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Several instructional options are typically used within a cluster, including: enrichment and extensions, higher-order thinking skills, pretesting and differentiation, compacting, an accelerated pace, and more complex content. [6] "Through cluster grouping the intellectual, social, and emotional needs of the gifted students can be addressed." [7]
Learning clusters: Students take three or more connected courses, usually with a common interdisciplinary theme uniting them. Freshman interest groups: Similar to learning clusters, but the students share the same major, and they often receive academic advising as part of the learning community.
See the algorithm section in cluster analysis for different types of clustering methods. 6. Evaluation and visualization Finally, the clustering models can be assessed by various metrics. And it is sometimes helpful to visualize the results by plotting the clusters into low (two) dimensional space. See multidimensional scaling as a possible ...
Conceptual clustering vs. data clustering [ edit ] Conceptual clustering is obviously closely related to data clustering; however, in conceptual clustering it is not only the inherent structure of the data that drives cluster formation, but also the Description language which is available to the learner.
Education for inclusion and diversity; Arising from the principle of inclusion and the recognition of the diversity of learning needs, Wedell became concerned with curriculum modification and systems of organising learning, considering how educational contexts can respond to pupil diversity. . [11] He recognised the complexity and dilemmas of ...
A Small Learning Community (SLC), also referred to as a School-Within-A-School, is a school organizational model that is an increasingly common form of learning environment in American secondary schools to subdivide large school populations into smaller, autonomous groups of students and teachers.
Consensus clustering is a method of aggregating (potentially conflicting) results from multiple clustering algorithms.Also called cluster ensembles [1] or aggregation of clustering (or partitions), it refers to the situation in which a number of different (input) clusterings have been obtained for a particular dataset and it is desired to find a single (consensus) clustering which is a better ...
Therefore, new algorithms based on BIRCH have been developed in which there is no need to provide the cluster count from the beginning, but that preserves the quality and speed of the clusters. The main modification is to remove the final step of BIRCH, where the user had to input the cluster count, and to improve the rest of the algorithm ...