Higher Education Data Research Articles

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.

An evolution of performance data in higher education governance: a path towards a ‘big data’ era?

ABSTRACT Performance data in higher education has gone through a major development in the last few decades. Simple input measures have given way to increasingly nuanced and dynamic output measures and performance indicators have become an integral part of management at the organisational and system level. The evolution of higher education performance measurement shows a reiterative relationship between data availability, its purpose in a governance system and its target audience. Digitalisation of learning, management and communication systems has revolutionised data availability, creating new possibilities for ‘big data’ use. Based on insights from the past evolution, current experiments with ‘big data’ and lessons from other sectors, the article explores what the new ‘big data’ era might mean for higher education governance. The high volume of data but also its speed of accumulation and related analytical techniques, are likely to substantially transform the current relationship between data and performance but also create some technical, ethical and policy challenges.

End-User Needs of Fragmented Databases in Higher Education Data Analysis and Decision Making

In higher education, a wealth of data is available to advisors, recruiters, marketers, and program directors. These large datasets can be accessed using an array of data analysis tools that may lead users to assume that data sources conflict with one another. As users identify new ways of accessing and analyzing these data, they deviate from existing work practices and sometimes create their own databases. This study investigated the needs of end users who are accessing these seemingly fragmented databases. Analysis of a survey completed by eighteen users and ten semi-structured interviews from five colleges and universities highlighted three recurring themes that affect work practices (access, understandability, and use), as well as a series of challenges and opportunities for the design of data gateways for higher education. We discuss a set of broadly applicable design recommendations and five design functionalities that the data gateways should support: training, collaboration, tracking, definitions and roadblocks, and time.

Understanding students’ effective use of data in the age of big data in higher education

ABSTRACT With the advancement of digital technologies, big data and learning analytics have become prevalent in the higher education. Various student-facing systems increased the amount of data available to students, and whether students can use big data and learning analytics effectively will affect their academic success. Most studies, however, have focused on how teachers and administrative personnel use student data to make data-driven instruction and management decisions. As a result, little attention has been given to students' use of relevant data that generated by big data and learning analytics to promote their own learning and growth. This study explored using social cognitive theory to identify possible environmental, personal, and behavioural factors that affect students' data use. We used an online questionnaire that collected 242 completed surveys from Chinese university students. Partial Least Squares (PLS) path modelling was used to analyse the data. The initial findings support the conclusion that university students could be encouraged to effectively use data in three ways: (1) through the promotion of university-wide cultures of data use and sustained improvements in data quality, (2) through the professional development of student data literacy, and (3) through the support of student data autonomy, student data reflectiveness, and students' digital identities.

Churn Prediction on Higher Education Data with Fuzzy Logic Algorithm

Colleges are an optional final stage of formal education. However, with time, the Management Section finds the fact that the student churn rate in the university scope becomes a problem. The purpose of this research is to predict whether the student will be churn or loyal in the future, the data will be taken from 2014. The analytical technique used in this research process is the Fuzzy method of C-Means. At a university, the variable that will be tested is the Length of students who are currently attending higher education at the University or campus, when the last student pays enrollment, the last payment period, and the student's total payment. The data used as many as 100 datasets were collected in this research, starting from 2014 to 2020. Of the 100 Datasets of informatics engineering, students gained 92% of loyal students, and 8% of students predicted to churn.

Knowledge Discovery for Higher Education Student Retention Based on Data Mining: Machine Learning Algorithms and Case Study in Chile.

Data mining is employed to extract useful information and to detect patterns from often large data sets, closely related to knowledge discovery in databases and data science. In this investigation, we formulate models based on machine learning algorithms to extract relevant information predicting student retention at various levels, using higher education data and specifying the relevant variables involved in the modeling. Then, we utilize this information to help the process of knowledge discovery. We predict student retention at each of three levels during their first, second, and third years of study, obtaining models with an accuracy that exceeds 80% in all scenarios. These models allow us to adequately predict the level when dropout occurs. Among the machine learning algorithms used in this work are: decision trees, k-nearest neighbors, logistic regression, naive Bayes, random forest, and support vector machines, of which the random forest technique performs the best. We detect that secondary educational score and the community poverty index are important predictive variables, which have not been previously reported in educational studies of this type. The dropout assessment at various levels reported here is valid for higher education institutions around the world with similar conditions to the Chilean case, where dropout rates affect the efficiency of such institutions. Having the ability to predict dropout based on student’s data enables these institutions to take preventative measures, avoiding the dropouts. In the case study, balancing the majority and minority classes improves the performance of the algorithms.

