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The Triarchic Theory of Intelligence andComputer-Based Inquiry Learning

Bruce C. HowardSteven McGeeNamsoo ShinRegina Shia

Sternberg’s (1985) triarchic theory of humanintelligence distinguished among three types ofintellectual abilities: analytic, creative, andpractical. Our study explored the relationshipsbetween student abilities and the cognitive andattitudinal outcomes that resulted fromstudent immersion in a computer-basedinquiry environment. In particular, weexamined outcome variables related to contentunderstanding, problem solving, andscience-related attitudes. Results indicated that more practical abilitiespredicted greater content understanding andtransfer of problem-solving skills. Highanalytic abilities were predictive of contentunderstanding but not transfer ofproblem-solving skills. High creative abilitiespredicted problem solving, but were notpredictive of performance on contentunderstanding. In terms of science-relatedattitudes, students who were dominant inpractical abilities had significantly morepositive posttest attitudes than those dominantin analytic abilities. The results from thisstudy were used to make recommendationsregarding design principles used in thesubsequent development of computer-basedinquiry environments.

In this study, we sought to examine the triar-chic theory of intelligence (Sternberg, 1985, 1996,1997) in the context of a computer-based inquirylearning environment. Sternberg’s theorydescribes three types of intellectual abilities:analytic, creative, and practical. According toSternberg, these abilities are interdependentconstructs, and every student demonstrates adistinct blend of strengths in one, two, or allthree triarchic ability categories.

Analytic abilities are those needed to analyze,evaluate, explain, and compare or contrast. Thestereotype for students high in analytic abilitiesis that of the “good student”—that is, such stu-dents have been found to excel at the kinds oftasks fostered and reinforced within the UnitedStates school system (Sternberg, 1997, 1998a).Creative abilities are those involved in creating,designing, discovering, or inventing. Creativethinking entails applying problem-solvingprocesses to relatively novel and unfamiliarproblems. Students with dominant creativeabilities are valued for being able to generatenew ideas. Practical abilities are those needed toutilize, implement, and apply problem-solvingprocesses to concrete and relatively familiareveryday problems. Practical students aremotivated by, and appreciative of knowledgethey can take with them when they leave theclassroom. Students with strong practicalabilities are considered “street smart”—able toquickly adapt to and shape their environment toachieve a concrete goal.

The research of Sternberg and colleagues hasfocused on testing new models of instruction

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that integrate and utilize triarchic theory. Areview of this research revealed that four typesof instructional models have been examined: (a)traditional instruction, (b) traditional instructionwith training in adaptive learning strategies, (c)matched instruction, and (d) triarchic instruc-tion. Each of these instructional models isdescribed below.

Traditional instruction. Sternberg asserts thatstudents with strong analytic abilities excel intraditional settings because they are primarilyheld accountable for declarative-type knowl-edge and memorization (Sternberg, 1997,1998a). The converse is also maintained.Students’ creative or practical abilities are notreinforced or even regarded as useful in tradi-tional instruction (Gardner, Krechevsky,Sternberg, & Okagaki, 1994; Sternberg, 1997,1998a; Sternberg & Spear-Swerling, 1996;Sternberg, Wagner, Williams, & Horvath, 1995;Sternberg & Williams, 1997). In the research ofSternberg and colleagues, traditional instructionis often used as the comparison or control fortesting new instructional models (e.g.,Sternberg, 1997, 1998a; Sternberg & Clinken-beard, 1995; Sternberg, Ferrari, Clinkenbeard, &Grigorenko, 1996).

Traditional instruction with training in adaptivelearning strategies. Research has shown general-ly positive results regarding training in adaptivelearning strategies. Adaptive learning strategiesenable students to take responsibility for theirown learning by helping them to adapt tovarious learning demands (Gardner et al., 1994).In one study, Sternberg and colleagues ex-amined what would happen if students weretrained in adaptive learning strategies for use inthe traditional classroom (Gardner et al., 1994).Strategy training helped students to understandtheir learning strengths and weaknesses, toreflect on the overall learning process, and tomanage the general school environment. Resultsindicated that this approach could be potentiallyvery powerful for helping students to succeed inthe classroom environment even with the tradi-tional instructional limitations.

Matched instruction. Several studies have ex-amined the effect of providing instruction that is

“matched” to students’ triarchic abilities. Instudies by Sternberg (1997), Sternberg andClinkenbeard (1995), and Sternberg et al. (1996),students exhibited differential outcomes accord-ing to how well the instructional environmentmatched their unique blend of triarchic abilities.That is, when students were placed in an in-structional context that emphasized theirabilities, they performed better than when therewas a mismatch. Other research indicates thatmismatching may result in lowered motivation,especially in the case of students high in creativeor practical abilities (Sternberg & Clinkenbeard,1995). Since most students have a blend of triar-chic abilities, the results of these studies shouldnot be taken to support the notion of providingindividualized instructional contexts. Instead,Sternberg and colleagues assert that it is impor-tant to provide an instructional context in whichstudents may capitalize on their strengths andalso learn how to compensate for and remediatetheir weaknesses (cf. Cronbach & Snow, 1977;Sternberg, 1998b; Sternberg, Grigorenko, Fer-rari, & Clinkenbeard, 1999).

Triarchic instruction. To create such an instruc-tional context, teachers and instructional desig-ners have turned to the model of triarchicinstruction, wherein teachers strategically createlearning activities that capitalize on the analytic,creative, and practical strengths of their studentsand that provide multiple pathways for encod-ing information to be learned via analytic, crea-tive, and practical ways (Sternberg, Torff, &Grigorenko, 1998). In this manner, there aresome activities in which students may use theirstrongest abilities and some in which they arechallenged to develop new learning modalitiesto compensate for areas of weakness (Sternberg,1998b). Recent research has demonstrated thatthe use of triarchic instruction is associated withpositive learning outcomes (Sternberg, 1996;Sternberg et al., 1996; Sternberg et al., 1998). Forinstance, research conducted by Sternberg andcolleagues (1998) demonstrated that studentswho were taught using the triarchic instructionmodel made significant learning gains on bothperformance measures and traditional multiple-choice memory items when compared to stu-dents taught in traditional ways (with anemphasis on memorizing) and those taught


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using critical thinking (with an emphasis onanalytic reasoning).

The model of triarchic instruction holdstremendous promise for the development ofcomputer learning environments—particularlyinquiry-based environments. In many inquiry-based environments students engage actively in“investigations” in which students are posed anauthentic problem facing scientists today (e.g.,Julyan, 1991; Linn, 1995; Linn & Songer, 1991;Maor & Fraser, 1996; Means & Olson, 1997;Newman, 1990, 1992; Scardamalia & Bereiter,1993; White & Frederiksen, 1995, 1998). Workingcollaboratively, students conduct backgroundresearch, analyze data, and apply theirknowledge to create and evaluate potential solu-tions. In many respects, inquiry environmentshave many similarities to the type of environ-ment envisioned in the triarchic instructionmodel because of the integration of analysis(uses analytic abilities), application (uses practi-cal abilities), and creation of a solution (usescreative abilities).

Our study was part of a larger program of on-going research that examines inquiry-based in-structional software developed at the NASAClassroom of the Future™, which specializes inresearch in the learning sciences and thedevelopment of software for math and scienceeducation. The present study examined howstudents of various triarchic abilities performedin the context of an inquiry environment. In par-ticular, our purpose was to examine howstudents’ triarchic abilities would lead to dif-ferential performance and attitudinal outcomes.To this end, we developed a set of hypothesesbased on research and our knowledge of thelearning environment that predicted how stu-dents of each ability would perform.

The next two sections outline the instruction-al context (software titled Astronomy Village®:Investigating the Universe™) and the designprinciples, and explain our hypotheses regard-ing students’ triarchic abilities and outcomes re-lated to problem-solving and science attitudes.

