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Learning From Examples:Fostering Self-Explanationsin Computer-Based LearningEnvironmentsAlexander Renkl & Robert K. AtkinsonPublished online: 09 Aug 2010.
To cite this article: Alexander Renkl & Robert K. Atkinson (2002) Learning From Examples:Fostering Self-Explanations in Computer-Based Learning Environments, InteractiveLearning Environments, 10:2, 105-119
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Learning From Examples: Fostering Self-Explanations inComputer-Based Learning Environments
Alexander Renkl1 and Robert K. Atkinson 2
1Physchological Institute, University of Freiburg, Germany and 2 Department of CounselorEducation and Educational Psychology, Mississippi State University, MS, USA
ABSTRACT
Cognitive skills acquisition involves developing the ability to solve problems in knowledge-richtask domains, and is particularly important for any individual attempting to meet the challengesof our modern, knowledge-driven economy. This type of economy argues for reconceptualizingcognitive skills acquisition as a lifelong process. Research has shown that worked-out examplesare the key to initial cognitive skill acquisition and, therefore, critical to lifelong learning. Theextent to which learners' pro®t from the study of examples, however, depends on how well theyexplain the solutions of the examples to themselves. This paper discusses our own research ondifferent types of computer-based learning environments that indirectly foster self-explanationsby (a) fostering anticipative reasoning, (b) supporting self-explanations during the transitionfrom example study to problem solving, and (c) supporting self-explanation activities withinstructional explanations. It also discusses ways of leveraging new computer and videotechnologies to enhance these environments by representing problem situations and theirconcepts dynamically. The paper concludes by suggesting that these learning environments, ifemployed successfully, can encourage systematic, lifelong learning.
INTRODUCTION
Cognitive skills acquisition involves developing the ability to solve problemsin knowledge-rich task domains such as medical diagnosis, physics, orthermodynamics (VanLehn, 1996). It is particularly important for any individ-ual attempting to meet the demands of the rapidly evolving globalized,knowledge-driven economy that exists in contemporary life. The intellectualchallenges imposed by this type of dynamic global economy means that
Correspondence: Alexander Renkl, Psychological Institute, University of Freiburg, Belfortstr.16, 79085 Freiburg, Germany. Tel.: �49-0761-203-3003, Fax: �49-0761-203-3100, E-mail:[email protected]
Interactive Learning Environments 1049-4820/02/1002-105$16.002002, Vol. 10, No. 2, pp. 105±119 # Swets & Zeitlinger
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cognitive skills acquisition and, more generally, learning must be regarded as alifelong process that goes beyond traditional schooling to learning in theworkplace or at home. It appears likely that those individuals who embrace thenotion that the conclusion of their schooling years does not mark the end oftheir education, but rather the beginning, will be the ones to live up to theirpotential and experience the greatest success in and out of the workplace duringthe 21st century. The challenge for educators and learning architects is increating learning environments that facilitate the development of these cogni-tive skills in an individual attempting to learn at every stage of his /her life.
The authors of this contribution (in part, together with colleagues) haveexperimented with a number of computer-based learning environments de-signed to enhance cognitive skill acquisition (Atkinson & Derry, 2000;Atkinson & Renkl, 2001; Renkl, 1997, in press). In particular, our focus hasbeen on environments designed to foster learning from worked-out examples,an approach that is critical to the initial stages of cognitive skill acquisition. Inthis article, we provide an overview of this line of research.
The Importance of Worked-Out Examples for Cognitive SkillsAcquisitionLearning from worked-out examples is of major importance for initialcognitive skills acquisition in well-structured domains such as mathematics(Atkinson et al., 2000; VanLehn, 1996). Moreover, learning from worked-outexamples is an effective mode of learning and one that is preferred by novices.Numerous studies performed by Sweller and coworkers (for an overview seeSweller, van MerrieÈnboer, & Paas, 1998) demonstrated that learning fromworked-out examples (or, more precisely, from example±problem pairs) canbe more effective than learning by problem-solving. This ®nding is explainedby the argument that problem-solving requires so much working memorycapacity that it interferes with learning in the sense of schema acquisition andthat, given this load, too few resources are left for the induction of abstract andgeneralizable problem-solving schemata (Sweller et al., 1998).
