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关键词： 优化   模块化   多样化   探究性实验  

摘要： 本文从《电路与电子学》课程特点及学情分析入手,提出以当前国际形势、思政教育、就业方向为教学指导思想,通过模块化的教学内容、多样化的教学方法与手段开展理论教学,将探究性实验与理论结合的教学改革策略,进一步提高了教学质量,激发了学生的积极性,使学生由填鸭式学习向自主式学习转变。

关键词： 民办高校   计算机类本科   实践教学  

摘要： 本文旨在探索民办高校计算机类本科中的“3+1”应用型人才培养模式。本文概述了“3+1”应用型人才培养模式能有效提升计算机类本科应用型人才的培养的原因,分析了民办高校在实施“3+1”人才培养模式过程中遇到的挑战和问题,提出了改革策略和具体措施。本文还通过学生和企业的反馈评价和改进建议评估了这一模式的实践效果。本文展望了该模式的改进方向和未来发展前景,并强调了深化和创新高等教育教学模式的重要性以及人才培养模式的理论探索和实践创新之间的相互关系。

关键词： Collaborative learning   Computer science learning   Elementary school   Virtual learning companions  

摘要： Virtual learning companions, or pedagogical agents situated as “near peers”, have shown great promise for supporting learning, but little is known about their potential to scaffold other practices, such as collaboration. We report on the development and evaluation of a first-of-their-kind pair of virtual learning companions, designed to model good collaborative practices for dyads of elementary school learners, that are integrated within a block-based coding environment. Results from a study with fifteen dyads of children indicate that the learning companions fostered more higher-order questions and promoted significantly higher computer science attitude scores than a control condition. Qualitative analyses revealed that most children perceived the virtual learning companions as helpful, felt that the companions changed their interaction with their partners, and wanted to have the companions in their future work. These results highlight the potential for virtual learning companions to scaffold collaboration between young learners and provide direction for future investigation on the role that near-peer agents play in collaborative and task support.

关键词： MOOCs   QCA   LDA   Learner satisfaction   Computer science course  

摘要： The nation's explosive growth in Massive Open Online Courses (MOOCs) is likely to lead to low effectiveness of MOOCs, therefore, it is necessary to promote the high-quality and long-term development of MOOCs through understanding learner satisfaction. The present research adopted the Latent Dirichlet Allocation (LDA) to derive factors affecting the satisfaction of the learners and conducted a fuzzy-set qualitative comparative analysis (QCA) to analyze configurations of the high-level and low high level of learning satisfaction with computer science MOOCs. This study identified how course design, teaching style, teaching content, communication, exercise, and platform impact learner satisfaction based on 27,316 students' reviews of MOOCs. It has been found that five sufficient configurations are demonstrated to achieve high satisfaction while the other three configurations do not result in high satisfaction. In addition, although the six factors cannot alone constitute the necessary conditions for configurations of learner satisfaction, course design, teaching styles, and exercise play an indispensable role in improving learner satisfaction. Overall, this study adds to the literature by examining specific learner-level factors and extending configural understanding of the factors that affect MOOC learner satisfaction.

关键词： Explainable artificial intelligence   Finance   Systematic literature review   Machine learning   Review   G00   L50  

摘要： Digitalization and technologization affect numerous domains, promising advantages but also entailing risks. Hence, when decision-makers in highly-regulated domains like Finance implement these technological advances-especially Artificial Intelligence-regulators prescribe high levels of transparency, assuring the traceability of decisions for third parties. Explainable Artificial Intelligence (XAI) is of tremendous importance in this context. We provide an overview of current research on XAI in Finance with a systematic literature review screening 2,022 articles from leading Finance, Information Systems, and Computer Science outlets. We identify a set of 60 relevant articles, classify them according to the used XAI methods and goals that they aim to achieve, and provide an overview of XAI methods used in different Finance areas. Areas like risk management, portfolio optimization, and applications around the stock market are well-researched, while anti-money laundering is understudied. Researchers implement both transparent models and post-hoc explainability, while they recently favored the latter.

关键词： Travel decision making   Recommender systems   Decision support systems   Flickr   Concept modelling  

摘要： Travel location recommendation systems have long been used by travellers for their ability to suggest destinations and potential travel experiences that match travellers' desires. Recently, a new type of domestic travel has emerged, namely domestic overseas-like travel experiences. These experiences are attractive to travellers who have a preference for exotic locations but no desire to travel internationally. Existing travel recommendation systems were not designed for such applications, nor do they have the relevant ability to recommend domestic overseas-like travel experiences to support travel decision making. To address this challenge, this paper focuses on the development of a recommendation model based on the visual content of photos for domestic overseas-like travel experiences and a prototype application. The application uses the latest advancement in computer vision - the concept model - to learn high-level concepts in an overseas travel destination photo collection to identify similar domestic travel experiences. We demonstrate the usability of the prototype application with a large-scale data set of approximately 479,000 travel photos taken in several countries and evaluate its utility and efficacy through four focus groups with target users.