The Instructional Context and Design Principles

For this project, we were interested in using themodel of triarchic instruction to refine our

design principles for inquiry-based environ-ments. In this sense, our research was akin to thenotion of “design experiments” (Brown, 1992;Collins, 1992), in which research is undertakenwithin the naturalistic conditions that exist inthe school setting to determine the essential fea-tures that must be in place to create the desiredlearning outcomes. Our purpose was to examinethe use of the learning environment as awhole—not to make particular judgments aboutredesigning individual activities. Indeed, an ex-amination of individual activities would havebeen unfeasible given the number of variablesinvolved (10 investigations to choose from, eachwith 12–19 activities, across approximately 20instructional periods). The design principlesused to develop Astronomy Village are part of aframework called TETEP (Testing EducationalTheory through Educational Practice—McGee &Howard, 2000). These design principles aresummarized in Table 1.

The Third International Math and ScienceStudy (TIMSS) (Robitaille et al., 1993) describedscientific inquiry as comprising identifying ques-tions to investigate, designing investigations,conducting investigations, and formulating andcommunicating conclusions. As shown in Table1, the TETEP framework uses these categories toorganize a set of design principles that supportthe inquiry process within Astronomy Village.

Students using Astronomy Village inves-tigate problems in contemporary astronomy.Student research teams, aided by a virtual men-tor, are immersed in science concepts andscience inquiry skills as they explore the vil-lage—a mountaintop observatory completewith a library, auditorium, conference center,and laboratory (see Pompea & Blurton, 1995, fora detailed description). During each investiga-tion, students progress through five phases asshown on a research path diagram: (a) back-ground research, (b) data collection, (c) dataanalysis, (d) data interpretation, and (e) presen-tation of results. Within each phase, studentsmay complete up to six activities, such assimulations, hands-on experiments, thoughtquestions, LOGBOOK entries, and library re-search. Figure 1 shows the village interface andthe five phases of the research path diagram forthe Stellar Nursery investigation.


INTELLIGENCE AND INQUIRY LEARNING 51

Table 1 Testing Educational Theory through Educational Practice (TETEP) framework forAstronomy Village®

Scientific Inquiry (TIMSS1) Instructional Design Principles

Identifying questions to investigate Present students with contemporary questions in astronomy.

Designing investigations Investigations should be:• ill-structured, having multiple solution methods, and multiple

criteria for evaluating solutions• authentic, having a relevant or realistic environment.

Conducting investigations Designers should provide support by:• designing activities that meet the needs of different learning styles,• scaffolding students to develop appropriate and successful

solutions,• allowing choice in the selection of activities,• encouraging participation in team research.

Formulating and communicating Learning activities should provide opportunities for:conclusions • students to develop formative conclusions, safely present these

to colleagues and experts, and receive valuable feedback,• students to communicate their understanding of complex content

using a variety of media.

1. Third International Math and Science Study.

Figure 1 The Astronomy Village® interface and the five phases of the research path diagramfor the stellar nursery investigation.


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When students join the Stellar Nursery re-search team, they set out to solve the problem ofwhich wavelengths of light are best for observ-ing star-forming regions known as stellar nur-series. Student teams proceed along a path ofactivities represented by various icons in the re-search path diagram. Each icon guides them tothe village’s virtual research facilities. Headingout from their laboratory, the students gainbackground information in the auditorium lis-tening to lectures on the electromagneticspectrum and the star-forming regions of theOrion nebula. In the library they read articlesthat further prepare them for their turn in theobservatory. Here they download images of theOrion nebula taken at different wavelengths oflight. In the computer lab the students useimage-processing techniques to count the num-ber of stars visible at each wavelength. Duringthis exercise they discover which wavelengths oflight successfully penetrate Earth’s outer layersof dust and gas. Throughout the investigationstudents record their notes in their onlineLOGBOOKs. After completing all the phases oftheir research, the students develop a presenta-tion describing their solution to the problem.

Design Principles

In line with the TETEP model, the developers ofAstronomy Village used the following designprinciples:

Identifying questions to investigate. S t u d e n t swere presented with contemporary issues inastronomy.

Designing investigations. Developers presentedstudents with ill-structured investigations, inthe sense that they allowed students to use mul-tiple methods to address the problem and multi-ple criteria for evaluating solutions, whileleading them to a more- or less-defined answer(Jonassen, 1997). In addition, the simulated en-vironment provided an authentic research con-text based on Kit Peak Observatory in Arizona.By simulating the physical world of a researchvillage around an observatory, the environmentwas intended to promote learning throughsimulating (Gredler, 1996) authentic research in

stellar astronomy (Brown, Collins, & Duguid,1989; Cognition and Technology Group atVanderbilt, 1993).

Conducting investigations. The environment wasdesigned to provide scaffolding to help studentssuccessfully complete the investigation. Thesoftware contains all of the resources that studentsneed to conduct the investigation—all articles, alldata-analysis tools, and organizational helps. Ifstudents needed it, they could refer to the researchpath diagram (see Figure 1). In addition, thestudents’ virtual mentors were programmed tosend e-mail messages providing scaffolding onhow to analyze the image data using a popularimage-processing software program.

In addition, students were given choices inselecting activities that would be most beneficialto solving the problem. They were also en-couraged to conduct their research as part of asmall team. The teacher manual articulates sug-gested roles for the students in this regard.

Formulating and communicating conclusions.Designers built into the software opportunitiesfor students to develop formative conclusionsand receive feedback from their peers and theteacher. In particular, the LOGBOOK provides aplace for students to jot down their observations,develop written conclusions, and drawdiagrams. Designers also incorporated oppor-tunities for students to express their under-standing of complex content throughpresentations. At the end of the investigation,students host a virtual press conference in whichthey “click” on various reporters who ask themquestions about the investigation. Studentsrespond in written form to the questions andstore the responses in their LOGBOOK. Finally,students present their findings to theirclassmates in an oral presentation format.

Objectives: The Triarchic Theory ofIntelligence Applied to Astronomy Village

We set out to apply the triarchic theory of intel-ligence to the use of Astronomy Village. There isgrowing recognition among researchers that the



study of problem solving must focus on bothcognitive outcomes and attitudinal outcomes,especially in the use of instructional tech-nologies (Simonson & Maushak, 1996). Afterreviewing the problem-solving literature andconsulting with experts, the authors ofBenchmarks for Science Literacy (American As-sociation for the Advancement of Science, 1993)concluded that “students’ ability and inclinationto solve problems effectively depend on theirhaving certain knowledge, skills, and attitudes” (p.282, italics added). In the present research, weexamined student changes in these three areas.Specifically, we examined content understanding,transfer of problem-solving skills, and science-re-lated attitudes.

As in triarchic instruction, the activities inAstronomy Village were not matched to par-ticular triarchic abilities. Instead, students wereafforded the opportunity to select from a num-ber of activities and to work in cooperativegroups. In this manner, students ostensiblychose activities that made use of their strengths

and were able to benefit from peer modeling tocompensate for areas of weakness.

Research Hypotheses

Hypotheses one through three refer to problemsolving as the dependent measure. The fourthhypothesis refers to science-related attitudes asthe dependent measure. Table 2 summarizeseach of the triarchic abilities, illustrates how theymight be applied in the context of AstronomyVillage, and summarizes the hypotheses.

