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Paulina Berríos; Estefanía Álvarez; Karen Gutiérrez; Antonia Santos – Association for Institutional Research, 2024
This article delves into the challenges of institutional data collection processes in higher education, particularly regarding diversity reporting. The study this article is based on focuses on enhancing inclusivity by introducing a nonbinary sex category into the institutional data of a distinguished Chilean public university. In the Chilean…
Descriptors: Gender Identity, LGBTQ People, Classification, Data Collection
Cross-Classified Item Response Theory Modeling with an Application to Student Evaluation of Teaching
Sijia Huang; Li Cai – Journal of Educational and Behavioral Statistics, 2024
The cross-classified data structure is ubiquitous in education, psychology, and health outcome sciences. In these areas, assessment instruments that are made up of multiple items are frequently used to measure latent constructs. The presence of both the cross-classified structure and multivariate categorical outcomes leads to the so-called…
Descriptors: Classification, Data Collection, Data Analysis, Item Response Theory
Sghir, Nabila; Adadi, Amina; Lahmer, Mohammed – Education and Information Technologies, 2023
The last few years have witnessed an upsurge in the number of studies using Machine and Deep learning models to predict vital academic outcomes based on different kinds and sources of student-related data, with the goal of improving the learning process from all perspectives. This has led to the emergence of predictive modelling as a core practice…
Descriptors: Prediction, Learning Analytics, Artificial Intelligence, Data Collection
Li, Maximilian Xiling; Nadj, Mario; Maedche, Alexander; Ifenthaler, Dirk; Wöhler, Johannes – Technology, Knowledge and Learning, 2022
With the advent of physiological computing systems, new avenues are emerging for the field of learning analytics related to the potential integration of physiological data. To this end, we developed a physiological computing infrastructure to collect physiological data, surveys, and browsing behavior data to capture students' learning journey in…
Descriptors: Physiology, Computation, Artificial Intelligence, Psychological Patterns
Hayama, Tessai; Odate, Hidetaka; Ishida, Naoto – International Journal on E-Learning, 2020
The field of learning analytics has been limited by its frequent dependence on learning logs created by students while learning. Most of the research has dealt with the relationships between learning during a course and the achieved results. Although students' in-class behavior affects learning achievement, this remains a challenging aspect to…
Descriptors: Student Behavior, Data Collection, Measurement Equipment, College Students
Jasinski, Lisa; Nokkala, Terhi; Juusola, Henna – European Journal of Higher Education, 2021
When conceived of as both a research strategy and mode to present findings, vignettes can be combined with various qualitative methodologies in higher education research. Rather than being a discipline unto itself, higher education research is best understood as a multi and cross-disciplinary field of study that, we contend, is made richer by its…
Descriptors: Vignettes, Educational Research, Educational Benefits, Research Methodology
Wonkyung Choi; Jun Jo; Geraldine Torrisi-Steele – International Journal of Adult Education and Technology, 2024
Despite best efforts, the student experience remains poorly understood. One under-explored approach to understanding the student experience is the use of big data analytics. The reported study is a work in progress aimed at exploring the value of big data methods for understanding the student experience. A big data analysis of an open dataset of…
Descriptors: College Students, Data Analysis, Data Collection, Learning Analytics
Choi, Sangdo – Decision Sciences Journal of Innovative Education, 2019
Operations and Supply Chain Management (OSCM) courses may cover supply chain strategies, supply chain classification, and supply chain performance. Familiarity with various manufacturing and logistics firms would help students to better understand such topics. Information on the Dow Jones Industrial Average indexed firms and top 50 supply chain…
Descriptors: Supply and Demand, Business Administration Education, Industry, Classification
Ali, Amira D.; Hanna, Wael K. – Journal of Educational Computing Research, 2022
With the spread of the COVID-19 pandemic, many universities adopted a hybrid learning model as a substitute for a traditional one. Predicting students' performance in hybrid environments is a complex task because it depends on extracting and analyzing different types of data: log data, self-reports, and face-to-face interactions. Students must…
Descriptors: Predictor Variables, Academic Achievement, Blended Learning, Independent Study
So, Joseph Chi-ho; Wong, Adam Ka-lok; Tsang, Kia Ho-yin; Chan, Ada Pui-ling; Wong, Simon Chi-wang; Chan, Henry C. B. – Journal of Technology and Science Education, 2023
The project presented in this paper aims to formulate a recommendation framework that consolidates the higher education students' particulars such as their academic background, current study and student activity records, their attended higher education institution's expectations of graduate attributes and self-assessment of their own generic…
Descriptors: Pattern Recognition, Artificial Intelligence, Higher Education, College Students
Ruiperez-Valiente, Jose A.; Munoz-Merino, Pedro J.; Alexandron, Giora; Pritchard, David E. – IEEE Transactions on Learning Technologies, 2019
One of the reported methods of cheating in online environments in the literature is CAMEO (Copying Answers using Multiple Existences Online), where harvesting accounts are used to obtain correct answers that are later submitted in the master account which gives the student credit to obtain a certificate. In previous research, we developed an…
Descriptors: Computer Assisted Testing, Tests, Online Courses, Identification
Nahar, Khaledun; Shova, Boishakhe Islam; Ria, Tahmina; Rashid, Humayara Binte; Islam, A. H. M. Saiful – Education and Information Technologies, 2021
Information is everywhere in a hidden and scattered way. It becomes useful when we apply Data mining to extracts the hidden, meaningful, and potentially useful patterns from these vast data resources. Educational data mining ensures a quality education by analyzing educational data based on various aspects. In this paper, we have analyzed the…
Descriptors: Learning Analytics, College Students, Engineering Education, Data Collection
Jaiswal, Garima; Sharma, Arun; Yadav, Sumit Kumar – International Journal of Information and Communication Technology Education, 2019
In the world of technology, tools and gadgets, a huge amount of data is produced every second in applications ranging from medical science, education, business, agriculture, economics, retail and telecom. Higher education institutes play an important role in the overall development of any nation. For the successful operation of these institutions,…
Descriptors: Prediction, Dropouts, Dropout Rate, Classification
Motz, Benjamin; Busey, Thomas; Rickert, Martin; Landy, David – International Educational Data Mining Society, 2018
Analyses of student data in post-secondary education should be sensitive to the fact that there are many different topics of study. These different areas will interest different kinds of students, and entail different experiences and learning activities. However, it can be challenging to identify the distinct academic themes that students might…
Descriptors: Data Collection, Data Analysis, Enrollment, Higher Education
Maden, Asli – Educational Policy Analysis and Strategic Research, 2020
The present study aimed to review the articles published in Turkey on electronic books. In the study, descriptive content analysis method was employed. In the study, national databases such as UlakbimUVT, Asos Index, Turkish Education Index (TEI) and international databases such as ERIC, DOAJ, EBSCO, Google Scholar and past issues of educational…
Descriptors: Foreign Countries, Electronic Publishing, Books, Electronic Learning