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JOURNAL OF GEOSCIENCE EDUCATION 63, 140–146 (2015)Google Earth Mapping Exercises for Structural Geology Students—APromising Intervention for Improving Penetrative Visualization AbilityScott Giorgis1,aABSTRACTThree-dimensional thinking skills are extremely useful for geoscientists, and at the undergraduate level, these skills are oftenemphasized in structural geology courses. Google Earth is a powerful tool for visualizing the three-dimensional nature of datacollected on the surface of Earth. The results of a 5 y pre- and posttest study of the three-dimensional visualization abilities ofundergraduate students (N 75) enrolled in a structural geology class at a small, liberal arts college are presented. The datasuggest students achieved statistically significant gains in three-dimensional visualization skills over the course of thesemester. Mean pretest scores for female students tended to be lower than those of male students. This gender gap, however,was no longer statistically significant in the posttest scores, with female students showing higher average gains in spatial skillscompared to their male counterparts. These data show a correlation between the introduction of Google Earth mapinterpretation exercises, available on the Science Education Resource Center’s Web site and developed by Tewksbury, andimproved student visual penetrative thinking ability. Results support the hypothesis that individuals with greater contextualknowledge are able to more successfully circumvent lower three-dimensional spatial visualization ability. The exercise appearsto be most effective in improving penetrative visualization ability for those students who have sufficient backgroundknowledge. Those with less geological knowledge appear to benefit less from the Google Earth–based intervention studiedhere. Ó 2015 National Association of Geoscience Teachers. [DOI: 10.5408/13-108.1]Key words: Google Earth, spatial visualization, visual penetrative ability, contextual knowledgeINTRODUCTIONTitus and Horsman, 2009; Ormand et al., 2014). Geosciencemajors display a wide range of spatial visualization abilities(Ormand et al., 2014); therefore, successful teachingstrategies must effectively address this range in ability.Google Earth is commonly used in both teaching andresearch (e.g., Tewksbury, 2008; Whitmeyer et al., 2010) andis one potential tool for improving visualization skills instudents. In 2011, the Geological Society of America held aPenrose Conference at Google headquarters with thespecific goal of more broadly distributing Google Earth–based education materials. The various viewpoints availablein Google Earth, varying from a bird’s-eye view to anoblique perspective view, afford users the opportunity toengage data sets from numerous perspectives (e.g., Tewksbury, 2010; SERC, 2015). It is well documented that manystudents have difficulty visualizing the three-dimensionalstructure of a region based solely on looking at a geologicmap and cross section (Piburn et al., 2002; Kastens andIshikawa, 2006). Google Earth is potentially a powerful toolfor bridging the cognitive gap between a two-dimensionalgeologic map and a two-dimensional cross section (Whitmeyer et al., 2010).Barbara Tewksbury (Hamilton College) developed andposted on the Science Education Resource Center’s (SERC)On the Cutting Edge Web site a series of Google Earthexercises for undergraduate structural geology students(Tewksbury, 2008, 2010, 2011; SERC, 2015). The exerciseswere designed to have students ‘‘discover’’ the concepts ofcontacts, strike, and dip by engaging in mapping and crosssection construction exercises based in Google Earth. Usingthese exercises, students work with inclined contactsexposed in arid regions of the world (e.g., Utah; Fig. 1).The lack of vegetation in arid regions makes it possible toconstruct a geologic map using the aerial photo imageryavailable in Google Earth. The ability to change perspectiveThree-dimensional thinking skills are widely recognizedto be of critical importance to geoscientists and thereforehave been the subject of a wide variety of studies seeking toimprove teaching and learning of spatial skills (e.g., Kali andOrion, 1996; Reynolds et al., 2006; Titus and Horsman, 2009;Ormand et al., 2014). Surveys of three-dimensional thinkingskills clearly demonstrate that some students are better thanothers (Lord, 1985; Kali and Orion, 1996; Piburn et al., 2002)and that these skills can be improved with