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Kebede, Mihiretu M.; Le Cornet, Charlotte; Fortner, Renée Turzanski – Research Synthesis Methods, 2023
We aimed to evaluate the performance of supervised machine learning algorithms in predicting articles relevant for full-text review in a systematic review. Overall, 16,430 manually screened titles/abstracts, including 861 references identified relevant for full-text review were used for the analysis. Of these, 40% (n = 6573) were sub-divided for…
Descriptors: Automation, Literature Reviews, Artificial Intelligence, Algorithms
Tanja Käser; Giora Alexandron – International Journal of Artificial Intelligence in Education, 2024
Simulation is a powerful approach that plays a significant role in science and technology. Computational models that simulate learner interactions and data hold great promise for educational technology as well. Amongst others, simulated learners can be used for teacher training, for generating and evaluating hypotheses on human learning, for…
Descriptors: Computer Simulation, Educational Technology, Artificial Intelligence, Algorithms

Milligan, Glenn W. – Multivariate Behavioral Research, 1981
Monte Carlo validation studies of clustering algorithms, including Ward's minimum variance hierarchical method, are reviewed. Caution concerning the uncritical selection of Ward's method for recovering cluster structure is advised. Alternative explanations for differential recovery performance are explored and recommendations are made for future…
Descriptors: Algorithms, Cluster Analysis, Literature Reviews, Methods
Geis, George L. – Journal of Instructional Development, 1984
Discussion of checklists and their variations--job aids, algorithms, heuristics, and decision tables--covers what they are, how they are generated, and some implications of checklisting for instruction, evaluation, and learning. Examples of various kinds of checklists and 11 references are provided. (MBR)
Descriptors: Algorithms, Check Lists, Criteria, Curriculum Development

Willett, Peter – Information Processing and Management, 1988
Reviews recent research into the use of hierarchic agglomerative clustering methods for document retrieval. The topics discussed include the calculation of interdocument similarities, algorithms used to implement clustering methods on large databases, validity testing of document hierarchies, appropriate search strategies, and other applications…
Descriptors: Algorithms, Bibliometrics, Cluster Analysis, Comparative Analysis