ICAP is a theory of active learning that differentiates students' engagement based on their behaviors. ICAP postulates that Interactive engagement, demonstrated by co-generative collaborative behaviors, is superior for learning to Constructive engagement, indicated by generative behaviors. Both kinds of engagement exceed the benefits of Active or Passive engagement, marked by manipulative and attentive behaviors, respectively. This paper discusses a 5-year project that attempted to translate ICAP into a theory of instruction using five successive measures: (a) teachers' understanding of ICAP after completing an online module, (b) their success at designing lesson plans using different ICAP modes, (c) fidelity of teachers' classroom implementation, (d) modes of students' enacted behaviors, and (e) students' learning outcomes. Although teachers had minimal success in designing Constructive and Interactive activities, students nevertheless learned significantly more in the context of Constructive than Active activities. We discuss reasons for teachers' overall difficulty in designing and eliciting Interactive engagement.

Two experiments investigated category inference when categories were composed of correlated or uncorrelated dimensions and the categories overlapped minimally or moderately. When the categories minimally overlapped, the dimensions were strongly correlated with the category label. Following a classification learning phase, subsequent transfer required the selection of either a category label or a feature when one, two, or three features were missing. Experiments 1 and 2 differed primarily in the number of learning blocks prior to transfer. In each experiment, the inference of the category label or category feature was influenced by both dimensional and category correlations, as well as their interaction. The number of cues available at test impacted performance more when the dimensional correlations were zero and category overlap was high. However, a minimal number of cues were sufficient to produce high levels of inference when the dimensions were highly correlated; additional cues had a positive but reduced impact, even when overlap was high. Subjects were generally more accurate in inferring the category label than a category feature regardless of dimensional correlation, category overlap, or number of cues available at test. Whether the category label functioned as a special feature or not was critically dependent upon these embedded correlations, with feature inference driven more strongly by dimensional correlations.Keywords Categorization . Concepts . Inductive reasoning . Expertise . MemoryThe experimental study of human categories has an extensive history, beginning with the seminal study of Hull (1920) and continuing today into the identification of variables critical to the shaping of concepts and the development of formal, quantitative models of classification.Hull introduced the classification paradigm that dominates most current research today. In this paradigm, the subject initially assigns a number of patterns into designated categories, followed by a transfer test containing old and new instances. By manipulation of variables in the learning phase and then evaluating transfer performance, Hull was able to draw a number of conclusions about the learning and representation of concepts; for example, concepts were learned more rapidly in the order from simple to complex rather than the reverse, transfer was better following learning of many patterns shown infrequently rather than a few patterns presented numerous times, and so forth.However, categories provide functions above and beyond classification. Bruner, Goodnow, and Austin (1966) summarized a number of additional utilities of categories: Once learned, they permit generalization to novel instances, thereby reducing the need for new learning; they simplify the incredible complexity of the environment into a manageable set of units, thereby facilitating a host of cognitive functions, including logical reasoning and communication; they are adaptive so that harmful or threatening stimuli can be responded to appropriately; and they permit i...

The present study explored feature-to-feature and label-to-feature inference in a category task for different category structures. In the correlated condition, each of the 4 dimensions comprising the category was positively correlated to each other and to the category label. In the uncorrelated condition, no correlation existed between the 4 dimensions comprising the category, although the dimension to category label correlation matched that of the correlated condition. After learning, participants made inference judgments of a missing feature, given 1, 2, or 3 feature cues; on half the trials, the category label was also included as a cue. The results showed superior inference of features following training on the correlated structure, with accurate inference when only a single feature was presented. In contrast, a single-feature cue resulted in chance levels of inference for the uncorrelated structure. Feature inference systematically improved with number of cues after training on the correlated structure. Surprisingly, a similar outcome was obtained for the uncorrelated structure, an outcome that must have reflected mediation via the category label. A descriptive model is briefly introduced to explain the results, with a suggestion that this paradigm might be profitably extended to hierarchical structures where the levels of feature-to-feature inference might vary with the depth of the hierarchy.

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The present study investigated whether the later learning of a category could affect the representation of other categories learned previously. Participants initially learned two or three categories, where each stimulus was composed of features that were distinctive to a category, shared with one or both of the other categories, or were idiosyncratic. When two categories were initially learned, a subsequent learning phase involved the learning of a third category that either shared distinctive features with categories learned previously, thereby discounting those features as diagnostic or was composed of features unrelated to the original categories. A common transfer test contained old, new, and prototype stimuli for classification, as well as critical items that revealed whether discounting of previously diagnostic features had occurred. The results revealed that stimuli assigned to a particular category in the two-category condition were assigned to the third category learned subsequently when the later learning discounted previously diagnostic features. These results suggest that later learning of a category can indirectly modify the representation of categories learned previously.