COGNITIVE SCIENCE 18, 439-477 (1994)(All thought the page numbering is been changed, the content of this document is the same as in
the journal edition)

Eliciting Self-Explanations Improves Understanding
MICHELENE T. H. CHI, NICHOLAS DE LEEUW,
MEI-HUNG CHIU, AND CHRISTIAN LAVANCHER
University of Pittsburgh

Learning involves the integration of new information into existing knowledge. Generating explanations tooneself (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 thatfinding, showing that self-explanation can also be facilitative when it is explicitly promoted, in the context oflearning 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 passage 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 agreater gain from the pretest to the posttest. Moreover, prompted students who generated a large number of selfexplanations (the high explainers) learned with greater understanding than low explainers. Understanding wasassessed 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 analysis 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 asreasons for the gains in deeper understanding.

Preparation of this article was supported in part by an Office of Educational Research and Improvement
Institutional grant for the Center of the Study of Learning. The opinions expressed do not necessarily reflect theposition of the sponsoring agencies, and no official endorsement should be inferred.
We are grateful for comments provided by Kurt VanLehn, and by the action editor and reviewers, Keith
Holyoak, Miriam Bassok, Allan Collins, and Pete Pirolli.
Correspondence and requests for reprints should be sent to Michelene T. H. Chi, 821 Learning Research andDevelopment Center, University of Pittsburgh, 3939 O'Hara Street, Pittsburgh, PA 15260. Requests by e-mail may
be sent to chi@ vms.cis.pitt.edu.
Mei-Hung Chiu is now an associate professor at the Graduate Institute of Science Education, National Taiwan
Normal University, 88, Sec. 5 Roosvelt Road, Taipei, Taiwan 117.

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Processes of learning are often considered in terms of either comprehension, skill acquisition, or both.
In the case of comprehension, the declarative information that is to be understood (such as a story) maps
onto knowledge that is already stored and organized in memory. The emphasis in comprehension istherefore on the instantiation of existing knowledge. In the case of learning a procedural skill, two classesof mechanisms are proposed: knowledge acquisition and compilation. In knowledge acquisition an initial
version of a skill (such as a set of instructions on how to send e-mail) is directly encoded from the source of
instruction. In knowledge compilation, the encoded skill is slowly transformed so that it becomes more
efficient, The emphasis in skill acquisition is, therefore, on the progression of a skill once encoded.
However, new instruction of either declarative or procedural knowledge cannot always be either
instantiated or directly encoded; often it requires the integration of new information with existingknowledge. This integration process can be facilitated by asking students to actively construct what they
are learning. In Chi, Bassok, Lewis, Reimann, and Glaser (1989), a special form of construction activity
called "self-explaining" was shown to be effective at improving the acquisition of problem-solving skills.
Apparently, spontaneously generating explanations to oneself as one studies worked-out examples from atext is a process that promotes skill acquisition even though it is neither the direct encoding of instruction
nor the compilation of an encoded skill.
The original Chi et al. (1989) study concerned the learning of a procedural skill from examples
provided in a physics text. Generally, a worked out solution example. takes the form of a sequence ofaction statements, such as "consider the knot ... to be the body" or "choosing the x- and y-axes as shown"
without any explanations or justifications for the actions chosen. It was hypothesized, therefore, that inorder for students to learn maximally from sparsely stated worked-out examples, they must provide theirown explanations for the actions in the examples. Eight college students, who first studied the prose
sections of an introductory physics text were then asked to explain (on a voluntary basis) whatever they
understood from reading statements from three examples. The basic result is termed the self-explanation
effect: The 4 students who were subsequently more successful at solving problems at the end of the chapter(averaging 82% correct in the posttest) were the ones who spontaneously generated a greater number ofself-explanations while studying the examples (15.3 explanations per example). The 4 less successful
students averaged 46% correct on the posttest and generated only 2.8 explanations per example (Chi et al.,1989).
The self-explanation effect has been directly replicated in other laboratories, all in the domain oflearning a procedural skill. Pirolli and Recker (1994) used a similar design, with LISP coding as the task
domain. Ferguson-Hessler and de Jong (1990) had subjects give protocols as they studied a manual on

3applications of principles of electricity and magnetism to the Aston mass spectrometer. Nathan, Mertz, andRyan (1994) examined the effect of self-explaining on solving algebra word problems. All of these studies
found the number of self-explanations correlated with problem-solving successes.
There are numerous other findings in the literature that can be interpreted to indirectly support the selfexplanation effect. Most notable are the robust findings of Webb (1989). Citing 19 published studies onlearning mathematics and computer science in small groups, Webb found that giving elaborate
explanations was positively related to individual achievement, whereas receiving elaborate explanations
had few significant positive relationships with achievement. Such findings are consistent with the selfexplanation effect because the advantages gained by explaining to others and to oneself are comparable. In
testing the effectiveness of using a computer-based analysis tool for instructional design in teacher training,
Russell and Kelley (1991) serendipitously found that requiring the students to explain their design decisions
dramatically and beneficially altered their understanding of course materials. In a completely different
context, it has long been known that reading out loud to young children is beneficial for their intellectual,social, and emotional development. However, such intellectual stimulation is further enhanced if the parent
engages the child in discussions of the stories (Dole, Valencia, Greer, & Wardrop, 1991). More remotely,
the phenomena of the generation effect (Slamecka & Graf, 1978), the hypothesis generation effect (KIahr& Dunbar, 1988), and cognitive tuning set (Zajonc, 1960), may all be interpreted to be broadly consistent
with the self-explanation effect.
Concurrently, there is also a general momentum in the science education literature toward talking,reflecting, and explaining as ways to learn, especially for challenging science domains. The whole idea of
the new "talking science" approach (Hawkins & Pea, 1987; Lemke, 1990) is that students should learn tobe able to talk science (to understand how the discourse of the field is organized, how viewpoints arepresented, and what counts as arguments and support for these arguments), so that students can participate
in scientific discussions, rather than just hear science. Alternatively, our interpretation of the benefit oftalking science is the provision of self-explanations: a constructive inferencing activity. Although both ofthese views encourage the students to talk more, and both make the same prediction concerning thebeneficial learning outcome, they do make diverse predictions with respect to other forms of constructive
activity, such as diagram drawing. The talking science view focuses on learning the discourse of science,
whereas the self-explanation view accepts the possibility that any form of constructive activity may bebeneficial to some degree, even diagram drawing.
Here, we explore the generality of this self-explanation effect by extending it from skill acquisition to
the learning of a coherent body of new knowledge. Moreover, this study shows that the beneficial effect of

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self-explanations can be achieved merely by prompting students to self-explain. Thus, this current study
has five specific goals:
1.

To see if the self-explanation effect can be generalized to a different (nonprocedural) domain, adifferent task, a different outcome measure, and a different age group.

2.

To develop an assessment of understanding based on complex questions that were designed in a
principled way, so that the questions can be differentiated on the basis of what kind of knowledge(directly encoded, integrated, or inferred) they tap, as opposed to the more standard method of simply
grading the difficulty of questions.

3.

To develop an analysis to capture what understanding is, in terms of the changes from the students'
initial to their final mental models.

4.

To address the processing characteristics of self-explaining that may make self-explanations especiallyamendable for theory revision.

5.

To see whether the same improvement in learning can be achieved when students are merely prompted
to self-explain.

The last goal is particularly important in light of the fact that the self-explanation effect could simply beinterpreted as yet another indication of two distinct learning types, comparable to binary learning
approaches suggested by many other investigators. Bereiter and Scardamalia (1989), for instance,
identified two kinds of learners: high intentional and low intentional. Marton and Saljo (1976) likewise
postulated two learning approaches: surface versus depth oriented. More recently, Ng and Bereiter (1991)
suggested that learners differ in the type of goals they maintain: task-completion goals, instructional goals,
and personal knowledge-building goals. It is also the case that spontaneous generation of self-explanations
can be correlated with ability. Therefore, in order to claim that it is the activity of generating selfexplanations that fosters greater understanding rather than some basic difference in learning style or ability
per se, we must further show that this activity can promote learning when elicited, independent of ability.
This study therefore attempts to replicate the self-explanation effect with some important changes:
1.

Using a different domain (a biological domain instead of a physics domain);

2.

Using a different age group (eighth graders instead of college students);

3.

Self-explaining expository text rather than worked-out solution examples;5

4.

Focusing on declarative understanding of concepts (as assessed by question answering and inductionof function) rather than learning a procedural skill (as assessed by problem-solving); and mostimportantly,

5.

Prompting students for self-explanations rather than relying on spontaneous generation of selfexplanations.
In addition, this study includes a control group of 10 students who were not prompted to self-explain;instead, they were given an opportunity to read the text twice, so as to roughly equate time on task with theprompted group. Moreover, prior knowledge and ability were assessed by a set of pretests and achievement
measures.
To review, two groups of eighth-graders were asked to read a passage about the human circulatory
system, taken from a popular high school biology text. Prior to reading they were given a set of pretests toassess their prior knowledge, and after reading, they took posttests to assess their understanding. During
the reading phase, the prompted students were asked to explain, after reading each sentence, what they
understood about that sentence. The unprompted students were asked to read the passage twice so as toroughly equate time spent on the task. California Achievement Test (CAT) scores for both groups ofstudents were used as a measure of their abilities.