The Importance of Self-Explanations in Learning from Worked-OutExamplesThe employment of learning from worked-out examples does not, of course,guarantee effective learning. The extent to which learners pro®t from the studyof examples depends on how well they explain the solutions of the examples tothemselves or, in other words, to what extent the learners employ the learning
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strategy of self-explaining. Chi, Bassok, Lewis, Reimann, and Glaser (1989)showed that the extent to which learners pro®t from the study of physicsworked-out examples depends on the quality of their self-explanations (`self-explanation effect'). Speci®cally, in comparison to the less successful learn-ers, Chi et al.'s successful learners: (a) more frequently elaborated on theapplication conditions and goals of operators, (b) more frequently relatedoperators to domain principles (principle-based explanations), (c) were lesslikely to suffer from an illusion of understanding, and (d) devoted more time tothe study of worked-out examples (more learning time).
In a follow-up study, Renkl (1997) ®xed the learning time for each individ-ual so that the pure impact of qualitative differences in self-explanations couldbe isolated. Renkl found that the quality of self-explanations was signi®cantlyrelated to learning outcomes even when learning time was kept constant.Using cluster analyses, Renkl classi®ed his participants as either successfulor unsuccessful learners. The successful and the unsuccessful learners differ-ed especially with respect to the following aspects: (a) Frequently assignedmeaning to operators by identifying the underlying domain principle(principle-based explanations), (b) frequently assigned meaning to operatorsby identifying the subgoals achieved by these operators (explication of goal-
operator combinations), and (c) tended to anticipate the next solution stepinstead of looking it up (anticipative reasoning).
In addition, Renkl (1997), found that there were two successful ways toapproach learning from worked-out examples. Some good learners concen-trated their self-explanation efforts on the assignment of meaning to operators,both by principle-based explanations and by explicating goal±operatorcombinations (principle-based explainers). Other successful learners focusedtheir effort on frequently anticipating solution steps (anticipative reasoners).However, most learners showed poor self-explanations ( passive explainers andsuper®cial explainers). For this reason, it is crucial that educators and learningarchitects search for instructional interventions that foster self-explanationactivities and, as a consequence, positive learning outcomes. In the next section,several techniques for fostering self-explanations are discussed.
Fostering Self-Explanations: Direct and Indirect Approaches for LifelongLearningThere are two possibilities by which learning strategies or, in this case, self-explanations, can be fostered (Renkl, 1999). In direct interventions, self-explanations are directly trained. For example, Renkl, Stark, Gruber, and
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Mandl (1998) showed in their experiment that a short training session effec-tively fostered the explication of goal±operator combinations and principle-based explanations in a subsequent learning phase. On the other hand, inindirect interventions, self-explanations are not directly trained. Instead, thelearning materials are structured in a way that fosters self-explanations.For example, Catrambone (1998) found that designing worked-out examplesin a way that makes each subgoal salient within an example fostered self-explanations about what these steps accomplished. As a result, learningoutcomes were enhanced.
Taking the need for lifelong learning and the context of computer-basedlearning into account, the direct approach has several limitations. First,designing learning strategy trainings that have lasting and widely transferableeffects are dif®cult to implement and need some long-term approach (Pressley,1995). Despite the fact that Renkl et al. (1998) demonstrated that direct self-explanation training effectively fostered self-explanations, their short trainingsession was merely intended to induce `only' short-term effects. The ques-tionable transfer effects of short term trainings reduce the usefulness (even) ofsuccessful direct interventions with a lifelong learner who is involved in jobobligations. Second, it is dif®cult to implement training before or connectedwith content-related learning in situations where a lifelong learner is using thecomputer to pursue his /her education through nonformal means of education.
In contrast, the option of indirectly fostering self-explanations ± and there-by positive learning outcomes ± by optimizing the design of the learningmaterials has substantial advantages in the context of learning throughout life.First, since the structure of learning materials themselves are responsible forfostering self-explanations, an indirect approach does not require the physicalpresence of a trainer. Second, unlike the direct approach, there is no uncertain-ty about the long-term and transfer effects with an indirect approach since theinstructional component is integrated directly into the learning materials and,thus, will always be present. In the next section, several learning environmentsare described that deliver worked-out examples which contain structuralmanipulations designed to foster self-explanations and enhance learningoutcomes.
Computer-Based Learning Environments that Foster Self-Explanationsand Example-Based LearningBefore discussing the features of our computer-based learning environmentsthat are favorable for example-based learning, a quali®cation is in order.
