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Direct linkDong Zhang; Yan Luo – Physical Educator, 2024
This study aimed to formulate a course classification process for identifying and classifying courses in the curriculum for physical education teacher education (PETE). The study recruited five experts in physical education (PE), PETE, and pedagogy. The participants were professors at a college in the Northeastern United States. The five…
Descriptors: Physical Education Teachers, Preservice Teacher Education, Teacher Education Programs, College Faculty
Download full textNational Forum on Education Statistics, 2023
This forum guide was developed to meet the need for common, widely understood, standardized course codes. The purpose of this guide is to introduce the voluntary School Courses for the Exchange of Data (SCED) classification system, including information on the structure of SCED codes, the process for ensuring that SCED remains up to date and…
Descriptors: Courses, Classification, Coding, Data
Johannes König; Sandra Heine; Charlotte Kramer; Jonas Weyers; Michael Becker-Mrotzek; Jörg Großschedl; Charlotte Hanisch; Petra Hanke; Thomas Hennemann; Jörg Jost; Kai Kaspar; Benjamin Rott; Sarah Strauß – Journal of Curriculum Studies, 2024
Numerous reviews have synthesized the empirical research on the effectiveness of teacher education, highlighting teacher education effectiveness research (TEER) as an emerging research paradigm. Our systematic search identified 27 reviews related to TEER, wherein teacher education is broadly understood as comprising all stages of teacher…
Descriptors: Teacher Education Programs, Program Effectiveness, Educational Research, Faculty Development
Hadj Kacem, Yessine; Alshehri, Safa; Qaid, Talal – Journal of Information Technology Education: Innovations in Practice, 2022
Aim/Purpose: This paper presents a machine learning approach for analyzing Course Learning Outcomes (CLOs). The aim of this study is to find a model that can check whether a CLO is well written or not. Background: The use of machine learning algorithms has been, since many years, a prominent solution to predict learner performance in Outcome Based…
Descriptors: Outcomes of Education, Artificial Intelligence, Educational Assessment, Classification
Holly R. Turner; David S. Jackson; Max Sender; Trina E. Orimoto; Lesley A. Slavin; Charles W. Mueller – Administration and Policy in Mental Health and Mental Health Services Research, 2022
This study utilized latent profile analysis to categorize youth served by a public mental health setting into homogenous classes. Then, associations between class membership and meeting clinical criteria by the latest assessment were examined. Caregiver responses to the Ohio Scales, Short Form, Problem Severity Scale for 1090 youth completed at…
Descriptors: Mental Health, Mental Disorders, Health Services, Program Effectiveness
Christopher Dann; Petrea Redmond; Melissa Fanshawe; Alice Brown; Seyum Getenet; Thanveer Shaik; Xiaohui Tao; Linda Galligan; Yan Li – Australasian Journal of Educational Technology, 2024
Making sense of student feedback and engagement is important for informing pedagogical decision-making and broader strategies related to student retention and success in higher education courses. Although learning analytics and other strategies are employed within courses to understand student engagement, the interpretation of data for larger data…
Descriptors: Artificial Intelligence, Learner Engagement, Feedback (Response), Decision Making
Ignacio L. Montoya; Julien De Jesus; Macario Mendoza-Carrillo – Language Documentation & Conservation, 2024
This paper focuses on the development, planning, and implementation of Numu (Northern Paiute) language classes at the University of Nevada, Reno. The authors' engagement with the Numu classes as well as the description and analysis presented in this paper are guided by principles of decolonization, language reclamation, and community-based…
Descriptors: Expertise, Decolonization, Universities, Courses
