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David Williamson Shaffer; Yeyu Wang; Andrew Ruis – Journal of Learning Analytics, 2025
Learning is a multimodal process, and learning analytics (LA) researchers can readily access rich learning process data from multiple modalities, including audio-video recordings or transcripts of in-person interactions; logfiles and messages from online activities; and biometric measurements such as eye-tracking, movement, and galvanic skin…
Descriptors: Learning Processes, Learning Analytics, Models, Data
Denisa Gándara; Rosa Maria Acevedo; Diana Cervantes; Marco Antonio Quiroz; Isabel McMullen; Tarini Kumar – Innovative Higher Education, 2025
Substantial shares of eligible students forgo or lose access to tuition-free college benefits, in part due to limited access to information on eligibility and other requirements. Given students' dependence on the Internet for information on how to pay for college, we examine the availability and digital accessibility of critical program…
Descriptors: Tuition, Eligibility, State Programs, Costs
Lynn Rosalina Gama Alves; William de Souza Santos – Information and Learning Sciences, 2024
Purpose: This study aims to analyze the platforming scenario at a Brazilian university as well as the data security process for students and professors. Design/methodology/approach: This research brings an analysis through a qualitative approach of the platformization process in a Brazilian teaching institution. Findings: The results point to a…
Descriptors: Foreign Countries, Universities, Data, Information Security
Schweizer, Karl; Gold, Andreas; Krampen, Dorothea – Educational and Psychological Measurement, 2023
In modeling missing data, the missing data latent variable of the confirmatory factor model accounts for systematic variation associated with missing data so that replacement of what is missing is not required. This study aimed at extending the modeling missing data approach to tetrachoric correlations as input and at exploring the consequences of…
Descriptors: Data, Models, Factor Analysis, Correlation
Annabel L. Davies; A. E. Ades; Julian P. T. Higgins – Research Synthesis Methods, 2024
Quantitative evidence synthesis methods aim to combine data from multiple medical trials to infer relative effects of different interventions. A challenge arises when trials report continuous outcomes on different measurement scales. To include all evidence in one coherent analysis, we require methods to "map" the outcomes onto a single…
Descriptors: Children, Body Composition, Measurement Techniques, Sampling
Robert C. Lorenz; Mirjam Jenny; Anja Jacobs; Katja Matthias – Research Synthesis Methods, 2024
Conducting high-quality overviews of reviews (OoR) is time-consuming. Because the quality of systematic reviews (SRs) varies, it is necessary to critically appraise SRs when conducting an OoR. A well-established appraisal tool is A Measurement Tool to Assess Systematic Reviews (AMSTAR) 2, which takes about 15-32 min per application. To save time,…
Descriptors: Decision Making, Time Management, Evaluation Methods, Quality Assurance
Quan Yuan; Lin Lv; Yolanda Cordero – International Journal of Web-Based Learning and Teaching Technologies, 2023
Relying on the nation's first judicial big data research base for people's courts in Southeast University, Southeast University Law School has set up a training direction for graduate students in legal big data and artificial intelligence, and explored the "three-dimensional, small-scale, wide-ranging, and large-scale ecology." The…
Descriptors: Law Schools, Legal Education (Professions), Graduate Students, Data
Burress, Theresa – College & Research Libraries, 2022
Undergraduate research is considered to be a high-impact practice; however, research into the data literacy of students conducting undergraduate research is lacking. In addition, institutionwide assessments of data practices are challenging because of varied disciplinary approaches to data. This study investigates the data practices of…
Descriptors: Data, Multiple Literacies, Undergraduate Students, Student Research
Brown, Neil C. C.; Weill-Tessier, Pierre; Sekula, Maksymilian; Costache, Alexandra-Lucia; Kölling, Michael – ACM Transactions on Computing Education, 2023
Objectives: Java is a popular programming language for use in computing education, but it is difficult to get a wide picture of the issues that it presents for novices; most studies look only at the types or frequency of errors. In this observational study, we aim to learn how novices use different features of the Java language. Participants:…
Descriptors: Novices, Programming, Programming Languages, Data
Usova, Tatiana; Laws, Robert – Journal of Information Literacy, 2021
Data literacy skills are becoming critical in today's world as the quantity of data grows exponentially and becomes the 'currency' of power. In spring 2020, a team of two librarians piloted a new one-credit course in data literacy and data visualisation. This report explains the rationale behind the project and discusses the place of data literacy…
Descriptors: College Credits, Information Literacy, Data, Visualization
Petya Ilieva-Trichkova; Pepka Boyadjieva; Ralitsa Dimitrova – European Journal of Higher Education, 2024
In the context of higher education's growing significance and the persistence of inequalities in it, the article aims to further develop the understanding of higher education as a public good and to explore its association with social cohesion in a European comparative perspective. It shifts the focus from the content of higher education as a…
Descriptors: Foreign Countries, Prosocial Behavior, Higher Education, Equal Education
Qiling Wu; Annemarie H. Hindman – Child & Youth Care Forum, 2025
Research indicates that parents' involvement in early literacy, particularly through book reading, matters for young children's language and literacy development. OBJECTIVE: However, little is known about the nature and extent of family book reading across the U.S. nation or about which factors support parents' involvement in book reading. In…
Descriptors: Kindergarten, Family Environment, Parents, Reading Habits
Liang Zhang; Jionghao Lin; John Sabatini; Conrad Borchers; Daniel Weitekamp; Meng Cao; John Hollander; Xiangen Hu; Arthur C. Graesser – IEEE Transactions on Learning Technologies, 2025
Learning performance data, such as correct or incorrect answers and problem-solving attempts in intelligent tutoring systems (ITSs), facilitate the assessment of knowledge mastery and the delivery of effective instructions. However, these data tend to be highly sparse (80%90% missing observations) in most real-world applications. This data…
Descriptors: Artificial Intelligence, Academic Achievement, Data, Evaluation Methods
Matt Giani; Madison E. Andrews; Tasneem Sultana; Fortunato Medrano – Annenberg Institute for School Reform at Brown University, 2025
This study examines College and Career Readiness (CCR) policy implementation through the lens of "decoupling." We investigate how high schools have jointly implemented Career and Technical Education (CTE) and Industry-Based Certifications (IBCs), and whether there is evidence of "curricular-credential decoupling" via…
Descriptors: Educational Policy, Credentials, High Schools, Data
Bulathwela, Sahan; Verma, Meghana; Pérez-Ortiz, María; Yilmaz, Emine; Shawe-Taylor, John – International Educational Data Mining Society, 2022
This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections. The paper introduces: (1) VLE, a novel dataset that consists of content and video based features extracted from publicly available scientific video lectures coupled with implicit and explicit signals related to…
Descriptors: Video Technology, Lecture Method, Data Analysis, Prediction