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Download full textOffice for Civil Rights, US Department of Education, 2023
The U.S. Department of Education's (ED) Office for Civil Rights (OCR) administers the Civil Rights Data Collection (CRDC), which is a mandatory survey of all public schools and school districts in the 50 states, Washington, D.C., and the Commonwealth of Puerto Rico. To support school districts' timely submission of accurate data, OCR takes steps…
Descriptors: National Surveys, Public Schools, School Districts, Federal Government
Peer reviewed
Direct linkPoschmann, Philipp; Goldenstein, Jan – Sociological Methods & Research, 2022
Despite the recent and ongoing progress in using text-mining tools to automatically analyze large text corpora, there remains significant potential to facilitate the study of social action in social science research. In this context, particularly the disambiguation (who is referred to in a text?) and specification (which demographic…
Descriptors: Web Sites, Collaborative Writing, Reliability, Accuracy
Regional Educational Laboratory Mid-Atlantic, 2021
Predicting incoming enrollment is an ongoing concern for the School District of Philadelphia (SDP) and similar districts with school choice systems, substantial student mobility, or both. Inaccurate predictions can disrupt learning as districts adjust to enrollment fluctuations by reshuffling teachers and students well into the fall semester. This…
Descriptors: Enrollment, Enrollment Projections, School Districts, Statistical Analysis
Perez-Vergara, Kelly – Strategic Enrollment Management Quarterly, 2020
Institutional staff such as enrollment managers, business officers, and institutional researchers are often asked to predict enrollments. Developing any predictive model can be intimidating, particularly when there is no textbook to follow. This paper provides a practical framework for generating enrollment projection options and for evaluating…
Descriptors: Enrollment Projections, Enrollment Management, Enrollment Trends, Models
Selwyn, Neil; Gaševic, Dragan – Teaching in Higher Education, 2020
A common recommendation in critiques of datafication in education is for greater conversation between the two sides of the (critical) divide -- what might be characterised as sceptical social scientists and (supposedly) more technically-minded and enthusiastic data scientists. This article takes the form of a dialogue between two academics…
Descriptors: Criticism, Data Analysis, Higher Education, Dialogs (Language)
Mbouzao, Boniface; Desmarais, Michel C.; Shrier, Ian – International Educational Data Mining Society, 2020
Massive online Open Courses (MOOCs) make extensive use of videos. Students interact with them by pausing, seeking forward or backward, replaying segments, etc. We can reasonably assume that students have different patterns of video interactions, but it remains hard to compare student video interactions. Some methods were developed, such as Markov…
Descriptors: Comparative Analysis, Video Technology, Interaction, Measurement Techniques
Choi, Youn-Jeng; Asilkalkan, Abdullah – Measurement: Interdisciplinary Research and Perspectives, 2019
About 45 R packages to analyze data using item response theory (IRT) have been developed over the last decade. This article introduces these 45 R packages with their descriptions and features. It also describes possible advanced IRT models using R packages, as well as dichotomous and polytomous IRT models, and R packages that contain applications…
Descriptors: Item Response Theory, Data Analysis, Computer Software, Test Bias
National Forum on Education Statistics, 2023
This guide is designed for use by school, district, and state education agency staff to improve the effectiveness of efforts to collect and use discipline data, including reporting accurate and timely data to the federal government. It explains the importance of collecting discipline data, identifies key considerations for agencies implementing…
Descriptors: Discipline, Data Collection, Data Analysis, School Districts
Erin C. Yang; Robby Divine; Christine S. Kang; Sidney Chan; Elijah Arenas; Zoe Subol; Peter Tinker; Hayden Manninen; Alicia Feichtenbiner; Talal Mustafa; Julia Hallowell; Isiac Orr; Hugh Haddox; Brian Koepnick; Jacob O'Connor; Ian C. Haydon; Karla-Luise Herpoldt; Kandise Van Wormer; Celine Abell; David Baker; Alena Khmelinskaia; Neil P. King – Journal of Chemical Education, 2022
Undergraduate research experiences can improve student success in graduate education and STEM careers. During the COVID-19 pandemic, undergraduate researchers at our institution and many others lost their work-study research positions due to interruption of in-person research activities. This imposed a financial burden on the students and…
Descriptors: Undergraduate Students, Teaching Methods, COVID-19, Pandemics
Wilks, Judith; Kennedy, Gillian; Drew, Neil; Wilson, Katie – Australian Universities' Review, 2018
In the Australian higher education sector, the challenges to successful engagement and retention experienced by Aboriginal and/or Torres Strait Islander students and communities are considerable. They persist despite many well-intentioned attempts to address this issue and to strengthen equity in participation in the sector. Implicated in this is…
Descriptors: Foreign Countries, Indigenous Populations, Academic Persistence, Higher Education
Guerra, Nelson Pérez – Journal of Chemical Education, 2017
A laboratory experiment in which students study the kinetics of the Viscozyme-L-catalyzed hydrolysis of cellulose and starch comparatively was designed for an upper-division biochemistry laboratory. The main objective of this experiment was to provide an opportunity to perform enhanced enzyme kinetics data analysis using appropriate informatics…
Descriptors: Undergraduate Study, Biochemistry, Kinetics, Laboratory Experiments
Capuano, Nicola; Loia, Vincenzo; Orciuoli, Francesco – IEEE Transactions on Learning Technologies, 2017
Massive Open Online Courses (MOOCs) are becoming an increasingly popular choice for education but, to reach their full extent, they require the resolution of new issues like assessing students at scale. A feasible approach to tackle this problem is peer assessment, in which students also play the role of assessor for assignments submitted by…
Descriptors: Participative Decision Making, Models, Peer Evaluation, Online Courses
Castro, Francisco Enrique Vicente; Adjei, Seth; Colombo, Tyler; Heffernan, Neil – International Educational Data Mining Society, 2015
A great deal of research in educational data mining is geared towards predicting student performance. Bayesian Knowledge Tracing, Performance Factors Analysis, and the different variations of these have been introduced and have had some success at predicting student knowledge. It is worth noting, however, that very little has been done to…
Descriptors: Models, Student Behavior, Intelligent Tutoring Systems, Data Analysis
Arenson, Ethan A.; Karabatsos, George – Grantee Submission, 2017
Item response models typically assume that the item characteristic (step) curves follow a logistic or normal cumulative distribution function, which are strictly monotone functions of person test ability. Such assumptions can be overly-restrictive for real item response data. We propose a simple and more flexible Bayesian nonparametric IRT model…
Descriptors: Bayesian Statistics, Item Response Theory, Nonparametric Statistics, Models
Casey, Kevin – Journal of Learning Analytics, 2017
Learning analytics offers insights into student behaviour and the potential to detect poor performers before they fail exams. If the activity is primarily online (for example computer programming), a wealth of low-level data can be made available that allows unprecedented accuracy in predicting which students will pass or fail. In this paper, we…
Descriptors: Keyboarding (Data Entry), Educational Research, Data Collection, Data Analysis
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