Dependent measures. We used the Problem Solv-ing Processes and Components Measure(PSPCM) to measure content understanding andproblem-solving skill (Hong, 1998; Hong, Jonas-sen, & McGee, in press). This measure is dis-cussed in greater detail in the Method section.We chose the PSPCM because it met theguidelines for authentic assessment as outlinedby Grabinger (1996) in that it provides a contex-

Table 2 Definition of each triarchic ability, how the abilities apply to Astronomy Village®predicted outcomes

Triarchic Ability Definition Strengths As Applied to Astronomy Village

Analytic Reasoning abstractly; acquiring • Analyzing Reading library articles, decoding knowledge; processing information; • Evaluating new vocabulary words, solving planning and executing strategies • Explaining math problems, interpreting

• Comparing and analyses of images, and examining contrasting graphical data

Creative Using experience, insight and • Creating Inventing solutions to ill-defined,creativity to solve new problems, • Designing novel problemscreate new ideas, or combine • Imaginingunrelated facts • Supposing

Practical Adapting to contexts; selecting • Using Conducting hands-on experiments;or shaping one’s environment • Applying explaining difficult concepts, such

• Implementing as light years, in practical terms

Table continues


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tualized, complex set of problems to be solvedalong with reliable and valid scoring rubrics.Moreover, the link between measuring sciencelearning and problem solving is clear. As notedby Simon (1981), “scientific discovery is a formof problem solving, and . . . the processeswhereby science is carried on can be explainedin terms that have been used to explain theprocesses of problem solving” (p. 48).

We also examined the change in studentscience-related attitudes as a result of using thesoftware. Simonson and Maushak (1996) as-serted that the overt activities associated withthe real-world feel of a student-led investigationproduces in learners favorable dispositions, asense of valuing, and often a desire to learnmore. Our purpose was to examine the way inwhich Astronomy Village would create changesin the disposition of students of various triarchicstrengths along dimensions such as attitudestoward science classes, careers in science, andpreference for experimentation.

To this end, we used the Test of Science-Re-

lated Attitudes (TOSRA—Fraser, 1978), whichhas six subscales as identified in the Smist, Ar-chambault, and Owen (1994) revalidation: (a)Attitude toward Science/Career & Leisure En-joyment, (b) Preference for Experimentation, (c)Social Importance of Science, (d) Normality ofScientists, (e) Attitude toward Science Classes,and (f) Openness to New Ideas. See Table 3 for adescription of each subscale and sample items.

Several of these attitudinal dimensions havebeen emphasized by the originators of Project2061 (American Association for the Advance-ment of Science, 1993) and by science educatorsfor many years (Klopfer, 1971; Moore & Foy,1996). Table 3 shows the alignment of theTOSRA with the six categories of affective aimsfor science education identified by Klopfer(1971) and certain selected benchmarks for stu-dent attitudes identified by the authors ofProject 2061. In the research cited here bySternberg and colleagues (e.g., Gardner et al.,1994; Sternberg, 1996; 1997, 1998a; Sternberg &Clinkenbeard, 1995; Sternberg et al., 1996,

Table 2 (continued)

Outcomes Expected forTriarchic Content Understanding and Outcomes Expected for Ability Problem-Solving Skll Science-related Attitudes

Analytic These are “good students"; they will Astronomy Village is “not like school”; students high in ana- show high scores on Content Under- lytic abilities may find that authentic scientific research is standing, but will devote few cognitive frustrating because abstract reasoning skills must be applied resources to learning problem-solving in context. These students will show an increase in positive skills (they will not show high scores attitude scores, but will not have as much of an increase on Problem-solving skill) as those with high Creative and high Practical abilities.

Creative Creative abilities will not predict These students will show an increase in positive attitude Content Understanding, but such scores.abilities will help these students to excel at solving hypothetical problems (they will show high scores on Problem-solving skill)

Practical Since Astronomy Village content is Students high in practical abilities will adapt quickly to grounded in “authentic” tasks, novel demands created by the authentic research activities, students with high practical abilities and will be able to apply and implement their knowledge will show high Content in most activities. Through experiencing science research Understanding. Applying such content first-hand, students’ attitudes toward science will improve.to problem solving is instinctive for these students, so they will show high Problem-solving skills as well.



Sternberg et al., 1998) there is little mention ofthe affective outcomes associated with either tri-archic or matched instruction, so our study wasdesigned to create inroads in that direction. Bymeasuring attitudinal outcomes, our goal was toexplicate the relationships between variousdimensions of science-related attitudes, triarchicabilities and use of the software.

Hypothesis 1. Analytic abilities will show adirect relationship with content understanding,but not problem-solving skill.

Because students with strong analytic

abilities excel in traditional classrooms (e.g.,Sternberg, 1997, 1998a), we hypothesized thatsuch students would exhibit high scores on con-tent understanding—the type of knowledge-development measure used in traditionalclassrooms. In terms of Astronomy Village, stu-dents with strong analytic abilities should excelat analytic activities (see Table 2) and shouldgain knowledge from them because these ac-tivities are “school-like.” Conversely, we alsohypothesized that students with strong analyticabilities would focus primarily on extractingand memorizing information and would devote

Table 3 The six subscales of the Test of Science-Related Attitudes (TOSRA)

Aims for Attitudinal Benchmarks IdentifiedScience Education by the American Association for the

Scale Sample Question Identified by Klopfer (1971) Advancement of Science Project 2061

Attitude toward “When I leave school, Development of interests in Students should raise questions about science, career I would like to work science and science-related the world around them and be willing & leisure with people who activities—development of to seek answers to some of them by enjoyment make discoveries interest in pursuing a career making careful observations and

in science.” in science trying things out

Preference for “It is better to ask Acceptance of scientific Students should know why curiosity, experimentation the teacher the inquiry as a way of thought honesty, openness, and skepticism are

answer than to find so highly regarded in science and how out by doing an they are incorporated into the way experiment.” science is carried out; exhibit those

traits in their own lives and value them in others.

Social “The government Having favorable attitudes Students should know that hypotheses importance should spend more toward science are valuable, even if they turn out not of science money on scientific to be true, if they lead to fruitful

research.” investigations.

Normality of “Scientists are less Having favorable attitudes Students should view science and scientists friendly than other toward scientists technology thoughtfully, being

people.” neither categorically antagonistic nor uncritically positive.

Attitude toward “Science classes are Enjoyment of science science classes fun.” learning experiences

Openness to “I dislike listening Students should offer reasons for new ideas to other people’s their findings and consider reasons

opinions.” suggested by others. Students shouldknow that often different explanationscan be given for the same evidence, andit is not always possible to tell whichone is correct.


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few of their cognitive resources to learning prob-lem solving from these or any other activities.

Hypothesis 2. Creative abilities will show adirect relationship with problem-solving skillbut not content understanding.

Sternberg and Spear-Swerling (1996) re-ported that students preferring creative thinkingoften have moderate to low grades and testscores in traditional classrooms. Our hypothesiswas that the same would hold true for studentsusing Astronomy Village—that creative abilitieswould not predict content understanding. How-ever, such abilities should have helped these stu-dents to excel at solving hypothetical problems,and they would show high scores on problem-solving skill. In Astronomy Village the researchinvestigations and topics are unfamiliar to theaverage student, creating an opportunity fortheir creative abilities to be expressed when in-venting solutions to novel problems, or whenanswering hypothetical questions, such as whatthe world would look like if human vision in-cluded the infrared spectrum.

Hypothesis 3. Practical abilities will show adirect relationship with both content under-standing and problem-solving skill.

According to Sternberg and Spear-Swerling(1996), students high in practical abilities excelin applying ideas to real-world situations. InAstronomy Village, such students would adaptquickly to the novel demands created by theauthentic research activities, and would be ableto apply and implement their knowledge effec-tively. Since the content is grounded in authentictasks, students with high practical abilitieswould show high content understanding. Ap-plying such content to problem solving is in-stinctive for these students, so they woulddemonstrate high problem-solving skill as well.

Hypothesis 4. All students will show increasesin positive science-related attitudes. Wehypothesize that the increase will be the least forstudents with strong analytic abilities, whencompared to students with strong creativeabilities and those with strong practical abilities.

