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Direct linkLeher Singh; Mihaela D. Barokova; Heidi A. Baumgartner; Diana C. Lopera-Perez; Paul Okyere Omane; Mark Sheskin; Francis L. Yuen; Yang Wu; Katherine J. Alcock; Elena C. Altmann; Marina Bazhydai; Alexandra Carstensen; Kin Chung Jacky Chan; Hu Chuan-Peng; Rodrigo Dal Ben; Laura Franchin; Jessica E. Kosie; Casey Lew-Williams; Asana Okocha; Tilman Reinelt; Tobias Schuwerk; Melanie Soderstrom; Angeline S. M. Tsui; Michael C. Frank – Developmental Psychology, 2024
Culture is a key determinant of children's development both in its own right and as a measure of generalizability of developmental phenomena. Studying the role of culture in development requires information about participants' demographic backgrounds. However, both reporting and treatment of demographic data are limited and inconsistent in child…
Descriptors: Data Collection, Young Children, Demography, Cultural Traits
Download full textDavid B. Warner; Lisa McKee – Journal of Cybersecurity Education, Research and Practice, 2024
Data collection, use, leveraging, and sharing as a business practice and advantage has proliferated over the past decade. Along with this proliferation of data collection is the increase in regulatory activity which continues to morph exponentially around the globe. Adding to this complexity are the increasing business disruptions, productivity…
Descriptors: Information Security, Privacy, Governance, Compliance (Legal)
Tom Manning – Learning Professional, 2024
The Standards Assessment Inventory (SAI) has provided relevant, educator-level data helping systems of all kinds -- states, districts, schools, provinces, and organizations -- gather and track data about the professional learning their educators experience. An online, confidential, valid, and reliable instrument administered to school-based…
Descriptors: Data Collection, Faculty Development, Program Improvement, Measures (Individuals)
Nehyba, Jan; Štefánik, Michal – Education and Information Technologies, 2023
Social sciences expose many cognitively complex, highly qualified, or fuzzy problems, whose resolution relies primarily on expert judgement rather than automated systems. One of such instances that we study in this work is a reflection analysis in the writings of student teachers. We share a hands-on experience on how these challenges can be…
Descriptors: Models, Language, Reflection, Writing (Composition)
Kearney, Christopher A.; Childs, Joshua – Improving Schools, 2023
School attendance and absenteeism are critical targets of educational policies and practices that often depend heavily on aggregated attendance/absenteeism data. School attendance/absenteeism data in aggregated form, in addition to having suspect quality and utility, minimizes individual student variation, distorts detailed and multilevel…
Descriptors: Data Analysis, Attendance, Educational Policy, Causal Models
Ghodoosi, Bahareh; Torrisi-Steele, Geraldine; West, Tracey; Li, Qinyi – International Journal of Adult Education and Technology, 2023
There is no single agreed-upon definition of data literacy because expectations of what it means to be data literate varies across contexts. The lack of agreement on a definition of data literacy across contexts is therefore necessary. However, definitions are important. Definitions embody our understanding of concepts and are the foundation for…
Descriptors: Definitions, Data, Information Literacy, College Graduates
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, Ak Wai; Sinnamon, Luanne S.; Kopak, Rick – Information and Learning Sciences, 2022
Purpose: The purpose of this study is to explore open data portals as data literacy learning environments. The authors examined the obstacles faced and strategies used by university students as non-expert open data portal users with different levels of data literacy, to inform the design of portals intended to scaffold informal and situated…
Descriptors: Data Collection, Multiple Literacies, Data, College Students
Keser, Sinem Bozkurt; Aghalarova, Sevda – Education and Information Technologies, 2022
Education plays a major role in the development of the consciousness of the whole society. Education has been improved by analyzing educational data related to student academic performance. By using data mining techniques and algorithms on data from the educational environment, students' performances can be predicted. In this study, a novel Hybrid…
Descriptors: Grade Prediction, Academic Achievement, Data Analysis, Data Collection
Basnet, Ram B.; Johnson, Clayton; Doleck, Tenzin – Education and Information Technologies, 2022
The nature of teaching and learning has evolved over the years, especially as technology has evolved. Innovative application of educational analytics has gained momentum. Indeed, predictive analytics have become increasingly salient in education. Considering the prevalence of learner-system interaction data and the potential value of such data, it…
Descriptors: Prediction, Dropouts, Predictive Measurement, Data Collection
Freddy Juarez; Jarred Pernier; Brittany Devies – New Directions for Student Leadership, 2025
The organizational change framework is a tool for understanding and facilitating organizational change and success, grounded in the principles of design thinking and the foundational leadership and organizational wellness (FLOW) model. This article dives into the components of the organizational change framework--collect the information, connect…
Descriptors: Organizational Change, Models, Data Collection, Program Implementation
Pavelko, Stacey L.; Owens, Robert E., Jr. – Perspectives of the ASHA Special Interest Groups, 2023
Purpose: The purposes of this tutorial are (a) to describe a method of language sample analysis (LSA) referred to as SUGAR (Sampling Utterances and Grammatical Analysis Revised) and (b) to offer step-by-step instructions detailing how to collect, transcribe, analyze, and interpret the results of a SUGAR language sample. Method: The tutorial begins…
Descriptors: Sampling, Language Tests, Data Collection, Data Analysis
Kai Li – International Association for Development of the Information Society, 2023
Assessing students' performance in online learning could be executed not only by the traditional forms of summative assessments such as using essays, assignments, and a final exam, etc. but also by more formative assessment approaches such as interaction activities, forum posts, etc. However, it is difficult for teachers to monitor and assess…
Descriptors: Student Evaluation, Online Courses, Electronic Learning, Computer Literacy
Arnold, Pip; Franklin, Christine – Journal of Statistics and Data Science Education, 2021
The statistical problem-solving process is key to the statistics curriculum at the school level, post-secondary, and in statistical practice. The process has four main components: formulate questions, collect data, analyze data, and interpret results. The Pre-K-12 Guidelines for Assessment and Instruction in Statistics Education (GAISE) emphasizes…
Descriptors: Statistics Education, Problem Solving, Data Collection, Data Analysis
National Forum on Education Statistics, 2021
"The Forum Guide to Strategies for Education Data Collection and Reporting (SEDCAR)" was created to provide timely and useful best practices for education agencies that are interested in designing and implementing a strategy for data collection and reporting, focusing on these as key elements of the larger data process. It builds upon…
Descriptors: Data Collection, Educational Research, Statistical Data, Data Analysis
