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Lisa A. Dieker; Rebecca Hines; Ilene Wilkins; Charles Hughes; Karyn Hawkins Scott; Shaunn Smith; Kathleen Ingraham; Kamran Ali; Tiffanie Zaugg; Sachin Shah – Journal of Special Education Preparation, 2024
The options for Artificial intelligence (AI) tools used in teacher education are increasing daily, but more is only sometimes better for teachers working in already complex classroom settings. This team discusses the increase of AI in schools and provides an example from administrators, teacher educators, and computer scientists of an AI virtual…
Descriptors: Artificial Intelligence, Elementary Schools, Preservice Teachers, Preservice Teacher Education
Cathy Cavanaugh; Bryn Humphrey; Paige Pullen – International Journal on E-Learning, 2024
To address needs in one US state to provide a professional development micro-credential for tens of thousands of educators, we automated an assignment scoring workflow in an online course by developing and refining an AI model to scan submitted assignments and score them against a rubric. This article outlines the AI model development process and…
Descriptors: Artificial Intelligence, Automation, Scoring, Microcredentials
Daniel Katz; Anne Corinne Huggins-Manley; Walter Leite – Applied Measurement in Education, 2022
According to the "Standards for Educational and Psychological Testing" (2014), one aspect of test fairness concerns examinees having comparable opportunities to learn prior to taking tests. Meanwhile, many researchers are developing platforms enhanced by artificial intelligence (AI) that can personalize curriculum to individual student…
Descriptors: High Stakes Tests, Test Bias, Testing Problems, Prior Learning
Yik, Brandon J.; Dood, Amber J.; Cruz-Ramirez de Arellano, Daniel; Fields, Kimberly B.; Raker, Jeffrey R. – Chemistry Education Research and Practice, 2021
Acid-base chemistry is a key reaction motif taught in postsecondary organic chemistry courses. More specifically, concepts from the Lewis acid-base model are broadly applicable to understanding mechanistic ideas such as electron density, nucleophilicity, and electrophilicity; thus, the Lewis model is fundamental to explaining an array of reaction…
Descriptors: Artificial Intelligence, Models, Formative Evaluation, Organic Chemistry
Schroeder, Kersten T.; Hubertz, Martha; Van Campenhout, Rachel; Johnson, Benny G. – Online Learning, 2022
While research in the learning sciences has spurred advancements in educational technology, the implementation of those learning resources in natural learning contexts advances teaching and learning. In this paper, two faculty members at the University of Central Florida used courseware generated with artificial intelligence as the primary…
Descriptors: Educational Technology, Artificial Intelligence, Courseware, College Faculty
Li, Chenglu; Xing, Wanli; Leite, Walter L. – Distance Education, 2022
Help-seeking is a valuable practice in online discussion forums. However, the asynchronicity and information overload of online discussion forums have made it challenging for help seekers and providers to connect effectively. This study formulated a new method to provide fair and accurate insights toward building a peer recommender to support…
Descriptors: Peer Relationship, Help Seeking, Electronic Learning, Distance Education
Xue, Kang; Huggins-Manley, Anne Corinne; Leite, Walter – Educational and Psychological Measurement, 2022
In data collected from virtual learning environments (VLEs), item response theory (IRT) models can be used to guide the ongoing measurement of student ability. However, such applications of IRT rely on unbiased item parameter estimates associated with test items in the VLE. Without formal piloting of the items, one can expect a large amount of…
Descriptors: Virtual Classrooms, Artificial Intelligence, Item Response Theory, Item Analysis
Michelle P. Banawan; Jinnie Shin; Tracy Arner; Renu Balyan; Walter L. Leite; Danielle S. McNamara – Grantee Submission, 2023
Academic discourse communities and learning circles are characterized by collaboration, sharing commonalities in terms of social interactions and language. The discourse of these communities is composed of jargon, common terminologies, and similarities in how they construe and communicate meaning. This study examines the extent to which discourse…
Descriptors: Algebra, Discourse Analysis, Semantics, Syntax
Erbeli, Florina; He, Kai; Cheek, Connor; Rice, Marianne; Qian, Xiaoning – Scientific Studies of Reading, 2023
Purpose: Researchers have developed a constellation model of decodingrelated reading disabilities (RD) to improve the RD risk determination. The model's hallmark is its inclusion of various RD indicators to determine RD risk. Classification methods such as logistic regression (LR) might be one way to determine RD risk within the constellation…
Descriptors: At Risk Students, Reading Difficulties, Classification, Comparative Analysis
Chini, Jacquelyn J.; Straub, Carrie L.; Thomas, Kevin H. – Physical Review Physics Education Research, 2016
Undergraduate students are increasingly being used to support course transformations that incorporate research-based instructional strategies. While such students are typically selected based on strong content knowledge and possible interest in teaching, they often do not have previous pedagogical training. The current training models make use of…
Descriptors: Undergraduate Students, College Science, Physics, Science Process Skills
Knauf, Rainer; Sakurai, Yoshitaka; Tsuruta, Setsuo; Jantke, Klaus P. – Journal of Educational Computing Research, 2010
University education often suffers from a lack of an explicit and adaptable didactic design. Students complain about the insufficient adaptability to the learners' needs. Learning content and services need to reach their audience according to their different prerequisites, needs, and different learning styles and conditions. A way to overcome such…
Descriptors: Prerequisites, College Instruction, Educational Experiments, Cognitive Style