Higher Education Data Research Articles

Big Data in Higher Education: Bibliometric Analysis

Big Data in Higher Education: Bibliometric Analysis

MODEL OF STUDENT PREPARATION IN HIGHER EDUCATION IN THE CONTEXT OF BIG DATA

Developing a robust educational system across all domains is essential for providing contemporary educational programs. The aim of our research work is to identify and practically implement the theoretical foundations of preparing students in the field of big data in higher education, which is the cornerstone of training amodern, competitive future specialist.Ensuring the high-quality organization of fundamental disciplines is crucial in developing specific competencies for future educators. Therefore, the main focus of the proposed article is on shaping an educated individual with a broad outlook and high culture of thought in preparing IT specialists at the L.N. Gumilyev Eurasian National University. The article provides a comprehensive definition of the model of student preparation in higher education in the context of big data, outlines and implements components, and discusses the model for achieving positive outcomes. Before considering the model for improving student preparation in the context of big data, let us briefly define big data and examine the concept of a model.

Learning recommendations from educational event data in higher education

Learning recommendations from educational event data in higher education

Decolonising data in higher education: critical issues and future directions

ABSTRACT This paper revisits the concept of ‘data colonialism’ and its critiques, suggesting a reframed conceptual framework tailored to interrogating and resisting data colonialism in the context of higher education, with the aim of advancing decolonial insights. Theorising data colonialism needs to move beyond simply identifying processes of data extractivism – e.g., how global university rankings and citation indexes are used to shape education policy, teaching and learning – towards a holistic project of decolonising higher education. A decolonial project that entails datafication interrupts both data extraction and data dispossession at all levels of higher education institutions – from the assessment methods of teaching, learning and research to student recruitment processes and so on. The analysis in this paper contributes to existing scholarship on datafication in higher education by paying explicit attention to the epistemic violence that is reproduced through data-dependent technologies and data practices and discussing how this violence may be counteracted.

"Academic Resonance: Women's Work-Life Harmony"

Women's pivotal societal role includes academia, yet balancing home management and professional duties presents distinct challenges for female educators. This study explores how women teaching professionals in higher education harmonize work and personal spheres. Demographic and key variable analyses investigate workplace influence on their work-life balance. 163 women employees' data in higher education were examined, encompassing age, experience, and perceptions of work-life balance factors. Data reliability used the Cronbach alpha Method. Outcomes highlight variables impacting academic performance and work-life balance. Challenges faced by women in higher education emerge, encompassing personal responsibilities, work-family conflicts, excessive workloads, and institutional policies. This research underscores women's intricate experiences, revealing multifaceted factors influencing equilibrium between professional and personal lives in higher education.

The Republic of North Macedonia's Research Ranking Platform for Academic Staff and Universities

In the present-day realm of higher education, the integration of open data and machine learning has become a crucial means of improving the evaluation and ranking of research publications by university professors. This research endeavours to address the pressing need for an effective computerized paradigm that combines the collection, analysis, and use of public data in higher education. The main objective is to provide recommendations in the form of informative reports for interested institutions and to rank university professors' scientific works on an open data platform. This research not only contributes to the advancement of educational evaluation methodologies but also responds to the growing importance of open data and machine learning in modern higher education. By automating the assessment of research publications, universities can better track and enhance their academic impact, ultimately fostering a culture of excellence in research and innovation.

Mathematical Modeling and Multi-Objective Optimization Methods for Big Data in Higher Education

Mathematical Modeling and Multi-Objective Optimization Methods for Big Data in Higher Education

Pengujian Regresi Otomasi pada Aplikasi Mobile Satudikti Menggunakan Katalon Studio

Satudikti is a one-stop application from the many services available at the Directorate General of Higher Education, to make it easier for users to access various higher education services in one application. Before the application can be widely used, a testing step is indispensable. This is due to potential problems in higher education data that can have a fatal impact, considering that this data is closely related to state information. And if there is an error in the software, the Directorate General of Higher Education must restart the development process that can harm the state. Therefore, the purpose of this study is to test the readiness of the application when it is widely used and reduce the risk of errors to ensure that the application remains of sufficient quality for users, as well as analyze the effectiveness of the application and then whether automated regression testing using Katalon Studio has more value than manual testing. Testing in this study was carried out manually with the black-box testing method and automated testing using Katalon Studio as an automated testing tool. Testing using Katalon Studio found that the functionality, features, and access to the tested pages worked well according to existing requirements. The implementation of automation testing using Katalon Studio has also proven to be more efficient in reducing test time and providing detailed test results to detect errors with easy-to-understand reporting.

