Western science has always been intrinsically a social enterprise. How the population of scientists organises itself to produce knowledge, though, has changed enormously during the last 150 years. Generally, these changes occurred instinctively and spontaneously, being rarely, if at all, planned beforehand or investigated a posteriori. The result of this process is that the actual organisation of the scientific society is being considered far from optimal to face the gigantic and complex challenges lying ahead. In inquiry domains aiming to understand problems of organised complexity it is even inadequate, although it is often difficult to state why and to identify where inadequacies lie. Grounding on organisations, a generalisation of the system concept, on the in-formation concept induced by them and on the ground-breaking achievements of the science of generic systems in the last century, I tentatively sketch a description of the scientific milieu and its social arrangements that allows for questioning about agonistic, antagonistic, and synergistic situations and patterns of interaction, collaboration, and knowledge-creation.

Project-based educational environment that focuses on real-world problems and crosssector collaboration instills students’ learning pathways with the proficiency to move from the domain-specific to the domain-general knowledge. We conceptualize a project-based curriculum model as central to undergraduate education. Focusing on iterative design and development during each year of the undergraduate degree allows for further enrichment of the undergraduate curriculum. We present a case study of a project-based model in action within Virginia Tech, a large public polytechnic university in the United States. Working with seventy-five industry partners serving as project mentors and twenty-seven transdisciplinary faculty, we discuss methodology key to ensure student learning and project outcomes within this model, including embedding industry partners within project teams, developing transdisciplinary project teams, and encouraging just-in-time implementation of disciplinary knowledge.

This work proposes the use of a fine-tuned Transformers-based Natural Language Processing (NLP) model called BERTimbau to generate the word embeddings from texts published in a Brazilian newspaper, to create a robust NLP model to classify news in Portuguese, a task that is costly for humans to perform for big amounts of data. To assess this approach, besides the generation of embeddings by the fine-tuned BERTimbau, a comparative analysis was conducted using the Word2Vec technique. The first step of the work was to rearrange news from nineteen to ten categories to reduce the existence of class imbalance in the corpus, using the K-means and TF-IDF techniques. In the Word2Vec step, the CBOW and Skip-gram architectures were applied. In BERTimbau and Word2Vec steps, the Doc2Vec method was used to represent each news as a unique embedding, generating a document embedding for each news. Metrics accuracy, weighted accuracy, precision, recall, F1-Score, AUC ROC and AUC PRC were applied to evaluate the results. It was noticed that the fine-tuned BERTimbau captured distinctions in the texts of the different categories, showing that the classification model based on this model has a superior performance than the other explored techniques.

Japanese schools have been found to develop their school-based curriculum and pedagogy. Based on Japan's social culture and the educational trends of the 1980s, which became the prototype for the school's organizational culture, the school had a consistently complex assessment. Therefore, we focused on their narrative data. However, lesson studies are only methodology and not theorized. Japan is no exception; each school’s lesson practice records are individualized, and the knowledge is buried. Therefore, it is necessary to increase the productivity of lesson study by interpreting the data theoretically and raising its level of abstraction. This study aims to establish a methodology for conceptualizing narrative data using qualitative analysis software. Lessons were regarded as a system, which comes from cybernetics to clarify the complex relationship between various factors in the classroom. Coding was conducted based on a frame (“kizuki”, “nakama”) from the narrative data, which was based on the concept of Japanese cultural context. The accumulation of such approaches will contribute to knowledge management in lesson study.

Gender equity in education is a priority aiming to promote the right to education for everyone. It is necessary to ensure equal access to girls and boys for completion of their education cycles, as well as empower equity all through the education education process.

Lack of equity between boys and girls schools is not a special specific of one country, but also a sensitive issue. Annual reports show that a considerable number of children, mainly girls, have interrupted their education in various levels of Albanian education system, due to a number of reasons.

Lack of gender equity in the education system is a big obstacle for dynamic development of the society. The education of girls and women in Albania is important, not only as a matter of respecting a basic human right for half of the population, but also as a powerful force for economic development and achieving other social goals such as improved health and civic involvement. This work will focus on gender disparity in Albanian education system, where number of boys and girls is not the same in all education cycles - primary, elementary, secondary, according to data in the largest region in the country. A coordination of the qualitative and quantitative analysis is provided to indicate the reasons for this disparity and compare data according to random chosen schools.

This work shall offer suggestions and recommendations to improve school curricula and determine the role of teachers in this aspect in order to create a favourable environment for both genders as well as making the education system more inclusive for all children includes improving standards, curricula and a focus on teacher training and development in Albania.

This paper studies the factors that contribute to students’ acceptance in improving their technical and academic writing skills by applying digital technology. The study also focuses on identifying the available digital technology that can improve students’ technical and academic writing performance. The study analyzes students’ acceptance of technology in academic writing from various theoretical models that stem from socio-psychological disciplines. Analyzing different well validated theoretical models established in sociology, psychology, and information technology, this study seeks to better understand the factors that may contribute to students’ acceptance to improve their writing skills. The ultimate goal of this study is to develop a theoretical framework to address the gaps in knowledge existing in current literature about different contributing factors and their level of significance on students’ motivation for improving writing skills and adopting digital technology for that purpose. Given the persistent limited writing performance and low motivation of learning academic and technical writing among the students, especially in the computer technology programs, this study is significant. The conceptual framework developed in this study can be the foundation for future empirical research extending the body of knowledge in the area of academic writing while applying digital technology in the higher education institutions.

