Human one-to-one tutoring has been shown to be a very effective form of instruction. Three contrasting hypotheses, a tutor-centered one, a student-centered one, and an interactive one could all potentially explain the effectiveness of tutoring. To test these hypotheses, analyses focused not only on the effectiveness of the tutors' moves, but also on the effectiveness of the students' construction on learning, as well as their interaction. The interaction hypothesis is further tested in the second study by manipulating the kind of tutoring tactics tutors were permitted to use. In order to promote a more interactive style of dialogue, rather than a didactic style, tutors were suppressed from giving explanations and feedback. Instead, tutors were encouraged to prompt the students. Surprisingly, students learned just as effectively even when tutors were suppressed from giving explanations and feedback. Their learning in the interactive style of tutoring is attributed to construction from deeper and a greater amount of scaffolding episodes, as well as their greater effort to take control of their own learning by reading more. What they learned from reading was limited, however, by their reading abilities.

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How do people use category membership and similarity for making inductive inferences? The authors addressed this question by examining the impact of category labels and category features on inference and classification tasks that were designed to be comparable. In the inference task, partieipants predicted the value of a missing feature of an item given its eategnry label and other feature values, In the classification task, participants predicted the category label of an item given its feature values. The results from 4 experiments suggest that category membership influences inference even when similarity information contradicts the category label. This tendency was stronger when the category label conveyed class inclusion information than when the label reflected a feature of the category. These findings suggest that category membership affects inference beyond similarity and that category labels and category features are 2 different things. Inductive inference is a fundamental use of categories. Current research identifies at least three crucial factors that govern inductive judgments using categories: (a) feature inheritance, (b) correlation of features across category members, and (c) focus on a single target category. Feature inheritance is facilitated by the hierarchical structure of many taxonomic categories in which specific categories are linked to more abstract categories with class inclusion relations. One reason that this structure is powerful is that it permits properties of subordinate categories to be inferred from superordinate categories (e.g., Quillian, 1968). For example, we may predict that a dolphin bears live young rather than laying eggs, because a dolphin is a mammal and mammals bear live young. Category-based induction is also influenced by the correlations among features of category members. Members of a category generally have features in common. Individual birds differ in appearance, but they share many attributes such as having wings, beaks, and feathers. People are sensitive to the correlation among attributes (Anderson &

Previous research suggests that learning categories by classifying new instances highlights information that is useful for discriminating between categories. In contrast, learning categories by making predictive inferences focuses learners on an abstract summary of each category (e.g., the prototype). To test this characterization of classification and inference learning further, the authors evaluated the two learning procedures with nonlinearly separable categories. In contrast to previous research involving cohesive, linearly separable categories, the authors found that it is more difficult to learn nonlinearly separable categories by making inferences about features than it is to learn them by classifying instances. This finding reflects that the prototype of a nonlinearly separable category does not provide a good summary of the category members. The results from this study suggest that having a cohesive category structure is more important for inference than it is for classification.