METHOD
Materials
Analysis and Choice of a Biological Topic
Understanding the circulatory system requires what philosophers have referred to as "systematic"
explanation (Haugeland, 1978), in that one must understand the "organized cooperative interactions" that
occur within the system. Such cooperative interaction can be explained by the systematic interaction of the
distinct components at all levels of the system. One way to specify this organized cooperative interaction isto decompose the circulatory system into its components and identify the structure, function, and behaviorof each component. Each physical entity, such as the atrium, was operationally defined as a component.
There are three kinds of "local" features to each component: the structure, function (or purpose), and the
behavior. Take the atrium, for example. One structural (S) property of the atrium is that it is a muscularchamber; the function (F) of the atrium is to serve as a holding bin for the blood returning from the body or
the lungs; the behavior (B) of the atrium is that it contracts and squeezes the blood into the ventricle. At
this first level of analysis, textbooks can be inadequate by omitting information about certain features of6

Figure 1. A schematic diagram of the knowledge pieces about components and their relations. Dotted lines andboxes indicate information missing from the text (S= structural property; F= function; B= behavior)each component. Typically, the functional information is omitted. See Figure 1 for a depiction of the three
local features of each component. Here, "local" is taken to mean features that pertain directly to thatcomponent.
There are relations within these three features of each component that could be specified in a textbook
but are often omitted, such as the relationship between the structure of the atrium and its behavior (asindicated by the upper question mark in Figure 1). In addition to these within a component relationalfeatures that are left unspecified, there are also relations among components. For each feature of anindividual component, one can ask about the relation between that feature (such as the function of theatrium as a holding reservoir) and a feature of another component (such as the behavior of the valves in theheart; see the lower question mark in Figure 1). If a learner understands this relationship, then the learnershould understand the health consequence of having a murmur or a leaky valve. The textbooks at the juniorand senior high school levels typically leave many of these within-a-component and among-components
relations unspecified. However, if a learner understands all the local features of each component, then,7presumably, these relational consequences within and among components can be inferred. They are
required for deep understanding.
So far, three kinds of information that could be specified in an expository passage have been
mentioned: the local features of each component, the relationships among the local features of eachcomponent, and the relations among the local features of different components. Besides these, for thecirculatory system at least, there are also hierarchical relationships between the local features of individualcomponents and the systemwide features of the circulatory system as a whole. For instance, the systemwide
goal of the circulatory system is to deliver nutrients and oxygen to the body's cells and to remove wastes.
There are relations among the local features of a component with this top-level purpose. So, for instance,
one structural feature of the heart is that it is divided in the middle by the septum. This feature has its own
local purpose, which is to separate the oxygenated from deoxygenated blood within the heart. But this local
function is subordinate to the systemwide purpose of optimizing the functioning of the circulatory system
as a whole by allowing for separate pulmonary and systemic circulations. Thus, in order to have a coherent
understanding of the circulatory system, an intricate system of relations must be understood not only
locally, but systemwide as well. In addition to these kinds of within-a-component, between-components,and hierarchical relations, "processes" such as diffusion or oxygenation constitute a fourth kind of
relationship involving several components.
All the information that is omitted from a text has been described here in great detail to illustrate threepoints. The first is that, although there have been general attempts to improve texts by inserting moreelaborations and so on (Beck, McKeown, Sinatra, & Loxterman, 1991), such undertakings seem pretty
hopeless because there are numerous relations that are left unspecified. Explicating all the relationships
would make texts ridiculously long. The second point is that because information is omitted, any
expository passage leaves a great deal of room for readers to provide their own inferences to bridge thegaps in the information provided. Hence, self-explaining seems to be a necessary activity in order tomaximize what is learned from any expository passage. The third point is that the design of the questions
for assessing understanding in this study was motivated largely by assessing these kinds of relational
understanding.

Choice of Text Passage
An analysis of four highly rated texts corroborate the analysis as described in the preceding section.1 It
appears that, in general, most of the texts are more or less adequate in explaining the global function of the1

We appreciate the help of Joanne Striley for this analysis.

8system, factual knowledge about the structure of components, and behavior of the components.
Deficiencies (in terms of the incompleteness of the account) occur primarily in the other factors, such as thefunction of components, local relations within and between components, systemwide relations, and generalprocesses such as diffusion. The four texts differed chiefly in the extent to which they relate anatomical
structure or function to physiological functioning of the entire system. Because such an analysis shows that
all texts have shortcomings of one kind or another, the final choice was to use Towle's (1989) Modern
Biology, a highly rated and popular text, usually used in the ninth grade.
Towle's (1989) unit on the circulatory system was kept intact, except that the three figures (notparticularly good ones) and a few sentences were deleted to keep the passage short. The edited version
contained a total of 101 sentences, relating to the major components of the primary topic. Sentences thatwere deleted either referred to figures in the original textbook or were excursions into related topics (suchas the lymphatic system), which were not mandatory for understanding the primary topic. Figures in the
text were deleted so as not to complicate the interpretation of the results with differences in the ability tolearn from diagrams (Mayer, 1993).
Consistent with other good texts, this one also contained information primarily about the global
function of the circulatory system, as well as the structure and behavior of each component. This unit
explicitly discussed 11 of the 22 components' functions. Even when the local function of a component was
explicitly specified, it was usually not related to the systemwide function of the circulatory system. For
instance, the passage would indicate the purpose of the pulmonary system and the structure of valves in theveins. However, it would not state why the pulmonary vein does not have a valve in it. Therefore, thestudents would have to make an inference (or a chain of inferences) in order to complete theirunderstanding.
How the Questions Were Designed
The design of the set of questions used in the pre- and posttests was intended to focus on tapping new
knowledge that the reader had to construct from the information presented. This constructed new
knowledge often corresponded to the links that are depicted by the question marks in Figure 1. To devise a
way of designing some of the questions, each sentence of the passage was coded as to what type of
information it contained, whether it described the function, and/or the structure, and/or the behavior of acomponent, and/or relationships between components. Occasionally, a sentence was coded as a "factlet,"which was a sentence that contained none of the three local features or any relations among thecomponents, such as "The heart continues to beat without interruption more than 2.5 billion times in the
average life span" (Towle, 1989, p. 655). Processes such as diffusion generally required several sentences

9

to describe. The purpose of such coding was to design questions in a principled way, so that one could
predict exactly what knowledge the question was assessing, and whether this knowledge could be directly
gained from a sentence, or whether it was inferred. For instance, if a sentence described only the structureof a component, such as the atrium, could a learner answer a question about its function?
Four sets of questions were constructed: Three sets of questions (Categories 1, 2, and 3 in ascending
difficulty) were designed to test what was learned from the passage, and Category 4 (or health) questions
tested students' use of this newly acquired knowledge to answer health-related questions.2 How thesequestions were constructed now follows.
Category 1 contained 14 "verbatim" questions that were generated from information explicitly stated in
the text. Usually, the information was presented in a single sentence but, occasionally, an implied agent
from the previous sentence was needed. Thus, these questions asked about the structure, function, or simple
knowledge about processes, as directly or explicitly presented in the text. Table 1 contains an example of a
Category 1 question along with the sentence from the text on which that question was based.
Category 2 contained 14 "comprehension inference" questions based on material explicitly presented in
the text. However, these questions required the student to integrate information from two or more lines oftext, or to integrate across nonconsecutive paragraphs. For example, one sentence in the passage described
the structure of the skeletal muscles, and another sentence described the behavior of blood flow. A question
was then posed concerning the relationship between the structure of the skeletal muscles and the directionof blood flow (see Table 1 for an example). Notice that most of the work in the comprehension literatureassesses understanding by this sort of comprehension-inferencing questions (e.g., see the questions designed
by Goldman & Duran, 1988; Graesser & Murachver, 1985; Nicholas & Trabasso, 1980; Nix, 1985), inthat the questions require either paraphrases, explicit comparisons, or integration of information within andacross paragraphs. Thus, these questions require comprehension inferencing of varying degrees, but all theknowledge needed to make the inferences is presented in the sentences.
In contrast, Category 3 contained 14 "knowledge inference" questions that required the generation of new
knowledge. Answering questions required a good deal of understanding, and the use of prior knowledge(either domain-relevant, commonsense, or everyday knowledge). These questions were of many types. The
first type assessed whether a learner could induce the function of a component if only the structure or
behavior was explicitly described in the text. This went beyond just asking students in a straightforward2

Two additional categories of questions, pertaining to medieval misconceptions, and to scientific discoveries, werealso designed and administered for different purposes. These questions were intended to compare and contrast
contemporary and historical misconceptions, and to examine the inferential processes in discovery. Because thequestions were not designed to test the self-explanation effect, no analyses of them will be reported.