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.

A decade of research into the application of big data and analytics in higher education: A systematic review of the literature

The need for data-driven decision-making primarily motivates interest in analysing Big Data in higher education. Although there has been considerable research on the value of Big Data in higher education, its application to address critical issues within the sector is still limited. This systematic review, conducted in December 2021 and encompassing 75 papers, analysed the applications of Big Data and analytics in higher education. The focus was on their usage in supporting learning, teaching and administration as reported in papers indexed in SCOPUS, Web of Science and IEEE Xplore. The key findings from the review revealed that Big Data and analytics are predominantly used to support learning and, to a lesser extent, guide teaching and informing administrative decision-making processes. The review also identified a set of studies focused on supporting student well-being. Further, we extend the use of Big Data in higher education to include the well-being of students and staff. This paper contributes to the growing debate on the practical use of Big Data and analytics to provide valuable insights for solving systemic challenges facing high education in the twenty-first century.

Pricing Strategies of Russian State Universities

The paper analyzes the pricing strategies of Russian state universities for bachelor and specialist programs. The analysis is carried out by studying the ratio coefficients of the tuition fees and the funding standards for budget students. On the basis of 2016–2019 Monitoring of enrollment quality at Russian universities and Russian Ministry of Science and Higher Education data, it is shown that Russian universities use three types of pricing strategies when attracting a certain contingent of fee-paying students. The first strategy is typical for the leaders of the Russian higher education market: their tuition fee is significantly higher than the funding standards for budget students, while the university’s market niche allows to consistently enroll fee-paying students with a high level of training. The second strategy is used by universities that are able to enroll the required number of fee-paying students with a good level of training on a competitive basis and have advantages at the local and cross-regional levels: tuition fee is approximately at the same level as the funding standards. The third strategy is used by non-selective universities, where the quality of the education allows to enroll fee-paying students only using tuition fees that are below the level of funding standards. The results of the analysis show that competitive universities with a higher level of financial capacity set tuition fees, which significantly exceed the funding standards. At the same time, universities with weak financial capacity and difficulties in fee-paying enrollment are prone to price dumping in violation of regulatory requirements.

Open data: an innovative learning resource for postgraduates

PurposeWith the continuous development of the global COVID-19 epidemic, mobile learning has become one of the most significant learning approaches. The mobile learning resource is the basis of mobile learning; it may directly affect the effectiveness of mobile learning. However, the current learning resources cannot meet users' needs. This study aims to analyze the influencing factors of accepting open data as learning resources among users.Design/methodology/approachBased on the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT), this study proposed a comprehensive theoretical research model. Data were obtained from 398 postgraduates from several universities in central China. Confirmatory factor analysis was used to determine the reliability and validity of the measurement model. Data has been analyzed using SPSS and AMOS software.FindingsThe results suggested that perceived usefulness, performance expectancy, social influence and facilitating conditions have a positive influence on accepting open data as learning resources. Perceived ease of use was not found significant. Moreover, it was further shown in the study that behavioural intention significantly influenced the acceptance of open data as learning resources.Originality/valueThere is a lack of research on open data as learning resources in developing countries, especially in China. This study addresses the gap and helps us understand the acceptance of open data as learning resources in higher education. This study also pays attention to postgraduates' choice of learning resources, which has been little noticed before. Additionally, this study offers opportunities for further studies on the continuous usage of open data in higher education.

Effective subunits in ineffective systems: Simultaneous tight and loose coupling in higher education data use

Higher education institutions are pressured to use data for university rankings, accreditation, decision making, and technical support of students and faculty. However, these organizations also experience barriers and resistance to such data-driven aspirations. Rather than simply focusing on the whole organization, this research attends to variations in organizational subunits and asks what processes help and hinder data use. It pays attention to the puzzle of how data use variation can lead to effective subunits just as data fragmentation contributes to ineffective systems. Although universities may create centralized coordinating bodies, the structural and cultural bases of loosely coupled university subunits may upset these efforts. Using the case of a university attempting to centralize its data systems, we employ in-depth interviews of senior and mid-level administrators to understand why technical changes happen within specific subunits even as only ceremonial changes happen for the larger organization. This suggests new insights for interrogating how loose and tight coupling can co-exist in the same organization.

