Research on Online Collaborative Learning Model Based on Cloud Computing Platform

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

Purpose The results from previous studies have shown that collaborative online studies are a popular trend selected by many students. This study aims to collect students’ inputs and reflections on the impacts of personality on the use of online collaborative platforms in higher education in Vietnam. Design/methodology/approach This study used Statistical Package for Social Scientists to collect data through survey questionnaires from 458 students in Hanoi to identify the impact of extroverted personality and agreeable calculation in the Vietnamese university context. It examined how extroversion and agreeableness affect collaborative learning performance on synchronous and asynchronous online platforms. Findings Findings from this study show that students have a better experience and motivation in learning via online collaborative platforms. However, students’ preference for face-to-face learning has the most obvious impacts. Extroversion and pleasantness have a positive but heterogeneous impact on both online collaborative learning and in face-to-face forms. Practical implications The leaders in the Vietnamese public universities might select the option of synchronous online collaborative teaching and learning when physical teaching and/or learning is impossible or geographically limited. While students’ confidence increases in online collaborative learning, their preference for face-to-face learning remains positive. Originality/value The findings from this study significantly support students and public universities in Vietnam in choosing synchronous online collaborative teaching and training in conditions where face-to-face teaching and learning cannot be carried out or are limited. However, it is also important to note that individual differences related to personality traits can have a certain impact on performance during collaborative learning.

Generative artificial intelligence (GAI) has the potential to assist instructors by providing effective formative feedback to students. Focus and complexity are important characteristics of formative feedback. Few studies have examined the combined effects of these factors in online collaborative learning. This study explored the effects of focus and complexity of GAI-assisted formative feedback (GAI-FF) on students’ online collaborative engagement, shared metacognition, And learning performance. A total of 114 preservice teachers were recruited from a public university in northwest China and randomized into four experimental conditions: task level and simple, task level and elaborated, multilevel and simple, and multilevel and elaborated GAI-FF. Participants were provided with four types of GAI-FFs longitudinally, and their pre-and post-tests, discussion processes, and learning performance were measured and recorded. Analysis of Variance (ANOVA) and its nonparametric alternatives were performed, and we found that (1) multi-level GAI-FF significantly promoted objective behavioral engagement, but not cognitive, positive, or negative emotional engagement; (2) the focus, not the complexity of GAI-FF, significantly impacted group shared metacognition; (3) there was a significant interaction effect between focus and complexity of GAI-FF on group shared metacognition; and (4) preservice teachers in multi-level GAI-FF conditions significantly outperformed those in task-level conditions in terms of group artifacts and course performance. The theoretical and practical implications of optimizing GAI-FF in collaborative learning to support learning and performance are discussed.

Grammar acquisition is essential in learning English as the fundamental component in leveraging language skills. However, attaining the grammar competency optimally for Indonesian EFL learners encounters the challenges with various factors. Hence, a proper applicable approach is necessary to be adopted such as incorporating self-regulation strategy, online collaboration learning, and technology enhancement in grammar instruction. This study attempts to investigate the students perspectives in self-regulated and online collaborative grammar learning via Google multi-platforms. Employing a qualitative case study with questionnaires that distributed to 104 and 10 for the interviews of undergraduate sophomore students. The findings revealed that the participants positively perceived on self-regulated strategy and online collaborative grammar learning via Google multi-platforms. The implication of the current study can be as the consideration for educators, researcher, and technology developers relating utilizing online setting, collaboration activity, and technology aid in a grammar instruction.

Cloud computing and big data: Technologies and applications

At present, advanced technologies such as Big Data, Artificial Intelligence, Cloud Computing, and the Internet of Things are gradually being applied to course teaching, which further promotes the informatization of the course. Under this background, correlated course teaching should actively explore the development of course teaching informatization. Considering the teaching of anesthesiology, it’s impossible to apply anesthesia technology and drugs on humans or animals at the beginning of learning. At this time, the use of virtual reality technology can effectively train students' practical operations and ability of anesthesiology, which is of great significance for further improving students' clinical practice ability. This article mainly introduces the advantages of virtual reality technology application, studies the current status of virtual reality technology, and analyzes the effective application strategies of virtual reality technology in anesthesiology teaching.