关键词： Vehicles   Pricing   Charging stations   Vehicle dynamics   Urban areas   Computer science   Training   Data-driven   electric vehicle   for-hire vehicle   fairness   deep reinforcement learning  

摘要： With the worldwide mobility electrification initiative to reduce air pollution and energy security, more and more for-hire vehicles are being replaced with electric ones. A key difference between gas for-hire vehicles and electric for-hire vehicles (EFHV) is their energy replenishment mechanisms, i.e., refueling or charging, which is reflected in two aspects: (i) much longer charging processes versus much shorter refueling processes and (ii) time-varying electricity prices versus time-invariant gasoline prices during a day. The complicated charging issues (e.g., long charging time and dynamic charging pricing) potentially reduce the daily operation time and profits of EFHVs, and also cause overcrowded charging stations during some off-peak charging pricing periods. Motivated by a set of findings obtained from a data-driven investigation and field studies, in this paper, we design a fairness-aware vehicle displacement system called FairMove to jointly optimize the overall profit efficiency and profit fairness of EFHV drivers by considering both the passenger travel demand and vehicle charging demand. We first formulate the EFHV displacement problem as a Markov decision problem, and then we present a fairness-aware multi-agent actor-critic approach to tackle this problem. More importantly, we implement and evaluate FairMove with real-world streaming data from the Chinese city Shenzhen, including GPS data and transaction data from over 20,100 EFHVs, coupled with the data of 123 charging stations, which constitute, to our knowledge, the largest EFHV network in the world. Extensive experimental results show that our fairness-aware FairMove effectively improves the profit efficiency and profit fairness of the EFHV fleet by 26.9% and 54.8%, respectively. It also improves the charging station utilization fairness by 38.4%.

关键词： Solid modeling   Mathematical models   Psychology   Computer science   Production   Dynamics   Sociology   Dynamic models   paradox of information access   polarization   social networks  

摘要： This article derives a new nonlinear stochastic model of the evolution of human beliefs that demonstrates how an increase in democratized information production and sharing, combined with consumers' confirmation bias and natural bias for outlying content, results in increased polarization. The model shows that the evolution of human beliefs can be approximated by a nonlinear diffusion-drift equation, in which systematic psychological biases contribute to drift, whereas other random influences contribute to diffusion. The nonlinear formulation predicts a growth in polarization that is attributable to increasing information production and sharing. While the core contribution is analytical, an anecdotal model parameter fitting empirical data is also presented. Specifically, we show that our model closely predicts the changing and increasingly polarized distribution of ideology of members of the US congress over the last quarter century (taken as an approximate proxy for shifts in the US population ideology), when we take the mobile-phone penetration curve as a proxy for the democratization of information access. The model suggests that escaping the polarizing forces in the age of information access may be an uphill battle.

关键词： Uncertainty   Training   Measurement uncertainty   Computer science   Labeling   Entropy   Data models   Active learning (AL)   clustering environment   informativeness   label acquisition   representativeness   unsupervised domain adaptation (UDA)  

摘要： Despite the significant progress in unsupervised domain adaptation (UDA), the performance of UDA methods is still far inferior to that of the fully supervised ones. In practical scenarios, it is usually feasible to acquire labels on a small portion of the target data through active learning (AL), which aims to train an effective model with as few queried instances as possible. However, due to the domain shift, the instances selected by existing AL algorithms can be uninformative, redundant, or outlying. To address this issue, we propose a novel approach, namely, clustering environment-aware learning (CEAL), for active domain adaptation (ADA). CEAL selects potentially the most valuable instances under domain shift by exploring the informativeness and representativeness of target samples in a clustering environment-aware manner. Specifically, for the informativeness, we not only leverage the knowledge of individual points but also their nearby neighbors, by measuring the proposed clustering environment aware informativeness score (CEAIS), thus ensuring that the selected samples are highly informative. For the representativeness, we design two schemes called point distance release (PDR) and informativeness score difference exclusion (ISDE) to guarantee the diversity and validity of the selected samples. Furthermore, we fully utilize the large amount of unlabeled data from target domain via pseudo labeling and adopt information maximization to improve the reliability of the target pseudo labels, thereby further improving the performance of the model. The effectiveness of our method is empirically verified on various benchmark datasets against recent state-of-the-art algorithms.