Exploration of students’ information behaviour and experience of assignment completion process

Assignments play an important role in consolidating knowledge for university students. Understanding students’ information behaviour and experience in this type of work task, i.e., students’ assignments, would be beneficial to the design of learning platforms or search systems to better support effective and efficient information behaviours. A 37-day online observation of 14 university students in China, working on one assignment through client logging, combined with questionnaires and interviews were conducted in this study. This study used descriptive analysis to describe students’ information behaviours and experiences during the assignment completion processes at different stages. According to the proportion of efforts devoted to seeking information and working for the assignment in four evenly distributed periods, the students could be divided into four time-allocation types, namely 'Ninjas', 'Turtles', 'Time wasters' and 'Pursuers'. Different types of students had different information behaviours and experiences during assignment completion process. When applying Information Seeking Process model to analysis students’ information behaviours for the assignment completion, it is necessary to combine the time context and examine how each time allocation type of students would allocate their information seeking effort in task completion behaviours.Peer Reviewe

Learning domain-specific causal discovery from time series

Causal discovery (CD) from time-varying data is important in neuroscience, medicine, and machine learning. Techniques for CD encompass randomized experiments, which are generally unbiased but expensive, and algorithms such as Granger causality, conditional-independence-based, structural-equation-based, and score-based methods that are only accurate under strong assumptions made by human designers. However, as demonstrated in other areas of machine learning, human expertise is often not entirely accurate and tends to be outperformed in domains with abundant data. In this study, we examine whether we can enhance domain-specific causal discovery for time series using a data-driven approach. Our findings indicate that this procedure significantly outperforms human-designed, domain-agnostic causal discovery methods, such as Mutual Information, VAR-LiNGAM, and Granger Causality on the MOS 6502 microprocessor, the NetSim fMRI dataset, and the Dream3 gene dataset. We argue that, when feasible, the causality field should consider a supervised approach in which domain-specific CD procedures are learned from extensive datasets with known causal relationships, rather than being designed by human specialists. Our findings promise a new approach toward improving CD in neural and medical data and for the broader machine learning community.Comment: 16 main pages, 7 figures. Accepted by TML

A Study on Natural Lighting Design Strategies for Teaching Buildings in Hot-summer and Cold-winter Zone of China—A case of the Arts and Sciences Building of Xinyang Normal University

The natural lighting of buildings plays an important role in creating a comfortable indoor light environment and reducing the energy consumption of artificial lighting. Teaching buildings have special requirements for the indoor light environment. Classroom glare, corridor backlit, and low natural illumination in corridor are light pollution problems easily appear in teaching building, which cannot be ignored in the design of teaching building. Regarding the issues above, the paper took the Arts and Sciences Building of Xinyang Normal University as an example, through the architectural modeling, space forms, facade effects and other features, used VELUX simulation software to simulate the illuminance and daylighting parameters of different sunroofs and provided solutions for classroom glare and corridor lighting. Ultimately, the paper analyzed the building lighting energy saving schemes based on regional climate and environment, and found out the best balance point for the energy saving design of lighting and thermal environment, meanwhile, provided valuable and practical reference for lighting design of corridor skylights in the region

Implicit Graphon Neural Representation

Graphons are general and powerful models for generating graphs of varying size. In this paper, we propose to directly model graphons using neural networks, obtaining Implicit Graphon Neural Representation (IGNR). Existing work in modeling and reconstructing graphons often approximates a target graphon by a fixed resolution piece-wise constant representation. Our IGNR has the benefit that it can represent graphons up to arbitrary resolutions, and enables natural and efficient generation of arbitrary sized graphs with desired structure once the model is learned. Furthermore, we allow the input graph data to be unaligned and have different sizes by leveraging the Gromov-Wasserstein distance. We first demonstrate the effectiveness of our model by showing its superior performance on a graphon learning task. We then propose an extension of IGNR that can be incorporated into an auto-encoder framework, and demonstrate its good performance under a more general setting of graphon learning. We also show that our model is suitable for graph representation learning and graph generation.Comment: 3 figure

A Pathway to Climate Neutral Buildings：Definitions, Policy and Stakeholder Understanding in Sweden and China

In recent years, \u27climate neutral buildings\u27 has become one of the most popular emerging terms in the context of global warming and the built environment. However, due to a vague definition, the term still lacks real-world uptake in practice. While initial research focuses on \u27climate neutral buildings\u27, few have discussed this term from the perspective of different countries or stakeholders. To address this gap, this paper explores the current understanding and future development of the term \u27climate neutral buildings\u27 in Sweden and China. Through a literature review of related definitions, an investigation of current regulations, and stakeholder interviews in both countries, we find that Sweden and China are in different stages of development towards climate neutral buildings. Sweden seems to surpass China in terms of theoretical research, regulation development and stakeholder understanding. Despite this, the two countries share similar issues regarding the future development of climate neutral buildings. Both countries lack an official interpretation of \u27climate neutral buildings\u27, sufficient regulations, and collaborations among different stakeholders. This paper suggests a foundation for the future development of climate neutral buildings

An Empirical Study on the Improvement of Attention by Exercise Game Intervention in Children with ADHD

This study aims to verify the attention improvement of ADHD children after sports game intervention, and provide a green and safe intervention path to help ADHD children improve their attention. 16 children with ADHD (experimental group: 10, control group: 5) were selected to carry out exercise game intervention for 24 weeks, 4 times a week, each intervention lasted about 60 minutes. D2 attention test was carried out twice in the experimental group and the control group respectively to observe the attention of children, and the results of attention change in ADHD children before and after the intervention were analyzed. Data were analyzed in SPSS23.0 by independent sample t-test and paired sample t-test. Before the experiment, there was no significant difference between the experimental group and the control group in the scores of each dimension of attention test. After the experiment, the experimental group had significant differences in TN and CP dimensions, but did not have significant differences in other dimensions. Intervention of rich and interesting sports games can effectively improve the selective attention and concentration ability of ADHD children. Therefore, in order to develop the attention of children with ADHD, sports game environment can be created to carry out challenges of team cooperation