Collaborative learning in virtual classrooms has tremendous potential in institutions, as it encourages cooperative knowledge production and skill development. This study investigates how a pedagogical model for virtual learning can incorporate this methodology, emphasizing the significance of meticulous preparation, an appropriate dynamic for establishing groups, the relevance of student practices to everyday technology utilization, an evolution to educator responsibilities and learning autonomy. The research underlines the value of technology in education. This article discusses Virtual Collaborative Learning (VCL) as an effective approach to quality assurance in the age of digitization. It presents design characteristics from an academic standpoint and optimizes these approaches through a qualitative examination of written opinions by VCL participants. The research identifies and prioritizes critical criteria for collaborative learning effectiveness from the students' perspective, generating further multi-perspective design recommendations. Adaptive collaborative virtual learning is a technology-enabled technique that employs algorithms to evaluate student data and adjust to a student's learning style, pace, and accomplishments. It capitalizes on gamification to make learning more engaging and interactive, while artificial intelligence (AI), machine learning, virtual reality (VR), and augmented reality (AR) have transformed conventional education. This article explores at how cutting-edge technologies are being created and implemented into the educational system and classroom for enhancing student education and learning environments. This chapter examines the adoption of AI and machine learning in intelligent learning, emphasizing their potential for enhancing learning experiences, personalize education, and improve outcomes. It also encompasses ethical and security issues, highlighting the importance of stringent laws to protect students’ rights. By implementing these guidelines, educators and policymakers may build a more intelligent and successful classroom setting. This study visualizes AI-enabled learning systems, identifies types of interventions, and discusses popular analytical methodologies. It serves as a reference for future studies regarding the development of AI-enabled educational platforms that address specific learning challenges while enhancing user experiences, ultimately directing future research in this area of study.

This research project developed a web-based application to facilitate out-of-class collaborative learning; the aim was to transform a student culture that facilitated very little independent study outside the classroom and classroom learning that treated students mainly as passive participants. Additionally, an innovative flipped learning course utilizing this web-based application was implemented in order to integrate online and in-class collaborative learning. We hypothesized that students’ online collaborative learning could be activated and sustained throughout the semester. Based on pre- and post-questionnaire results, it was apparent that the students actively participated in discussions outside class (Active Learning (AL): p < .10, AL externalization: p < .05). Furthermore, Spearman’s rank correlation analysis for the frequency of category appearances and the indicators for social and cognitive presences, which was guided by the Community of Inquiry framework with regard to out-of-class student interactions and the pre- and post-questionnaire gains revealed that the cognitive presence was strongly correlated with the pre- and post-questionnaire gains (p < .001). These results indicate that the web-based application developed in this study supported out-of-class student collaboration.

This collaborative learning aims to improve the quality and quantity of cooperation between universities in eastern Indonesia through the implementation of collaborative online learning. Collaborative online learning training involves six educators who will teach.. Based on the results of a survey that has been conducted, it is stated that IPB Cirebon students still use LMS as a lecture system, while Uniflor students have a face-to-face lecture system. Therefore, a collaborative online learning program (PDK) was carried out between IPB Cirebon and Uniflor. The PDK program is implemented using three stages. These stages include starting from initial studies, planning, implementation, monitoring and evaluation. Using stages in the PDK program is the right approach. Active student involvement in every meeting, the development of LMS-based digital teaching materials will be more oriented towards student needs and aspirations. Through active student participation, the PDK program has the potential for better achievements, realizing learning in 2045. Collaborative online learning between two universities can increase digital literacy between students and educators. Collaborative online learning can also develop students' abilities in the 3T area. Through this learning, students can collaborate with other university students.

The outbreak of COVID-19 has accelerated the construction of educational informationization and strengthened the integration and application of education and digitalization. Educators have increased demands for smart education to guarantee the quality of online teaching during the epidemic. As a key factor of smart education, smart classroom is expected to produce an important effect on education and teaching. However, at the present stage, there are some problems with smart classroom, such as complex equipment environment and inadequate combination with professional knowledge. Exercise Physiology is a course with strong practicality. For the traditional teaching mode, it is hard for students to gain a deep understanding of related knowledge. In view of this, this paper attempts to introduce a research-based teaching mode based on the smart classroom environment, make it run through three parts of the class, i.e., pre-class, in-class and post-class, make use of all kinds of equipment and functions in the smart classroom to form an interactive teaching mode that integrates pre-class, in-class and post-class. To be specific, the process includes storing in the cloud, pushing smartly, asking questions, creating scenarios, forming an individual learning mode, combining online teaching method with collaborative learning mode, giving quantitative results according to the status of collaborative learning and finally performing feedback evaluation and expanding after-class knowledge. Through the practical teaching of Exercise Physiology, the teaching mode established in this study can achieve a good teaching effect and teaching quality. In particular, it is of great significance for promoting students’ classroom satisfaction and ability to analyze problems and developing good learning habits in them.

In the field of education, cloud computing, as a basic environment and platform for the future network learning, will bring positive effect on construction of the learners' personal learning environment, on construction of school resources, on development of the educational information system. Based on the analysis of the cloud-computing influence on collaborative learning, this paper puts forward the construction strategy of collaborative learning environment supported by cloud-computing and the specific application case, which will provide reference for cloud-computing application in education informatization and social network learning.