Developing a measure of innovation from research in higher education data

The benefit of science is often limited to the contribution to general knowledge. The definition of innovation of research is complex and multidimensional. Size of research expenditure budget and publication and citation metrics are most often used to measure innovation of institutional research. This project aims to construct a measure for innovation of institutional research that has convergent validity with societal benefit from university research. The sample included 143 institutions in the US who responded to the 2014 Association of University Technology Managers (AUTM) Licensing Survey Data on faculty size, research expenditure, publications, citations, intellectual property outcomes, clinical trials registration and results, and contributions to clinical practice guidelines were included. Exploratory Structural Equation Modeling (ESEM) was used to determine the most parsimonious model with all available indicators from the AUTM Licensing Survey Data. A Second-Order Confirmatory Factor Analysis (CFA) was used to validate the ESEM results. Second order CFA confirmed hypothesis of an overall latent factor of research innovation. These results indicate that innovation of institutional research can be evaluated on three factors: contributions to knowledge, public health innovation, and economic impact. There have been no previous efforts to empirically measure the multidimensionality of innovation of institutional research with the inclusion of public health impact.

How stakeholders\u2019 data literacy contributes to student success in higher education: a goal-oriented analysis

Student success is becoming a shared vision for quality in higher education. Majority data in higher education have not been transformed into actionable insights for quality enhancement. Data are dispersed among stakeholders, and stakeholders’ data literacy influences the effectiveness of using data for student success. However, existing studies mainly focus on students’ data literacy; the analysis of other stakeholders’ data literacy for student success is still few. This study aims to explore how stakeholders’ data literacy contributes to student success in a holistic view. The salience model is used to identify core stakeholders. The goal-modeling language iStar is used to present how stakeholders contribute to student success. A competencies matrix of data literacy is used to discuss the specific data literacy competencies that stakeholders should focus on promoting student success. A survey is conducted to validate the goal-oriented analysis and the discussions on specific competencies of data literacy for stakeholders. The goal-oriented analysis presents the complexity of interactions and dependencies among stakeholders for student success. This study helps to raise stakeholders to be aware of the importance of their data literacy and the necessity of collaboration on exploiting vast available data for student success.

Implementation of OLAP and K-Medoids Clustering for Accreditation Data Analysis of Study Programs

At present to maintain the quality of the data in the study program is very much needed, given the current accreditation based on PDDIKTI(Pangkalan Data Pendidikan Tinggi) data which must be reported every semester. Higher education data are generally still separate, there is no valid data warehouse and system for data analysis so that it complicates data quality control. The purpose of this study is to apply data warehouse, OLAP (Online Analytical Processing) and k-medoids clustering for data prediction and control according to the study program’s accreditation self-evaluation report matrix. This study uses data the last 3 years for prospective new students, students, students, study periods, achievements and lecturers to be analyzed using OLAP and the k-medoids cluster. The results showed OLAP can see information in an informative, real-time data accreditation matrix and the k-medoids cluster produces accurate cluster estimates with an evaluation value of Davies Bouldin Index of 0.2927 and said to be a good cluster

Do Cooperative-Based Learning Groups Help Students Learn Microeconomics?

As many college students voluntarily form cooperative-based learning groups to study course materials, this article investigates whether or not such type of learning improves their academic performance. This is the first research using Taiwan’s higher education data to study cooperative learning in the field of economic education. The data used herein are from 10 intermediate microeconomics courses from 2006 to 2016 at a public university in Taiwan. The sample size is 1,389, which encompasses 120 to 150 enrolled students in each semester. We use an instrumental variables (IVs) approach to remedy the potential endogeneity problem associated with forming cooperative-based learning groups, and the key IV employed is the number of students with the same academic major. The IV estimation results show that forming cooperative-based learning groups does significantly improve college students’ learning outcomes. In addition, the cooperative learning effect is heterogeneous across groups. Students with poor attendance records benefit more from voluntarily formed cooperative learning groups in learning microeconomics. Moreover, it is worth noting that the magnitude of the cooperative learning effect from our analysis is greater than the typical class attendance effect.

