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Deep learning methods for salient object detection (SOD) have been studied actively and promisingly. However, it is still challenging for the studies with two aspects. The first one is a single type of label from the network to convey limit information, which leads to the poor generalization ability of the network. The second one is the difficulty to improve the accuracy and detect details of target. To address these challenges, we develop a novel approach via joint weakly supervised, unsupervised and supervised learning for SOD (WUSL–SOD), which differs from existing methods just based on ground-truth or other sparse labels. Specifically, to optimize the objective of the image, the unsupervised learning module (ULM) is designed to generate coarse saliency feature and suppress background noises via attention guiding mechanism. Then, we propose the weakly supervised learning module (WLM) based on scribbles for producing relatively accurate saliency feature. Note that this structure is used to enhance the details and remedy the deficiency of scribbles in WLM. For further refining information from the ULM and WLM, we propose a supervised learning module (SLM), which is not only applied to process and refine information from the ULM and WLM, but also enhance the image details and capture the entire target area. Furthermore, we also exchange information between the SLM and the WLM to obtain more accurate saliency maps. Extensive experiments on five datasets demonstrate that the proposed approach can effectively outperform the state-of-the-art approaches and achieve real-time.

Background/purpose. Teachers' pedagogical competence in Indonesia has become one of the central issues in improving the quality of national education. This issue can affect students' academic achievements and influence education quality. This study aims to explore the need to develop a new pedagogical module tailored to current educational challenges that can significantly enhance teachers' pedagogical competence.Materials/methods. This developmental research adopts a methodological approach based on operational research using the Design-Based Research (DBR) framework.Results. The findings reveal that the use of the Pedagogical Content Learning (PCL) module significantly improves teachers' pedagogical competence, as evidenced by the differences in the mean scores of the pre-test and post-test. Statistical analysis using a t-test indicates a significant increase in pedagogical competence scores after implementing the module, while the Effect Size test demonstrates a substantial impact of the module on enhancing teachers' pedagogical skills.Conclusion. These findings suggest that the development of the PCL module not only contributes positively to the pedagogical competence of economics teachers but also has the potential to improve overall educational quality. The module enables teachers to connect economic materials with real-life contexts, fostering the development of critical thinking skills and digital literacy. Thus, the PCL module serves as an effective tool to assist teachers in addressing the complexities of 21st-century education.

This paper presents GRATE–DRL–AI, an Artificial Intelligence (AI)–driven algorithm designed to enhance the efficiency and accuracy of distribution system planning. Leveraging advanced AI methodologies, including graph learning, transfer learning, deep reinforcement learning (DRL), and physics‐guided neural networks, this model efficiently addresses the growing complexity and uncertainties in modern distribution grids with high penetration of distributed energy resources. Case studies on the Institute of Electrical and Electronics Engineers 33‐bus and 123‐bus systems show that GRATE–DRL–AI reduces planning cost by up to 8.5%, achieves 99%–100% feasibility, and significantly lowers computation time (e.g., 580 s vs. 1610 s for the 342‐bus system). Even under ±30% uncertainty in demand and renewable generation, feasibility remains above 99%. In addition to strong performance gains, the study also highlights limitations, such as data availability, computational requirements, and regulatory considerations, which must be addressed for real‐world deployment of AI‐driven planning frameworks.

Climate change is an issue that concerns all countries, but surveys report that only a fraction of Filipinos are well-informed about it. Building climate science literacy is vital for citizens to understand the impacts of climate change and develop solutions to mitigate climate change and adapt to its effects. Making connections between human actions and their effects on climate change is included in the learning competencies of the Programme for International Student Assessment (PISA). Systems thinking is an approach that emphasizes understanding the relationships and feedback among variables in a system. Given the inherent interconnections of the Earth system, it becomes critical to integrate systems thinking into comprehending climate science and managing climate change. This research study utilized a descriptive research design, with the objective of developing an e-learning module for climate change with the integration of systems thinking. Five experts validated the e-learning module: one each in the field of Earth and environmental science; science education; systems thinking; language, grammar, and style; and information technology. Descriptive statistics were used to identify the level of acceptability of the e-learning module. The results showed that the module can be used teach climate change topics to junior high school students in the Philippines.