appropriateinterventions (e.g., Lord 1985; Uttal and Cohen, 2012; Uttalet al., 2013). For example, Sorby and Baartmans (2000)developed a 10 week training program for freshmanengineering majors focused on improvement of spatialvisualization skills. Over a 6 y period, students thatcompleted this program consistently improved their visualization skills, were more likely to stay in the major, andfinished the major in less time. On a different scale, Terleckiet al. (2008) engaged students in playing Tetris on a regularbasis, which improved their ability to do three-dimensionalmental rotation. These are two examples of how wellconceived interventions can improve student spatial visualization skills.There is high degree of interest in the geosciencecommunity in improving our quantitative understanding ofthe relative efficacy of different interventions for improvingthree-dimensional visualization skills (e.g., Piburn et al.,2005; Kastens and Ishikawa, 2006; Reynolds et al., 2006;Received 2 December 2013; revised 9 September 2014 and 26 February 2015;accepted 12 March 2015; published online 13 May 2015.1Department of Geological Sciences, SUNY Geneseo, 1 College Circle,Geneseo, New York 14454, USAaAuthor to whom correspondence should be addressed. Electronic 7140Q Nat. Assoc. Geosci. Teachers

J. Geosci. Educ. 63, 140–146 (2015)Using Google Earth to Improve Penetrative Visualization Ability141FIGURE 1: (A) Bird’s-eye view geologic map of the Raplee Anticline near Mexican Hat, UT. Contact lines are drawnover Google Earth imagery, and units are numbered from oldest (1) to youngest (8). The line X-X’ marks theorientation of the cross section drawn by students. (B) The folded nature of the rocks is readily apparent in an obliqueview of the same structure in Google Earth. Both images are from Tewksbury (SERC, 2015).from map view to oblique view provides students anopportunity to dynamically engage the three-dimensionalrelationship between contacts on a geologic map and thetopography.This study presents the results of a 5 y pretest/posttestsurvey of the spatial visualization skills of students enrolledin an undergraduate structural geology course. The datasuggest that using Google Earth correlates with increasedstudent visual penetrative ability, but these gains areapparently limited to those students with sufficient geologicknowledge to take advantage of the exercises.METHODOLOGYStudy Population and DesignThe study took place between 2007 and 2011 in astructural geology course at an undergraduate, liberal artscollege populated by approximately 5,000 students mostlyranging from 18 to 21 y old with an average SAT score of1329 for students enrolled during the study period.Approximately 15%–20% of the student body during thistime period self-identified as an ethnic minority, whileapproximately 5%–10% of geology majors identified asminorities. The student body was 58% female and 42% male,and the study group was almost evenly split between maleand female students (37 female, 38 male). All majors wererequired to take structural geology, which is a 300-levelcourse intended for juniors and seniors. Most students tookat least four required geology classes prior to taking thestructural geology course (physical, historical, mineralogy,petrology). Students who chose to take the class as seniorshad also completed two or three additional required courses(paleontology, stratigraphy, and/or geomorphology).Tewksbury’s Google Earth exercises were introducedinto the structural geology class starting in 2009. Subdivisionof the study population is therefore based on year ofenrollment rather than random assignment. Those studentsenrolled in structural geology in 2007 and 2008 were used asbaseline to gauge the effect of the Google Earth exercises in2009–2011. The lack of a random assignment of students toeither a control group (pre–Google Earth) or an experimen-tal group (post–Google Earth) makes this research nonexperimental in design (e.g., Shadish et al., 2002). Students’baseline three-dimensional visualization abilities were measured on the first day (pretest) and last day (posttest) of classusing the visualization exam assembled by Titus andHorsman (2009). The goal of this study was to test thehypothesis that implementation of Tewksbury’s GoogleEarth exercises improves students’ three-dimensional visualization skills.Google Earth InterventionThe exercises designed by Tewksbury (SERC, 2015) aresubdivided into four main modules: inclined contacts, strikeand dip, horizontal and vertical