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Without exception, the development of the computer-based environmentsdescribed below was not driven by the intent to maximize the potential impactof this medium for complex, multimedia, or interactive learning (`technology-driven approach of educational technology') but instead was supported bysound example-based instructional design principles (`instruction-drivenapproach of educational technology'). Nevertheless, many of the instructionaldesign principles associated with this type of learning could hardly beimplemented without the assistance of technology. Against this background,the following section discusses several different types of computer-basedlearning environments including environments designed to (a) foster anticipa-tive reasoning, (b) support self-explanations during the transition fromexample study to problem solving, and (c) support self-explanation activitieswith instructional explanations (for an overview see Table 1; see Conati, 1999;Schult & Reimann, 2001, for related work by other researchers).
Table 1. Overview of Studies Using Computer-Technology to Support Learn-ing from Worked-Out Examples.
Fostered aspect Productive instructional measure Authors
Anticipative reasoning Learner-paced step-by-steppresentation
Renkl (1997)
Anticipative reasoning Step-by-step presentation� blanksin worked-out examples
Stark (1999)
Self-explanations in generalduring the transition toproblem-solving
Fading worked-out steps from aseries of examples
Renkl,Atkinson,Maier andStaley (2001)
Principle-based explanations Instructional explanations providedon learner demand
Renkl (2001)
Principle-based explanations Instructional explanations providedon learner demand or externally inan integrated format
Atkinson andRenkl (2001)
Anticipative Reasoning�Explication of subgoal structure
Step-by-step presentation�subgoal salience + auralinstructional explanations
Atkinson andDerry (2000)
Explanations in general Instructional explanationsprovided by an animated agent
Atkinson(2001)
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Fostering Anticipative Reasoning
Renkl (1997) employed a computer-based environment for example-basedlearning in which the worked-out examples were presented in a step-by-stepmode. The problem speci®cation and the solution steps of each worked-outexample were shown on four screen pages. On the ®rst page, the problemsgiven were displayed. The learner could read them and then go to the nextpage on which the ®rst solution step was presented in addition to the problemformulation. After inspecting this solution step on the second page, theparticipants proceeded to the following page where the next solution stepwas added, and so on. After the entire solution of a problem was presented, thelearner would move to the next page where the cycle began all over again withthe ®rst page of a new example being presented. The fact that the nextsolution-step was not immediately present or visible ± as is the case with usualpaper±pencil presentations of a worked-out example ± was intended to serveas a situational `incentive' to anticipate the next solution step. As previouslymentioned, part of the learners in the study of Renkl (1997) extensivelyanticipated solution steps and, thereby, achieved high learning outcomes.
It can be assumed that anticipating is effective because elements of problemsolving are integrated into learning from examples, a feature known to fosterlearning (Stark, 1999). In addition, anticipating can reduce the frequentlyfound `illusions of understanding' (Chi et al., 1989) since ± when the learnerproceeded to the next page in Renkl's (1997) learning program ± the correctstep and, thereby, some direct feedback, was provided. In addition, thisapproach encouraged learners to begin to do what they were ultimatelyexpected to do, namely to solve problems (i.e., to generate solution steps).
In response to Renkl's (1997) research, Stark (1999) tested to what extentthe learners can be `forced' to anticipate by omitting text and inserting blanksinto the worked-out examples deployed in a computer-based learning envir-onment. The learners' task was to try to ®ll in the steps that were missing.After attempting to ®ll in the blanks, the learners received feedback on thecorrectness of their responses as the complete step was presented after thenext mouse click. Stark found that, compared to studying complete examples,incomplete examples fostered self-explanations and reduced ineffectivelearning activities, such as re-reading or paraphrasing. As a consequence,incomplete examples enhanced the transfer of learned solution methods.
Taken together, the instructional idea of fostering anticipative reasoningand providing subsequent feedback on the correctness of the anticipation canbe implemented best by using computers. It is dif®cult to imagine how this
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same approach could be reached in an alternative way without the use oftechnology except perhaps by one-on-one tutoring arrangements, an optiononly available for those individuals prosperous enough to afford the cost ofhiring a professional tutor to deliver individualized instruction.
Supporting the Transition from Example Study to Problem SolvingAs outlined above, worked-out examples are known to effectively supportinitial cognitive skill acquisition. However, for later learning stages, especiallywhen proceduralized or automatized routines should be acquired, it is clearthat examples are not well suited. Against the background of an example-based learning model postulated by Anderson, Fincham, and Douglass (1997),it can be postulated that the ef®cacy of learning from examples is limited tothe ®rst stage (analogy) and the second stage (declarative rules) within acognitive skill acquisition framework consisting of four overlapping stages.Learning from examples is not the preferred method when learners reach thethird stage (proceduralized rules), where problem-solving practice is theoptimal instructional approach. A question that to date has been neglectedto a large extent is how to structure the transition from example study in earlystages of cognitive skill acquisition to problem solving in the later stages.