At the University of California, Irvine, School of Medicine (UCISOM), the COVID‐19 pandemic is accelerating the transition of face‐to‐face didactic lectures to online platforms. Institutions nationwide have opted to transition their lectures into remote instruction for the upcoming Fall 2020 academic year. UCISOM’s pre‐clerkship Medical Immunology course in the Spring 2020 serves as a template for other medical courses to successfully transform lecture content into virtual presentations. To help facilitate successful large‐scale transition to online courses, UCI developed institutional support and implemented a Division of Teaching Excellence and Innovation (DTEI) Fellowship and iMedEd programs to support medical educators throughout Summer. Previously developed E‐learning modules for renal and acid‐base physiology serve as the foundation for novel pulmonary E‐learning modules at UCISOM. In preparation for the new academic year, in a collaboration between faculty, UCISOM’s top performing second‐year medical students (MS2s) and DTEI fellows worked together during the summer to transition UCISOM’s Medical Physiology and Pathophysiology course online. With over 100 first‐year medical students attending the Medical Physiology course over live synchronous Zoom instruction, formative and summative assessments were incorporated into Canvas modules along with peer‐led review sessions and new E‐learning modules to educate and monitor student progress. The course will maintain existing in‐person active learning activities for students to get hands‐on experience using the latest medical devices while maintaining social distancing. Successful transition to online medical education at UCISOM will depend on increasing use of formative assessments, increased utilization of peer‐led review sessions, and efficient communication to help foster self‐directed learning.

The digital gender divide is a major challenge that needs to be addressed in developing countries. Thus, the focus of this study is to address the digital il-literacy of girls and women that also fuels the digital gender divide. The goal is to produce an e-learning module that focused on the skills to be measured in assessing ICT skill in Sustainable Development Goals (SDG) 4. This can be used during training as a tool to capacitate participants like marginalized women and girls. The development of this e-module follows the research and development using the 4D model process that begins in define phase, followed by the design of e-learning content and development activities, and lastly disseminate. The impact of the e-learning module was evaluated during ICT literacy training for marginalized women and girls. This study found that utilizing e-learning modules in the development of skills among participants was significant. This study was a humble step towards gaining technological skills of the marginalized girls and women in the Philippine community to-wards ICT4D.

Massage therapists are uniquely positioned to identify skin cancer. Seminal work in 2013 revealed that 40% of massage therapists do not receive any training in skin cancer identification (Campbell et al. J Cancer Educ 28:158–164, 2013). Limited work has been published assessing optimal training methodologies to close this educational gap. We present the results of a study in which students were given access to a 30-min self-driven web-based learning module designed to teach the high yield points of melanoma demographics and clinical features. The students completed pre- and post-testing, the results of which indicated improved knowledge levels and improved confidence in detecting suspected melanoma. We conclude that a 30-min learning module may be sufficient to improve massage therapists’ ability and comfort level in identifying melanoma. The ease of delivery of web-based modules may make this an important approach in ensuring that massage therapists receive basic training in skin cancer identification.

Machine learning is a popular topic in data analysis and modeling. Many different machine learning algorithms have been developed and implemented in a variety of programming languages over the past 20 years. In this article, we first provide an overview of machine learning and clarify its difference from statistical inference. Then, we review Scikit-learn, a machine learning package in the Python programming language that is widely used in data science. The Scikit-learn package includes implementations of a comprehensive list of machine learning methods under unified data and modeling procedure conventions, making it a convenient toolkit for educational and behavior statisticians.

The constantly growing offering of online learning materials to students is making it more difficult to locate specific information from data pools. Personalization systems attempt to reduce this complexity through adaptive e-learning and recommendation systems. The latter are, generally, based on machine learning techniques and algorithms and there has been progress. However, challenges remain in the form of data-scarcity, cold-start, scalability, time consumption and accuracy. In this article, we provide an overview of recommendation systems in the e-learning context following four strands: Content-Based, Collaborative Filtering, Knowledge-Based and Hybrid Systems. We developed a taxonomy that accounts for components required to develop an effective recommendation system. It was found that machine learning techniques, algorithms, datasets, evaluation, valuation and output are necessary components. This paper makes a significant contribution to the field by providing a much-needed overview of the current state of research and remaining challenges.

We have investigated the efficacy of on-line, multimedia learning modules (MLMs) as preparation for in-class, lecture-based tutorials in electromagnetism in a physics course for natural science majors (biology and marine science). Specifically, we report the results of a multiple-group pre/post-test research design comparing two groups receiving different treatments with respect to activities preceding participation in Tutorials in Introductory Physics. The different pre-tutorial activities were as follows: (1) students were assigned reading from a traditional textbook, followed by a traditional lecture; and (2) students completed on-line MLMs developed by the Physics Education Research Group at the University of Illinois at Urbana Champaign (UIUC), and commercially known as FlipItPhysics. The MLM treatment group earned significantly higher mid-term examination scores and larger gains in content knowledge as measured by the Conceptual Survey of Electricity and Magnetism (CSEM). Student attitudes towards “reformed” instruction in the form of active-engagement tutorials were also improved. Specifically, post-course surveys showed that MLM-group students believed class time was more effective and the instructor was more clear than reported by non-MLM students, even though there was no significant difference between groups with respect to in-class activities and the same instructor taught both groups. MLM activities can be a highly effective tool for some student populations, especially when student preparation and buy-in are important for realizing significant gains.