Data-based individualization (DBI) is a systematic approach to intensifying and individualizing interventions for students who require more support. Diagnostic data represent the third step in the DBI process. When progress monitoring data indicate that a student is not making adequate progress in an intervention, educators use diagnostic data to…

This study aims to produce a more personalized recommendation system for online learning using multicriteria in collaborative filtering and data from the Binus Online Learning repository as a knowledge base. The study uses forecasting (regression) and consists of three stages: (1) collecting data on the results of the learning process; (2) adding…

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…

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…

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…

Students with severe and persistent academic or behavioral challenges may benefit from data-based individualization (DBI). Starting with an evidence-based standard protocol and systematic progress monitoring, teachers can evaluate growth and implement individualized interventions to meet students' needs. Specifically, this article addresses the…

The Diversifying the Education Profession Ohio Taskforce consists of aspiring teachers, K-12 educators, human resources personnel, educator preparation program representatives, community members, State Board of Education representation, and staff members from the Ohio Department of Education and Ohio Department of Higher Education and convened to…

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…

This report describes how big data and an evidence framework can align across five contexts of educational improvement. It explains that before working with big data, there is an important prerequisite: the proposed innovation should align with deeper learning objectives and should incorporate sound learning sciences principles. New curriculum…

Discrete trial instruction or naturalistic, incidental teaching: How do you choose which approach to use with young children with autism? Now there's no need to "pick a side"--this groundbreaking book helps professionals skillfully blend the best of both behavioral approaches to respond to "each child's individual needs". Developed by one of the…

This first in a series of six learning modules on instructional planning is designed to assist secondary and postsecondary vocational teachers in becoming familiar with the variety of techniques that can be used to determine their students' needs and interests (particularly vocational), and to give practice in using these skills to obtain…