Privacy Of Student Data Research Articles

Dimensions of privacy and its implications for learning analytics. Preliminary insights from/for African higher education

ABSTRACT This paper delves into dimensions of privacy within the African context, especially in relation to Learning Analytics (LA) in higher education. It highlights how prevailing notions of privacy, influenced by Global North perspectives, may not fully encapsulate the cultural and contextual nuances in Africa. Through phenomenological interpretive research, the study gathers insights from 13 African higher education experts, revealing that African understandings of privacy are complexly interwoven with factors like colonial history, culture, religion, and education. The findings challenge the universality of current privacy notions, suggesting that African perspectives offer a more diversified view that intertwines individual and communal aspects. This research underscores the need for contextually sensitive approaches to student data privacy in LA, particularly as African higher education increasingly digitises. The paper concludes with implications for developing inclusive, culturally aware privacy standards in LA, contributing to a more nuanced global understanding of privacy in the digital age.

Global Education Development Plan to Build Sustainable Education Based on Artificial Intelligence

Global education is an important aspect in preparing a generation ready to face the challenges of the future. In an effort to develop sustainable education, the use of artificial intelligence is one of the interesting potentials to consider. The research aims to explain the design of global education development aimed at developing AI-based sustainable education through library studies. The study uses a library study method by collecting, analysing, and synthesizing research, scientific articles, reports, and other relevant literary sources. The research focuses on three important aspects in the design of AI-based global sustainable education development, namely, learning personalization, adaptive curriculum development, and student progress assessment and monitoring. The library study results show that the use of AI in education can enable more effective learning personalization. With AI, learning materials can be tailored to individual student needs and preferences, enhancing their engagement and understanding. Furthermore, AI-supported adaptative curricular development allows learning experiences that are adapted to student development and capabilities. Student progress evaluation and surveillance can also be enhanced through the application of AI. This technology can provide more objective and comprehensive assessment of student progress, helping teachers to provide more timely feedback and support better decision-making in learning planning. While the potential for the use of AI in sustainable global education is promising, there are challenges and consequences to bear in mind. Ethical aspects, student data privacy, and fairness in access and use of AI technology should be key concerns in designing sustainable global education development. The study provides insight into the design of AI-based sustainable global education development through library studies. These findings could provide a basis for further research and provide practical guidance to stakeholders in adopting and implementing AI technologies responsibly to develop sustainable education at a global level.

THE IMPLICATIONS OF ARTIFICIAL INTELLIGENCE FOR EDUCATIONAL SYSTEMS: CHALLENGES, OPPORTUNITIES, AND TRANSFORMATIVE POTENTIAL

With advancements in Artificial Intelligence (AI) and machine learning, education systems are transforming. This paper analyzes the challenges AI poses for schools and teachers and the opportunities it presents for personalized learning. It evaluates three central challenges: updating curriculums with AI disciplines, adopting adaptive teaching techniques, and developing evaluation metrics for new paradigms. Policymakers must incorporate data science and machine learning into core frameworks. Self-paced learning platforms require new classroom dynamics. Assessments must prioritize higher-order thinking. The article emphasizes three crucial opportunities within an AI-driven education framework - broadening access, strengthening educators, and tailoring education. Online learning currently extends admission beyond geographical and economic hurdles. Intelligent content provision enables personalization for learners with disabilities. AI liberates precious teaching hours from routine tasks to concentrate on student welfare. Moreover, evolving learning technologies persistently amend lesson designs based on immediate responses. However, to actualize this vision, we need to tackle ethical concerns like the privacy of student data and the inherent biases that could infiltrate algorithms. In conclusion, despite some inevitable hitches in current systems, AI brings forth hopeful remedies to persistent issues such as inclusivity, resource limitations, and personalized guidance on a large scale. The article highlights that policy, institutional readiness, and public consciousness are equally important in steering this transformation. Educators need to acknowledge the potential of AI, prompting culture modifications centered around new perceptions of educational quality, accomplishment, and preparedness for the workforce. Further national initiatives merging education and AI will set the course for the future.