Problems and Prospects for the Art Education Development in Higher Educational Institutions Based on Big Data Technologies and Digital Platforms

In order to build an effective system of providing educational services in the art direction, it is necessary to possess an assessment of the results of Big Data initial implementation. Previously conducted studies on the specifics of implementing digital platforms in the educational art space are incomplete and insufficient. Most universities of different countries introduce these systems independently, and, therefore, there are no methods and principles for implementing such measures in the educational process. The purpose of the present research is to assess the role of Big Data and digital platforms in improving the quality and efficiency of art education. An analysis of assessing the implementation and functioning of digital systems in the leading higher educational institutions of the art direction in Poland, the Czech Republic, and Ukraine was carried out. This made it possible to generalize the experience gained and identify the main trends of this process. This made it possible to generalize the experience gained and identify the main trends of this process. In particular, 70% of students have a positive attitude towards using digital platforms that allow them to expand their awareness and informativeness, and 73% note these platforms as a source of obtaining available information. This made it possible to generalize the experience gained and identify the main tendencies of this process. As a result, modelling of the concept of using Big Data in higher education in the art direction has been presented. The main methods and examples of using Big Data in art education have been defined and characterized. Prospective directions for further application and implementation of innovative digital systems have been indicated. The use of research results creates opportunities for more flexible expansion of existing digital systems and the formation of new directions for subsequent implementation in the educational process. The research pointed to the most significant problem of comprehensive using Big Data by students due to ignorance and lack of awareness of the potential of Big Data in terms of planning, forecasting behavioral actions both in the process of learning, and in future professional activities. Further development of the research topic should focus on the quantitative and qualitative assessment of existing systems and the formation of a detailed methodology for the introduction of the latest educational services in the art education system.

Using Education Data Mining (EDM) and Tracer Study (TS) Data as Materials for Evaluating Higher Education Curriculum and Policies

The policy-making process is influenced by the results of a good evaluation process. However, to get valid results, the evaluation process requires supporting data and technology. Higher education produces huge amounts of data but is still lacking in turning it into useful information. With the rapid development of information technology, higher education data must be processed into information that can be used as an evaluation and basis for making quick and accurate policies. Educational Data Mining (EDM) is a technique used to measure students’ academic achievement to improve their results, assess the learning process, evaluate the overall quality of education, etc. Besides, the relevance of the curriculum followed by a university and the competencies possessed can be analyzed using a tracer study (TS). The current study uses EDM to extract information from higher education data and tracer study results. The relationship between higher education data and TS data was analyzed. The results indicated that not all students with higher GPA scores get jobs per the competencies they had in college, however, those with higher GPAs and who were actively organized during college are easier to find work. The study results must be considered as material for curriculum evaluation and for making higher education policies.
 Keywords: higher education, Educational Data mining development, Tracer Study, curriculum evaluation, policy making

Returning the data gaze in higher education

ABSTRACT Learning analytics offer centralization of a particular understanding of learning, teaching, and student support alongside data-informed insight and foresight. As such, student-related data in higher education can be imagined and enacted as a ‘data frontier’ in which the data gaze is expanding, intensifying, and performing new meanings and practices. But the gaze is not necessarily one-way. In this article we conceptualize different ways the data gaze might be returned. Drawing on more-than-human theorizing, we map three descriptive accounts to better understand the tangle of datafied bodies complicit in the gaze of learning analytics and to inform how the data gaze may be re-shaped, re-directed, and re-storied as forms of data activism. This includes uncovering what algorithms do, improvising passages, and talking back with data-bodies. Such theorization offers insights for new modes of proactive data activism to address the problematics and opportunities of the intensified datafication of learning analytics platforms.