For students with strong analytic abilities weproposed that the attitudinal benefits of triarchic

instruction within Astronomy Village would beneutralized somewhat by their frustration at thenontraditional nature of the learning. In past re-search, teachers have indicated that their stu-dents like Astronomy Village because it is “notlike school,” in the sense that students are askedto use their reasoning skills in an authentic con-text (McGee, Hong, Shia, & Purcell, 1998).Having to apply knowledge in context, how-ever, is not a strength for students high inanalytic abilities and might create frustration(Sternberg & Spear-Swerling, 1996). In light ofthis, it seemed unlikely that their attitudestoward science would improve very much, butneither would their frustration be so strong as tocreate a decrease in positive attitudes.

Sternberg and Spear-Swerling (1996) statedthat students with creative strengths like todevelop their own ideas and be self-directed, butdo not like to follow directions. Such being thecase, we hypothesized that students with crea-tive strengths would benefit greatly from theself-directed nature of the investigations and usetheir insight and new ideas to complete the ac-tivities. We expected that these students wouldshow an increase in positive attitude scores asthey learned that science can be creative.

Students high in practical abilities often feelbored in school and disconnected from theirteachers (Sternberg & Spear-Swerling, 1996). Be-cause Astronomy Village presents an oppor-tunity for students with practical abilities tomake use of their strengths, we hypothesizedthat their boredom and disconnectedness wouldbe alleviated. That is, through experiencingscience research first-hand, their attitude towardscience would improve.

METHOD

Participants and Procedure

Participants were 88 ninth-grade students oftypical ability from four general science classes(same teacher) in a West Coast high school. Thegender breakdown included 46 females, 39males, and 3 who did not specify. The ethnicbreakdown included 31% White (n = 27), 17%Asian American (n = 15), 14% Hispanic or Latino



(n = 12), 2% Black (n = 2), and 35% (n = 32) whodid not specify. According to the teacher’sreport, student ability level and prior exposureto stellar astronomy were evenly distributedacross the four classes. Because of absences, 18students had to be dropped from most analyses.

The teacher coached students in the use of thesoftware, and facilitated their progress actively.Students were assigned to a triad and were al-lowed to choose their own research investiga-tion. The researchers did not specify how toassign students to groups. The teacher reportedthat he created groups that “worked togetherwell.” Students worked toward specified projectcompletion dates, and were graded as a groupon their final presentation—a 10- to 15-min over-view of their research question, data collection,data analysis and conclusions. From start tofinish, the use of Astronomy Village comprisedapproximately twenty 50-min class periods.

Before students began using the software,they completed Sternberg’s Triarchic AbilitiesTest (STAT—Sternberg, 1991, 1992) and theTOSRA (Fraser, 1978). Students were then as-signed to groups and spent the next three weekspursuing investigations concerning a researchquestion in astronomy. The TOSRA posttest andthe PSPCM were given after all Astronomy Vil-lage activities were completed.

Materials

Triarchic abilities. The STAT (Sternberg, 1991,1992) is made up of 12 different subscales, withfour questions apiece; that is, the three abilitiesare measured across four domains: quantitative,verbal, figural, and performance. Sternberg haswritten extensively about the three abilities andfour domains, and has created other versions ofa similar test (R.J. Sternberg, personal com-munication, August 26, 2000; Sternberg, 1985;1991; 1992; 1998a; Sternberg et al., 1996). TheSTAT and similar tests have been used innumerous research studies and continue to beused by Sternberg’s research group and others.Table 4 gives three sample items. The test is ap-propriate for the high school level, takes threehours to administer, and yields three total scoresfor each student (see Sternberg, 1991, 1992 for a

more detailed description). Due to time con-straints imposed by the teacher, we did not usethe performance sections in which studentscomplete three essays. Since our intent was toexamine triarchic abilities relative to each other,we assumed that inclusion of the performancedomain was not absolutely necessary for ade-quate validity. Some of Sternberg’s research hasalso excluded the performance domain (R. J.Sternberg, personal communication, October 15,1999; Sternberg et al., 1999).

Sternberg’s work has demonstrated that theSTAT is correlated with, but nonidentical to,other conventional tests. For instance, he reportssignificant correlations between the triarchicabilities scores on the STAT and the ConceptMastery Test, the Watson-Glaser Critical Think-ing Appraisal, the Cattell Culture-Fair Test, andother measures (Sternberg & Clinkenbeard,1995; Sternberg et al., 1996; Sternberg et al.,1999). He has used the measure in his own worknumerous times (e.g., Sternberg, 1991, 1997;Sternberg & Clinkenbeard, 1995; Sternberg et al.,1996; Sternberg et al., 1999).

Problem solving. The PSPCM includes threenovel astronomy-related problems presented inthe form of a scenario and takes approximately90 min to complete. To address the scenario, stu-dents first answer questions about their currentunderstanding of the relevant astronomy con-cepts (content understanding) and then writeanswers to the problems (problem-solving skill).Samples are given below:

Sample scenario: You are a member of a research teamthat has been asked to calculate the distance to a par-ticular star. A famous astronomer has suggested thatthe star is relatively close to Earth (within 25 lightyears).

Sample content-understanding question: Put an × in theboxes next to the five concepts (ideas) that are most im-portant to finding the distance to that star.

Sample problem-solving question: You have been askedto meet with the press to discuss how the team willproceed with this research. Assume that the peoplewho will be reading your explanation have little or noknowledge of astronomy. Write your explanation sothat it is clear enough for anyone to understand. Makesure you provide specific details of the procedures you


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will follow to measure the distance. You may want touse drawings to illustrate your thinking.

Responding to the content-understandingquestion requires students to analyze the prob-lem statement to determine which concepts arerelevant to the solution and apply to this prob-lem what they have learned in astronomy. Be-cause the problem-solving questions do nothave “correct” solutions, students must imaginethe scenario, design a solution, and implementtheir design.

From the outset, the PSPCM was intended tobe a posttest-only assessment of content under-standing and transfer of problem-solving skillslearned from using Astronomy Village (Hong,1998). On the assessment, students are presented

three problem-based scenarios of varying com-plexity, with a set of multiple open-ended ques-tions for each scenario. First, each set ofquestions addresses student knowledge relatedto the question. For example, one questionreads:

Dr. Smith, an astronomer, recently announced that amajor emergency will be occurring soon. He believesthat there is a good chance that a very large asteroid,will soon hit Earth. Put an × in the box next to the fivekey ideas that you think would be most important inresearching this problem.

Next, each set of questions addresses studentproblem-solving skills by asking them how theywould solve the problem, and to justify theirthinking. For example, after reading the scenario

Table 4 Sample questions from the Sternberg Triarchic Abilities Test

Analytic

Each passage contains an unknown word that is underlined. Read each passage and choose the word that hasthe same meaning as the unknown word as it is used in the question. The day was hot, and many people were outside enjoying the sunshine. Many tems were on the lake. Somepulled water-skiers.Tem most likely means:A. wave B. boat* C. raft D. duck

Creative

In each question below, there are three underlined words. The first two underlined words go together in acertain way. Choose the word that goes with the third underlined word in the same way that the first two gotogether.Each question has a “Pretend” statement. You must suppose that this statement is true. Sometimes the statementwill be important in helping you choose the correct answer and sometimes it will not. Think of the statement,and then decide which word goes with the third underlined word in the same way that the first two underlinedwords go together. Money falls off trees.Snow is to shovel as dollar is toA. bill B. rake* C. bank D. green

Practical

Each question gives you information about a situation involving a high school student. Read each questioncarefully. Choose the answer that provides the best solution, given the specific situation and desired outcome. John’s family moved to Iowa from Arizona during his junior year in high school. He enrolled as a new student inthe local high school two months ago but still has not made friends and feels bored and lonely. One of hisfavorite activities is writing stories. What is likely to be the most effective solution to this problem?A. Volunteer to work on the school newspaper staff.*B. Spend more time at home writing columns for the school newsletter.C. Try to convince his parents to move back to Arizona.D. Invite a friend from Arizona to visit during Christmas break.