Management of Data in Higher Education and the Quality of Academic Research of University Lecturers: A Case Study in South-South Nigeria

The existing body of knowledge has witnessed gaps arising from the paucity of research literature on the quality of educational research output in higher education. This study shows how the management of data in higher education affects the quality of academic research conducted by university lecturers in South-South Nigeria. The study adopted a factorial research design. The population of this study includes all the Faculty of Education academic staff in both public and private universities situated in South-South Nigeria. Using results of a survey administered to a sample of 602 accessible lecturers during data collection, one research question and one null hypothesis were resolved in the study. Multiple regression was employed in the analysis of data. Findings revealed amongst others that; data storage, data security, data retrieval, data sharing, and data re-use jointly contributed to the total variance in educational research effectiveness in terms of proper citations by 56.25%, problem-solving by 22.14%, knowledge creation by 34.50%, and generation of testable data by 36.88%; and their composite influence on educational research effectiveness in terms of proper citations, problem-solving, knowledge creation, and generation of testable data, is statistically significant. Based on the findings of this study, it was concluded that data management practices play an important role in higher education that significantly influences the quality of research output in universities in South-South Nigeria. It was recommended among others that, there should be an up-to-date database or data warehouse in Ministries of Education where researchers can easily access data in order to increase the quality of researches by eliminating the problems of data fabrication and falsification.

Comparative Analysis for Boosting Classifiers in the Context of Higher Education

Machine learning techniques are applied on higher education data for analyzing the interac-tion between the students and electronic learning systems. This type of analysis serves in predicting students’ scores, in alerting students-at-risk, and in managing the degree of stu-dent engagement to educational system. The approaches in this work implements the divide and conquer algorithm on feature set of an educational data set to enhance the analysis and prediction accuracy. It divides the feature set into a number of logical subgroups based on the problem domain. Each subgroup is analyzed separately and the final result is the combi-nation of the results of the analysis of these subgroups. The classifier that shows the best prediction accuracy is dependent on the logical non-statistical nature of the features in each group. Both traditional and boosting classifiers are utilized on each dataset, from which a comparison is conducted to show the best classifiers along with the best dataset. This ap-proach provides the possibility to apply a brute force algorithm in the selection of the best feature subgroups with a low computational complexity. The experimental work shows a high prediction accuracy of the students-at-risk relative to the current research, and provides a list of new important features in the field of electronic learning systems.

Data Management Practices and Educational Research Effectiveness of University Lecturers in South-South Nigeria

The existing body of knowledge has witnessed gaps arising from the paucity of research literature on the quality of educational research output in higher education. This study shows how the management of data in higher education affects the quality of academic research conducted by university lecturers in South-South Nigeria. A sample of 602 lecturers were accessed during data collection and responded to two questionnaires (Data Management Practices Questionnaire – DMPQ and "Educational Research Effectiveness Questionnaire – EREQ). Multiple regression was employed in the analysis of data. Findings revealed amongst others that; data storage, data security, data retrieval, data sharing, and data re-use jointly contributed to the total variance in educational research effectiveness of university lecturers in terms of proper citations by 56.25%, problem-solving by 22.14%, knowledge creation by 34.50%, and generation of testable data by 36.88%. The five data management practices compositely influences the educational research effectiveness of university lecturers in terms of proper citations (F=152.25,p<.05), problem-solving (F=33.90,p<.05), knowledge creation (F=62.78,p<.05), and the generation of testable data (F=69.65,p<.05), is statistically significant. Based on the findings of this study, it was concluded that data management practices (storage, security, retrieval, sharing, and re-use) jointly and relatively contributes to the total variance in educational research effectiveness in terms of proper citations, problem-solving, knowledge creation and generation of testable data at varying extents. It was recommended, among others that, lecturers should practice the habit of managing research data in their personal computers and through manual channels. These records could become vital in the future for further problem-solving.