contacts, and mappingfolded rock. Students begin by working on one limb of afold, i.e., a region with inclined contacts. After developing amap, determining dip directions, and constructing a crosssection, students are then introduced to the concepts ofstrike and dip. Next, Tewksbury introduces the outcroppatterns for horizontal and vertical contacts. In the finalstages of the exercise, students develop a map and crosssection through an area of folded rock (Fig. 1).In my experience, full implementation of Tewksbury’sprotocol took 2 to 3 weeks of class time, including lectureand laboratory. I deployed three of the four modules(excluding the horizontal and vertical contact module) andhad my students finish by constructing a map and crosssection of the Raplee Anticline near Mexican Hat, UT (Fig.1). Anecdotally, this exercise was extremely satisfying.Students seemed to grasp the concepts of contacts, the ruleof v’s, strike and dip, and cross-section construction morenaturally.Data CollectionThree tests of spatial thinking skills were given as preand posttests to students enrolled in the structural geologycourse. Students were required to take both the pre- andposttest, but they were graded solely on participation.Administration of the tests followed Titus and Horsman(2009), where students were given 3 min to complete eachsection of the test. Correct answers earned one point, and, in

142S. GiorgisJ. Geosci. Educ. 63, 140–146 (2015)TABLE I: 3D visualization exam results by year.1SpatialSpatialVisualRelations Manipulations PenetrativeAbilityTotalFall 2007 (N 10; F 6, M 4)Pretest3.7 (1.7)13.7 (4.1)5.7 (2.4)23.1 (5.5)Posttest4.7 (2.3)14.5 (6.0)5.0 (2.9)24.2 (8.3)1.10.7-0.71.1GainFall 2008 (N 14; F 3, M 11)Pretest4.4 (3.0)14.8 (5.8)5.8 (1.7)25.0 (8.2)Posttest4.6 (3.0)16.8 (3.6)5.5 (3.1)26.9 (8.4)0.22.0-0.31.9GainFall 2009 (N 15; F 11, M 4)Pretest3.7 (1.7)12.7 (4.6)4.9 (2.9)21.2 (6.7)Posttest4.5 (2.5)14.9 (5.0)6.0 (2.8)25.4 (8.1)0.82.2Gain1.24.2Fall 2010 (N 14; F 5, M 9)Pretest4.0 (1.7)11.6 (6.2)4.6 (2.5)20.2 (8.4)Posttest4.8 (3.3)12.8 (5.4)6.0 (3.2)23.6 (10.1)0.81.2Gain1.43.4Fall 2011 (N 22; F 12, M 10)Data AnalysisThe data collected were subjected to a standardstatistical analysis to address the following questions: (1)Do the Google Earth exercises developed by Tewksbury(SERC, 2015) improve students’ spatial thinking skills? (2)Are these effects evenly distributed across students withdifferent levels of background knowledge in geology?The study group was subdivided into subgroups basedon whether or not they took part in the Google Earthexercises and their level of background geologic knowledge.For each subgroup within the study, the following valueswere calculated using Microsoft Excel: Pretest4.2 (2.1)11.8 (6.1)5.5 (3.2)21.5 (9.4)Posttest4.6 (2.2)13.4 (6.3)7.8 (4.1)25.8 (10.1)0.41.6GainStudent grades in prior geology courses were gatheredfrom their transcripts to assess variation in baselinegeological knowledge. A contextual knowledge score wascalculated using seven required classes (noted above) that allgeology majors must complete. The score factored in boththe number of classes taken and student performance. Foreach class taken, the student earned up to four pointsdepending on his or her grade following a standard fourpoint grading scale (i.e., A 4, A- 3.7, etc.). A studentwith ‘‘A’s’’ in all seven classes prior to structure earned ascore of 28 (i.e., 7 · 4). The ‘‘average’’ student earned a scoreof 12, i.e., four classes with a B in each class (4 · 3).2.3 4.31All scores are presented as mean (one standard deviation); pretest andposttest scores give the mean score out of 10, 20, 15, and 45, respectively, foreach test; gain mean posttest - mean pretest; N total number ofstudents; F number of female students; M number of male students.an effort to discourage guessing, incorrect answers resultedin loss of a quarter of a point. Unanswered questionsreceived a score of zero.The three tests administered were assembled by Titusand Horsman (2009) and have been used in previousinvestigations of spatial thinking in the geosciences (Titusand Horsman, 2009; Ormand et al., 2014). Each test targeteda different category of spatial thinking ability: spatialrelations, spatial manipulations, and visual penetrativeability (Titus and Horsman, 2009). The spatial relationsportion of the assessment was drawn from the PurdueVisualization of Rotations Test (PVRT; Guay, 1976). Thissubtest measures the ability to rotate an object about itscenter. The spatial manipulation