In an effort to examine this issue, Renkl, Atkinson, and Maier (2000) andRenkl, Atkinson, Staley, and Maier (2001) extended the work on anticipativereasoning and proposed a new rationale that involved the introduction ofblanks into worked-out examples based on the Cognitive Apprenticeship
approach (Collins, Brown, & Newman, 1989). This instructional modelproposes a smooth transition from modeling to scaffolded problem solvingto independent problem solving in which instructional support fades duringthe transition, a rationale that can be straightforwardly applied to example-based learning. First, a complete example is presented (model). Second, anexample is given in which one single solution step is omitted (scaffoldedproblem solving). Then, the number of blanks is increased step-by-step untiljust the problem formulation is left, that is, a problem to-be-solved (indepen-dent problem solving). Such a fading procedure can be used to structure asmooth transition from modeling (complete example) to scaffolded problemsolving (incomplete example) to independent problem solving.
In order to implement such a learning environment, Renkl and coworkers(Renkl et al., 2000, 2001) adapted the computer-based learning program thathad been originally developed by Renkl (1997) and modi®ed by Stark (1999).They tested the effectiveness of such a fading procedure against the traditional
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method of employing example±problem pairs. In a ®eld experiment, and intwo more controlled laboratory experiments, Renkl and coworkers found thatthe fading procedure fosters learning, at least when near transfer performancewas considered, and that it was more favorable to fade out worked-outsolution steps in a backward manner (omitting the last solution step ®rst) ascompared to a forward manner (omitting the ®rst solution step ®rst).
Although the studies of Renkl et al. (2000, 2001) provided no directevidence that the effect of fading on learning outcomes was mediated by self-explanations, the work of Jones and Fleischmann (2001) strongly suggests thatthis was actually the case. Their intention was to model the fading effectreported in the Renkl et al. studies within the CASCADE framework (aprominent, computer-based simulation of example-based learning; VanLehn,Jones, & Chi, 1992). More speci®cally, they attempted to account for theeffect of fading on learning outcomes by assuming that the faded solutionsteps focus attention and trigger self-explanations in a productive way. Bybuilding these assumptions into the CASCADE model, Jones and Fleischmannfound that the fading effect could be successfully modeled. Thus, theirresearch provides more direct evidence that the effect of fading on learningoutcomes is in fact mediated by self-explanations.
Supporting Self-Explanation Activities by Instructional Explanations
Recent studies have revealed that learning solely on the basis of self-explanationsis connected with several restrictions, even when effective self-explaining istrained or elicited (Renkl et al., 1998; Stark, 1999). These limitations areprobably inherent in learning arrangements in which learners are totallydependent on their self-explanation activities. Speci®cally, learners sometimesprovide incorrect self-explanations and often do not notice when they fail tounderstand important aspects (illusions of understanding). Moreover, evenwhen learners notice comprehension impasses, they are not able to overcomethem. Against this background, Renkl (in press, 2001) developed a coherent setof principles for integrating instructional explanations into learning via self-explanations (self-explanation activity supported by instructional expla-nations [SEASITE principles]). The most important principles were that (a)the instructional explanations should be provided only on learner demand, (b)they are written in a minimalist way, and (c) if the minimalist explanation wasnot suf®cient, progressive (i.e., more extensive) help is available. The provi-sion of learner demand, in particular, makes it necessary to implement suchinstructional explanations in a computer-based learning environment. The
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aforementioned learning program used by Stark (1999) was enriched by suchinstructional explanations.
Renkl's (in press, 2001) research also investigated to what extent the learners'self-explanation activities could be effectively supported by instructional ex-planations that were designed according to the SEASITE principles. He foundthat the instructional explanations (a) were used only to a moderate extent, (b)were primarily demanded by learners with a low level of prior knowledge, and(c) were associated with some positive effects on far transfer performance.According to his research, there seemed to be some learners who did not needthe instructional explanations. For other learners, especially those withoutabove-average prior knowledge, the infrequent use of instructional explana-tions was obviously dysfunctional. There were, however, two groups withfrequent use of the instructional explanations. One group of learners withlow prior knowledge used the provided instructional explanations produc-tively. Another group did not especially pro®t from their frequent access toexplanations, presumably because there was a heavy cognitive load or perhapseven an overload during the processing of explanations due to a type of split-attention format of the explanations and the worked-out example at hand (bothinformation sources could not be inspected simultaneously; Sweller et al.,1998).
