Comparison of Online and Onsite Students’ Learning Outcomes and Experiences in a Massively Open Online Course in China

This paper compares the achievements and learning experiences of onsite and online students participating in a Massively Open Online Course (MOOC) in China. Altogether 192 Chinese students learned face-to-face, and another 311 Chinese learners participated online. In regard to learning performance, onsite learners had a lower attrition rate than the online students. However, for learners who had completed all their learning assignments, no significant difference was detected between the onsite and online participants’ average assignment scores, and they were equally likely to win two of the learning awards. As to their learning experiences, there was also no significant difference between the online and onsite students’ ratings of technology quality and usability, instructional content, and the design of learning assessment. Students also reported the challenges that they had encountered and provided suggestions to improve their learning experiences. At the end of the paper, lessons learned from running the MOOC are discussed. Findings from this first empirical study on a Chinese MOOC informs researchers and practitioners interested in introducing MOOCs to Chinese students

Six façons de mettre à profit l’expertise des professionnels en technopédagogie

We underutilize the knowledge and skills of Instructional Design and Technology (IDT) professionals, despite the frequent challenges in implementing learning technologies in medical education. This is largely due to a lack of understanding among stakeholders regarding the expertise of IDT professionals and their role in technology implementation processes. We seek to improve technology implementation outcomes by explaining the IDT field’s foundational tenets of a systems perspective and disciplined approach, clarifying the role that IDT professionals can play in educational technology initiatives, and providing guidance on how to foster productive collaborations in pursuit of effective technology-enhanced learning.Bien que souvent confrontés aux défis de la mise en oeuvre des technologies éducatives en éducation médicale, nous sous-utilisons les connaissances et les compétences technopédagogues. Cela est largement dû à un manque de compréhension, chez les parties prenantes, de l’expertise de ces professionnels et de leur rôle dans les processus de mise en œuvre des technologies. Dans le but d’améliorer les impacts de la mise en œuvre des technologies, nous expliquons les principes fondamentaux de la technopédagogie, à savoir une perspective systémique et une approche rigoureuse, tout en clarifiant le rôle que les technopédagogues peuvent jouer dans les initiatives technologiques éducatives. Nous proposons également des conseils sur la manière de favoriser des collaborations productives en vue d’un apprentissage efficace optimisé par les technologies

Economic feasibility analysis of a renewable energy project in the rural China

AbstractIn this paper, an economic feasibility analysis of a wind farm is presented. Three situations including cost benefit analysis of current situation, government wind power subsidy on wind power price, and Clean Development Mechanism (CDM) of wind farm are considered. Results show that wind power generating system is a good choice for both energy saving and GHG emission reduction compared with the other power generation systems. It is also proved that the construction of wind farm is an attractive choice for investors. Finally, CDM program and government subsidy on wind power are suggested as two efficient approaches to boost the wind power development

Understand and Analyzing Learning Objects: A Foundation for Long-Term Substantiality and Use for E-Learning

In this paper, we investigated the genres of learning objects (LOs) within eight e-learning courses that provide boating safety instruction in the United States. Guided by findings from our literature review, five genres of LOs emerged during the analysis, including interactive and non-interactive graphics, interactive and non-interactive animations, and interactive text feedback. We surveyed the use of each genre of LOs within the courses and found that more non-interactive LOs than interactive LOs were adopted. Also, interactive text feedback was the most popular interactive genre available for seven courses. In our discussion, we explore potential management mechanisms of LOs in digital repositories. Our genre analysis provides a foundation for appropriate deconstruction of LOs into components, which can assist with the management of digital repositories. Effective deconstruction of LOs allows instructors and designers to successfully discover LOs that they need and reuse them in new learning units

Intersection Signal-Vehicle Coupled Coordination with Mixed Autonomy Vehicles

Connected and autonomous vehicles (CAVs) are predicted to alleviate traffic congestion, particularly at road intersections, which are the major bottleneck of the urban road network. This paper proposes a signal-vehicle coupled optimal control strategy for mixed traffic flows of CAVs and human-driven vehicles. The method follows a two-layer architecture, which formulates the signal-vehicle control tasks as two cascaded optimization problems by a notion of mixed platoons so that they can be efficiently solved by the central coordinator. In particular, the upper layer is designed to minimize the total waiting time of all vehicles in the intersection, while the lower layer is formulated to minimize the aggregated vehicle energy consumption by adequately exploiting the signal plan, number of crossing vehicles and target crossing speed obtained in the upper layer. Extensive simulation results are provided to examine the performance of the proposed signal-vehicle joint control framework and to reveal the impact of the introduction of the new algorithm at different CAV penetration rates, traffic demands and electric vehicle ratios. The comparisons with existing methods demonstrate the benefit of the proposed method in terms of fuel usage and traffic throughput

Klein-Gordon Equations on Modulation Spaces

We consider the Cauchy problem for a family of Klein-Gordon equations with initial data in modulation spaces Mp,1a. We develop the well-posedness, blowup criterion, stability of regularity, scattering theory, and stability theory

A Hybrid Wireless Image Transmission Scheme with Diffusion

We propose a hybrid joint source-channel coding (JSCC) scheme, in which the conventional digital communication scheme is complemented with a generative refinement component to improve the perceptual quality of the reconstruction. The input image is decomposed into two components: the first is a coarse compressed version, and is transmitted following the conventional separation based approach. An additional component is obtained through the diffusion process by adding independent Gaussian noise to the input image, and is transmitted using DeepJSCC. The decoder combines the two signals to produce a high quality reconstruction of the source. Experimental results show that the hybrid design provides bandwidth savings and enables graceful performance improvement as the channel quality improves

Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges

The past decade has witnessed the rapid development of ML and DL methodologies in agricultural systems, showcased by great successes in variety of agricultural applications. However, these conventional ML/DL models have certain limitations: They heavily rely on large, costly-to-acquire labeled datasets for training, require specialized expertise for development and maintenance, and are mostly tailored for specific tasks, thus lacking generalizability. Recently, foundation models have demonstrated remarkable successes in language and vision tasks across various domains. These models are trained on a vast amount of data from multiple domains and modalities. Once trained, they can accomplish versatile tasks with just minor fine-tuning and minimal task-specific labeled data. Despite their proven effectiveness and huge potential, there has been little exploration of applying FMs to agriculture fields. Therefore, this study aims to explore the potential of FMs in the field of smart agriculture. In particular, we present conceptual tools and technical background to facilitate the understanding of the problem space and uncover new research directions in this field. To this end, we first review recent FMs in the general computer science domain and categorize them into four categories: language FMs, vision FMs, multimodal FMs, and reinforcement learning FMs. Subsequently, we outline the process of developing agriculture FMs and discuss their potential applications in smart agriculture. We also discuss the unique challenges associated with developing AFMs, including model training, validation, and deployment. Through this study, we contribute to the advancement of AI in agriculture by introducing AFMs as a promising paradigm that can significantly mitigate the reliance on extensive labeled datasets and enhance the efficiency, effectiveness, and generalization of agricultural AI systems.Comment: 16 pages, 2 figure