Pemanfaatan Media Cyber Konseling dalam Meningkatkan Mutu Layanan Bimbingan Konseling di MAN 3 Langkat

Cyber counseling media can be an effective tool in improving the quality of counseling services at MAN 3 Langkat School. This media can help students who are difficult to reach directly to get counseling, provide easy access, improve the effectiveness and efficiency of services, expand the reach of services, and increase security. However, in using cyber counseling media, it is necessary to pay attention to several things such as student data privacy and security, technology security, and applicable regulations. The school must ensure that cyber counseling services are carried out in compliance with professional ethics and applicable laws in Indonesia. In this case, cyber counseling media can help improve the quality of counseling services at MAN 3 Langkat School.

Optimization of Smart Campus Cybersecurity and Student Privacy Protection Paths Based on Markov Models

Abstract This paper starts with the application of hyper-convergence technology, builds the framework of a university smart campus based on it, and gives the framework description of the smart campus. In order to analyze the network security for the smart campus, the Markov model is used as the basis combined with the reinforced Q learning algorithm for network node security detection, and a specific simulation analysis is given. The encryption performance and defense performance of the elliptic curve cryptosystem are analyzed through the elliptic curve cryptosystem to formulate the encryption scheme for students’ private data in the smart campus. The results indicate that the Markov model node detection combined with reinforcement Q-learning in this paper takes a maximum time of about 5.75s when the network node size reaches 150. When the number of nodes in the smart campus network is 30, under brute force attack, the whole network is captured only when the number of malicious nodes increases to more than 22, while under random attack, it takes as many as 30 malicious nodes to join before the network completely falls. This illustrates that the use of the Markov model can be realized to analyze the network security of the smart campus and that student privacy protection needs to further improve the awareness of student data privacy protection and develop the habit of assessing the privacy risk beforehand in their daily network behavior.

Contextualizing privacy with wearable data in higher education

AbstractThis conceptual study uses dynamic systems theory (DST) and phenomenology as lenses to examine data privacy implications surrounding wearable devices that incorporate stakeholder, contextual and technical factors. Wearable devices can impact people's behaviour and sense of self, and DST and phenomenology provide complementary approaches for emphasizing the subjective experiences of individuals that occur with the use of wearable data. Privacy is approached through phenomenology as an individual's lived bodily experience and DST emphasizes the self‐regulation and feedback loops of individuals and their uses of wearable data. The data collection, analysis and communication of wearable data to support learning systems alongside privacy implications for each are examined. The IoT, cloud computing, metadata and algorithms are discussed as they relate to wearable data, pointing out privacy risks and strategies to minimize harm. Practitioner notesWhat is already known about this topic Data privacy is a complex topic and is approached through different perspectives, influencing the degree of an individual's data autonomy. Wearable technology is increasing in the consumer market and offers great potential to learning environments. What this paper adds Extends extant literature on dynamic systems theory and phenomenology, contributing these perspectives to educational research in the context of student data privacy and wearable technologies. Provides a framework to understand the complex and contingent ways that privacy can be understood in the collection, analysis, and communication of wearable data to support learning. Implications for practice and/or policy Higher education faculty and educational policymakers should consider various interactions in systems and among systems of how wearable data collection may be analysed, communicated and stored, potentially exposing students to privacy harms. Multiple actors in learning systems must engage in continuous and evolving feedback loops around data security, consent, ownership and control to determine who has access to student data, how it is used and for what purposes. The EU's General Data Protection and Regulation offers one of the most comprehensive frameworks for higher education institutions and faculty around the world to follow for protecting student data privacy.

Ethics of Artificial Intelligence in Education: Student Privacy and Data Protection

Rapid advances in artificial intelligence (AI) technology are profoundly altering human societies and lifestyles. Individuals face a variety of information security threats while enjoying the conveniences and customized services made possible by AI. The widespread use of AI in education has prompted widespread public concern regarding AI ethics in this field. The protection of pupil data privacy is an urgent matter that must be addressed. On the basis of a review of extant interpretations of AI ethics and student data privacy, this article examines the ethical risks posed by AI technology to student personal information and provides recommendations for addressing concerns regarding student data security.

Analisis Penerapan Teknologi dalam Pendidikan dan Dampaknya terhadap Kesehatan di Lingkungan Sekolah

This study aims to analyze the application of technology in education and its impact on health in the school environment. The research method used is literature study by collecting data and information from trusted sources. The results of the study show that the use of technology in education has complex positive and negative impacts. Application of technology can help improve the quality of learning and assessment, open up opportunities for remote learning, develop students' digital skills, and pay attention to the safety and privacy of student data. However, the unwise use of technology can have negative impacts on students' health, such as sleep disturbances, eye fatigue, stress, and gadget addiction. The discussion in this study includes the factors that influence the application of technology in education, the positive and negative impacts of using technology on students, as well as strategies that can be implemented to minimize the negative impacts and maximize the positive impacts of the application of technology in education.