In a subsequent study, Atkinson and Renkl (2001) examined techniques forincreasing the frequency with which learners access instructional explana-tions. They speculated that one possibility for inducing more frequent use ofinstructional explanations was requiring the learning program to collect someindicators for lack of understanding and then providing explanations whensuch indicators were detected. One such indicator was a wrong anticipation ofa to-be-determined probability in the context of a set of probability problems.To elicit this indicator, learners were required to type in anticipated prob-abilities in their learning environment. The program checked accuracy of theanticipation and decided on this basis whether an explanation was to bepresented or not. After the ®rst error was detected, a very minimalist explana-tion was given after which the learner was provided a second chance to typein the correct answer. After a second error, a more extensive explanationfollowed that included the correctly worked-out solution step. Contrary tothe predicted outcome, Atkinson and Renkl (2001) found in their experimentthat the learners in a control condition (without any instructional explana-tions) outperformed their counterparts in the two experimental conditions(self-regulated and other-regulated provision of explanations) on subsequent
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problem solving. They speculated, however, that this outcome might haveresulted from the split-attention format of the instructional explanations andworked-out examples (see above).
More speci®cally, Atkinson and Renkl (2001) realized that this outcomemight simply be an artifact of where the explanations were located. Theexplanations were not located on the same page as the examples themselves,which, according to cognitive load theory, could impose a high cognitive loadon learners' working memory by forcing them to integrate disparate sources ofinformation. In a subsequent experiment, the examples were reformatted sothat the instructional explanations were integrated into the examples them-selves, thereby signi®cantly reducing the likelihood of a split-attention effectreoccurring. The results of the second experiment indicate that, as predicted,their redesign ameliorated the split-attention effect found in the ®rst experi-ment. In fact, they found that the learners assigned to the two experimentalconditions (self-regulated and other-regulated provision of instructionalexplanations) outperformed their peers in the control condition on subsequentproblem solving. The reduction in cognitive load made it easier for learners torelate the instructional explanations to the examples before them and ulti-mately bene®t from their pedagogical content.
In a recent study, Atkinson and Derry (2000) examined the effectiveness ofa computer-based multimedia environment designed to maximize learningfrom examples. To accomplish this, they provided learners with multicompo-nent worked examples that (a) were structured to accentuate problem subgoals(i.e., subgoal-oriented), (b) included a second modality that was synchronizedwith the presentation of problem states (i.e., visually presented solution stepscoupled with aurally presented instructional explanations), and (c) weresequential ± such that the examples, similar to the examples used by Renkl(1997) and Stark (1999), consisted of a sequential presentation of problemstates. Atkinson and Derry found that learners presented with these sequential,subgoal-oriented (SE /SO) examples outperformed learners who were expos-ed to more traditional, simultaneous, non-subgoal-oriented examples onproblem-solving transfer, despite the fact that the examples in both conditionswere dual mode.
Atkinson (2001) examined ways of further enhancing the effects of thesequential, dual mode, SE/ SO worked examples. In particular, Atkinson(2001) examined two types of additional structural manipulations: (a) Incor-porating an instructional format consisting of instructional explanations andprompts designed to encourage learners to explain the example to themselves,
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or both and (b) the use of a noninteractive animated pedagogical agent todeliver instruction. In Experiment 1, which compared examples containinginstructional explanations, self-explanations prompts, or both, Atkinson estab-lished that instructional explanations alone aided learners in subsequentproblem solving.
In Experiments 2 and 3, Atkinson (2001) attempted to optimize the learningenvironment established in the initial experiment by adding an animatedpedagogical agent that was programmed to deliver the subgoal-orientedinstructional explanations aurally while simultaneously directing the learnerto focus attention on the relevant part of the example. In particular, thelearning environment was structured so that an animated character created byMicrosoft Agent ± a collection of programmable pieces of software availablefrom Microsoft for creating animated agents ± exploited verbal (e.g., instruc-tional explanations) as well as nonverbal forms of communication (e.g., gaze,gesture) within the examples themselves in an effort to promote a learner'smotivation toward the task and his / her cognitive engagement in it. The resultsof Experiment 2 showed that relying on the agent's text-to-speech enginecreated an environment that inhibited learning. After the text-to-speech enginewas replaced with human voice ®les, a subsequent experiment was conducted.The results of this experiment indicated that participants presented with ananimated agent using a human voice reported higher levels of understandingand lower levels of perceived dif®culty with regard to the examples they werepresented than their counterparts in a control condition. Moreover, theseparticipants also outperformed their control peers on both near and fartransfer. In sum, Atkinson's results implied that learners can bene®t on avariety of cognitive and affective measures by working within an example-based learning environment that contains an animated pedagogical agent, inparticular, an agent capable of delivering instructional explanations aurallywhile simultaneously exploiting nonverbal forms of communication to sup-port learning.
