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The following hierarchy is an example of a cognitive model task performance for the knowledge and skills in the areas of ratio, factoring, function, and substitution (called the Ratios and Algebra hierarchy). [9] This hierarchy is divergent and composed of nine attributes which are described below.
The model of hierarchical complexity (MHC) is a formal theory and a mathematical psychology framework for scoring how complex a behavior is. [4] Developed by Michael Lamport Commons and colleagues, [3] it quantifies the order of hierarchical complexity of a task based on mathematical principles of how the information is organized, [5] in terms of information science.
The primary significance of the hierarchy is to identify prerequisites that should be completed to facilitate learning at each level. Prerequisites are identified by doing a task analysis of a learning/training task. Learning hierarchies provide a basis for the sequencing of instruction.
Attainment based vs. time based progress – The instruction can either be based on the focus on the mastery of the concept or the time spent on learning the concept. Customized vs. standardized instruction – The instruction can be different for different learners or the instruction can be given in general to the entire classroom
When computing a t-test, it is important to keep in mind the degrees of freedom, which will depend on the level of the predictor (e.g., level 1 predictor or level 2 predictor). [5] For a level 1 predictor, the degrees of freedom are based on the number of level 1 predictors, the number of groups and the number of individual observations.
The taxonomy divides learning objectives into three broad domains: cognitive (knowledge-based), affective (emotion-based), and psychomotor (action-based), each with a hierarchy of skills and abilities. These domains are used by educators to structure curricula, assessments, and teaching methods to foster different types of learning.
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The Reverse Hierarchy Theory (RHT), proposed by Ahissar & Hochstein, aims to link between learning dynamics and specificity and the underlying neuronal sites. [54] RHT proposes that naïve performance is based on responses at high-level cortical areas, where crude, categorical level representations of the environment are represented.