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Soyoung Park; Pamela M. Stecker; Sarah R. Powell – Intervention in School and Clinic, 2024
This article provides teachers with a toolkit for assessing students in the context of data-based individualization (DBI) in mathematics. Assessing students is a critical component of DBI because it provides teachers with information about what they may need to modify in their instructional programs. In this article, we provide teachers with…
Descriptors: Student Evaluation, Individualized Instruction, Mathematics Instruction, Progress Monitoring
Steven Snead – ProQuest LLC, 2024
Data-based decision-making has been a frequently used policy and practice intervention used in schools to help inform the decision-making processes of educational practitioners, with the aim of improving student outcomes. Interim benchmark assessments are designed by commercial test developers to support educators in this framework. In fact, the…
Descriptors: Student Evaluation, Data Analysis, Educational Practices, Decision Making
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Nathan Helsabeck; Jessica A. R. Logan – International Journal of Research & Method in Education, 2024
Assessing student achievement over multiple years is complicated by students' memberships in shifting upper-level nesting structures. These structures are manifested in (1) annual matriculation to different classrooms and (2) mobility between schools. Failure to model these shifting upper-level nesting structures may bias the inferences…
Descriptors: Academic Achievement, Student Evaluation, Growth Models, Data Analysis
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Achmad Bisri; Supardi; Yayu Heryatun; Hunainah; Annisa Navira – Journal of Education and Learning (EduLearn), 2025
In the educational landscape, educational data mining has emerged as an indispensable tool for institutions seeking to deliver exceptional and high-quality education. However, education data revealed suboptimal academic performance among a significant portion of the student population, which consequently resulted in delayed graduation. This…
Descriptors: Data Analysis, Models, Academic Achievement, Evaluation Methods
Thomas R. Guskey, Editor; Cassandra Erkens, Contributor; Katie White, Contributor; Mandy Stalets, Contributor; Garnet Hillman, Contributor; Tim Brown, Contributor; Tom Hierck, Contributor; Tom Schimmer, Contributor; Sharon V. Kramer, Contributor; Sarah Schuhl, Contributor; Anthony R. Reibel, Contributor; Joellen Killion, Contributor – Solution Tree, 2025
In "The Teacher as Assessment Leader, Second Edition," editor Thomas R. Guskey and expert contributors offer research-backed strategies for re-envisioning assessment to enhance student learning and teacher instruction. The authors provide actionable steps, practical examples, and strategies for utilizing formative assessments. These…
Descriptors: Teacher Leadership, Formative Evaluation, Data Analysis, Evidence Based Practice
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Boesdorfer, Sarah B.; Del Carlo, Dawn I.; Wayson, Jessica – Research in Science Education, 2022
Despite the promotion of data-driven or data-informed instructional practices in teacher education and professional development, past research indicates that teachers use a limited number of sources for student data to make short-term adjustments to their teaching in order to address deficiencies in student learning. Science teachers, with a more…
Descriptors: Secondary School Teachers, Data Use, Teaching Methods, Data Analysis
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Boutilier, Justin J.; Chan, Timothy C. Y. – INFORMS Transactions on Education, 2023
Artificial intelligence (AI) and operations research (OR) have long been intertwined because of their synergistic relationship. Given the increasing popularity of AI and machine learning in particular, we face growing demand for educational offerings in this area from our students. This paper describes two courses that introduce machine learning…
Descriptors: Artificial Intelligence, Operations Research, Undergraduate Students, Engineering Education
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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
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Shou, Zhaoyu; Tang, Mengxue; Wen, Hui; Liu, Jinghua; Mo, Jianwen; Zhang, Huibing – International Journal of Information and Communication Technology Education, 2023
In this paper, a key node mining algorithm of entropy-CRITIC combined weighted GRA-TOPSIS method is proposed, which is based on the network structure features. First, the method obtained multi-dimensional data of students' identities, seating relationships, social relationships, and so on to build a database. Then, the seating similarity among…
Descriptors: Social Networks, Algorithms, Network Analysis, Databases
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Yanhui Wang – International Journal of Web-Based Learning and Teaching Technologies, 2024
In recent years, China has accelerated the process of internationalization and made more and more achievements in transnational communication and cooperation. English learning is very important for contemporary college students. And English reading is an important means to acquire English language knowledge, understand external information and…
Descriptors: Algorithms, College Students, English (Second Language), Reading Ability
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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
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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
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Rebecka Rundquist; Kristina Holmberg; John Rack; Zeynab Mohseni; Italo Masiello – Journal of Learning Analytics, 2024
The generation, use, and analysis of educational data comes with many promises and opportunities, especially where digital materials allow usage of learning analytics (LA) as a tool in data-based decision-making (DBDM). However, there are questions about the interplay between teachers, students, context, and technology. Therefore, this paper…
Descriptors: Learning Analytics, Elementary Secondary Education, Mathematics Education, Data Analysis
Regan, Kelley; Evmenova, Anya S.; Hutchison, Amy; Day, Jamie; Stephens, Madelyn; Verbiest, Courtney; Gafurov, Boris – TEACHING Exceptional Children, 2022
The process of analyzing student data to determine an appropriate instructional decision is crucial for student academic growth. This article details how teachers can make data-driven decisions to carefully design writing instruction. Steps are presented for teachers to follow throughout the data driven decision-making process in order to meet…
Descriptors: Writing Instruction, Decision Making, Essays, Data Analysis
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Çetinkaya-Rundel, Mine; Dogucu, Mine; Rummerfield, Wendy – Statistics Education Research Journal, 2022
Many data science applications involve generating questions, acquiring data and preparing it for analysis--be it exploratory, inferential, or modeling focused--and communicating findings. Most data science curricula address each of these steps as separate units in a course or as separate courses. Open-ended term projects, however, allow students…
Descriptors: Introductory Courses, Data Analysis, Statistics Education, Units of Study
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