Several caveats remain, however. The preceding discussion about thepossibilities of fostering self-explanation and learning outcomes by structur-ing learning materials is not complete (Atkinson et al., 2000) since it onlyfocuses on the features that are best implemented by computer technology. Itis also noteworthy that the aforementioned learning programs did not exploitall of the rich possibilities for creating animated and interactive learningenvironments using computer technology. As previously alluded to, this canbe attributed to the instruction-driven orientation that we adopted. Looking to
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the future, however, it appears that computer-based learning environmentsdesigned to foster self-explanations and example-based learning will increas-ingly rely on animation and interactive elements. The next section reportsabout several new possibilities in this area.
COMPUTER-BASED LEARNING ENVIRONMENTS THATMAY FOSTER SELF-EXPLANATIONS AND
EXAMPLE-BASED LEARNING
Although the results from the research described in the preceding pages areencouraging, much work remains, particularly as new instructional paradigmsdevelop and as new computer and video technologies enhance a learningarchitect's capability for representing problem situations and their conceptsdynamically, using visualization and modeling. For instance, it may be possi-ble to create an example-based learning environment that utilizes authentic,interactive videos to capture the problem formulation of a worked-out exam-ple and to encourage learners to engage in self-explanation. It may also bepossible to create example-based learning environments that leverage thevisualization capacity of computers by presenting learners with animatedgraphics, models, and other visual representations of information that mayhelp them learn.
As previously mentioned, research is presently focusing on testingcomputer-based environments for example-based learning that containanimated pedagogical agents (Atkinson, 2001). The next logical step is toexamine ways of fostering self-explanations and example-based learning bycreating a truly interactive agent that is capable of responding to a learner'svoice. In fact, the technology used by Atkinson (2001) ± called MicrosoftAgent ± incorporates a program that supports user interaction through aconversational interface, which is designed to simulate certain aspects ofhuman social communication. For instance, an agent created by MicrosoftAgent can not only respond to mouse and keyboard input but it can respondto voice commands using its speech recognition capability as well. Thistype of interaction might dramatically improve learning from examplesby exploiting verbal communication typically reserved for human±humaninteractions. For example, an agent could be programmed to behave in a`lifelike' manner by focusing a learner's attention by using gaze andgesture, providing verbal feedback, responding to verbal input, as well as
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conveying and eliciting emotion or other behavior associated with effectivehuman tutors.
To date, the learning environments described in the preceding sections havebeen operationalized as `local' products (i.e., non-Web). There is no reason,however, that the delivery of these environments should be limited in thismanner especially in the light of the proven utility of the World Wide Web as apowerful delivery mechanism. In fact, all of the aforementioned computer-based learning environments can be delivered through the Web, therebycircumventing the barriers of time and distance that may preclude theirmore widespread adoption. Furthermore, the process of converting theaforementioned local learning environments to Web-based instructionalapplications that can be accessed by any learner with an Internet connectionis relatively straightforward. Obviously, this type of deployment would, inparticular, support lifelong learners who may not be learning in a traditionalsetting, instead relying on the Web as their primary source of pedagogicalcontent.
CONCLUSION
The research discussed in this article shares one underlying objective: tocontinuously optimize learning from worked-out examples, a learning modethat is particularly well suited for initial cognitive skill acquisition. In particular,the discussion focused on computer-based learning environments that effectivelysupport this learning mode. These environments can be employed withinstructured educational activities, such as those associated with a formal class-room, or adapted for use in more tentative explorations of understanding, such asthose associated with the informal use of the Web. Since these learning environ-ments are ¯exible and help people in the acquisition of cognitive skills, it can beargued that they could be successfully employed to encourage systematic lifelonglearning, thereby helping people negotiate the challenges presented by thegrowing role of information and knowledge in contemporary life.
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