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Direct linkRyan S. Baker; Stephen Hutt; Nigel Bosch; Jaclyn Ocumpaugh; Gautam Biswas; Luc Paquette; J. M. Alexandra Andres; Nidhi Nasiar; Anabil Munshi – Educational Technology Research and Development, 2024
In this paper, we propose a new method for selecting cases for in situ, immediate interview research: detector-driven classroom interviewing (DDCI). Published work in educational data mining and learning analytics has yielded highly scalable measures that can detect key aspects of student interaction with computer-based learning in close to…
Descriptors: Electronic Learning, Anxiety, Metacognition, Data Collection
Clay Gransden; Matthew Hindmarsh; Ngoc Chi Lê; Thi-Huyen Nguyen – Higher Education, Skills and Work-based Learning, 2024
Purpose: There is an increase globally of students using technology to support their learning. The purpose of this paper is to outline the technical aspects of adaptive learning and contribute to the development of pedagogy that incorporates this method in teaching and learning. Design/methodology/approach: This is a technical review article that…
Descriptors: Foreign Countries, Teaching Methods, Technology Uses in Education, Asynchronous Communication
Stephen G. Sireci; Javier Suárez-Álvarez; April L. Zenisky; Maria Elena Oliveri – Grantee Submission, 2024
The goal in personalized assessment is to best fit the needs of each individual test taker, given the assessment purposes. Design-In-Real-Time (DIRTy) assessment reflects the progressive evolution in testing from a single test, to an adaptive test, to an adaptive assessment "system." In this paper, we lay the foundation for DIRTy…
Descriptors: Educational Assessment, Student Needs, Test Format, Test Construction
