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Full-Text Articles in Medicine and Health Sciences
Elementary Classroom Teachers’ Self-Reported Use Of Movement Integration Products And Perceived Facilitators And Barriers Related To Product Use, Roddrick Dugger, Aaron Rafferty, Ethan Hunt, Michael W. Beets, Collin Andrew Webster, Brian Chen, Jeffrey Michael Rehling, Robert Glenn Weaver
Elementary Classroom Teachers’ Self-Reported Use Of Movement Integration Products And Perceived Facilitators And Barriers Related To Product Use, Roddrick Dugger, Aaron Rafferty, Ethan Hunt, Michael W. Beets, Collin Andrew Webster, Brian Chen, Jeffrey Michael Rehling, Robert Glenn Weaver
Faculty Publications
Movement integration (MI) products are designed to provide children with physical activity during general education classroom time. The purpose of this study was to examine elementary classroom teachers’ self-reported use of MI products and subsequent perceptions of the facilitators of and barriers to MI product use. This study utilized a mixed-methods design. Elementary classroom teachers (n = 40) at four schools each tested four of six common MI products in their classroom for one week. Teachers completed a daily diary, documenting duration and frequency of product use. Following each product test, focus groups were conducted with teachers to assess facilitators …
Explainable Ai Using Knowledge Graphs, Manas Gaur, Ankit Desai, Keyur Faldu, Amit Sheth
Explainable Ai Using Knowledge Graphs, Manas Gaur, Ankit Desai, Keyur Faldu, Amit Sheth
Publications
During the last decade, traditional data-driven deep learning (DL) has shown remarkable success in essential natural language processing tasks, such as relation extraction. Yet, challenges remain in developing artificial intelligence (AI) methods in real-world cases that require explainability through human interpretable and traceable outcomes. The scarcity of labeled data for downstream supervised tasks and entangled embeddings produced as an outcome of self-supervised pre-training objectives also hinders interpretability and explainability. Additionally, data labeling in multiple unstructured domains, particularly healthcare and education, is computationally expensive as it requires a pool of human expertise. Consider Education Technology, where AI systems fall along a …