Michelene T. H. Chi

Eliciting Self-Explanations Improves Understanding

Learning involves the integration of new information into existing knowledge. Generoting explanations to oneself (self-explaining) facilitates that integration process. Previously, self-explanation has been shown to improve the acquisition of problem-solving skills when studying worked-out examples. This study extends that finding, showing that self-explanation can also be facilitative when it is explicitly promoted, in the context of learning declarative knowledge from an expository text. Without any extensive training, 14 eighth-grade students were merely asked to self-explain after reading each line of a possage on the human circulatory system. Ten students in the control group read the same text twice, but were not prompted to self-explain. All of the students were tested for their circulatory system knowledge before and after reading the text. The prompted group had a greater gain from the pretest to the posttest. Moreover, prompted students who generated o large number of self-explanations (the high explainers) learned with greater understanding than low explainers. Understanding was assessed by answering very complex questions and inducing the function of a component when it was only implicitly stated. Understanding was further captured by a mental model onolysis of the self-explanation protocols. High explainers all achieved the correct mental model of the circulatory system, whereas many of the unprompted students as well as the low explainers did not. Three processing characteristics of self-explaining are considered as reasons for the gains in deeper understanding.

Learning from human tutoring

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.

Active-constructive-interactive: a conceptual framework for differentiating learning activities.

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.

Understanding Tutor Learning: Knowledge-Building and Knowledge-Telling in Peer Tutors’ Explanations and Questions

Prior research has established that peer tutors can benefit academically from their tutoring experiences. However, although tutor learning has been observed across diverse settings, the magnitude of these gains is often underwhelming. In this review, the authors consider how analyses of tutors’ actual behaviors may help to account for variation in learning outcomes and how typical tutoring behaviors may create or undermine opportunities for learning. The authors examine two tutoring activities that are commonly hypothesized to support tutor learning: explaining and questioning. These activities are hypothesized to support peer tutors’ learning via reflective knowledge-building, which includes self-monitoring of comprehension, integration of new and prior knowledge, and elaboration and construction of knowledge. The review supports these hypotheses but also finds that peer tutors tend to exhibit a pervasive knowledge-telling bias. Peer tutors, even when trained, focus more on delivering knowledge rather than developing it. As a result, the true potential for tutor learning may rarely be achieved. The review concludes by offering recommendations for how future research can utilize tutoring process data to understand how tutors learn and perhaps develop new training methods.

Short-term memory limitations in children: Capacity or processing deficits?

This paper evaluates the assertion that short-term memory (STM) capacity increases with age. Initially an analysis is made of the STM system in terms of its parameters and control processes. No evidence was found that can suggest conclusively that either the capacity or the rate of information loss from STM varies with age. On the other hand, substantial evidence exists to show that the processing strategies used by adults are unavailable or deficient in children. Furthermore, considerable differences in the contents and complexity of the long-term memory (LTM) knowledge base (semantic and recognition networks can produce grossly different STM performance between age groups. The second half of this paper reviews three STM-related paradigms—memory span, serial probed recall, and recognition under limited exposure—that have consistently shown performance deficits in children. These deficits are explained in terms of the lack of proper control processes (or processing strategies), as well as an impoverished LTM knowledge base rather than a limitation in STM capacity.

The Processes and Challenges of Conceptual Change

Students engaged in learning a large body of related knowledge often possess some incorrect naive knowledge about the domain. These “misconceptions” must be removed and/or the correct conception must be built in order for students to achieve a deep understanding. This repair process is generally referred to as “conceptual change.” However, although conceptual change has been discussed for several decades within different research contexts, the literature nevertheless presents a somewhat blurry picture of what exactly misconceptions are, what constitutes conceptual change, and why conceptual change is difficult. In this chapter, we suggest that one should think of misconceptions as ontological miscategorizations of concepts. From this perspective, conceptual change can be viewed as a simple shift of a concept across lateral (as opposed to hierarchical) categories. We argue that this process is difficult if students lack awareness of when a shift is necessary and/or lack an alternative category to shift into. These ideas are explored using a detailed example (i.e. diffusion) from abroad class of science concepts (i.e. emergent processes) that are often robustly misunderstood by students.