Beneficios del uso de técnicas de minería de datos para extraer y analizar datos de twitter aplicados en la educación superior: una revisión sistemática de la literatura

En los últimos años, existe un creciente interés por los actores de la educación en la inclusión de las TIC en sus instituciones, como es el caso de las redes sociales, que lejos de ser un problema y mediante un uso guiado de las mismas, permiten innovar las sesiones de clases tradicionales y mejorar la comunicación entre docentes y estudiantes. En el presente estudio se plantearon dos objetivos: (1) realizar una revisión sistemática de la literatura, mediante la búsqueda de artículos publicados entre Enero/2007 y Marzo/2019, en bases de datos como ACM, IEEE, ScienceDirect, Springer, entre otras, para identificar las investigaciones que han aplicado técnicas de minería de datos, para la extracción y análisis de datos de Twitter en la educación superior; y, (2) destacar las prácticas pedagógicas que han incorporado Twitter y minería de datos para mejorar los procesos educativos. De los 315 artículos obtenidos, fueron seleccionados 65 que cumplieron con los criterios de inclusión. Los principales resultados indican que: (1) las técnicas de minería de datos más utilizadas son predictivas con tareas de clasificación; (2) Twitter se usa principalmente para: (a) determinar percepción estudiantil; (b) compartir información, material y recursos; (c) generar comunicación y participación; (d) fomentar habilidades; y (e) mejorar la expresión oral y el rendimiento académico; (3) Estados Unidos es el país con mayor número de trabajos; sin embargo, en países de Latinoamérica los hallazgos son pocos, por lo que, se apertura un campo de investigación en esta región; y (4) los estudios incluyeron modelos, métodos, estrategias, teorías o instrumentos como práctica pedagógica; de modo que, no existe un consenso en la forma en que los datos extraídos de Twitter podrían ser incorporados en la educación superior para mejorar los procesos de enseñanza y aprendizaje.

Management of Data in Higher Education and the Quality of Academic Research of University Lecturers: A Case Study in South-South Nigeria

The existing body of knowledge has witnessed gaps arising from the paucity of research literature on the quality of educational research output in higher education. This study shows how the management of data in higher education affects the quality of academic research conducted by university lecturers in South-South Nigeria. The study adopted a factorial research design. The population of this study includes all the Faculty of Education academic staff in both public and private universities situated in South-South Nigeria. Using results of a survey administered to a sample of 602 accessible lecturers during data collection, one research question and one null hypothesis were resolved in the study. Multiple regression was employed in the analysis of data. Findings revealed amongst others that; data storage, data security, data retrieval, data sharing, and data re-use jointly contributed to the total variance in educational research effectiveness in terms of proper citations by 56.25%, problem-solving by 22.14%, knowledge creation by 34.50%, and generation of testable data by 36.88%; and their composite influence on educational research effectiveness in terms of proper citations, problem-solving, knowledge creation, and generation of testable data, is statistically significant. Based on the findings of this study, it was concluded that data management practices play an important role in higher education that significantly influences the quality of research output in universities in South-South Nigeria. It was recommended among others that, there should be an up-to-date database or data warehouse in Ministries of Education where researchers can easily access data in order to increase the quality of researches by eliminating the problems of data fabrication and falsification.