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TABLE 1
Examples of Category 1- 4 Questions
and the Corresponding Lines of Text Needed to Answer Them (Towle, 1989)
Category 1: Verbatim
Sentence 127:
Hemoglobin is the molecule that actually transports oxygen and carbon dioxide.
Question:
What does hemoglobin transport?
Category 2: Comprehension Inferences
Sentence 71:
Many veins pass through the skeletal muscles.
Sentence 72:
During -movement, these muscles contract, squeezing blood through the veins
Question:
Besides the role of valves in the veins, what keeps blood from the lower body moving up
toward the heart through the veins, against gravity?
Category 3a: Knowledge Inferences
Sentence 17:
The septum divides the heart lengthwise into two sides.
Sentence 18:
The right side pumps blood to the lungs, and the left side pumps blood to other parts of thebody.
Sentence 30:
Blood returning to the heart, which has a high concentration of carbon dioxide and a low
concentration of oxygen, enters the right atrium.
Sentence 33:
In the lungs, carbon dioxide leaves the circulating blood and oxygen enters it.
Sentence 34:
The oxygenated blood returns to the left atrium of the heart.
Question:
Why would the distribution of oxygen (a systemwide function) be less efficient if there is ahole in the septum (a structure of the septum)?
Category 3b: Knowledge Inferences
Sentence 73:
Valves prevent the blood from moving backward or downward.
Sentence 80:
Pulmonary circulation is the movement of blood from the heart to the lungs and then backto the heart.
Sentence 86:
Oxygenated blood then flows into venules, which merge into the pulmonary veins that leadto the left atrium of the heart.
Sentence 87:
The pulmonary veins are the only veins that carry oxygenated blood.
Question:Why doesn't the pulmonary vein have a valve in it?
Category 4: Health
Sentence 3:
Sentence 64:
Sentence 76:
Question:

The blood moving through the vessels serves as the transport medium for oxygen, nutrients,and other substances.
Several venules in turn unite to form a vein, a large blood vessel that carries blood to theheart.
The English scientist William Harvey first showed the heart and blood vessels formed one
continuous, closed system of circulation.
Some snake bites can be dangerous because the snake venom causes muscle paralysis(muscles become immobile-can't move). How is it that a person can die in a short amountof time from such a snake bite, even when the bite is on the ankle?

way what the function of a component was: They had to induce the function in order to use that knowledge
to answer a question that was not directly related to the function.
A second type of question in this category required students to induce the function (or behavior or
structure) of a component and relate it to another feature (function, structure, or behavior) of anothercomponent. For example, suppose the behavior of the valve was described as opening and closing. A

11question then asked about the relationship between the opening and closing of valves and the purpose of theatrium.
A third type required relating a specific induced feature of a component to either a systemwide purpose
or some process such as diffusion. A question of this type is shown in Table 1, Category 3a.
Why would the distribution of oxygen (a systemwide function) be less efficient if there is a hole in the
septum (a structure of the septum)?

In order to answer such a question, the student needs to induce the function of the septum because noneof the relevant sentences in the text (Sentences 17, 18, 30, 33, and 34) explicitly mention it.
A fourth type of question asked for an explanation of an implied structure. Table 1, Category 3b,
illustrates this type of question. All the sentences (73, 80, 86, and 87) from the passage that were relevant
to valves and the pulmonary vein are shown in this table. No sentence mentioned anything about the role ofvalves in the pulmonary vein. Hence, a question relating these two components asked why the pulmonaryvein would not have a valve in it. In order to answer such a question, the student must reason about andinfer several notions: First that most veins have low pressure, but that the pulmonary circulation is only ashort distance away from the heart, therefore the heart can pump blood to the lungs with a great deal offorce, thereby eliminating the likelihood that the blood will flow backward, hence no valve is needed. A
great deal of the reasoning brings in commonsense knowledge, such as the shorter distance of thepulmonary circulation implies greater pressure. This factor is one of the reasons the conjecture is made that
self-explaining can facilitate understanding, because it encourages the student to utilize commonsense
knowledge.
Finally, Category 4 contained 14 "health" questions that typically assessed students' understanding of
the implications of the systemwide properties of the circulatory system. One such systemwide property isthat the circulatory system is a closed system of circulation. A closed system implies certain properties
such as, the same blood circulates throughout the system, and the total volume of blood does not change.
The snake bite question (shown in Table 1) assesses students' use of such knowledge.
The pre- and posttests also included two other tasks: drawing the pattern of blood flow on an outline ofa human body, and defining 23 terms that were taken from the unit.

Subjects
Eleven pilot students were tested prior to the actual study. These pilot students were tested for a varietyof purposes, such as modifying the loci of probes in the text for knowledge of functions, adding definitions

12of a couple of vocabulary words used in the passage that students did not understand, and timing theduration of reading the entire passage with prompting. The experimental subjects were 14 eighth graders (7boys and 7 girls) for the prompted group and 10 eighth graders (5 boys and 5 girls) for the unpromptedgroup, all recruited from a local public school. More students were tested for the prompted group with theintention to do some contrastive analyses. It would have been preferable to test more students in total, but
the subject sample was restricted to volunteers from all the eighth graders of an inner city school class (asmall sample to begin with) in order to maintain homogeneity. None of them had taken a biology course.
Students were chosen intentionally with a range of abilities in terms of their CAT scores, so that ability
differences could be examined. Even though students were paid for their participation, it was difficult to
recruit students with lower CAT scores because they tended not to volunteer for this kind of activity.
Procedure
There were three phases to the study. First, an initial interview session (a pretest) in which each student
discussed what they knew about 23 terms of the circulatory system, then drew the direction of blood flow
through the body and answered half of the questions in each category (1-4), in that order. This interview
was audiotaped. More specifically, in the first section of both the pre- and posttests, students were asked to
explain everything they knew about 23 terms taken from the circulatory system (i.e., heart, lungs,
pulmonary artery, etc.). They were asked to consider:
1.

What is it? What kind of thing is it? What does it refer to?

2.

Where is it found in the body?

3.

What is its structure, texture, or composition?

4.

What does it do?

5.

What is its purpose?

In the blood path section, students were presented with an outline of the human body and asked to draw thepath of blood flow throughout the body, making sure the path included the heart, lungs, brain, feet, andhands.
Second, each student then read the 101-sentence passage. Each sentence was printed on a separate
piece of paper. The prompting consisted of general instructions (shown in the Appendix) given at the
beginning of the reading phase. Then the students were told to explain what each sentence means. The
students had absolutely no trouble carrying out this general instruction. The prompt given by theexperimenter after every sentence was very general, more analogous to a reminder. In fact, after a few

13

initial prompts, the students often proceeded with self-explaining without any further prodding, perhaps
because the one-sentence-per-page format served as a sufficient cue.
In addition to these general prompts, a set of specific function prompts were inserted at 22 locations
throughout the 101-sentence passage. These locations corresponded to places in the text at which acomponent of the circulatory system was discussed, such as the atrium. The function prompt occurred after
the student generated the self-explanation of that sentence, and consisted of asking the student explicitly
what the function of the component was.
Besides the explanations they generated in response to reading each sentence, students may have been
prompted for further clarification by the experimenter if what they stated was vague, with comments suchas "Can you explain that?", "What do you mean?" or "Flowing through what?" (if the student said
"Flowing through there"). Students were told that they could also take notes and draw diagrams while
reading, although they were not prompted to do so in any systematic fashion.
Students in the control or unprompted group were not prompted for either self-explanations orfunctions. Instead, they were asked to read the materials twice. Reading it twice (as opposed to three orfour times) engaged the unprompted students with the text for roughly the same amount of time as having
the prompted students read and explain the text. This was determined a priori by measuring the total
amount of time a pilot group of prompted students took. The actual mean of the prompted group's studying
time was 2 hr 5 min (range = 1 hr 27 min to 2 hr 53 min), and the mean of the unprompted group was 1 hr
6 min (range = 22 min to 2 hr 47 min).
Third, after they read the material, students took a posttest, which included the 23 terms, the blood
path, and the entire set of the Category 1-4 questions, including those given in the pretest. In order toreduce stress and place more emphasis on learning as opposed to memory, students were allowed to lookback at the text or their notes to help explain any terms or answer any questions in the posttest.
The whole procedure was run in three to four sessions (some students took two sessions to read thetext), with each session spaced at least a week apart. Each session lasted from 1-3 hr. All sessions wereaudiotaped.

14

Figure 2. Percentage of correct answers on pre- and posttest across all 24 subjects for all four categories ofquestions (all gains are significant at the .001 level).