Proposed artificial intelligence algorithm and deep learning techniques for development of higher education

Artificial intelligence (AI) has been increasingly impacting various aspects of our daily lives, including education. With the rise of digital technologies, higher education has also been experiencing a transformation, and AI has been playing a crucial role in this transformation. The application of AI in higher education has been rapidly increasing, with a focus on improving student engagement, increasing efficiency, and enhancing the learning experience. The use of AI in higher education is not without its challenges and ethical considerations. One of the biggest challenges is ensuring the accuracy and fairness of AI algorithms, as well as avoiding potential biases. In addition, there are concerns about the privacy of student data, as well as the potential for AI to replace human instructors and support staff. Another challenge is ensuring that AI is used in a way that supports the overall goals of higher education, such as promoting critical thinking and creativity, rather than just being used as a tool for automating tasks and increasing efficiency. In this article, we will discuss the various ways in which AI is being applied in higher education where a proposed model for improving the cognitive capability of students is proposed and compared to other existing algorithms. It will be shown that the proposed model shows better performance compared to other models.

The answer is (not only) technological: Considering student data privacy in learning analytics

AbstractEvidence shows that appropriate use of technology in education has the potential to increase the effectiveness of, eg, teaching, learning and student support. There is also evidence that technology can introduce new problems and ethical issues, eg, student privacy. This article maps some limitations of technological approaches that ensure student data privacy in learning analytics from a critical data studies (CDS) perspective. In this conceptual article, we map the claims, grounds and warrants of technological solutions to maintaining student data privacy in learning analytics. Our findings suggest that many technological solutions are based on assumptions, such as that individuals have control over their data (‘data as commodity’), which can be exchanged under agreed conditions, or that individuals embrace their personal data privacy as a human right to be respected and protected. Regulating student data privacy in the context of learning analytics through technology mostly depends on institutional data governance, consent, data security and accountability. We consider alternative approaches to viewing (student) data privacy, such as contextual integrity; data privacy as ontological; group privacy; and indigenous understandings of privacy. Such perspectives destabilise many assumptions informing technological solutions, including privacy enhancing technology (PET). Practitioner notesWhat is already known about this topic Various actors (including those in higher education) have access to and collect, use and analyse greater volumes of personal (student) data, with finer granularity, increasingly from multiplatforms and data sources. There is growing awareness and concern about individual (student) privacy. Privacy enhancing technologies (PETs) offer a range of solutions to individuals to protect their data privacy. What this paper adds A review of the assumption that technology provides adequate or complete solutions for ensuring individual data privacy. A mapping of five alternative understandings of personal data privacy and its implications for technological solutions. Consideration of implications for the protection of student privacy in learning analytics. Implications for practice and/or policy Student data privacy is not only a technological problem to be solved but should also be understood as a social problem. The use of PETs offers some solutions for data privacy in learning analytics. Strategies to protect student data privacy should include student agency, literacy and a whole‐system approach.

Student Data Privacy and Automatic Textbook Billing

The textbook market in U.S. higher education is changing. In recent years, publishers have developed an automatic billing model, in which colleges and universities negotiate deals with publishers to provide ebooks and courseware to students, folding the cost into student fees. This model is commonly known as "inclusive access." Because it offers students first-day access to course materials - important to student success - as well as some savings over full-priced standard textbooks, it is becoming popular with faculty and administrators. But textbook publishers are promoting these plans for another reason: The data they can collect with digital materials opens a lucrative new market, allowing them to diversify into analytics services.

