Bibliographic record
Abstract
Gary J. Anglin is Program Coordinator of the Instructional Systems Design Program at the University of Kentucky, where he teaches graduate courses in instructional design, instructional theory, and foundations of instructional technology. His current research interests include e-Learning, in particular the adaptations students, instructional designers, and instructors will have to make in an e-learning environment. In particular, his instructional design research interests include the role of metacognition and cognitive load theory for the design of Web-based instruction.Mary Beth Bianco is a doctoral candidate at Penn State University in the Instructional Systems Department. She is currently employed as an education consultant with the BLaST, Itermediate Unit 17 in Williamsport, PA, after eight years as an elementary educator. Bianco recently finished her doctoral coursework and is in the process of completing her dissertation.Barbara Bichelmeyer is an assistant professor of instructional systems technology, Indiana University. Her primary interests in teaching, research and service activities are related to learners’ engagement, ownership and control of the learning process. Bichelmeyer is also interested in understanding the role that emotion plays in intelligence, learning and instruction. Currently, Bichelmeyer is engaged in research concerned with the relationships between learner emotions and learner engagement in Web-based learning environments.Wellesley R. (Rob) Foshay is the chief instructional architect of the PLATO Learning System. He has 25 years of experience doing instructional design in university and private sector environments. He is active in the International Society for Performance Improvement and the Association for Educational Communications and Technology, and serves on the editorial boards of two research journals. He has published over 50 major papers and book chapters on ID methodology, problem solving, e-Learning, and evaluation.Doug Harvey is currently director of the graduate program in Instructional Technology at the Richard Stockton College of New Jersey. His research focus is on the use of hypertext and online learning. He holds a doctoral degree from Penn State University in Instructional Systems.Tiffany Koszalka is an assistant professor of education and director of assessment and research for the Kids as Airborne Mission Scientist project at the Pennsylvania State University. She spent over a decade as an instructional designer creating and implementing a variety of paper-, classroom-, and technology-based training for adults in the corporate world. She then turned her attention to investigating teacher and student uses of Web-based information and human resources in K-12 science classrooms. She currently teaches instructional design and educational technology integration courses both in the classroom and through distance education. Her recent research in distance education has included computer-mediated communication, social interactions, and the design of distance education to support synchronous and asynchronous learning.Jung Lee is an assistant professor of Instructional Technology at the Richard Stockton College of New Jersey. She received a Ph.D. degree in Adult Learning and Technology from the University of Wyoming. Her research interests include visual design and human factors in hypermedia/multimedia design.Melanie Misanchuk is a doctoral student in Instructional Systems Technology at Indiana University. Her research interests include online learning communities, communities of practice, distance education, and language instruction.Richard Schwier is a professor of educational communications and technology at the University of Saskatchewan, where he teaches graduate courses in educational technology theory, instructional design, and multimedia design. Recent research interests include the use of virtual learning communities in distance education, usability research, and visual design for instructional multimedia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.536 | 0.313 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".