In order to create effective instruction, representations must be structured to promote comprehension and decrease cognitive load capacity. To accomplish this task, theorists have developed learning taxonomies. Learning taxonomies attempt to organize levels of information that are processed differently in a hierarchal order to increase learner comprehension of the material presented (Gagne, 1985). The purpose of a learning taxonomy is to order learning objectives in a hierarchal structure where information, such as facts and concepts, are presented as a prerequisite to high level processing strategies such as problem solving. Nitko (2004) states “To assess higher-order thinking abilities, it is often necessary to develop tasks for which the solutions or answers depend on a particular piece(s) of introductory material presented along with them.” (p. 232). Presenting information in the hierarchal structure allows designers to effectively align objectives in a structured way, which complements instruction and augments comprehension. While there are several theorists who hold their own version of the learning hierarchal structure, they all share the same basic theoretical paradigm. Examples include Gagne’s levels of complexity, the instructional consistency/congruency model, and the component display theory.
Gagne’s levels’ of complexity is a hierarchal learning taxonomy, which aligns information in categories of facts, concepts, rules, and high-order rules or problem solving techniques (Gagne, Wagner, & Briggs, 1998). Gagne explains that in order for learners to be able to perform high-level tasks such as rules and problem solving, they must first have the prerequisite facts and concepts.
Gagne states, “In solving problems for which instruction has prepared them, learners are acquiring some higher-order rules (that is, complex rules). Problem solving requires that they recall some simpler, previously learned rules and defined concepts. To acquire these rules, learners must have learned some concrete concepts, and to learn these concepts, they must be able to retrieve some previously learned discriminations.” (Gagne et al., 1992, p. 54).
Dwyer (1978) encourages the use of an instructional consistency/congruency model, which introduces prerequisite objectives prior to presenting information that is required to process high-level learning tasks, such as problem solving strategies. The instructional consistency/congruency model is structured similarly to Gagne’s levels and presents itself as levels of facts, concepts, rules/principles, and problem solving objectives. The model helps ensure that learning objectives, instructional content, and assessment items are congruent to each other so that information is both presented and assessed within the same level. Other models with similar theoretical foundations have been a proposed as well. For example, Merrill’s Component Display Theory classifies “learning objectives (or capabilities) along two dimensions: performance level (remember, use, or find) and content type (facts, concepts, principles, or procedures).” (Ragan and Smith, 2004, p. 632).
The purpose of these models is to effectively measure achievement of content, ensure that both high and low levels of learning are measured correctly, and that learning objectives are aligned in an order that promotes learning. This helps ensure that the instructional methods and strategies being implemented are structured in a way that benefits learners’ processing abilities.
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