RESULTS
Overall Gains
Student's answers on all four categories of questions were scored by matching answers to a template ofidealized answers constructed by the researchers. Each question had a maximum score of 6 points, withpartial credit available depending on the question. Collapsing across the two groups, Figure 2 shows
significant gains in all four categories of questions (p< .001), suggesting that both groups of students took
this learning task seriously, and were able to retain the knowledge for at least a week.
Differences Between the Prompted and Unprompted Groups
Both prompted and unprompted students gained significantly greater understanding from the pretest to theposttest, F(1, 22) = 183.6, p < .0001 (see Figure 2), and this gain was more pronounced for somecategories of questions than others, F(6, 132) = 24.7, p < .0001 (see Figure 2 again). However, from thepretest to the posttest, the gain was greater for the prompted group (32%) than the unprompted group(22%; see Figure 3). This interaction, between groups and tests, is significant, as shown by a 2 x 2 x 4(Prompted vs. Unprompted x Pretest vs. Posttest x Question Categories) analysis of variance (ANOVA)
with repeated measures, F(l, 22)=5.1, p < .05. There was no significant main effect for the groups, F (1,15Figure 3. Percentage correct answers on pre- and posttests comparing prompted (n=14) and unprompted (n=10)
students

22) < 1, as there were for tests and question categories, as noted before. The three-way interaction was nottested because categories were nested within tests.
The difference between the two groups' improvement is even more dramatic if only the more difficult
Category 3 and 4 questions are examined: The prompted group improved by 22.6% versus only 12.5% for
the unprompted group, t(22)=2.64, p< .01. These more difficult questions required knowledge inferences
and use of commonsense knowledge, and were apparently more sensitive measures of the improvement
afforded by self-explanations.
The greater gain of the prompted group, especially for the more complex questions, is particularly
impressive considering the following. First, the selected passage from Towle (1989) is already a very well
written text; even so, self-explaining can further enhance comprehension. Second, the students from theunprompted group were given the opportunity to read the passage twice, thus reinforcing theirunderstanding of the text. Nevertheless, the prompted group still outperformed them. Third, theunprompted group was not suppressed from self-explaining, so that some of the students may havespontaneously self-explained anyway while reading the materials twice (some evidence of this will be
provided later). Recall that the original self-explanation effect (Chi et al., 1989) was obtained bycontrasting spontaneous with nonspontaneous self-explainers. In other words, the contrast is not between
explainers and nonexplainers as this analysis does not capture the extent to which the prompted and the16

unprompted students were self-explaining. The data merely show the additional improvements gained byprompting students to self-explain. Finally, the greater gain was obtained by merely eliciting selfexplanations; no extensive training program was undertaken.
The prompted group did have higher CAT scores than the unprompted group (M = 87 % vs. M = 83 %but the difference was not significant. Using the gain scores (i.e., the difference between the pr- andposttests scores) as a dependent measure, the influence of ability on prompting outcomes was examinedwith multiple regression. CAT scores were not a significant predictor in the regression analysis, t (21) =1.479, p = .15. With CAT scores included, the prompted group retained a significant advantage, t (21)=2.147, p < .05. Whereas these results do not rule out the role of prior ability in the outcomes, it is clear thatability had a much smaller influence on a student's improvement than whether they were assigned to theprompted group. Additional analyses on ability, presented later, are consistent with this interpretation.
Within the Prompted Group Analyses
Codings of Self-explanations
Although it is clear from the preceding analyses that prompting is successful at eliciting self-explanations
and enhancing learning, the effect of the prompting intervention could have been more pronounced had it
been possible to ascertain the extent to which the unprompted group did or did not self-explain covertly.
Because the previous comparison between the prompted and the unprompted group was not a contrast
between explainers and non explainers, it may be more productive to know exactly how many selfexplanations each student in the prompted group generated and contrast the high explainers with the low
explainers. (Note that no analysis can be carried out with the unprompted group because they did not
overtly self-explain.)In order to determine the amount of self-explanations each student in the prompted group generated, it
was necessary to define and code what a self-explanation was. Self-explanation in the physics work (e.g.,
Chi et al., 1989) was operationalized as any utterance that went beyond the information given, namely, an
inference of new knowledge. Suppose Student 4 read the following sentences about the septum:
Sentence 17:

The septum divides the heart lengthwise into two sides.

Sentence 18:

The right side pumps blood to the lungs, and the left side pumps blood to the other partsof the body.

Ignoring what the student said after Sentence 17, the explanation generated after Sentence 18 was:

17

So the septum is a divider so that the blood doesn't get mixed up. So the right side is to the lungs, andthe left side is to the body. So the septum is like a wall that divides the heart into two parts... it kind oflike separates it so that the blood doesn't get mixed up....

There are two pieces of knowledge inference here: The fact that blood in the two sides does not mix, andthat the septum is solid like a wall. These pieces of knowledge are crucial for understanding, and many
historical and everyday false beliefs pertain to these ideas, such as that the septum is porous and permits
the blood from the two sides to exchange. These are inferences that make use of commonsense knowledge,
such as that dividing lengthwise can mean separating like a wall, which also implies that it may be solid.
Self-explanations were considered to be inferences that went beyond the text, excluding monitoringstatements, paraphrases, comprehension, or bridging inferences (these were considered kinds ofparaphrases). Comprehension inferences were either whole-sentence paraphrases, or direct translation ofindividual words (e.g., such as translating the word "transport" to the word "carry," or translating the word
"divides" to the word "separates," as shown in the previous self-explanation quote), or explicating implicit
references, and so forth. Table 2 shows an example of two utterances coded as self-explanations in contrast
to one coded as a paraphrase. (The underlined text is what the student read. The explanations uttered by thestudent are in quotation marks. The recoded content of each explanation follows.)
The identified comments in Table 2 were counted as two units of self-explanations, as indicated.
Hence, basically, parts of the protocol utterances were coded as knowledge inferences if the coder, who
was a project member, subjectively felt that new pieces of knowledge had been generated, as illustrated
before with respect to the "blood doesn't get mixed up" and "septum is like a wall" examples. Notice, also,that in that quote, even though the student mentioned the fact that "blood doesn't get mixed up twice," only
one explanation inference was counted. This coding produced a fairly coarse grain size, which is similar to
the grain size used in the original Chi et al. (1989) data.
This coarse level of coding might overlook more minute inferences that a student could have generated inthose uncoded parts of the protocols. Hence, for a second pass, another project member systematicallycoded all the utterances generated in response to reading each sentence, into units that corresponded moreor less to a proposition. Not surprisingly, an explanation could span several propositional units. The
segmentation into propositional sized units forced us to systematically consider whether every individual
proposition was an inference, and if not, whether it participated in the context of a larger sized inference.
This subsequent coding was extremely tedious, and took an additional 200 person hours. Naturally, a finerlevel of coding tended to capture more inferences at a smaller level. Table 3 illustrates and contrasts theoriginal coarser coding with the second finer coding. It shows the sentence the student read (the underlined
sentence), followed by her explanation of the sentence (in quotation marks). The slash marks (//) indicate

18

TABLE 2
Example of Self-explanation Coding
Sentence 118:

These substances (including vitamins, minerals, amino acids andglucose), are absorbed from the digestive system and transported
to the -cells.

Student:"Okay, so that's the point of what hepatic portal circulation [is].
To pick up these nutrients, I would guess."

Experimenter:"Okay why would you say that?"

Student:"Well because it says that it's absorbed from the digestive system,
um vitamins, minerals, amino acids, and glucose,

Paraphrase

and so that's why it's important to eat a balanced diet, or else yourcells won't get the right vitamins, minerals amino acids, andglucose."Explanation 2

Explanation 1:

Purpose of the hepatic portal circulation: to pick up nutrientsfrom digestive system.

Explanation 2:

Eating a balanced diet Is important for your cells.

Explanation 1

where proposition-like segments were coded.
Although the finer coding captured more inferences (3 vs. 1, see Table 3), the overall pattern of results
for the two sets of codings was largely identical. Therefore, the two sets of codings serve as a validity
check. The majority of the analyses in this article were then carried out with the coarser coding for threereasons. First, pragmatically, the coarser coding reduced the amount of work for many of the subsequent
analyses. Second, the self-explanations captured at the coarser level made more intuitive sense as a piece ofknowledge inference. Third, the finer coding sometimes seemed to capture comprehension inferences (as in
the case of Explanation 2 in Table 3) and redundant inferences (such as Explanation 3 in Table 3). On theother hand, because the finer coding considered all the utterances, it was necessary as a measure of theamount of talk, so that the effect of verbosity could be assessed.
Outcome Measures
High and Low Self-explainers. The previous contrast between the prompted and the unpromptedgroups did not give us a sense of how many explanations each group did generate, especially because theunprompted students could have spontaneously generated them covertly. Coding the prompted students'

19

TABLE 3
Example of Coarse and Fine Grain Coding of Self-explanations notions
Sentence 9:
Student:

During strenuous exercise, tissues need more oxygen.
"During exercise, the tissues, um, are used more, // and since they are used more,they need more oxygen and nutrients. // And um the blood, blood's transporting it[O2 and nutrients] to them.//”Coarser Coding
Explanation 1.

Purpose of blood is to transport oxygen and nutrients to the tissues.

Finer Coding
Explanation 2.
Explanation 3.
Explanation 4.