* Items marked with an asterisk indicate the response which is most in line with the triarchic ability of interest.



in which an asteroid might be heading towardEarth, students are asked, “What types of ex-perts will be needed to assist you in yourdecision? Write an explanation of your choice ofteam members that is clear enough for others tounderstand.”

Because of the open-ended format, each ofthe 16 questions is scored using a rating scaledevised by Hong (1998). Each question is worthfrom 2 to 20 points on the scale, with most beingworth 4 points. The questions are scored accord-ing to various dimensions, such as the degree towhich the response is scientific in nature versusbeing merely practical, or the degree to whichthe student demonstrates an organizedknowledge of related concepts. In its develop-ment, the PSPCM was subjected to an extensiveconstruct validation process in which test items(and the scoring rubric) were reviewed iterative-ly by subject matter experts, curriculumdevelopers, and high school teachers withregard to accuracy, content, readability, andvocabulary level, and also pilot tested with stu-dents. The scoring rubric was developed to besimilar to other rubric systems (e.g., Baxter,Glaser, & Raghavan, 1993; Lane, 1993), and wasvalidated using the method of “instructionalsensitivity,” which compares differences in theresponses of experts and novices (Gall, Borg, &Gall, 1996). The PSPCM rubric system wasshown to discriminate between students whodemonstrated a well-organized problem-solv-ing process and those who demonstrated a dis-organized process (p < .001) (Hong, 1998; Hong,Jonassen, & McGee, in press). The average over-all inter-rater reliability in these studies was .82.In addition, the instrument has been shown tocorrelate with measures of metacognition,science-related attitudes, and motivation (Hong,1998).

Science-related attitudes. In the 20 years since itsdevelopment, the TOSRA has been used exten-sively and is considered to be a singularly out-standing measure because of its thoroughempirical validation (Haladyna & Shaughnessy,1982; Smist et al., 1994). The TOSRA is made upof 70 test items with a 5-point Likert scale, whichincludes six subscales: (a) attitude toward science-career & leisure enjoyment, (b) preference for ex-

perimentation, (c) social importance of science, (d)normality of scientists, (e) attitude toward scienceclasses, and (f) openness to new ideas (Smist et al.,1994). By convention, the TOSRA is not typicallyused to yield a singular-attitude score. In theoriginal validation of the TOSRA, Fraser (1978)reported subscale reliabilities ranging from 0.67to 0.93 (M = 0.80). The mean test-retest (two-week interval) reliability was reported to be 0.78.

RESULTS

Because students used six class periods to com-plete the measures, the total number of par-ticipants taking each inventory varied accordingto classroom attendance for each day.

Learning Outcomes

To test Hypotheses One through Three, we usedmultiple regression analysis to explicate therelationships between triarchic abilities and thelearning outcomes of content understandingand problem-solving skill. Table 5 presents themeans and standard deviations for each triar-chic ability score and content understandingand problem-solving scores. Table 6 presentsPearson’s r correlations between scores on theSTAT and content understanding and problem-solving measures. Using regression analysis inaddition to correlational analysis allowed us toparcel out the variability shared between triar-chic scores. In the regression analysis, scores foreach of the triarchic abilities were entered intothe model simultaneously. In regards to contentunderstanding (M = 10.81, SD = 5.35), bothanalytic abilities and practical abilities were sig-nificant predictors (β = .22, p = .031 and β = .25, p= .016, respectively) accounting for 25% of thevariance (R2 =. 25, p = .001). Creative ability wasnot a significant predictor, β = .16, p = .129. Al-ternatively, for Problem-Solving Skill (M =18.40, SD = 12.56), both creative abilities andpractical abilities were significant predictors (β =.22, p = .048 and β = .21, p = .037, respectively) ac-counting for 17% of the variance (R2 = .17, p =.004). Analytic ability was not a significantpredictor, β = .10, p = .397.


60 ETR&D, Vol. 49, No. 4

Science-Related Attitudes

Table 7 presents students’ overall means for thesubscales of the TOSRA. To test the fourthhypothesis, we looked at the main effect forchanges in science-related attitudes (Did stu-dents show increases in positive science-relatedattitudes?), and we examined the interaction ef-fect (Did students with high Analytic abilitiesshow less of a gain in positive science-related at-titudes as compared to those with otherabilities?). Our custom analysis modelemployed a doubly multivariate repeatedmeasures analysis of variance (MANOVA)(Norusis, 1994), with two within-subjects vari-ables (testing occasions and TOSRA subscalescores) and one between-subjects variable (triar-chic ability categories). The MANOVA modelwas customized to test (a) the main effects forpre- to postchanges in science-related attitudeswithin each of the three triarchic abilitycategories, and (b) the two-way interaction effectbetween testing occasion and triarchic abilitycategory.

Before running the MANOVA, we examinedthe relationship between triarchic abilities andpretest science attitudes. Since the STATmeasures various abilities and the TOSRAmeasures various attitudes, one would not ex-pect them to be significantly related. If theywere, one might question their constructvalidity. We conducted 18 correlational analyses(Analytic, Creative, and Practical Ability scoresacross six TOSRA subscale scores) using a =

.0028 to control for Type I errors. As expected,correlations between triarchic abilities scoresand TOSRA pretest scores revealed no sig-nificant relationships.

To sort students by triarchic ability, wecategorized them according to their highest tri-archic ability score. For example, a student witha score of 8 on Analytic, 5 on Creative, and 6 onPractical was categorized as Analytic. Sternbergand colleagues used a similar procedure forcategorizing students according to theirstrongest showing (Sternberg 1997; Sternberg &Clinkenbeard; 1995; Sternberg et al., 1996). Thisprocedure resulted in 19 Analytic students, 30Creative students, and 15 Practical students. Six-teen students who did not have a single highestscore were left out of further analyses.

Table 5 Means and standard deviationsfor each triarchic ability scoreand content understanding andproblem solving scores

Triarchic Ability M SD

Analytic 3.59 2.15Creative 4.10 2.29Practical 4.68 2.01Content Understanding 10.81 5.35Problem Solving 18.40 12.56

Note: n = 82.

Table 6 Pearson’s R correlations betweenscores on the Sternberg TriarchicAbilities Test and contentunderstanding and problemsolving measures

Content ProblemTriarchic Ability Understanding Solving

Analytic .3991** .2561*Creative .3098** .2995**Practical .4157** .3442**

Note: n = 79. *p < .05 ** p < .01.

