The present paper analyzes the self‐generated explanations (from talk‐aloud protocols) that “Good” and “Poor” students produce while studying worked‐out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self‐explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such (...) learning results in example‐independent knowledge and in a better understanding of the principles presented in the text. “Poor” students do not generate sufficient self‐explanations, monitor their learning inaccurately, and subsequently rely heavily on examples. We then discuss the role of self‐explanations in facilitating problem solving, as well as the adequacy of current AI models of explanation‐based learning to account for these psychological findings. (shrink)

The representation of physics problems in relation to the organization of physics knowledge is investigated in experts and novices. Four experiments examine the existence of problem categories as a basis for representation; differences in the categories used by experts and novices; differences in the knowledge associated with the categories; and features in the problems that contribute to problem categorization and representation. Results from sorting tasks and protocols reveal that experts and novices begin their problem representations with specifiably different problem categories, (...) and completion of the representations depends on the knowledge associated with the categories. For, the experts initially abstract physics principles to approach and solve a problem representation, whereas novices base their representation and approaches on the problem's literal features. (shrink)

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. (shrink)

The present paper analyzes the self‐generated explanations (from talk‐aloud protocols) that “Good” and “Poor” students produce while studying worked‐out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self‐explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such (...) learning results in example‐independent knowledge and in a better understanding of the principles presented in the text. “Poor” students do not generate sufficient self‐explanations, monitor their learning inaccurately, and subsequently rely heavily on examples. We then discuss the role of self‐explanations in facilitating problem solving, as well as the adequacy of current AI models of explanation‐based learning to account for these psychological findings. (shrink)

Active, constructive, and interactive are terms that are commonly used in the cognitive and learning sciences. They describe activities that can be undertaken by learners. However, the literature is actually not explicit about how these terms can be defined; whether they are distinct; and whether they refer to overt manifestations, learning processes, or learning outcomes. Thus, a framework is provided here that offers a way to differentiate active, constructive, and interactive in terms of observable overt activities and underlying learning processes. (...) The framework generates a testable hypothesis for learning: that interactive activities are most likely to be better than constructive activities, which in turn might be better than active activities, which are better than being passive. Studies from the literature are cited to provide evidence in support of this hypothesis. Moreover, postulating underlying learning processes allows us to interpret evidence in the literature more accurately. Specifying distinct overt activities for active, constructive, and interactive also offers suggestions for how learning activities can be coded and how each kind of activity might be elicited. (shrink)

Closing the research‐practice gap cannot be achieved by one of the most promising methods, which is to distill and synthesize decades of research to see how the robust findings can work in practice. An alternative approach is proposed, which is to translate a theory of active learning for practitioners.

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. (shrink)

Studies exploring how students learn and understand science processes such as diffusion and natural selection typically find that students provide misconceived explanations of how the patterns of such processes arise (such as why giraffes’ necks get longer over generations, or how ink dropped into water appears to “flow”). Instead of explaining the patterns of these processes as emerging from the collective interactions of all the agents (e.g., both the water and the ink molecules), students often explain the pattern as being (...) caused by controlling agents with intentional goals, as well as express a variety of many other misconceived notions. In this article, we provide a hypothesis for what constitutes a misconceived explanation; why misconceived explanations are so prevalent, robust, and resistant to instruction; and offer one approach of how they may be overcome. In particular, we hypothesize that students misunderstand many science processes because they rely on a generalized version of narrative schemas and scripts (referred to here as a Direct-causal Schema) to interpret them. For science processes that are sequential and stage-like, such as cycles of moon, circulation of blood, stages of mitosis, and photosynthesis, a Direct-causal Schema is adequate for correct understanding. However, for science processes that are non-sequential (or emergent), such as diffusion, natural selection, osmosis, and heat flow, using a Direct Schema to understand these processes will lead to robust misconceptions. Instead, a different type of general schema may be required to interpret non-sequential processes, which we refer to as an Emergent-causal Schema. We propose that students lack this Emergent Schema and teaching it to them may help them learn and understand emergent kinds of science processes such as diffusion. Our study found that directly teaching students this Emergent Schema led to increased learning of the process of diffusion. This article presents a fine-grained characterization of each type of Schema, our instructional intervention, the successes we have achieved, and the lessons we have learned. (shrink)