Prominent social media sites such as Facebook and Twitter use content and filter algorithms that play a significant role in creating filter bubbles that may captivate many users. These bubbles can be defined as content that reinforces existing beliefs and exposes users to content they might have otherwise not seen. Filter bubbles are created when a social media website feeds user interactions into an algorithm that then exposes the user to more content similar to that which they have previously interacted. By continually exposing users to like-minded content, this can create what is called a feedback loop where the more the user interacts with certain types of content, the more they are algorithmically bombarded with similar viewpoints. This can expose users to dangerous or extremist content as seen with QAnon rhetoric, leading to the January 6, 2021 attack on the U.S. Capitol, and the unprecedented propaganda surrounding COVID-19 vaccinations. This paper hypothesizes that the secrecy around content algorithms and their ability to perpetuate filter bubbles creates an environment where dangerous false information is pervasive and not easily mitigated with the existing algorithms designed to provide false information warning messages. In our research, we focused on disinformation regarding the COVID-19 pandemic. Both Facebook and Twitter provide various forms of false information warning messages which sometimes include fact-checked research to provide a counter viewpoint to the information presented. Controversially, social media sites do not remove false information outright, in most cases, but instead promote these false information warning messages as a solution to extremist or false content. The results of a survey administered by the authors indicate that users would spend less time on Facebook or Twitter once they understood how their data is used to influence their behavior on the sites and the information that is fed to them via algorithmic recommendations. Further analysis revealed that only 23% of respondents who had seen a Facebook or Twitter false information warning message changed their opinion “Always” or “Frequently” with 77% reporting the warning messages changed their opinion only “Sometimes” or “Never” suggesting the messages may not be effective. Similarly, users who did not conduct independent research to verify information were likely to accept false information as factual and less likely to be vaccinated against COVID-19. Conversely, our research indicates a possible correlation between having seen a false information warning message and COVID-19 vaccination status.

Tandem mass spectrometry (MS/MS) is a popular technology for identifying small molecules involved in metabolism, better known as metabolites. Coupled with liquid chromatography (LC), LC-MS/MS instruments first separate, ionize, and fragment metabolites, then measure mass-to-charge ratios (m/z) and intensities of metabolite fragments. Understanding metabolite fragmentation is crucial to develop computational tools for identifying metabolites based on this spectroscopic data. Metabolite fragmentation patterns have large variations making it especially difficult for computer scientists to design and implement metabolite identification approaches. To address this interdisciplinary challenge, this article presents FragView, a web-based application providing the web service for visualizing metabolite fragmentation. Users can break chemical bonds to produce metabolite fragments and export 3D fragment structures for 3D printing. Developing FragView is an opportunity for exposing student participants to this interdisciplinary bioinformatics project. This paper summarizes the experience of training student participants in bootcamps and designing the implementation plan based on student backgrounds. Students were exposed to project meeting discussions on coding and raw data visualization and visited a lab with an LC-MS/MS instrument. FragView is an open source, freely accessible tool, released under the GPLv3 license. We will continue to improve and update FragView in the future based on feedback.

The COVID-19 pandemic has significantly impacted most countries in the world. Analyzing COVID-19 data from these countries together is a prominent challenge. Under the sponsorship of NSF REU, this paper describes our experience with a ten-week project that aims to guide an REU scholar to develop a physics-guided graph attention network to predict the global COVID- 19 Pandemics. We mainly presented the preparation, implementation, and dissemination of the addressed project. The COVID-19 situation in a country could be dramatically different from that of others, which suggests that COVID-19 pandemic data are generated based on different mechanisms, making COVID-19 data in different countries follow different probability distributions. Learning more than one hundred underlying probability distributions for countries in the world from large scale COVID- 19 data is beyond a single machine learning model. To address this challenge, we proposed two team-learning frameworks for predicting the COVID-19 pandemic trends: peer learning and layered ensemble learning framework. This addressed framework assigns an adaptive physics-guided graph attention network (GAT) to each learning agent. All the learning agents are fabricated in a hierarchical architecture, which enables agents to collaborate with each other in peer-to-peer and cross-layer way. This layered architecture shares the burden of large-scale data processing on machine learning models of all units. Experiments are run to verify the effectiveness of our approaches. The results indicate the proposed ensemble outperforms baseline methods. Besides being documented on GitHub, this work has resulted in two journal papers.

Exams, writing, business, and management studies are all included in a comprehensive description of the revision. Consequently, depending on the context in which it is used, the definition of "revision" shifts and adapts. But there is one thing that all of these definitions have in common: the act of reading something again after it has already been written, studied, or performed in order to either commit it to memory, modify it, or improve it.

The next teaching revision tools that are described below will primarily concentrate on revision in relation to studying and exam preparation, but they will also briefly touch upon the various definitions that revision may have depending on its intended application.

Revision is the process of looking over material that has already been studied or learned. It involves reading the material again, going over course materials again, and reviewing. Students could do this purely out of interest (for the love of learning), but more frequently, they revise to prepare for a test. For this reason, it's often referred to as exam revision.

Exam revision is a phrase that students might infer its meaning. Exam revision is the process of updating or revisiting students' course information so that they can succeed on their exams when they take them. Exam revision is a great method to bring together everything pupils have learned about a certain subject over the course of a given period if you approach it the appropriate way. Revision has several advantages, some of which are directly tied to the way the word "revision" is employed, while others cut across borders. For instance, the revision will enable students to see the boundaries of their knowledge and provide them with the means to transcend them.

Students can write more effectively and coherently by revising their work. Students can make sure that their thoughts are more logical and flow better. The revision will aid students in their studies by helping them recall crucial data, numbers, subjects, and approaches from prior coursework. Students will be better able to respond to test questions in exams thanks to the revision. The students feel ready. And the assurance and assurance that comes from knowing that pupils revised what they needed to would lessen test anxiety, which is a key step to performing well on an exam. The author of this paper will offer many techniques to aid students in revising before exams.