Tissues are used more during exercise
Since tissues are used more, they need more oxygen
Purpose of blood is to transport oxygen and nutrients to the tissues.

explanations into units tells us precisely the number of self-explanations each student did generate, so that
we can contrast the performance of the high explainers with the low explainers.3 Therefore, the students
were ranked according to the number of self-explanation inferences they generated (see Table 5 under the"Inferences" column). To maximize the contrast, the top 4 high explainers (Subjects 1-4) were comparedwith the bottom 4 low explainers (Subjects 9-12), not counting the 2 lowest subjects (because Subject 14
had an outlying CAT score, and Subject 13 had no available CAT score). The high explainers generated amean of 87 self-explanations and the low explainers generated a mean of 29 self-explanations across theentire passage using the coarser coding, t(6)= 5.64, p< .001. The difference remains significant even if theincorrect self-explanations are removed. That is, the high explainers (M = 61) articulated a greater numberof correct explanations than the low explainers (M= 14), t (6) = 5.89, p < .001. A similar difference wasobtained by using the finer grained coding. High explainers generated a mean of 117 self-explanation
propositions versus a mean of 60 for the low explainers, t(6) = 4.42, p < .01.
The high explainers' pre- to posttest gain scores improved more than the low explainers (38% vs.
27%), although this difference approached significance, t(6) = 2.14, p < .07. (It would be significant if thecorrect answers derived from subsequent looking up in the text passage were excluded, as will be shown
later.) Again, as was the case contrasting the prompted and unprompted groups, the improvement wasmore pronounced for Category 3 and 4 questions (33% vs. 17%), t(6) = 3.02, p < .05, which suggests thatthey acquired a deeper understanding of the topic (see Figure 4).
3

In the interest of seeing whether the current results replicate the previous finding in the domain of physics (Chi etal., 1989), instead of contrasting high and low explainers, one can also contrast the more and less successful
learners, splitting the subjects on the basis of their question-answering scores, comparable to the successful andless successful problem solvers in physics. An identical pattern of results was obtained.

20

Figure 4. Percentage of correct answers for category 1-4 questions on pre- and posttests comparing highexplainers (n=4) and low explainers (n=4).

Recall how knowledge inference and health questions were constructed. These were the mostcomplicated questions, tapping knowledge of system-wide features (such as closed and double circulation),and asking for a causal explanation of an implied structure. Another way to think about the nature of thesequestions is that the majority of them required an inference of either the function, the structure, thebehavior of a component, and/or the relation between the inferred structure, function, and behavior withanother component's structure, function, and behavior. The structure of these knowledge inference andhealth questions can be seen by coding the questions in the same way that each sentence of the passage wascoded. Such an analysis emphasizes the number of inferences that a student needs to construct in order tobe able to answer these questions. Because high explainers answered significantly more Category 3 and 4
questions correctly than low explainers, it is evident that they induced what was only implicitly presented(as in the case of the question shown in Category 3a of Table 1, where the student must induce the functionof the septum), and used the inferred knowledge to answer questions appropriately.
Because the question-answering task was an open-book one, students were free to refer to the text
passage if they wished. High explainers, on average, referred to the text for only 2 of the 54 posttest
questions, whereas low explainers, on average, referred to it for 11 of the questions. This suggests that thehigh explainers absorbed more understanding from reading the text with self-explanations, and did not needto reference it again. If the correct answers due to looking up information in the text are excluded from theanalysis (mostly in the case of low explainers), the difference in pre- to posttest gain scores between high21

and low explainers becomes even more pronounced (37% gain for high and 15% gain for the low
explainers when looked up answers are excluded), t(6) = 3.22, p < .02.
Interestingly, only one of the high explainers' references regarded answers for Category 3 and 4
questions, whereas about 35% of the low explainers' text references were for Category 3 and 4 questions.
This suggests that the high explainers may have known that knowledge inference and health questions did
not contain answers that one could look up in the text. These findings on text referencing are consistent
with the example-referencing behavior of the more and less successful problem solvers in the Chi et al.
(1989) study, in that the more successful solvers made fewer references to examples and reread fewer lines
per example, suggesting that they knew exactly what they needed to look up.
Using the finer grained analysis, the high explainers did speak more than the low explainers (650
propositional units, SD = 198, vs. 439 propositional units, SD = 7 1, respectively), but the difference was
not significant. Many factors contribute to the source of variance for verbosity: the number of repetitions,
irrelevant comments, metastatements, paraphrases, and so forth. For instance, some students repeat
themselves more often than others, as seen earlier in the quote of Student 4's explanation. Although it isdifficult to partial out the effects of general verbosity from generating self-explanations, it is true that thehigh explainers had a higher proportion of self-explanation propositions (117/650=18%) than the low
explainers (69/439 = 14%). This is at least suggestive that the high explainers were doing more than justtalking more.4
One characteristic of self-explaining is that it is a constructive activity. Naturally, one wonders whether
other forms of constructive activity could also enhance learning. This notion can be tested by comparing
the effectiveness of generating self-explanations with drawing diagrams in the process of self-explaining.
Recall that the students were encouraged (but were not systematically prompted) to draw diagrams andtake notes if they wished. Diagrams included drawings of the circulatory system, often showing the4

One question that often arises is the legitimacy of considering self-explanations as a source of verbal protocols
because the process of generating self-explanations alters the processing of the to-be-learned materials. This is tobe contrasted with protocols collected while a subject solves a problem, as reported in Ericsson and Simon (1984),
which presumably does not alter the processing of the task. The difference is that in the traditional protocolcollection context, subjects are only supposed to articulate the information passing through their short-term or
active memory as they are solving a problem. Verbal protocols in that context are used to capture the processing
sequences (of problem space states and operators applied) that the solver is undertaking. The solvers are prohibitedor discouraged from any kind of analyses or reflection of the content of their short-term memory. Indeed, reflection
on problem solving (retrospective protocols) is not a reliable indicator of processing (Ericsson & Simon, 1984), in
part, because self-explanations may occur. Only concurrent protocols are considered reliable traces of problemsolving processes. Self-explaining (i.e., generating new knowledge), whether prompted or spontaneous, in contrast,
is a process of reflection. Students are encouraged to reflect, think about, and infer what they are reading. In sum,
the process of giving protocols is just displaying the problem solving, whereas the process of generating

22

interrelationships among the components, as well as treelike diagrams of concepts, but excluded notes,
which were often copying of the text sentences. High explainers drew, on average, 18 diagrams (SD =22.0) versus 1 diagram (SD = 1.4) for the low explainers, but the difference was not statisticallysignificant. There was a great deal of variability in the number of diagrams drawn among the highexplainers even though there was very little variability among the low explainers. All of the students whodrew many diagrams were high explainers. However, the converse is not true: Not all of the students whodrew few diagrams were low explainers. This suggests that drawing diagrams may be an alternative
constructive activity for enhancing learning, so that the benefit of talking science may be its constructive
nature, rather than the learning of the discourse and argument structure of science. However, it is not clear
whether drawing diagrams alone would have been adequate at promoting greater learning, or whether it
merely accompanied the activity of self-explaining. One of the problems is that the number of data points isslim compared to the number of self-explanations, so that drawing diagrams is not as sensitive a measure
as self-explanations. It needs to be studied more systematically.
As an aside, because all frequent diagram drawers were high explainers, we can use the frequency of
diagram drawing to diagnose whether students in the unprompted control group were covertly selfexplaining. It turns out that 2 of the 10 unprompted students drew 21 and 17 diagrams, suggesting that atleast 2 of the unprompted students were covertly spontaneously self-explaining.
High- and Low-Ability Students. One important question to ask regarding ability is the issue ofaptitude-treatment interaction, that is, can lower ability students profit just as much from self-explanation
prompting as higher ability students? By selecting 5 explainers with the highest CAT scores (98%) and 5
explainers with the lowest CAT scores (72%), two groups of explainers with significantly different ability
differences were attained, t(8) = 3.33, p < .01. Their pre- to posttest gain scores on Category 1-4 questions
shows that both higher and lower ability students profited the same amount, with gains of about 30% (seeFigure 5).
The Content and Structure of Self-explanations. One can analyze the content of self-explanations inorder to ascertain how they are produced. In the Chi et al. (1989) physics work, the content of selfexplanations was captured by translating all of the self-explanations into if-then conditional statements,
and then classifying them into one of four categories: systems, technical procedures, principles, andconcepts. From this coding, each individual if-then conditional statement was determined to have been

explanations is adding inferences and constructing a mental structure. For a more extended discussion and manualon how to carry out alternative methods of verbal analyses, see Chi (1994).

