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Kathy Strunk; Andrew R. Hinkle; Sheryl S. Lazarus; Carol Seay; Kascinda Fleming – National Center on Educational Outcomes, 2024
For many years state education agencies (SEAs) have sought to create assessment systems that include all students, including students with disabilities. In order to improve outcomes for students with disabilities, there is an urgent need for cross-agency collaboration between special education and assessment offices, but other offices (e.g.,…
Descriptors: Agency Cooperation, Student Evaluation, Students with Disabilities, State Departments of Education
Natasha Arthars; Kate Thompson; Henk Huijser; Steven Kickbusch; Samuel Cunningham; Gavin Winter; Roger Cook; Lori Lockyer – Australasian Journal of Educational Technology, 2024
Assessing group work formatively in higher education poses a significant challenge. The complexity of evaluating individual contributions is compounded by the lack of efficient and effective methods for tracking, analysing and assessing individual engagement and contributions, which can impede timely feedback and the development of group work…
Descriptors: Formative Evaluation, Cooperative Learning, College Students, Student Evaluation
Peng, Chao-Ying Joanne; Chen, Li-Ting – Education Sciences, 2021
Due to repeated observations of an outcome behavior in N-of-1 or single-case design (SCD) intervention studies, the occurrence of missing scores is inevitable in such studies. Approximately 21% of SCD articles published in five reputable journals between 2015 and 2019 exhibited evidence of missing scores. Missing rates varied by designs, with the…
Descriptors: Intervention, Program Evaluation, Scores, Incidence
Matayoshi, Jeffrey; Cosyn, Eric; Uzun, Hasan – International Journal of Artificial Intelligence in Education, 2021
Many recent studies have looked at the viability of applying recurrent neural networks (RNNs) to educational data. In most cases, this is done by comparing their performance to existing models in the artificial intelligence in education (AIED) and educational data mining (EDM) fields. While there is increasing evidence that, in many situations,…
Descriptors: Artificial Intelligence, Data Analysis, Student Evaluation, Adaptive Testing
Arnold, Jessica; Crane, Eric W. – Region 15 Comprehensive Center, 2021
In the summer and fall months of a "typical" year, many education leaders would receive reports of student performance on annual summative state assessments administered each spring. Using these data, administrators begin a process of sense-making -- identifying areas that need support and integrating new information offered by the data…
Descriptors: Decision Making, Data Use, Student Evaluation, COVID-19
Bernstein, Sima; Barnett, W. Steven; Ackerman, Debra J. – National Institute for Early Education Research, 2019
Because pre-academic and social-emotional capabilities at kindergarten entry predict later academic and social success, there has been great concern with the level of "readiness" at kindergarten entry. Many states require a kindergarten readiness assessment (KRA). However, how readiness is defined, who or what entity is doing the…
Descriptors: School Readiness, Kindergarten, Definitions, Student Characteristics
Kathryn Richardson; Greta Rollo – Australian Journal of Education, 2024
The National Assessment Program for Literacy and Numeracy (NAPLAN) is intended to assess all Australian students in Years 3, 5, 7 and 9 to ensure equity and inclusion. This paper explores the extent to which equity and inclusion are achieved for students with disability through NAPLAN reporting. Document analysis of publicly accessible NAPLAN…
Descriptors: Literacy, Numeracy, Student Evaluation, National Competency Tests
Goran Trajkovski; Heather Hayes – Digital Education and Learning, 2025
This book explores the transformative role of artificial intelligence in educational assessment, catering to researchers, educators, administrators, policymakers, and technologists involved in shaping the future of education. It delves into the foundations of AI-assisted assessment, innovative question types and formats, data analysis techniques,…
Descriptors: Artificial Intelligence, Educational Assessment, Computer Uses in Education, Test Format
Jacqueline M. Nowicki; Sarah Kaczmarek; A. Nicole Clowers – US Government Accountability Office, 2024
The early elementary school years are widely understood to be critical to a child developing a strong foundation for future academic success. Children arrive at kindergarten from diverse settings and experiences, and not all of them are "kindergarten ready." Kindergarten readiness refers to the skills and abilities needed to succeed in…
Descriptors: Elementary Secondary Education, Kindergarten, Young Children, School Readiness
Provasnik, Stephen – Large-scale Assessments in Education, 2021
This paper presents the concepts and observations in the author's keynote address at the May 2019 "Opportunity versus Challenge: Exploring Usage of Log-File and Process Data in International Large-Scale Assessments" conference in Dublin, Ireland. This paper recaps briefly some key points that emerged at the December 2018 ETS symposium on…
Descriptors: Data Collection, Cognitive Processes, Ethics, Student Evaluation
Good, Megan R. – Assessment Update, 2023
This issue of "Assessment Update" presents a three part series on fixing assessments. In Part 1: "Assessment Standards are Broken," David Eubanks highlights the challenges with the assessment standards articulated by institutional accreditors. In this article, Part 2: Megan R. Good, discusses the opportunity costs faced and…
Descriptors: Administrative Organization, Educational Quality, Outcomes of Education, Student Evaluation
Jensen, Joanne L.; Goldstein, Jessica; Brunetti, Matthew A. – WestEd, 2021
Researchers have long traced achievement gaps in the elementary years back to readiness gaps at the start of kindergarten. Educators seeking to address these gaps, which are associated with race and ethnicity, home language, disability status, and indicators of economic disadvantage, need systematic ways to identify which children need what kinds…
Descriptors: Kindergarten, Young Children, School Readiness, Student Evaluation
Sandra Leaton Gray; Mutlu Cukurova – Cogent Education, 2024
Debates surrounding the use of data science in educational AI are frequently rather entrenched, revolving around commercial models and talk of teacher replacement. This article explores the potential for digital textual analysis within humanities and social science education, advocating for a sociologically-driven approach that complements, rather…
Descriptors: Humanities, Social Sciences, Social Science Research, Research Methodology
Bhattacharya, Madhumita; Coombs, Steven – Journal of Interactive Learning Research, 2018
This paper explores the usefulness of formalytics for sustainable learning improvement linked to lifelong learning. This can be achieved in two ways; firstly, by providing students with automatic VLE generated feedback of information giving an analysis of their personal data gathered on their learning engagement and performance; secondly, by…
Descriptors: Feedback (Response), Sustainability, Lifelong Learning, Educational Research
Selwyn, Neil – British Journal of Sociology of Education, 2018
This article reviews two recent books on the rising use of digital data in schools and university education, reflecting on areas of further research, analysis, and action. The books discussed are: (1) "The datafication of primary and early years education: playing with numbers," by A. Bradbury and G. Roberts-Holmes, London, Routledge,…
Descriptors: Educational Technology, Technology Uses in Education, Data Collection, Data Analysis

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