Table 7 Overall means for the subscalesof the Test Of Science RelatedAttitudes (TOSRA)

Pretest PosttestTOSRA Subscale M SD M SD

Career & leisure 3.02 .37 2.92 .42Preference for 3.11 .49 2.99 .42experimentationSocial importance 3.06 .37 3.09 .50Normality of scientists 3.09 .32 3.10 .47Attitudes toward 2.99 .44 2.96 .55science classesOpenness to 2.65 .57 2.83 .65new ideas



Tests for main effects were consistent withhypotheses. Table 8 presents pretest and posttestscores on the subscales of the TOSRA, brokendown by triarchic ability category. The main ef-fect of most interest was changes from pretest toposttest on science-related attitudes. The multi-variate test for within-subjects effects (pre- to

postchanges) revealed a significant main effect,F (1, 58) = 2.68, p = .024, for testing occasion ashypothesized. However, posthoc analysesshowed no significant differences from pretestto posttest for any of the individual six TOSRAsubscales. An examination of the pre- topostchanges for each of the triarchic abilities

Table 8 Pretest and posttest scores on the subscales of the test of science-related attitudes,broken down by triarchic ability category

Career & Preference for SocialLeisure Experimentation Importance

Triarchic Ability M SD M SD M SD

Analytic Pre 3.16 0.50 3.32 0.68 3.11 0.40Post 2.73 0.33 2.78 0.48 3.01 0.37

Practical

Pre 2.93 0.35 2.98 0.48 3.01 0.30Post 3.04 0.46 3.12 0.23 3.22 0.62

Creative

Pre 2.98 0.28 3.11 0.36 3.04 0.28Post 3.07 0.25 3.17 0.37 3.20 0.36

Table 9 Change scores from pretest to posttest on the subscales of the Test of ScienceRelated Attitudes, broken down by triarchic ability category, and Scheffe’s post hocanalysis results

Career & Preference for SocialLeisure Experimentation Importance

Change from Pretest to Posttest

Analytic – 0.43 – 0.54 – 0.10Creative 0.11 0.14 0.21Practical 0.09 0.06 0.16

Posthoc results Std Std StdError p Error p Error p

Practical>Analytic .1761 .0073 .1937 .0029 .2011 .2219Creative>Analytic .2038 .0366 .2242 .0281 .2327 .5043Practical>Creative .1865 .9807 .2052 .9274 .2130 .9326

Table continues

Table continues


62 ETR&D, Vol. 49, No. 4

categories (Table 9) shows why: Scores for stu-dents categorized as primarily Analyticdecreased, while the scores of Creative and Prac-tical students generally increased. These find-ings are discussed further in the examination ofinteraction effects below.

The remaining two tests for main effects were

not pertinent to our research. That is, resultsshowing significant main effects for TOSRA sub-scale score differences would have indicatedthat students scored differently on different sub-scales. Since the scales are considered inde-pendent factors (Fraser, 1978; Smist et al., 1994)this would have been expected. Further, results

Table 8 (continued)

Normality of Attitudes Toward Openness Scientists Science Classes to New Ideas

Triarchic Ability M SD M SD M SD

Analytic Pre 3.23 0.35 2.97 0.47 2.80 0.58Post 3.00 0.55 2.75 0.36 2.54 0.40

Practical

Pre 2.98 0.25 2.89 0.30 2.55 0.38Post 3.19 0.40 3.15 0.69 3.09 0.77

Creative

Pre 3.09 0.32 3.20 0.49 2.61 0.39Post 3.23 0.35 3.12 0.39 2.89 0.40

Table 9 (continued)

Normality of Attitudes Toward Openness toScientists Science Classes New Ideas

Change from Pretest to Posttest

Analytic – 0.23 – 0.22 – 0.26Creative.21 0.21 0.26 0.54Practical 0.14 - 0.08 0.28

Post hoc results Std Std StdError p Error p Error p

Practical>Analytic .1623 .0231 .1946 .0245 .2328 .0026Creative>Analytic .1878 .1568 .2251 .8120 .2694 .1172Practical>Creative .1719 .8646 .2061 .1588 .2465 .5337



showing significant main effects for triarchicability categories would have indicated that stu-dents in each category showed differences inhow they responded to the TOSRA. This alsowould have been expected since the studentswere not assigned randomly to the triarchicability categories.

The test for interaction effects revealed somevery interesting findings. By examining the in-teraction, we were able to compare the differen-ces in pre- to postchanges in science-relatedattitudes between triarchic ability categories.That is, we wanted to know if students withhigh Analytic abilities showed less of a gain inpositive science-related attitudes as compared tothose with other abilities. In fact, the analysisrevealed a significant effect for the two-way in-teraction between testing occasion and triarchicability category, F (2, 57) = 7.05, p = .002. Posthocanalyses revealed that the testing occasion bytriarchic ability category interaction was sig-nificant for five of the six TOSRA subscales. SeeTable 10 for details on univariate F tests for in-teraction effects and effect sizes. The only subs-cale that evidenced no interaction effect was theSocial Importance of Science.

Based on these results, we computed changescores from pretest to posttest for each of the sixTOSRA subscales. Figure 2 illustrates the dif-ference in change scores as a function of triarchicability category. Positive change scores indicatepositive attitude changes. Using a linear model,

we tested for differences in change scores be-tween the three triarchic ability categories foreach TOSRA subscale. For the five subscaleswhere interactions were found, Scheffé’s post-hoc analyses revealed that change scores for stu-dents categorized as Analytic were significantlylower than corresponding change scores for stu-dents categorized as Practical. Table 9 shows thechange scores and the results of the posthocanalyses. Students with high practical abilitiesappear to have benefited the most, followed bythose with high creative and high analyticabilities.

DISCUSSION

In this study, we explored the relationships be-tween student abilities and the cognitive and at-titudinal outcomes that resulted from studentimmersion in a computer-based inquiry en-vironment. Our presumption was that studentuse of such software would yield differentialoutcomes according to their triarchic abilities. Infact, the results proved this presumption true.Results indicated that more practical abilitiespredicted greater content understanding andtransfer of problem-solving skills. High analyticabilities were predictive of greater content un-derstanding, but not transfer of problem-solvingskills. High creative abilities predicted positiveoutcomes for problem solving, but were notpredictive of performance on content under-standing. In terms of science-related attitudes,students who were dominant in practicalabilities had significantly better posttest at-titudes than those dominant in analytic abilities.

Implications Regarding the TriarchicTheory of Intelligence

Clearly, activities in Astronomy Village were agood match for those with practical abilities. It isour conclusion that the learning environmentmade use of many practical abilities, such asadapting to the navigation interface, usingtutorials, and applying image-processing know-how to a set of data. Students high in practicalabilities demonstrated increases in positive at-

Table 10 Interaction effect for TOSRA**subscales: Univariate F tests with(2,58) D. F.

TOSRA Subscale F value p value Effect size

Career & leisure 5.95 .004 .641Preference for 7.06 .002 .698experimentationSocial importance 1.56 .220 .328Normality of scientists 4.17 .020 .536Attitudes toward 4.44 .016 .553science classesOpenness to new ideas 6.63 .003 .676

* Effect size uses Cohen’s d (Wolf, 1986).** Test of Science Related Attitudes


64 ETR&D, Vol. 49, No. 4

titudes, perhaps because of the good fit betweentheir strengths and preferences, and the simu-lated realism in Astronomy Village. It seemsplausible to suppose that Astronomy Villagecreated a learning environment based on the ac-tivities of real-world science, which in turnenabled those with practical abilities to excel.

As hypothesized, high analytic abilities werepredictive of greater content understanding. Bytraditional standards, these students had a “suc-cessful” outcome, because content under-standing is so often equated with “knowing.” Itshould be noted, however, that higher analyticabilities were not related to greater transfer ofproblem-solving skills. Also, scores for analyticstudents decreased over time for all six science-related attitude subscales, but not significantly.It seems likely that these results may be ex-plained by the nontraditional nature of the in-struction. Analytic students were required tocollaborate with peers, make inferences from theresults of hands-on activities, and think creative-

ly to solve problems—all activities that analyti-cal students may have found to be uncomfort-able and difficult. These results are consistentwith other research indicating that where thereis a mismatch between the nature of the activityand students’ strongest abilities, student motiva-tion levels decrease (Sternberg & Clinkenbeard,1995). It also is plausible that four weeks in thislearning environment was not enough time foranalytic students to adapt to the realism.

It was expected that creative abilities wouldpredict positive outcomes for problem solving,but would not be predictive of performance oncontent understanding. The results support thishypothesis. The novelty of Astronomy Villageappears to be a good match for students high increative abilities—allowing them to invent solu-tions to novel problems. Students also had theopportunity to expand their problem-solvingrepertoire by learning to use their insight to cre-ate new ideas and explanations.

