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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
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
Frost, Raymond; Matta, Vic; Kenyo, Lauren – Journal of Information Systems Education, 2021
Student learning benefits from individual support and feedback. This type of support does not scale well especially in large classes. A system was built to automate the delivery of individual support and feedback on Excel assignments in information systems and analytics courses. The system embeds instructional scaffolding in the distributed…
Descriptors: Automation, Scaffolding (Teaching Technique), Formative Evaluation, Individualized Instruction
Mamcenko, Jelena; Kurilovas, Eugenijus; Krikun, Irina – Informatics in Education, 2019
The paper aims to present application of Educational Data Mining and particularly Case-Based Reasoning (CBR) for students profiling and further to design a personalised intelligent learning system. The main aim here is to develop a recommender system which should help the learners to create learning units (scenarios) that are the most suitable for…
Descriptors: Case Method (Teaching Technique), Individualized Instruction, Intelligent Tutoring Systems, Cognitive Style
Pardo, Abelardo; Bartimote-Aufflick, Kathryn; Shum, Simon Buckingham; Dawson, Shane; Gao, Jing; Gaševic, Dragan; Leichtweis, Steve; Liu, Danny; Martínez-Maldonado, Roberto; Mirriahi, Negin; Moskal, Adon Christian Michael; Schulte, Jurgen; Siemens, George; Vigentini, Lorenzo – Journal of Learning Analytics, 2018
The learning analytics community has matured significantly over the past few years as a middle space where technology and pedagogy combine to support learning experiences. To continue to grow and connect these perspectives, research needs to move beyond the level of basic support actions. This means exploring the use of data to prove richer forms…
Descriptors: Individualized Instruction, Data Analysis, Learning, Feedback (Response)
Tucker, William; Long, Don – National Association of State Boards of Education, 2018
With roots in student-centered pedagogies that go back at least to the 1900s, personalized learning meets students' learning goals, needs, context, and pace while incorporating their interests and preferences. Personalized learning in today's classroom depends upon and creates an abundance of rich student data, which simultaneously fosters new…
Descriptors: Data Collection, Data Analysis, Student Records, Individualized Instruction
Prior-Grosch, Ariadne; Woodruff, Karen – Science Teacher, 2022
Fall 2020 presented myriad challenges for teachers trying to plan curricula to meet students' social-emotional and learning needs following an unprecedented spring and summer of isolation and loss due to the pandemic caused by SARS-CoV-2 (Rivera and Wallace 2020). The result of creative planning and adjusting of curricula for remote instruction…
Descriptors: COVID-19, Pandemics, School Closing, Distance Education
Aguilar, Stephen J. – TechTrends: Linking Research and Practice to Improve Learning, 2018
We are still designing educational experiences for the "average" student, and have room to improve. Learning analytics provides a way forward. This commentary describes how learning analytics-based applications are well positioned to meaningfully personalize the learning experience in diverse ways. In so doing, learning analytics has the…
Descriptors: Instructional Design, Social Justice, Educational Research, Data Collection
Filderman, Marissa J.; Toste, Jessica R. – TEACHING Exceptional Children, 2018
Reading proficiency is fundamental to school success. However, up to 50% of students with reading disabilities are not making adequate progress. Students who demonstrate persistent and severe reading difficulties require increasingly intensive instruction individualized to meet their instructional needs Individualizing instruction with…
Descriptors: Reading Difficulties, Reading Skills, Individualized Instruction, Decision Making
Ryan, Sarah; Cox, Joshua D. – Regional Educational Laboratory Northeast & Islands, 2016
Many states are moving away from approaches that base student advancement on credits and "seat time" toward competency-based learning approaches that provide schools with the flexibility to link a student's advancement to mastery of content. Regional Educational Laboratory Northeast & Islands, in partnership with the Northeast…
Descriptors: Competency Based Education, Student Surveys, Student Experience, High School Students
Hussin, Anealka Aziz – International Journal of Education and Literacy Studies, 2018
Almost everyone is talking about the 4th Industrial Revolution (4IR). The 4IR wave is so strong that change is inevitable, including within the education setting, making Education 4.0 the famous buzzword among educationists today. What is Education 4.0? Do educators really understand it or they simply follow what others are doing. Education 4.0 is…
Descriptors: Educational Change, Teaching Skills, Student Attitudes, Electronic Learning
de Freitas, Sara; Gibson, David; Du Plessis, Coert; Halloran, Pat; Williams, Ed; Ambrose, Matt; Dunwell, Ian; Arnab, Sylvester – British Journal of Educational Technology, 2015
With digitisation and the rise of e-learning have come a range of computational tools and approaches that have allowed educators to better support the learners' experience in schools, colleges and universities. The move away from traditional paper-based course materials, registration, admissions and support services to the mobile, always-on and…
Descriptors: Higher Education, Student Records, Data Analysis, Information Utilization
Snow, Erica L. – International Educational Data Mining Society, 2015
Intelligent tutoring systems are adaptive learning environments designed to support individualized instruction. The adaptation embedded within these systems is often guided by user models that represent one or more aspects of students' domain knowledge, actions, or performance. The proposed project focuses on the development and testing of user…
Descriptors: Intelligent Tutoring Systems, Models, Individualized Instruction, Needs Assessment
Ola, Ade G.; Bai, Xue; Omojokun, Emmanuel E. – Research in Higher Education Journal, 2014
Over the years, companies have relied on On-Line Analytical Processing (OLAP) to answer complex questions relating to issues in business environments such as identifying profitability, trends, correlations, and patterns. This paper addresses the application of OLAP in education and learning. The objective of the research presented in the paper is…
Descriptors: Profiles, Database Management Systems, Information Management, Progress Monitoring
National Forum on Education Statistics, 2015
The National Forum on Education Statistics (Forum) organized the College and Career Ready (CCR) Working Group to explore how state and local education agencies (SEAs and LEAs) can use data to support college and career readiness initiatives. The working group determined that high-quality data in integrated K12, postsecondary, and workforce data…
Descriptors: Data Analysis, Data Collection, College Readiness, Career Readiness
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