Social Networking Media in Higher Education: A Review

Social media has become a constant presence in the daily lives of young people. As a result, many educational procedures and processes have been influenced by the widespread use of social networks. The number of people using social media has continuously increased. Students’ perceptions on how social media affects their learning is a prominent subject of research. Students’ school achievement has been significantly impacted by their interaction with professors, peers, and online knowledge-sharing behaviors. Several aspects are involved in this review, which aims to shed light on how students use social media in higher education. Findings reveal that the usage of online social media for cooperative learning encourages students to be innovative, dynamic, and research oriented. In other words, it is a matter of academic competence. This study gathered and analyzed social media data in higher education for computing disciplines. Digital libraries from EBSCO, IEEE, and ACM were utilized for data gathering. All of the above traits were found to be positively influenced by social networks, which suggest that their usage in higher education should be broadened. Teachers and educational institutions have not taken full use of internet social networks. Keywords: social media; social networks; higher institutions education

Big Data in Higher Education for Student Behavior Analytics (Big Data-HE-SBA System Architecture)

Big data is an important part of innovation that has recently attracted a lot of interest from academics and practitioners alike. Given the importance of the education industry, there is a growing trend to investigate the role of big data in this field. Much research has been undertaken to date in order to better understand the use of big data in many sectors for diverse reasons. Big data in higher education, however, still lacks a complete examination. Thus, the purposes of the research were (1) to design the system architecture of big data in higher education for student behavior analytics and (2) to evaluate the system architecture of big data in higher education for student behavior analytics. The research procedure was divided into two phases. The first phase is designing a system architecture for big data in higher education for student behavior analytics, and the second phase is the architecture evaluation by experts. Purposive sampling was used to select ten experts in big data and student behavior analytics. Data collection tools were the system and the assessment of an appropriate model with a five-level rating scale. The statistics used in the data analysis were means and standard deviation. The results showed that the system architecture of big data in higher education for student behavior analytics consists of four elements: a) Big Data Sources for Behavioral Analytics; b) Big Data Sources for Behavioral Analytics Sub-Domains; c) Big data capture and storage for behavioral analytics; and d) big data behavioral analysis. The experts' opinions on the system architecture were at the most appropriate level.

Analisa Data Penerimaan Siswa Pada Perguruan Tinggi melalui Jalur SNMPTN menggunakan Algoritma Fuzzy C-Means (Studi Kasus : SMAN 5 Kota Bengkulu)

SMA Negeri 5 Bengkulu City is one of State Senior High Schools in Bengkulu City that has been accredited A with a total of 58 educators and 23 administrative staff and employees. In addition, SMA Negeri 5 Bengkulu City has also won many achievements both in academics (graduates of SMA N 5 Bengkulu City are accepted at the best universities in Indonesia through test and non-test pathways) as well as in the fields of science (Olympics), Sports, IMTAQ and the arts for the provincial and national levels. Application of student admissions data in higher education through SNMPTN using Fuzzy C-Means Algorithm at SMA N 5 Bengkulu City is an application that can help analyze data grouping based on student admission data into 3 groups. Data Analysis of Student Admissions in higher education through SNMPTN was made using the Visual Basic.Net programming language and SQL Server 2008 database by applying the Fuzzy C-Means algorithm. This application is able to provide information on the results of the analysis of student admissions at Higher Education through SNMPTN. The more data on student admissions in Higher Education, the more accurate the grouping results. Based on the results of the tests that have been carried out, the Application of Student Admission Data in Higher Education through SNMPTN can provide information on the results of data grouping divided into 3 groups, namely high, medium and low

Optimizing Information Dissemination Model for Improvement of College Students’ Education Based on Learning Community

In the context of the popularization and diversified application of information technology in higher education, efficient information dissemination has a significant impact on the learning effect of the learning community. Improving the efficiency of information dissemination and driving the force of learning to enhance the learning effect are the hot issues in the field of higher education data analysis. This paper proposes a new method of feature fusion using information entropy and ReliefF algorithm, applies the improved PageRank algorithm and K-means algorithm to optimize the information transfer mode, and finally develops a new and efficient network information model. The comparative test results show that the new model can complete the dissemination of the same amount of information with a smaller delivery ratio. The research results can play an advantageous role in information interaction feedback, curriculum quality analysis, and teaching information transmission.