DEVELOPING FOR AND DEPLOYING WEBWORK ACROSS DISCIPLINES IN SECOND-YEAR ENGINEERING

Online homework systems provide immediate feedback to students, enhancing student learning. However, paid online homework from textbook publishers or other sources systems can be costly and also raise concerns about student data privacy. WeBWorK is an open-source online homework system that can be setup on local servers, is free to students and has been in use since its development in the mid-1990s. Previous to this work around 200 engineering problems were openly shared on the WeBWorK platform, limiting opportunity for adoption. In order to address this, we have developed, deployed, and evaluated nearly 1000 new engineering problems across a wide range of engineering topics at the second-year level. 
 Student perceptions of WeBWorK have been evaluated using surveys at the start and end of courses where it is deployed. These surveys indicate that students generally prefer the WeBWorK system to other online homework systems they have used. Surveys also indicate that students were generally motivated to both attempt and complete all assigned problems that contributed to their grade, and believed WeBWorK enhanced their learning. The creation of error-free WeBWorK questions was difficult, however the hope is that the ability to re-use and share these questions ensures they provide a higher value over the long term than paper-based homework problems.

The syllabus as a student privacy document in an age of learning analytics

PurposeThe purpose of this paper is to reveal how instructors discuss student data and information privacy in their syllabi.Design/methodology/approachThe authors collected a mixture of publicly accessible and privately disclosed syllabi from 8,302 library and information science (LIS) courses to extract privacy language. Using privacy concepts from the literature and emergent themes, the authors analyzed the corpus.FindingsMost syllabi did not mention privacy (98 percent). Privacy tended to be mentioned in the context of digital tools, course communication, policies and assignments.Research limitations/implicationsThe transferability of the findings is limited because they address only one field and professional discipline, LIS, and address syllabi for only online and hybrid courses.Practical implicationsThe findings suggest a need for professional development for instructors related to student data privacy. The discussion provides recommendations for creating educational experiences that support syllabi development and constructive norming opportunities.Social implicationsInstructors may be making assumptions about the degree of privacy literacy among their students or not value student privacy. Each raises significant concerns if privacy is instrumental to intellectual freedom and processes critical to the educational experience.Originality/valueIn an age of educational data mining and analytics, this is one of the first studies to consider if and how instructors are addressing student data privacy in their courses, and the study initiates an important conversation for reflecting on privacy values and practices.

Backtalk

Unfortunately, all of the efforts around student data privacy have done little to allay the concerns of parents, say the authors, pointing to a recent survey showing that 79% of parents cited privacy as a concern when their students use technology for school. Compliance with privacy laws is not sufficient. These laws do not address the issue at the heart of privacy concerns — and that is trust. School leaders, online service providers, education associations, and other stakeholders must begin to build trust around the collection and use of data.

Student data privacy is cloudy today, clearer tomorrow

An introduction to the big picture conversation on student data privacy and the norms that are coming out of it. The author looks at the current state of federal law, and ahead to proposed legislation at the federal level. The intent is to help educators become familiar with the key issues regarding student data privacy in education so as to participate in the conversation.

New technology “clouds” student data privacy

As technology has leaped forward to provide valuable learning tools, parents and policy makers have begun raising concerns about the privacy of student data that schools and systems have. Federal laws are intended to protect students and their families but they have not and will never be able to keep up with rapidly evolving technology. School systems can help themselves and their students by following a list of guidelines, the authors say.

Demographics of Deaf Education: More Students in More Places

We have witnessed important changes in the demographics of the deaf and hard of hearing student population receiving special education services during the past quarter century. The ethnic, intervention, and educational setting profiles are more diverse and dispersed. On top of the federal policy changes driving emerging intervention and continuing educational setting changes, there is now an increasing demand for deaf and hard of hearing students to participate in the general curriculum and school accountability systems. Over the same time period, stricter control over student data privacy and greater scrutiny of human subject protection have been incorporated into federal regulations. We highlight some consequences for deaf education research resulting from the convergences of these parallel trends in changing demographics, shifting school policy contexts, and regulation of federally funded research.

Privacy of data in education

With cheaper mass data storage equipment and computers that can process data with great speeds there is the perception of an increasing danger that the privacy of student data will be violated. Responses to this have varied with countries. Some are in the initial stage of becoming aware of the problem while others have reached the final stage of enacting and enforcing laws that protect the privacy of student data. These national developments are identified and from (his survey there appears to be a trend in many countries towards the acceptance of certain rights as they relate to privacy of student data. They are the rights of notification, access, challenge, prompt correction, erasure, control and finally, the right to redress. These are discussed along with the possible responses and strategies by educational management. The cost and other implications of each of these strategies are also discussed. The cost is estimated based on experience with implementing privacy rights in the government sector and by a simulation cost model for the education sector. Also discussed are the possible consequences for educational management of not complying with the privacy laws. In some countries this could result in fines, imprisonment or both.