23

Figure 5. Percentage of correct answers on pre- and posttests comparing higher ability (n=5) and lower ability(n=5) prompted students.

deduced from information presented earlier in the prose parts of the text or from information contained in
the examples information presented earlier in the prose parts of the text or from information contained in
the examples themselves. In general, those results (Chi & VanLehn, 1991) showed that self-explanations
were minute inferences that were inferred either from the example lines, or else from integrating what was
encoded from the example lines with commonsense and other background knowledge.
The picture that has emerged from the analyses of the structure of self-explanations in biology isessentially the same. It was determined how each self-explanation inference could have been generated, thatis, with what source of knowledge was the inference generated, such as by integrating the currently readline of text with previously read line(s) of text, or else by integrating the currently read line of text withsome commonsense or background knowledge. Explanation 2 in Table 2 (eating a balanced diet) is an
example of a self-explanation that integrates newly presented information with commonsense knowledge.
Likewise, analogizing the septum to a wall (quoted earlier) is also an example of the use of everyday
knowledge. It was found that 30% of the self-explanations were produced by integrating new information
with commonsense and background knowledge; 41% of the self-explanations were integration of new
information with prior sentences; and the other 29% fell into various types, such as integrating withepisodic experiences, using analogy, translating words or phrases, logical inferences, indeterminate ones,
and so forth. In general, no differences in the distribution of the structure of these three kinds of selfexplanations were found. That is, both high and low explainers generated self-explanations by integrating

24

with prior knowledge at least 30% of the time because many of the ambiguous ones (29% of them) could
be liberally classified as uses of prior knowledge.
Because self-explanation inferences are produced in around 41% of the cases by integrating thecurrently read line of text with previously read sentences, one can distinguish between links that integrate
either across or within the same topic area (such as the structure of the heart, circulation in the heart, bloodvessels, etc.). Hence, the number of references made to a sentence within the same topic area versus thenumber between topic areas was tabulated. For the high explainers, there was a 2:1 ratio in the number ofintegration within topics (M= 34 links) and between topics (M= 17 links). Low explainers, on the other
hand, had a 5:1 ratio in the number of integration within topics (M = 18 links) versus across topics (M = 3
links). The fact that half of the high explainers' integrations were across topic areas suggests that they are
more likely to produce a more coherent mental model in which distinct components of the mental model
would be related to each other. Hence, the differential pattern of integration between the high and low
explainers not only suggests a fruitful way to prompt students to generate utterances that constitute an
explanation inference, but also may have implications for the coherence of their mental models, as will beshown later.
More Direct Assessment of Understanding
Induction of Function
Of all the outcome measures discussed in the preceding section, only one of them assessed the depth ofunderstanding, namely, the high explainers' greater advantage in answering the more complex Category 3
and 4 questions than the low explainers because of the way these questions were constructed. Anotherexplicit measure of understanding is provided by a test of how well students learned the function ofcomponents. Students' difficulty in grasping domains such as the human circulatory system is often blamedon texts that do not adequately supply information about the function of each component. In our passage,
there were 22 identifiable components, of which only 11 had explicitly stated functions, so that thefunctions of the 11 others were implied. Thus, as a measure of understanding, all the students in theprompted group were explicitly probed for their explanations of the function of each of the components
either right after or shortly after the text's introduction of that component. For example, the septum wasintroduced in Line 17 of the text. Student 4 was probed for the function of the septum after she had achance to read and explain Sentence 18, as shown earlier see sentences 17 and 18 on Table 1. After Student
4 have her explanation, the experimenter probed her with the question:
Experimenter: Can you explain to me what you think the purpose of the septum is?

25

Figure 6. Number of correct answers to explicit and implicit function probes comparing high (n=4) and low (n=4)
explainers (the interaction is significant at the .01 level).

Student:

Well, I basically think that the purpose is to separate the right chamber, the right part of theheart [from] the left part of the heart, because each one has a separate function, and so theright side pumps to the lungs, the left is to the body. So you need something to separate
them so [that blood] won't mix, and that's the septum's job.

On the whole, students could correctly articulate 84.6% of the explicitly presented functions, and 73.2% of
the implicitly presented functions. The extremely high performance in inducing the function answers atheoretically intriguing question about whether it is possible to induce the function of a system orcomponent when only the behavioral and structural information is given (see, e.g., the work of deKleer &Brown, 1983). These data suggest that it is quite possible for students to induce the function when it is not
specified.
Moreover, when the function was explicitly described, both the high and the low explainers could
articulate it equally well when probed (see Figure 6). However, when the function was only implicitly
stated (or not stated at all), the high explainers were able to induce it significantly better than the low
explainers (10.5 vs. 7.8 of 11 probed), t(6)= 3.05, p< .05. This result accounts, in part, for the superior
performance of the high explainers in answering knowledge inference questions because many of thosequestions required the induction of a function that was not explicitly presented, as illustrated earlier with
the question about the septum. Thus, not only do we have evidence that prompted students actually can

26

induce the function when it is not explicitly stated (thereby suggesting that one way to minimize theinadequacy of texts is to practice self-explaining rather than revising the texts) but the high explainers'
greater facility at this induction, along with their increased advantage in answering knowledge inference
and health questions, further supports the interpretation that high explainers gained greater understanding
than the low explainers.

Analyses of Mental Models
Instead of a piecemeal and cross-sectional look at the display of understanding, as afforded by averaged
performance measures across students such as the mean number of Category 3 and 4 questions answered
correctly, or the mean number of implicit functions induced, an integrated assessment of the changes in the
representation as a function of understanding was needed. With this goal in mind, a way to capture theinitial and final mental model that each student had of the circulatory system was developed. This analysis
depicted the status of each student's mental model prior to and after learning, as a measure of
representational changes occurring with deep understanding. In this case, status refers to the mental model'scorrectness and completeness in regard to the anatomical connections among the blood vessels, heart, lungs,
and body.
The following section describes how a student's mental model was assessed. The student's initial modelof how the circulatory system works as a whole was depicted from statements giving evidence of the
anatomical connections and the direction of blood flow that students believed to exist in the system. Thesestatements were taken only from the terms and blood path sections of the pretest (for the model prior tolearning) and then again from these same sections of the posttest (for the model after learning). Unlike thequestion and answer sections of the pre- and posttests, which often challenged the students' initialconceptions of how the circulatory system works, both the terms and blood path sections allowed students
to explain their knowledge of the system without interference from direct questions. It was for this reason
that only the protocols from the terms and blood path sections were used for this analysis.
All relevant statements and evidence from the blood path drawings concerning the flow of blood andrelations between circulatory components were identified and then combined to form a model of circulation.
This involved integrating statements that were sometimes compatible and sometimes conflicting. Thesestatements were graphically represented with drawings of connections between components and the
direction of blood flow. As more statements were read, more and more detail could be added to thedrawing. For the most part, students' protocols were rich with statements suggesting how the system
worked. Often students would attempt to explain the whole system (at least the general workings of the27

Figure 7. Depiction of a student’s mental model.

system) in a few well-integrated statements. For example, one student explained the term "artery" in thepretest as follows:
Artery is a general term for all tubes that are from the heart and they carry the clean blood from theheart to all the body ... it [the body] always needs clean blood and the blood travels once through thearteries and when it's used, it travels back up in the veins to go back to the heart, the heart cleans it
again, ummm, replenishes it with oxygen, umm and then it goes again to all the parts of the body.

From this explanation of one term the student was credited with the knowledge that:
1. Blood flows from the heart to the body in arteries.
2. Blood flows from the body to the heart in veins.
3. The body used the "clean" blood in some way, rendering it unclean.
4. Blood is "cleaned" or "replenished with oxygen" in the heart.
5. Circulation is a cycle.
These statements might be represented in a drawing as shown in Figure 7. This initial model was further
enriched by considering and integrating additional statements from the protocols. For example, the same
student later explained the term "heart":

28

Figure 8. Continued depiction of a student’s more enriched mental model.

The blood goes in at the upper right chamber and it then goes down to the downward right chamber,then it goes to the downward left chamber then it goes to the upward chamber then it goes out of theheart. And each chamber is divided by a valve that makes sure the blood goes in one direction.

