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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.
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.
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.
In the United States, diversity, equity, and inclusion (DEI) are organizational frameworks that seek to promote the fair treatment and full participation of all people, particularly groups who have historically been underrepresented or subject to discrimination based on identity or disability. [1]
[43] [44] [45] Teachers also report spending less time addressing disciplinary issues in high-track classrooms than in low-track classes, which leads to differences in content coverage. [46] Importantly, research finds that instructional differences across track levels goes beyond what would be expected based simply on differences in student ...
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 ...
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