portion examines the abilityto mentally rearrange an object into a different configurationwith questions drawn from the Educational Testing Service(Ekstrom et al., 1976). Lastly, penetrative visualizationinvolves the ability to mentally envision the inside of a solidobject (Kali and Orion, 1996; Titus and Horsman, 2009). Thisability is tested with the Planes of Reference Test (Ormand etal., 2014) using questions developed by Crawford andBurnham (1946), Myers (1953), and Titus and Horsman(2009). Traditionally, the ability of a student to draw anappropriate cross section is used to assess the accuracy of thethree-dimensional model of a map area. Therefore, GoogleEarth exercises should improve students’ three-dimensionalpenetrative visualization ability. mean pretest and posttest scores for each of the threevisualization tests;mean gain (mean posttest minus mean pretest);p-value, using a paired, two-tailed t-test, of pretestscores versus posttest scores; andp-value, using an unpaired, two-tailed t-test, ofposttest scores for the pre–Google Earth group versusthe post–Google Earth group.All populations analyzed with the t-test passed theKolmogorov-Smirnov test for a normal distribution.RESULTSOver a 5 y period, students improved their spatialvisualization test scores while enrolled in the structuralgeology course (Tables I and II and Fig. 2). These gains wererealized in all three categories of spatial thinking skills. On ayear-by-year basis, the gains in spatial relations and spatialmanipulations varied considerably (Table I). Pretest scoresfor male students were significantly better than those forfemale students. However, female students improved morethan their male counterparts, and the difference between theposttest scores for male and female students was notsignificant at the 95% confidence level (Table II and Fig. 3).In 2007 and 2008, students on average scored lower ontheir visual penetrative ability at the end of the structuralgeology course compared to the beginning (Table I). Recallthat the visual penetrative ability test measures students’skill at mentally ‘‘cross-sectioning’’ an object. Drawing crosssections of deformed bodies of rock is a required skill withinstructural geology, which makes these negative scoresparticularly distressing. Introduction of the Google Earthexercises in 2009 correlated with a change in this trend(Table I and Fig. 4). Gains in visual penetrative abilityincreased (p 0.05) after the introduction of the exercises.Those gains were maintained through the final 3 y of thisstudy (Table I). These gains were most pronounced in

J. Geosci. Educ. 63, 140–146 (2015)Using Google Earth to Improve Penetrative Visualization Ability143TABLE II: 3D visualization exam results by group.1SpatialRelationsSpatialVisualManipulations PenetrativeAbilityTotalAll (N 75, F 37, M 38)Pretest4.0 (2.1)12.8 (5.5)5.3 (2.7)22.1 (8.0)Posttest4.6 (2.6)14.4 (5.4)6.3 (3.4)25.3 (9.0)0.61.61.03.2 0.05 0.05 0.05 0.05Gainp-valueFemale (N 37)Pretest3.4 (1.8)11.9 (5.8)4.7 (2.5)20.0 (8.1)Posttest4.0 (2.5)13.7 (6.2)5.8 (3.4)23.5 (9.7)Gain0.61.81.13.5p-value0.13 0.050.05 0.05Male (N 38)Pretest4.6 (2.2)13.6 (5.1)5.9 (2.7)24.1 (7.5)Posttest5.3 (2.6)15.0 (4.5)6.8 (3.4)27.0 (8.0)Gain0.71.40.82.9p-value0.05 0.050.05 0.05Pre–Google Earth (N 24, F 9, M 15)Pretest4.1 (2.5)14.3 (5.1)5.8 (2.0)24.2 (7.1)Posttest4.6 (2.7)15.8 (4.8)5.3 (3.0)25.8 Google Earth (N 51, F 28, M 23)Pretest4.0 (1.9)12.0 (5.6)5.1 (2.9)21.1 (8.3)Posttest4.6 (2.6)13.7 (5.6)6.8 (3.5)25.1 (9.4)Gainp-value0.61.71.74.0 0.05 0.05 0.05 0.051All scores are presented as mean (one standard deviation); pretest andposttest scores give the mean score out of 10, 20, 15, and 45, respectively, foreach test; gain mean posttest - mean pretest; p-value is the result of apaired two-tailed t-test comparing the pretest and posttest scores for eachgroup; N number of students; F number of female students; M number of male students.FIGURE 2: Aggregate spatial visualization test resultsfrom 2007–2011. Maximum scores for each portion of thetest vary (spatial relations 10, spatial manipulations 20, visual penetrative ability 15, total 45). Meangains (D) are the difference between the mean posttestand mean pretest scores. The p-values were calculatedusing a paired t-test with a two-tailed distribution. Allgains are statistically significant at the 95% confidencelevel.statistically significant in posttest scores (Fig. 3). Femalegains tend to be higher than their male counterparts, but thedifference in gains is not statistically