Big Educational Data & Analytics: Survey, Architecture and Challenges

The proliferation of mobile devices and the rapid development of information and communication technologies (ICT) have seen increasingly large volume and variety of data being generated at an unprecedented pace. Big data have started to demonstrate significant values in higher education. This paper gives several contributions to the state-of-the-art for Big data in higher education and learning technologies research. Currently, there is no comprehensive survey or literature review for Big educational data. Most literature reviews from a few authors have focused on one of these fields: educational mining, learning analytics with discussions on one or two aspects such as Big data technologies without educational focus, social media data in education, etc. Most of these literature reviews are short and insufficient to provide more inclusive reviews for Big educational data. In this paper, we present a comprehensive literature review of the current and emerging paradigms for Big educational data. The survey is presented in five parts: (1) The first part presents an overview and classification of Big education research to show the full landscape in this field, which also gives a concise summary of the overall scope of this paper; (2) The second part presents a discussion for the various data sources from education platforms or systems including learning management systems (LMS), massive open online courses (MOOC), learning object repository (LOR), OpenCourseWare (OCW), open educational resources (OER), social media, linked data and mobile learning contributing to Big education data; (3) The third part presents the data collection, data mining and databases in Big education data; (4) The fourth part presents the technological aspects including Big data platforms and architectures such as Hadoop, Spark, Samza and Big data tools for Big education data; and (5) The fifth part presents different approaches of data analytics for Big education data. This part provides a more inclusive discussion on data analytics which is beyond traditional forms of learning analysis in higher education. This includes predictive analytics, learning analytics including collaborative, behavior, personal learnings and assessment, followed by recommendation systems, graph analytics, visual analytics, immersive learning and analytics, etc. The final part of the paper discusses social (e.g. privacy and ethical issues) and technological challenges for Big data in education. This part also illustrates the technological challenges faced by giving an example for utilizing graph-based analytics for a cross-institution learning analytics scenario.

Factor analysis and evaluation of China's higher education development in big data era

Big data has become an indispensable part of higher education development now, and most of the related data in higher education of China can be applied to evaluation and analysis. However, currently there is a lack of research on the development of higher education using the latest China Statistical Yearbook data. In this paper, based on the latest data, by using SPSS to analyse and evaluate the development of higher education, the new issues can be discovered and initial reliability recommendations can be proposed. Therefore, we will have more profound and unique insights and applications in the practice of the big data era, and will further provide evidence and clues to better support and develop China's higher education.

Factor analysis and evaluation of China's higher education development in big data era

Big data has become an indispensable part of higher education development now, and most of the related data in higher education of China can be applied to evaluation and analysis. However, currently there is a lack of research on the development of higher education using the latest China Statistical Yearbook data. In this paper, based on the latest data, by using SPSS to analyse and evaluate the development of higher education, the new issues can be discovered and initial reliability recommendations can be proposed. Therefore, we will have more profound and unique insights and applications in the practice of the big data era, and will further provide evidence and clues to better support and develop China's higher education.

LIGHTNING TALK: Impact Of Academic Advising On Retention: Care Within The Context Of Student Success

Location: George's Student data in higher education is increasingly available, yet understanding how to better support students given this data remains a challenge. Computational models help synthesize the data in a way where trends and patterns become more easily understood. The ability to use modeling software to test theories of student support help us understand, predict, and mitigate challenges. By capitalizing on the access to data and technology we can better support our students for better retention.

Policy networks, performance metrics and platform markets: Charting the expanding data infrastructure of higher education

AbstractDigital data are transforming higher education (HE) to be more student‐focused and metrics‐centred. In the UK, capturing detailed data about students has become a government priority, with an emphasis on using student data to measure, compare and assess university performance. The purpose of this paper is to examine the governmental and commercial drivers of current large‐scale technological efforts to collect and analyse student data in UK HE. The result is an expanding data infrastructure which includes large‐scale and longitudinal datasets, learning analytics services, student apps, data dashboards and digital learning platforms powered by artificial intelligence (AI). Education data scientists have built positive pedagogic cases for student data analysis, learning analytics and AI. The politicization and commercialization of the wider HE data infrastructure is translating them into performance metrics in an increasingly market‐driven sector, raising the need for policy frameworks for ethical, pedagogically valuable uses of student data in HE.Practitioner NotesWhat is already known about this topicLearning analytics, education data science and artificial intelligence are opening up new ways of collecting and analysing student data in higher education.UK government policies emphasize the use of student data for improvements to teaching and learning.What this paper addsA conceptual framework from “infrastructure studies” demonstrates how political objectives and commercial aims are fused to HE data systems, with data infrastructure becoming a key tool of government reform.A critical infrastructure analysis shows that student data processing technologies are being developed and deployed to measure university performance through student data.Implications for practice and/or policyEducators and managers in universities need to prepare robust institutional frameworks to govern their use of student data.Learning analytics practitioners, data scientists, learning scientists and social science researchers need to collaborate with the policy community and education technology developers on new policy frameworks to challenge narrow uses of student data as performance metrics.