This was integrated with the previous statements, adding more detail with the blood flow through the heart,
resulting in a more enriched model, as shown in Figure 8.
A representation of each student's initial and final mental model was constructed in this manner, after
several iterations through the protocols. At this level of analysis, an attempt was made to capture as muchdetail of each student's model as was possible. A great deal of variation existed between students' models,particularly in the functioning, behavior, and structure of specific components as well as relations between
components. For example, this student's model is a single loop type of model where the blood flows fromthe heart to the body and then back to the heart with an added feature that the heart oxygenates the blood.
She also talked about a specific blood flow through the heart involving valves and chambers. Anotherstudent had a similar single loop type of model, where the blood flowed from the heart to the body and thenback to the heart, but with the added feature of the lungs delivering air to the heart in order for oxygenation
to occur there. And still another student with this single loop type of model did not even consider that blood
was oxygenated anywhere in the system. These models are quite different in their details, yet they all have29

the basic overall structure of being a single loop: Blood flows from the heart to the body and then blood
returns to the heart from the body. Six different general types of models were discerned, ranging from leastaccurate to most accurate: (1) no loop model; (2) ebb and flow model; (3) single loop model; (4) single
loop with lungs model; (5) double loop-1 model; and (6) double loop-2 model. The double loop-2 model
depicts the most accurate flow of blood through the circulatory system.
Because this first attempt at constructing models yielded various models in a more or less subjective
way, this subjective analysis was validated by defining a set of necessary features for each of the six
models, as shown in Table 4. Each student's model was then reclassified based on these features. Evidence
for every feature had to be exhibited in order for that particular classification to be given to that model. For
example, in order to be credited with a double loop-2 model, a student's protocols had to mention all thefeatures listed for the double loop-1 model, plus all the ones listed for the double loop-2 model. Thus, thecriteria for achieving the double loop-2 model were very stringent. This second pass through the verbal
protocols, looking for specific features, served as a validity check for the first more subjective analysis.
Looking at all 24 subjects as a whole (both prompted and unprompted), there was an overall
improvement in the formation of a correct mental model from reading the text (see Table 5), consistent withthe overall gain in the question scores, as shown in Figure 2. The majority of students started with lessaccurate models such as the no loop model (21%) and the single loop model (50%), but after reading thetext, the majority of students formed more accurate models, notably, double loop-1 (25%) and double
loop-2 (46%). So reading the text and self-explaining did seem to improve the accuracy of students' mental
models.

Prompted versus Unprompted Groups. Prompted students developed a correct model more often thanunprompted students. Comparing the results of the posttest models, 8 of 14 (57%) prompted students
attained the most accurate double loop-2 model, whereas only 2 of 9 unprompted students (22%) reachedthis level of accuracy in the formation of their models (see Table 5). (Student 19 in the unprompted group
was excluded from this 22% calculation as having achieved the double loop-2 model because he began witha double loop-2 model.)High Explainers versus Low Explainers. Within the prompted group, all 4 high explainers (100%)
attained the most accurate double loop-2 model, whereas only 1 of 4 low explainers (25%) developed amodel to this level (see Table 5 again). This shows that high explainers, those generating on average 87

30

TABLE 4
Necessary Features for Each Type Of Mental Model
No Loop
1. Blood is Pumped from the heart to the body.
2. Blood does not return to the heart.
Ebb and Flow
1. Blood is primarily contained in blood vessels.
2. Blood is pumped from the heart to the body.
3. Blood returns to the heart by way of the some blood vessel.
Single Loop
1. Blood is primarily contained in blood vessels.
2. Blood is pumped from the heart to the body.
3. Blood returns to the heart from the body.
Single Loop with Lungs
1. Blood is primarily contained in blood vessels.
2. Heart pumps blood to body or to lungs.
3. Blood returns to heart from body or from lungs.
4. Blood flows from lungs to body or from body to lungs without return to heart in between.
5. Lungs play a role in the oxygenation of blood.
Double Loop-1
1. Blood is primarily contained in blood vessels.
2. Heart pumps blood to body.
3. Blood returns to heart from body.
4. Heart pumps blood to lungs.
5. Blood returns to heart from lungs.
6. Lungs play a role in the oxygenation of blood.
Double Loop-2
1. All features from Double Loop-1
2. Heart has four chambers
3. Septum divides heart lengthwise-sense of preventing mixing of blood.
4. Blood flow through heart is top to bottom.
5. At least three of the following:
Blood flows from right ventricle to the lungs.
Blood flows from lungs to left atrium.
Blood flows from left ventricle to body.
Blood flows from body to right atrium.

explanations, all reached an understanding of the ultimate correct double-loop structure. Low explainers,
those generating on average 29 explanations, attained the double loop-2 model in about the same
proportion as the 9 unprompted students.

31

TABLE 5
Prompted and Unprompted Students’ Pre- and Posttest Mental Models
Student
Prompted
High
1
2
3
4
Medium
5
6
7
8
Low
9
10
11
12
Excluded
13
14
Unprompted
15
16
17
18
19
20
21
22
23
24

Inferences

Pretest Model

Posttest Model

111
87
84
64

Single loop
Single loop-lungs
Single loop
Single loop

Double loop-2
Double loop-2
Double loop-2
Double loop-2

47
47
42
40

Single loop
Single loop
Ebb and flow
Single loop

Single loop-lungs
Double loop-2
Double loop-2
Double loop-2

35
33
28
20

Single loop-lungs
No loop
Single loop
Single loop-lungs

Double loop-2
Single loop-lungs
Double loop-1
Double loop-1

20
7Single loop
No loop

Single loop (no CAT score)
No loop (outlying CAT score)Single loop
Single loop-lungs
No loop
Single loop
Double loop-2
Ebb and flow
No loop
Single loop
No loop
Single loop

Undeterminable
Double loop-2
Double loop-1
Double loop-1
Double loop-2
Double loop-1
No loop
Double loop-1
No loop
Double loop-2

DISCUSSION
Eliciting self-explanations clearly enhances learning and understanding of a coherent body of new
knowledge, whether one compares the amount learned by the prompted and the unprompted students, orwhether one compares the amount learned by the high and low explainers. In either case, generating a large
number of self-explanation inferences facilitated learning, when pre- to posttest gain scores wereconsidered. Self-explaining also promoted deeper understanding of expository materials, as displayed by
the high explainers' ability to answer more complex questions, as well as being more able to induce theimplicit function of a component. Besides assessing the display of understanding, understanding was also
captured by changes in the students' mental models of the circulatory system. Many more prompted
students, including all the high explainers, represented the circulatory system by the correct double loop-2

32model, whereas few unprompted students achieved the correct model. Clearly, not only does eliciting selfexplanations promote greater learning, but the more students self-explain, the deeper their understanding.
Therefore, learning a body of declarative knowledge can neither simply be the mere instantiation of existing
stored knowledge nor the direct encoding of it. Rather, learning is the use of existing knowledge inconjunction with new information to create more new knowledge. In some ways, self-explaining is thinking
with what one knows.
What might mediate learning from self-explaining? Three processing characteristics of self-explaining
may shed light on why it is a particularly effective learning activity. The first characteristic of selfexplaining is that it is a constructive activity. This means that new declarative or procedural knowledge isconstructed. Constructed declarative knowledge was directly captured from the explanation protocols inthis study as explanation inferences, and constructed procedural knowledge was captured in the Chi et al.
(1989) physics explanation protocols as if-then conditional rules (Chi & VanLehn, 1991).
Constructed procedural rules can be captured in other indirect ways as well, namely, by developing arule-based model for solving problems, and then seeing when these rules are used. VanLehn, Jones, andChi (1992) found that only 50-60% of the rules needed to solve physics problems of the kind used in Chi etal. (1989) were presented in the physics text. This means that the remaining rules needed for solving
problems had to be constructed by the solver. Presumably, self-explaining facilitated the construction ofthese rules. This finding reiterates the point made in the introduction, that learning a procedural skill cannot
be solely the direct encoding and subsequent speeding up of instruction.
An alternative way to infer the construction of rules is to examine the problem-solving protocols in the
Chi et al. (1989) data for errors committed by high explainers (those who generated more than 40 selfexplanations) and low explainers (those who generated fewer than 20 self-explanations across the three
examples). Missing rules was the only category where high explainers made significantly fewer errors thanlow explainers (VanLehn & Jones, 1993). Low explainers seemed to have more errors arising from the lackof usable rules, which presumably could have been constructed had they self-explained more. Thus, in thephysics study, self-explanations seem to provide opportunities to construct rules that can subsequently be
used during problem-solving; in the current biology study, self-explanations provide opportunities toconstruct knowledge inferences (such as inducing the function of a component) that can be subsequently
used to answer complex questions.
The fact that self-explaining is a form of constructive activity brings into question whether other forms
of nonverbal constructive activity are equally effective at enhancing learning because the majority of theones investigated in the literature (such as peer problem-solving and reciprocal teaching) are verbal in33

nature. This issue can be partially addressed by examining the diagram-drawing activity that accompanies
self-explanations. Although high explainers drew more diagrams than low explainers, there was also agreater variability among high explainers. Low explainers, however, consistently drew fewer diagrams. The
diagrams drawn were also very sketchy, hence they could not be coded for quality. These data do notaddress whether drawing diagrams per se, without self-explaining, would be an adequate form of
constructive activity to promote learning. Basically, to the extent that an activity (such as diagram
drawing) is a constructive one, then it could probably facilitate learning to some degree.
The process of self-explaining contains an important second characteristic: It encourages theintegration of newly learned materials with existing knowledge. Taken at face value, people often wonder
how students can construct explanations when they have incomplete knowledge of the domain. The data
here show that at least 30% of the self-explanations are produced from integrating new information withold knowledge. The implication of this characteristic of self-explaining is that other kinds of constructive
activities that discourage integration, such as summarizing, may be less effective at promoting learning.
An outcome of integration with prior knowledge is that a self-explanation can result in an incorrect
piece of knowledge. In both the physics data and the current data, about one fourth of the self-explanations
are incorrect, although generating incorrect self-explanations was not detrimental to learning (because thepattern of results remained the same when incorrect ones were partialed out). It is even conceivable thatgenerating incorrect self-explanations can provide a learning experience. Here is one possible
interpretation. Having articulated an incorrect explanation, a student continues to read the next sentence orsequence of sentences in the text. Eventually, the text sentences, because they always present correct
information, may contradict knowledge embodied in the incorrect self-explanation. This will create a caseof conflict. Hence, one interpretation is that creating an incorrect self-explanation merely objectifies thatpiece of knowledge, which allows it to be examined in the face of conflicting information from subsequent
sentences, thus establishing the opportunity for self-repair to resolve the conflict. Suggestions that much oflearning takes place during confrontations, conflict situations, or at points of impasses have been presented
in the developmental (Kuhn, 1972), social (Doise, Mugny, & Perret-Clermont, 1975), and cognitive
science literature (VanLehn, 1988). Presumably, such confrontations and conflicts create opportunities forresolving them.
However, the fact that self-explanation involves the integration of new knowledge with old knowledge
suggests that learning a new body of knowledge must also take into account the nature and coherence ofstudents' existing mental models and/or conceptions. For some concepts (such as forces, heat, natural
selection), the nature of the initial (mis)conceptions are "ontologically incompatible" with the veridical