The goals of this study are to evaluate a relatively novel learning environment, as well as to seek greater understanding of why human tutoring is so effective. This alternative learning environment consists of pairs of students collaboratively observing a videotape of another student being tutored. Comparing this collaboratively observing environment to four other instructional methods—one‐on‐one human tutoring, observing tutoring individually, collaborating without observing, and studying alone—the results showed that students learned to solve physics problems just as effectively from observing tutoring (...) collaboratively as the tutees who were being tutored individually. We explain the effectiveness of this learning environment by postulating that such a situation encourages learners to become active and constructive observers through interactions with a peer. In essence, collaboratively observing combines the benefit of tutoring with the benefit of collaborating. The learning outcomes of the tutees and the collaborative observers, along with the tutoring dialogues, were used to further evaluate three hypotheses explaining why human tutoring is an effective learning method. Detailed analyses of the protocols at several grain sizes suggest that tutoring is effective when tutees are independently or jointly constructing knowledge: with the tutor, but not when the tutor independently conveys knowledge. (shrink)

INTRODUCTION G. Soros I was hoping to deliver a paper at the Wuhan Conference on Karl Popper's philosophy, but business interfered. ...

A large body of research links testosterone and cortisol to male-male competition. Yet, little work has explored acute steroid hormone responses to coalitional, physical competition during middle childhood. Here, we investigate testosterone, dehydroepiandrosterone, androstenedione, and cortisol release among ethnically Chinese boys in Hong Kong, aged 8–11 years, during a soccer match and an intrasquad soccer scrimmage, with 63 participants competing in both treatments. The soccer match and intrasquad soccer scrimmage represented out-group and in-group treatments, respectively. Results revealed that testosterone showed (...) no measurable change. DHEA increased during both treatments in the majority of participants and the degree of change had no relation to independent variables or covariate measures. Most boys experienced androstenedione increases during match play, but no significant differences during the intrasquad soccer scrimmage competitions. The magnitude of change differed significantly between treatments and was positively associated with age. These latter findings suggest boys’ androstenedione responses may be sensitive to competitor type. For most subjects, cortisol significantly increased during match play, decreased during the intrasquad soccer scrimmage, and differed significantly between treatments, suggesting each treatment promoted a different psychological state among competitors. Cortisol/DHEA molar ratio decreased during the intrasquad scrimmage, suggestive of a more relaxed mental state. These data shed new light on potential proximate mechanisms associated with coalitional competition among prepubescent boys, with relevance to adrenarche and life history theory. (shrink)

In post-Gricean pragmatics, communication is said to be successful when a hearer recovers a speaker’s intended message. On this assumption, proposals for ‘what is said’ – the semantic, propositional meaning of a speaker’s utterance – are typically centred around the content the speaker aimed to communicate. However, these proposals tend not to account for the fact that speakers can be deliberately vague, leaving no clear proposition to be recovered, or that a speaker can accept a hearer’s misconstrual even though the (...) speaker didn’t intend it. In such cases, identifying ‘what is said’ is more contentious, even though communication is arguably no less successful. Building on recent interactionist approaches to meaning, this chapter offers a proposal for ‘what is said’ that can account for cases of ‘imperfect’ communication in which the recovery of a speaker’s intention goes awry. This involves taking ‘what is said’ as the co-constructed meaning that the speaker and hearer jointly agree on, giving ‘grounded meanings’ – those mutually and manifestly accepted into the discourse by speaker and hearer – higher precedence in a theory of communication than simply the speaker’s intended meaning. In such cases, rather than speaker and hearer converging at the level of explicit utterance content, communication is expected to be successful as long as the hearer recovers a proposition which is compatible with the speaker’s higher-order discursive goal. The hearer’s uptake is then allowed to override the content of the speaker’s intended meaning in the process of negotiating ‘what is said’. (shrink)

Philosophy, social problems, and global environmental change.

History of government under the Lý-Trần dynasty, from XI-XIV centuries in Vietnam.

History of political ideology in Vietnam.

On the influence of Confucianism to the social and politics in Vietnam.