ensure timely submission of the issued books, this work develops a Book-Bot which solves these complexities. The bot can commute from point A to point B, scan and verify QR Codes and Barcodes. The bot will have a certain payload capacity for carrying books. The QR code and Barcode scanning will be enabled by a Pi Camera, OpenCV and Raspberry Pi, thus making the exchange of books safe and secure. The odometry maneuvers of the bot will be controlled manually via a Blynk App. This paper focuses on how human intervention can be reduced and automates the issue part of library management system with the help of a bot.
Gradient based learning using error back-propagation (``backprop'') is a well-known contributor to much of the recent progress in AI. A less obvious, but arguably equally important, ingredient is parameter sharing - most well-known in the context of convolutional networks. In this essay we relate parameter sharing (``weight sharing'') to analogy making and the school of thought of cognitive metaphor. We discuss how recurrent and auto-regressive models can be thought of as extending analogy making from static features to dynamic skills and procedures. We also discuss corollaries of this perspective, for example, how it can challenge the currently entrenched dichotomy between connectionist and ``classic'' rule-based views of computation.
In this paper, we study the various ways 3rd-5th grade educators in Wisconsin utilized Jo Wilder and the Capitol Case, a historical inquiry game, as part of their classroom instruction. The 15 educators involved in the study were all grade school teachers in Wisconsin who took part in the "Doing History Fellowship" program, a professional development opportunity offered by the authors, designed to increase their understanding of historical inquiry instruction and game-based learning. As part of the program, the educators planned and implemented the game within their own classroom context and reported their results back to the authors and other educators. Through their reports, surveys and semi-structured interviews we discovered the educators were motivated by five distinct instructional purposes, which influenced how the game was integrated into their curriculum. In this paper, we name and describe these five purposes. We see these findings as useful insights into how educators think about games and how educational video games and corresponding professional development activities may be designed in the future.