Collaborative learning has been utilised throughout the years to deliver educational content for various levels of students in schools. Previously, less emphasis was given to the online support mode as the conventional method exists. However, as schools across the globe closed in response to the Covid-19 pandemic, students from various levels experienced a severe disruption to their learning process. Though most schools quickly began offering some online learning, there have been growing concerns about the effects of this unprecedented shift. In addition, such a paradigm shift has also witnessed parents' significant role in ensuring the effective delivery of educational content through online platforms. In such a situation, parents were expected to support the learning process. However, less support from other stakeholders makes it challenging for parents to support their children's learning online. Although various strategies and best practices exist globally, engaging students, parents, teachers, and relevant stakeholders towards collaborative online learning is crucial. Based on relevant previous studies, engagement towards online learning is highly influenced by the user's cultural background in getting successful collaboration among all stakeholders. Thus, the proposed study aims to investigate and propose a conceptual framework for a collaborative online learning model based on a cultural-based approach. It applies a Systematic Literature Review (SLR) based on relevant previous studies. A preliminary study has conducted a comparative review based on strategies and best practices. Initial findings divulge that cultural background influences the teacher-parent engagement towards collaborative online learning provided by the school and relevant authorities. Therefore, findings from the proposed study could improve the quality of learning in primary schools, in line with SDG4 and 4IR.

At the forefront of technological development, cloud computing, the Internet of Things (IoT), big data, artificial intelligence (AI), and blockchain (CTDAB) are not mutually exclusive, but complementary and integrated. The connotative characteristics and correlation of cloud computing, IoT, big data, AI, and blockchain are mainly introduced in this study, and their application in maritime field are summarized. Cloud computing, big data, and AI are the core technologies for realizing IoT technology. Blockchain technology supplements the functions of IoT. The cross-support and comprehensive application of these emerging technologies has started in the process of people’s exploration of the maritime field, which has played a key role in promoting the intelligent development of the maritime field.

As the number of patients treated in-home are increasing exponentially mainly in countries such as Japan, US A and Europe etc., these people often enter into a critical situation that may require help immediately (e.g. when facing an accident, or becoming depressed). Researches and Advances in computing areas and the Internet of Things (IOT) have provided efficient and cheap equipments including wireless communication and cameras, such as smart phones or embedded devices that enable the deployment of Health S mart Homes (HS H) that can provide medical treatment of patients in their homes. The images captured from these cameras can help nurses or caretakers of patients to provide timely help. The use of such patient images and emotional detection to assist patients and elderly people within home is provided in this article. Internet of Things and Cloud computing can work together, which can address to the Big Data problems. Big data is actually a term used for a huge data set that performs operations like storage of data, analysis, sharing, transfer, predictive analysis; updating etc. data set can grow rapidly. Data sets have different analytics on data that involve process of inspecting, transformation and modeling of data so as to discover new information, and to reach on some particular decision. There are some particular data analysis techniques from which data mining is a popular one which focuses on modeling and discovery of new facts. The discovered knowledge helps in predictive analysis purposes as well as text analysis. Further on data mining is performed on data set which involves discovery of patterns by applying different operations at databases, and using artificial intelligence, machine learning, statics etc. Keywords: Cloud computing, IOT, AI, face recognition, big data, data mining.

The digital revolution has profoundly reshaped the landscape of global finance, giving rise to an era where speed, security, and intelligence define the success of financial services. Innovations in Digital Finance and Intelligent Technologies: A Deep Dive into AI, Machine Learning, Cloud Computing, and Big Data in Transforming Global Payments and Financial Services aims to illuminate this transformation by exploring disruptive technologies at the core of today’s financial evolution. In recent years, Artificial Intelligence (AI), Machine Learning (ML), Cloud Computing, and Big Data have moved from theoretical concepts to practical tools that power every facet of modern finance—from algorithmic trading and fraud detection to credit scoring and personalized banking. These innovations are not just enhancing operational efficiency; they are enabling real-time decision-making, improving financial inclusion, and reshaping the way individuals, businesses, and institutions engage with money. This book is crafted for a wide spectrum of readers including financial professionals, technologists, researchers, policy-makers, and students. It unpacks the mechanisms behind intelligent financial systems, provides real-world case studies, and discusses emerging trends such as decentralized finance (DeFi), embedded banking, digital identity, and AI-driven risk assessment. Special attention is given to the regulatory, ethical, and cybersecurity challenges that come with this rapid digitalization. What makes this work timely is its holistic view of innovation—not just as a product of technology, but as a driver of systemic change in global economies. From digital wallets and instant cross-border payments to cloud-native banking platforms, the book outlines how intelligent technologies are setting new standards for agility, transparency, and customer experience. As financial ecosystems grow increasingly complex and interconnected, this book serves as a compass guiding stakeholders toward building secure, inclusive, and intelligent financial futures.