In terms of science-related attitudes, students

Figure 2 Triarchic ability type and changes in science attitudes on the six TOSRA (Test ofScience-Related Attitudes) subscales.



who were high in creative abilities showedneither a statistically significant increase nor adecrease in attitudes from pretest to posttest.This is contrary to the hypothesis that theywould demonstrate more positive attitudes. Per-haps students with high creative abilities wouldhave preferred a far more unstructured learningenvironment, or the environment did not pro-vide enough opportunities for invention. Inretrospect, we realize that many of the activitiesdesigned to spark creative thinking were notcentral to student investigations, and may havebeen completed in a too casual fashion.

Implications for the Design of Instruction

Research indicates that one way of optimizingstudent problem solving in inquiry-based en-vironments is to develop software that accom-modates the different abilities and learningstrengths of students (Bell, Davis, & Linn, 1995;Jonassen & Reeves, 1996; Linn, 1996; Sternberg,1998b; White & Frederiksen, 1998; Yager, 1995).Our results coincide with this notion in that stu-dents demonstrated distinctive learning out-comes because of their unique blends of abilities.As a research and development group, we wereinterested in revising our software design prin-ciples based on these findings.

There are two important findings from thisstudy that point to limitations in the original setof design principles (see Table 1). The first find-ing was the disparity in change scores betweenthe science-related attitudes of practical studentsand analytical students. The second finding wasthat creative abilities did not predict perfor-mance on the content-understanding test andanalytical abilities did not predict problem-solv-ing performance. We suggest enhancements thatprovide a better balance of challenge and sup-port to encourage more effective learning forstudents with these strengths. Table 11 shows arevised TETEP framework outlining, in italics,the additions to the design principles.

Identifying questions to investigate. There areseveral ways to engage students of variousabilities in answering the question. In general,research questions should be presented in such a

way as to pique student curiosity and sparktheir ideas about the answer. Those with strongpractical abilities might answer in terms of thesolution’s practical significance. Those whohave creative strengths might generate novelanswers. Those who have analytic strengthsmight try to develop an answer based on scien-tific principles they already know. Prior researchsupports the notion that how research questionsare phrased is important for developing studentinterest in pursuing the solution. For instance,McGee and Howard (1999) found that a carefulphrasing of the research question contributedtoward a more effective integration of activities.

Designing investigations. We suggest providingmultiple solution methods, multiple criteria forevaluating solutions, and multiple possibleanswers. This would foster opportunities forcreative students to apply their insight to trulynovel solutions.

Conducting investigations. We suggest includinga balance of both challenge and support. That is,activities should capitalize on student abilities (asupport) while also encouraging students todevelop modes of compensation for areas ofweakness (a challenge) (Sternberg, 1998b). Forinstance, in Astronomy Village the nonlinear na-ture of the investigation allowed students topursue different solution methods. By providingchoice, students could capitalize on theirstrengths, but there was no mechanism to chal-lenge students to compensate for areas of weak-ness. That is, creative students could applyproblem-solving techniques without paying at-tention to the content and analytic studentscould pay attention to the content without rely-ing on problem-solving techniques.

Another way to provide support would be toallow students to “practice” the process of in-quiry, by providing research investigations thatare shorter in duration. Within Astronomy Vil-lage, students typically complete one investiga-tion within a four-week time frame. It may bebeneficial to shorten the investigations so stu-dents can participate in more iterations. In thismanner, students who have analytic strengthswill have the opportunity to become comfort-able in this nontraditional learning environ-


66 ETR&D, Vol. 49, No. 4

ment, and will learn over time to devote cogni-tive resources to problem-solving tasks.

As developers of educational technology, weare often asked, “Does educational technologylead to better learning outcomes?” Our answeris that it depends on the combination of the op-portunities made available in the software andhow these opportunities are used by the stu-dents. This study provided evidence that educa-tional multimedia such as Astronomy Villagecan provide learning opportunities that accom-modate distinctive blends of creative, practical,and analytic thinking. By accommodating in-dividual student abilities, such software reducesthe mismatch between practical or creativeabilities and current traditional classroom ac-tivities.

Bruce C. Howard, Steven McGee, Namsoo Shin, and Regina Shia are with the Wheeling Jesuit University,NASA Classroom of the Future/Center forEducational Technologies.

REFERENCES

American Association for the Advancement of Science.(1993). Benchmarks for science literacy: Project 2061.New York: Oxford University Press.

Baxter, G.P., Glaser R., & Raghavan, K. (1993). Analysisof cognitive demand in selected alternative science assess-ments. Pittsburgh, PA: CRESST/Learning Researchand Development Center. (ERIC DocumentReproduction Service No. ED 368 776)

Bell, P., Davis, E.A., & Linn, M.C. (1995). The knowledgeintegration environment: Theory and design. Universityof California, Berkeley.

Brown, A.L. (1992). Design experiments: Theoretical

Table 11 Revised TETEP* framework for Astronomy Villager (new design principles are initalics). Taken from McGee & Howard (2000).

Scientific Inquiry (TIMSS**) Instructional Design Principles

Identifying questions to investigate A research question should:• be important and not fully answered by scientists,• pique students’ curiosity,• spark students’ ideas about the answer.

Designing investigations Investigations should be:• ill-structured, having multiple solution methods, multiple

criteria for evaluating solutions, and multiple possible answers• authentic, having a relevant or realistic environment.

Conducting investigations Designers should provide a balance of challenge and support by:• designing activities that capitalize on students’ learning strengths

and that challenge students to develop modes of compensation for areas of weakness.

• scaffolding students to develop appropriate and successful solutions,

• allowing choice in the selection of activities,• encouraging participation in team research,• providing practice at inquiry through multiple investigations.

Formulating and Learning activities should provide opportunities for:communicating conclusions • students to develop formative conclusions, safely present these

to colleagues and experts and receive valuable feedback,• students to communicate their understanding of complex content

using a variety of media.

* Testing Educational Theory through Educational Practice (McGee & Howard, 2000).** Third International Math and Science Study (Robitaille et al., 1993)



and methodological challenges in creating complexinterventions in classroom settings. The Journal of theLearning Sciences, 2(2), 141–178.

Brown, J.S., Collins, A., & Duguid, P. (1989). Situatedcognition and the culture of learning. Educational Re-searcher, 18(1), 32–42.

Collins, A. (1992). Toward a design science of educa-tion. In E. Scanlon & T. O’Shea (Eds.), New directionsin educational technology. New York: Springer-Ver-lag.

Cognition and Technology Group at Vanderbilt(CTGV). (1993). Integrated media: Toward atheoretical framework for utilizing their potential.Journal of Special Education Technology, 12(2), 76–89.

Cronbach, L.J., & Snow, R.E. (1977). Aptitudes and in-structional methods. New York: Irvington.

Fraser, B.J. (1978). Development of a test of science-re-lated attitudes. Science Education, 62, 509–515.

Gall, M.D., Borg, W.R., & Gall, J.P. (1996) Educationalresearch: An introduction (6th ed.). New York:Longman Publisher.

Gardner, H., Krechevsky, M., Sternberg, R.J., &Okagaki, L. (1994). Intelligence in context: Enhanc-ing students’ practical intelligence for school. In K.McGilly (Ed.), Classroom lessons: Intergrating cognitivetheory and classroom practice (pp. 105–127).Cambridge,MA: Bradford Books.

Grabinger, R.S. (1996). Rich environments for activelearning. In D.H. Jonassen (Ed.), Handbook of researchfor educational communications and technology: Aproject of the Association for Educational Communica-tion and Technology (pp. 665–692). New York: Simonand Schuster MacMillan.

Gredler, M.E. (1996). Educational games and simula-tions: A technology in search of a (research)paradigm. In D.H. Jonassen (Ed.), Educational com-munications and technology. New York: Simon &Schuster Macmillan.

