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Secil Caskurlu; Yasin Yalçin; Jaesung Hur; Hui Shi; James D. Klein – TechTrends: Linking Research and Practice to Improve Learning, 2025
This exploratory qualitative study examined how instructional designers use data to make decisions during the instructional design process. Participants included full-time instructional designers (n = 9) who were involved in one or more phases of the ADDIE (Analysis, Design, Development, Implementation, Evaluation) across different job sectors,…
Descriptors: Data Use, Instructional Design, Decision Making, Data Collection
Hiroaki Ogata; Changhao Liang; Yuko Toyokawa; Chia-Yu Hsu; Kohei Nakamura; Taisei Yamauchi; Brendan Flanagan; Yiling Dai; Kyosuke Takami; Izumi Horikoshi; Rwitajit Majumdar – Technology, Knowledge and Learning, 2024
This paper explores co-design in Japanese education for deploying data-driven educational technology and practice. Although there is a growing emphasis on data to inform educational decision-making and personalize learning experiences, challenges such as data interoperability and inconsistency with teaching goals prevent practitioners from…
Descriptors: Educational Technology, Instructional Design, Cooperation, Data Use
Schoepp, Rosemarie – ProQuest LLC, 2023
Data-driven decision making (DDDM) is an important teaching practice that can positively influence student achievement by using assessment data to make instructional decisions. The problem is that teachers do not always use this practice. Teachers' perspectives of DDDM can influence their data use practices. This basic qualitative study was…
Descriptors: Teacher Attitudes, Data Use, Instructional Design, Decision Making
Taub, Michelle; Azevedo, Roger – New Directions for Teaching and Learning, 2023
The goal of this chapter is to propose a cyclical process of how teachers can use multimodal multichannel data of cognitive, affective, metacognitive, motivational, and social processes to assist with the understanding of their own and their students' self-regulated learning (SRL), and their subsequent instructional decision making. What…
Descriptors: Independent Study, Learning Processes, Instructional Design, Decision Making
Papamitsiou, Zacharoula; Filippakis, Michail E.; Poulou, Marilena; Sampson, Demetrios; Ifenthaler, Dirk; Giannakos, Michail – Smart Learning Environments, 2021
In the era of digitalization of learning and teaching processes, Educational Data Literacy (EDL) is highly valued and is becoming essential. EDL is conceptualized as the ability to collect, manage, analyse, comprehend, interpret, and act upon educational data in an ethical, meaningful, and critical manner. The professionals in the field of…
Descriptors: Multiple Literacies, Instructional Design, Tutors, Electronic Learning
Groth, Randall E.; Bergner, Jennifer A.; Austin, Jathan W.; Burgess, Claudia R.; Holdai, Veera – Mathematics Teacher Educator, 2020
Undergraduate research is increasingly prevalent in many fields of study, but it is not yet widespread in mathematics education. We argue that expanding undergraduate research opportunities in mathematics education would be beneficial to the field. Such opportunities can be impactful as either extracurricular or course-embedded experiences. To…
Descriptors: Student Research, Undergraduate Students, Mathematics Education, Data Use
Fladd, Laurie; Heacock, Laurie; Hill-Kelley, Jennifer; Lawton, Julia; Pechac, Sharmaine; Shamah, Devora; Woodruff, Amber – Achieving the Dream, 2021
This guidebook is designed for institutional leaders and student success teams who are ready to talk openly about the students they serve and who are eager to learn practical strategies from national experts and peer institutions. We cannot design an experience that meets our students where they are unless we holistically understand who they are.…
Descriptors: Instructional Leadership, Instructional Design, Holistic Approach, Higher Education
Center on Positive Behavioral Interventions and Supports, 2022
This practice guide is an updated version of "Supporting and Responding to Behavior: Evidence-based Classroom Strategies for Teachers" (see ED619696) that replaces, rather than supplements, the first version. This guide summarizes evidence-based, positive, and proactive practices that support and respond to students' social, emotional,…
Descriptors: Evidence Based Practice, Student Behavior, Intervention, Classroom Techniques
Prestigiacomo, Rita; Hunter, Jane; Knight, Simon; Martinez Maldonado, Roberto; Lockyer, Lori – Australasian Journal of Educational Technology, 2020
Data about learning can support teachers in their decision-making processes as they design tasks aimed at improving student educational outcomes. However, to achieve systemic impact, a deeper understanding of teachers' perspectives on, and expectations for, data as evidence is required. It is critical to understand how teachers' actions align with…
Descriptors: Preservice Teachers, Preservice Teacher Education, Elementary Secondary Education, Undergraduate Students
Huang, Ronghuai, Ed.; Kinshuk, Ed.; Price, Jon K., Ed. – Lecture Notes in Educational Technology, 2016
This book aims to capture the current innovation and emerging trends of digital technologies for learning and education in k-12 sector through a number of invited chapters in key research areas. Emerging Patterns of innovative instruction in different context, Learning design for digital natives, Digital learning resources for personalized…
Descriptors: Elementary Secondary Education, Computer Uses in Education, Instructional Innovation, Instructional Design
Office of Special Education Programs, US Department of Education, 2015
The purpose of this document is to summarize evidence-based, positive, proactive, and responsive classroom behavior intervention and support strategies for teachers. These strategies should be used classroom-wide, intensified for support small-group instruction, or amplified further for individual students. These strategies can help teachers…
Descriptors: Evidence Based Practice, Student Behavior, Intervention, Positive Behavior Supports

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