Three Types of Conceptual Change: Belief Revision, Mental Model Transformation, and Categorical Shift

Learning of complex material, such as concepts encountered in science classrooms, can occur under at least three different conditions of prior knowledge. First, a student may have no prior knowledge of the to-be-learned concepts, although they may have some related knowledge. In this case, prior knowledge is missing, and learning consists of adding new knowledge. Second, a student may have some correct prior knowledge about the to-be-learned concepts, but that knowledge is incomplete. In this incomplete knowledge case, learning can be conceived of as gap filling. In both missing and incomplete knowledge conditions, knowledge acquisition is of the enriching kind (Carey, 1991). In a third condition, a student may have acquired ideas, either in school or from everyday experience, that are “in conflict with” the to-be-learned concepts (Vosniadou, 2004). Knowledge acquisition under this third case is of the conceptual change kind. It is customary to assume in this case that the prior “in conflict with” knowledge is incorrect or misconceived, and the to-be-learned information is correct, by some normative standard. Thus, learning in this third condition is not adding new knowledge or gap filling incomplete knowledge; rather, learning is changing prior misconceived knowledge to correct knowledge. This chapter focuses on this conceptual change kind of learning. Although this definition of conceptual change appears straightforward, conceptual change kind of learning entails several complex, non-transparent, and interleaved issues. Some of the key non-transparent ideas are: (a) In what ways is knowledge misconceived? (b) Why is such misconceived knowledge often resistant to change? (c) What constitutes a change in prior knowledge? and (d) Ho should instruction be designed to promote conceptual change? The existence of decades of research on conceptual change speaks to the complexity of these issues. This chapter hopes to add clarity to some of these issues by laying out three different grain sizes in which knowledge can be “in conflict with” the to-be-learned materials, postulating for each grain size the processes by which such “in conflict with knowledge” can be changed, and speculating on the kind of instruction that might achieve such change. We start by providing some definitions and assumptions about concepts and categories in conceptual change.

Content Knowledge: Its Role, Representation, and Restructuring in Memory Development

Publisher Summary This chapter provides a knowledge-based framework that could be useful in interpreting much of the memory development literature of the 1970s and 1980s. The resulting framework was largely composed of a scaffolding of content knowledge of various types. It argues that most of the age differences that were observed in previous studies could be explained in terms of the ways in which content knowledge developed. Thus, the chapter focuses on a reanalysis of these studies almost exclusively in the context of age-related differences in content knowledge. A danger in this approach is that it may create the impression that changes in content knowledge are the sole source of knowledge-based developmental differences in memory. Many other types of knowledge also change: planning knowledge, “meta” knowledge, and procedural skills.

The ICAP Framework: Linking Cognitive Engagement to Active Learning Outcomes.

This article describes the ICAP framework that defines cognitive engagement activities on the basis of students’ overt behaviors and proposes that engagement behaviors can be categorized and differentiated into one of four modes: Interactive, Constructive, Active, and Passive. The ICAP hypothesis predicts that as students become more engaged with the learning materials, from passive to active to constructive to interactive, their learning will increase. We suggest possible knowledge-change processes that support the ICAP hypothesis and address the limitations and caveats of the hypothesis. In addition, empirical validation for the hypothesis is provided by examining laboratory and classroom studies that focus on three specific engagement activities: note taking, concept mapping and self-explaining. We also consider how ICAP can be used as a tool for explaining discrepant findings, dictate the proper choice of a control condition, and evaluate students’ outputs. Finally, we briefly compare ICAP to existing theories of learning.

Span and Rate of Apprehension in Children and Adults.

Abstract Children and adults quantified random patterns of dots, under unlimited exposure duration. For adults and children two distinct processes appear to operate. For adults the quantification of collections of from one to three dots is essentially errorless, and proceeds at the rate of 46 msec per item, while the quantification rate for from 4 to 10 dots is 307 msec per dot. For children the same operating ranges appear to hold, however children are much slower. The lower slope is 195 msec per dot, while the upper is 1049. Although the results for adults and children are similar except for the overall rates, the nature of the isomorphism between children and adults is unclear.