Haladyna, T., & Shaughnessy. J,. (1982). Attitudestowards science: A quantitative synthesis. ScienceEducation, 66, 547–563.

Hong, N. (1998). The relationship between well-structuredand ill-structured problem solving in multimedia simula-tion. Unpublished doctoral dissertation. The Pen-nsylvania State University, State College.

Hong, N.S., Jonassen, D.H., & McGee, S. (in press).Predictors of well-structured and ill-structuredproblem solving in an astronomy simulation. Journalof Research in Science Teaching.

Jonassen, D.H. (1997). Instruction design models forwell- structured and ill-structured problem-solvinglearning outcomes. Educational Technology Researchand Development, 45(1), 65–94.

Jonassen, D.H., & Reeves, T.C. (1996). Learning withtechnology: Using computers as cognitive tools. InD.H. Jonassen (Ed.), Handbook of research for educa-tional communications and technology. New York:Simon and Schuster Macmillan.

Julyan, C. (1991). Getting connected to science. HandsOn! 14(1), 4–7.

Klopfer, L.E. (1971). Evaluation of learning in science.In B.S. Bloom, J.T. Hastings, & G.F. Madaus (Eds.),Handbook on summative and formative evaluation of stu-dent learning. New York: McGraw-Hill.

Lane, S. (1993). The conceptual framework for thedevelopment of a mathematics performance assess-ment instrument. Educational Measurement: Issuesand Practice, 12(2), 6–13.

Linn, M.C. (1995). Designing computer learning en-vironments for engineering and computer science:The scaffolded knowledge integration framework.Journal of Science Education and Technology, 4(2), 103–126.

Linn, M.C. (1996). Key to the information highway.Communications of the AMC, 39, 34–35.

Linn, M.C., & Songer, N.B. (1991). Teaching ther-modynamics to middle school students: What areappropriate cognitive demands? Journal of Researchin Science Teaching, 28, 885–918.

Maor, D., & Fraser, B.F. (1996). Use of classroom en-vironment perceptions in evaluating inquiry-basedcomputer assisted learning. International Journal ofScience Education, 18(4), 401–421.

McGee, S., Hong, N., Shia, R., & Purcell, S. (1998).Results of a survey assessing the impact of AstronomyVillage®: Investigating the Universe™. [On-line].Retrieved December 15, 2000:http://www.cet.edu/research/papers.

McGee, S., & Howard, B.C. (1999, June). Using designexperiments as a means of guiding software development.Paper presented at the annual ED-MEDIA con-ference, sponsored by the Association for the Ad-vancement of Computing in Education. Seattle, WA.

McGee, S., & Howard, B.C. (2000). Testing educationaltheory through educational practice: A framework for thedesign of inquiry-based multimedia learning environ-ments [Unpublished manuscript, on-line]. RetrievedDecember 15, 2000:http://www.cet.edu/research/papers.

Means, B., & Olson, K., (1997). Technology and educationreform: Studies of education reform. Office of Educa-tional Research and Improvement, U.S. Departmentof Education.

Moore, R., & Foy, R.L.H. (1996). The scientific attitudeinventory: A revision (SAI II). Journal of ScienceTeaching, 34(4), 327–336.

Newman, D. (1990). Opportunities for research on theorganizational impact of school computers. Educa-tional Researcher, 19(3), 8–13.

Newman, D. (1992). Technology as support for schoolstructure and school restructuring. Phi Delta Kappan,308–315.

Norusis, M. (1994). SPSS advanced statistics 6.1.Chicago: SPSS Inc.

Pompea, S.M., & Blurton, C. (1995, Jan/Feb). A walkthrough the Astronomy Village. Mercury, 32–33.

Robitaille, D.F., Schmidt, W.H., Raizen, S., McKnight,C., Britton, E., & Nicol, C. (1993). Curriculumframeworks for mathematics and science. Vancouver,Canada: Pacific Educational Press.


68 ETR&D, Vol. 49, No. 4

Scardamalia, M., & Bereiter, C. (1993). Technologies forknowledge-building discourse. Communications ofthe AMC, 36(5), 37–41.

Simon, H.S. (1981). The psychology of scientific prob-lem solving. In R.D. Tweney, M.E. Doherty, & C.R.Mynalt (Eds.), On scientific thinking (pp. 41–54). NewYork: Columbia University Press.

Simonson, M., & Maushak, N. (1996). Instructionaltechnology and attitude change. In D.H. Jonassen(Ed.), Handbook of research for educational communica-tions and technology: A project of the Association forEducational Communication and Technology (pp. 984–1016). New York: Simon and Schuster Macmillan.

Smist, J.M., Archambault, F.X., & Owen, S.V. (1994,April). Gender differences in attitude toward science.Paper presented at the meeting of National Councilon Measurement in Education, New Orleans, LA.

Sternberg, R.J. (1985). Beyond IQ: A triarchic theory ofhuman intelligence. New York: Cambridge UniversityPress.

Sternberg, R.J. (1991). Theory-based testing of intellec-tual abilities: Rationale for the triarchic abilities test.In H. Rowe (Ed.), Intelligence: Reconceptualization andmeasurement (pp. 183–202). Hillsdale, NJ: LawrenceErlbaum.

Sternberg, R.J. (1992). Sternberg triarchic abilities test.Unpublished test.

Sternberg, R.J. (1996). Successful intelligence: How practi-cal and creative intelligence determine success in life.New York: Plume.

Sternberg, R.J. (1997, March). What does it mean to besmart? Educational Leadership, 20–24.

Sternberg, R.J. (1998a). Applying the triarchic theory ofhuman intelligence in the classroom. In R.J.Sternberg and W.M. Williams (Eds.), Intelligence, in-struction, and assessment. Mahwah, New Jersey:Lawrence Erlbaum Associates.

Sternberg, R.J. (1998b). Principles of teaching for suc-cessful intelligence. Educational Psychologist, 33(2/3),65–72.

Sternberg, R.J., & Clinkenbeard, P. (1995). The triarchicmodel applied to identifying, teaching, and assess-ing gifted children. Roeper Review, 17(4), 255–260.

Sternberg, R.J., Ferrari, M., Clinkenbeard, P., &Grigorenko, E.L. (1996). Identification, instruction,and assessment of gifted children: A constructvalidation of a triarchic model. Gifted ChildrenQuarterly, 40, 129–137.

Sternberg, R.J., Grigorenko, E.L., Ferrari, M., &Clinkenbeard, P. (1999). A triarchic analysis of anaptitude- treatment interaction. European Journal ofPsychological Assessment, 15(1), 1–11.

Sternberg, R.J., & Spear-Swerling, L. (1996). Teachingfor thinking. Washington, DC: American Psychologi-cal Association.

Sternberg, R.J., Torff, B., & Grigorenko, E.L. (1998).Teaching triarchically improves school achieve-ment. Journal of Educational Psychology, 90(3), 374–384.

Sternberg, R.J., Wagner, R.K., Williams, W.M., & Hor-vath, J.A. (1995). Testing common sense. AmericanPsychologist, 50, 912–927.

Sternberg, R.J., & Williams, W.M. (1997). Does thegraduate record examination predict meaningfulsuccess in the graduate training of psychologists?American Psychologist, 634–641.

White, B.Y., & Frederiksen, J.R. (1995). The ThinkerToolsinquiry project: Making scientific inquiry accessible tostudents and teachers (Causal Models Research GroupReport CM-95-02). University of California atBerkeley, School of Education.

White, B.Y., & Frederiksen, J.R. (1998). Inquiry, model-ing, and metacognition: Making science accessible toall students. Cognition and Instruction, 16, 3–118.

Yager, R.E. (1995). Constructivism and the learning ofscience. In S.M. Glynn & R. Duit (Eds.), Learningscience in the schools: Research reforming practice (pp.35–57). Mahwah, NJ: Lawrence Erlbaum.



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