34

conceptions. That is, the students' initial conceptions of these concepts belong to a different ontologicalcategory, so that integrating new information with them leads to further misunderstandings that are robust
and persistent (Chi, 1992, 1993; Chi & Slotta, 1993; Chi, Slotta, & de Leeuw, 1994). It is unclear at thispoint whether self-explaining can facilitate understanding of these ontologically incompatible concepts. In
the case of ontologically compatible concepts and systems (such as the circulatory system), however,integration of new information with existing faulty conceptions allows the new information to revise theinitial false beliefs more readily (de Leeuw, 1993; an example of this revision will be shown shortly). The
remaining discussion applies only to ontologically compatible concepts.
Although an incorrect self-explanation or a faulty initial mental model can produce conflicts andconfrontations, it is not always the case that conflicts and contradictions lead to resolution and learning, assome of the literature on the role of anomalies shows (see discussion in Chi, 1992, pp. 152-158). However,a plausible account of why self-explaining may be a powerful mechanism for removing conflicts has to dowith its third processing characteristic, namely, that self-explaining is carried out in a continuos, ongoing,and piecemeal fashion, thereby often resulting in partial, incomplete, and fragmented self-explanations.
Suppose one thinks of self-explaining as the process of creating or revising a mental structure (in thisbiology case, a mental model of the system, and in the previous physics case, a mental structure of thesolution example). While reading (either expository text sentences or example statements), there are many
opportunities whereby what is read contradicts what is being created or existed a priori in one's mental
structure. Self-explaining thereby gives rise to multiple opportunities to see conflicts between one's
evolving mental structure and the veridical description of it from the text. For instance, Student 9's initial
mental model was a single loop with lungs that links the blood path from the lungs directly to the rest of thebody, without another stopover in the heart. He said during the pretest of the term "blood" thatblood from the heart into the lungs, where oxygen is taken out of the lungs and held in the blood andthen, uh ... the blood goes throughout the rest of the body.

And then, again, during the blood path portion of the pretest, he said
[blood] comes back out through the lungs and then goes all over the body through arteries.

Upon reading Sentences 33 and 34 in the text as shown
Sentence 33: In the lungs, carbon dioxide leaves the circulating blood and oxygen enters it.
Sentence 34: The oxygenated blood returns to the left atrium of the heart.

he then explained:

35

Okay. It's just saying that the blood has to go back to the heart before it goes to the rest of the body. So
it's pumped through another time.

Thus, the explanation generated upon reading Sentence 34 obviously revised his initial mental model, which
omitted the blood's stopover at the heart before going on to the rest of the body. The point of this example
is that ongoing piecemeal self-explaining allows conflicts to be recognized and resolved at many loci, where
the changes are more minute and more easily repaired.
Contrast this with an alternative case, in which a student read (probably without self-explaining) a
theory and attempted a complete explanation using that theory. An excellent illustration of this outcome
can be seen in some protocols that Scardamalia (1992, July) collected. After reading about blood clots, astudent was asked to explain how a cut heals. The first response the student gave, on the basis of reading
the text material, was the following:
When you get a cut it bleeds. In your blood there are things called platelets. Platelets made a shield on
your cut, it is called a scab, it protects your skin when it is healing.

The student was then asked to use his own theory to answer the same question. Here is the student's second
response to the same question:
My theory is that when you get a cut the blood vessel that got cut dies and the heart stops sending bloodto that vessel until it heals.

What is so telling about these two explanations is that each is complete, and each is generated from a
coherent theory, and each theory has its own components (platelets and scab in the scientific explanation,and dying blood vessel and heart stops sending blood in the naive explanation). That is, the student's oldtheory obviously was not subverted by the new theory when the new theory was learned, as was the case in
the example of Student 9's revision of his old theory about the destination of blood path after leaving thelungs. Although the student may see that one explanation is pitted against the other, it is difficult to see
how the two opposing explanations can be integrated and resolved at this global complete explanation level.
Noticing conflicts and resolving them must be attempted at a more fine-grained piecemeal level, the level ofwhat happens to blood when one gets a cut (whether it bleeds, or whether the heart stops sending blood).
Thus, explaining on the basis of the text's theory in an isolated and self-contained way cannot be taken tomean that the student understands it and has rejected other explanations. In order to assimilate the newtheory exclusively, it must be incorporated with existing knowledge at a fine-grained level. This example

36

and our data emphasize the importance of the second and third processing characteristics of selfexplaining, namely, that learning involves the integration of new knowledge with old knowledge, and suchintegration is best carried out in a more minute and ongoing fashion.
These second and third processing characteristics have direct implication for what kind of constructive
activity might be more or less efficient at promoting learning. Some existing training studies have requested
that students generate explanations that are scientific (Coleman, 1992), and/or based on the scientifictheory presented in the text (Ohlsson, 1992). These kinds of constructive activities may preclude anddiscourage students from integrating their prior misconceived knowledge with the newly acquired scientificknowledge, thereby possibly producing isolated and self-contained explanations, as the preceding quotation
showed about what happens to a cut. Instead of asking students to generate complete explanations based ona given theory, it might be more productive to ask them to generate microscopic and partial selfexplanations because these minute and fragmented ones may have a better chance of repairing one's
erroneous initial mental model. In sum, self-explanation has three important characteristics that may
contribute to its effectiveness as a learning skill. Many alternative activities, such as diagram drawing,summarizing, generating complete theory-based explanations, may be more limited as learning skills
because each of them lacks all of the characteristics of self-explanations. However, these alternative
activities may be ideal for other purposes, such as means of assessing understanding.
The general claim of this article, that generating more self-explanations promotes greater learning, is
supported by the contrast between the prompted and unprompted conditions, as well as between the highand low explainers. However, the fact that some prompted students generated more self-explanations thanothers, suggests that perhaps some other underlying factors may be mediating the effectiveness ofprompting. The results here suggest at least two possible alternative factors. First, although the benefit ofself-explaining seems to be independent of ability (or achievement) and prior domain-relevant knowledge(as assessed in the pretests) it is possible that prior general world knowledge may play a key role because
at least 30% of the self-explanations resulted from integrations with prior commonsense knowledge. A
second alternative mediating factor may have to do with the strategy of integrating the new to-be-learned
materials with previously encoded text information in a more extensive way, namely, across topics rather
than just within a given topic (because the high explainers had a much higher proportion of across-topic
links). This may be a strategy that can be taught in order to achieve maximal generation of explanation
inferences. In this study, because we had visions of implementing an automated prompting system, we
wanted to see the extent to which a very nonspecific and unguided prompt to elicit self-explanations would
promote learning. The results of this study certainly point to ways in which more extensive training can

37

perhaps guide the elicitation of even more self-explanations. Successful efforts along the lines of using
more elaborate training procedures can be found in Bielaczyc et al. (in press) and King (1994).

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APPENDIX
Instructions Given to Prompted Subjects Prior to Reading the Text
The following is a chapter on the human circulatory system which was taken from a high school text book.
We are trying to learn more about how students read and learn from a textbook, as well as what makessome textbooks better than others. In order for us to assess what information the text book is good atmaking understandable, it is important that you read every line very carefully–as if studying for an exam.
The text is presented one sentence at a time so that you will have time to really think about what
information each sentence provides and how I this relates to what you've already read.
We would like you to read each sentence out loud and then explain what it means to you. That is, what
new information does each line provide for you, how does it relate to what you've already read, does it give
you a new insight into your understanding of how the circulatory system works, or does it raise a questionin your mind. Tell us whatever is going through your mind–even if it seems unimportant.
You may need to go back and re-read parts of the text to really understand all the material. Also, somepeople find it helpful, when reading difficult material, to draw a picture or take notes. Please feel free to dowhat is best for you–please use these transparencies for this purpose. Let me know when you'd like to starta new transparency.