significant. These datasuggest that gender differences in spatial ability can bereduced through the practice involved in a ‘‘traditional’’structural geology curriculum.Although these results are promising, two potentialshortcomings call these findings into question. First,previous work strongly suggests that simply taking a spatialtest twice can lead to substantial gains (Titus and Horsman,2009; Uttal and Cohen, 2012; Ormand et al., 2014).students with average background knowledge or better(Table III and Fig. 5).DISCUSSIONStructural geology at the study institution consists ofmany of the laboratory exercises typically associated with theclass: three-point problems, calculation of strike and dipfrom a geologic map, stereonet analysis, cross-sectionconstruction, and map interpretation. Previous work (e.g.,Lord, 1987; Titus and Horsman, 2009) suggests that spatialthinking skills can be improved with practice. This 5 y dataset (Table I and Fig. 2) supports the hypothesis that exercisesin a ‘‘traditional’’ structural geology curriculum generallyimprove students’ three-dimensional visualization skills.Additionally, the pretest data (Fig. 3) support the widelyaccepted notion that male students tend to have higherspatial visualization skills than female students (e.g., Linnand Petersen, 1985). This gender gap, however, is no longerFIGURE 3: Pretest and posttest spatial visualization testresults subdivided by gender. Maximum score for theentire test is 45 points. Mean gains (D) are the differencebetween the mean posttest and mean pretest scores. Thep-values were calculated using a paired t-test with atwo-tailed distribution.

144S. GiorgisJ. Geosci. Educ. 63, 140–146 (2015)FIGURE 5: Gains in visual penetrative ability prior toand after the introduction of Google Earth exercisessubdivided by contextual knowledge. The difference inpre- versus postintroduction mean gains is noted (D).The p-values were calculated using an unpaired t-testassuming a two-tailed distribution.FIGURE 4: Comparison of final visualization test resultsand gains prior to (2008–2009) and after (2009–2011) theintroduction of Google Earth exercises. Differences inpre– versus post–Google Earth scores are noted (D). Thep-values were calculated using an unpaired t-testassuming a two-tailed distribution.Therefore some of the gains observed in this data set arelikely to arise from repeat testing and cannot be uniquelyattributed to teaching interventions in the course. Second,the nonexperimental (e.g., Shadish et al., 2002) nature of thisstudy means that the experimental and control groups werenot randomly assigned. Students self-selected to join thestudy simply by becoming geology majors. Assignment tothe pre–Google Earth versus post–Google Earth groups wasbased only on year of enrollment. The lack of randomassignment indicates the possibility that some (or all) of thegains in three-dimensional visualization ability could be dueto systematic differences between the pre– versus post–Google Earth groups. For example, studies show that generalspatial thinking ability correlates with math SAT scores (e.g.,Casey et al., 1995) and visual penetrative ability specifically(Cohen and Hegarty, 2012). Math SAT score data were notcollected as part of this study; therefore, they cannot be ruledout as a potential cause of the observed gains. However, thisis likely not the case given that the pretest scores for thepost–Google Earth group were lower than those of the pre–Google Earth group (Table II).If we assume that gains arising from taking a spatial testtwice are constant between groups taking the same test,then there is a correlation between the introduction of theGoogle Earth exercises and increased gains in spatialthinking skills (Table II and Fig. 4). While no clear changesin student gains in spatial relations and spatial manipulations were present, the data show an increase in visualpenetrative ability (Table II). Disappointingly, student’spenetrative ability scores dropped between the pre- andTABLE III: Visual penetrative ability scores of pre– and post–Google Earth groups subdivided by contextual knowledge score.1Pre–Google EarthHigh (‡12)(N 14, F 5, M 9)Low ( 12)(N 16, F 8, M 8)High (‡12)(N 35, F 20, M 15)Prior knowledge9.3 (1.4)17.0 (3.2)9.4 (3.0)17.6 (3.9)Pretest5.4 (2.2)6.1 (1.8)5.3 (3.8)5.0 (2.5)Posttest4.6 (3.1)5.8 (2.8)6.5 (4.0)6.9 (3.4)1.23.50.11 0.05Gainp-value1Post–Google EarthLow ( 12)(N 10, F 4, M 6)-0.80.43-0.30.78All scores are presented as mean (one standard deviation); N number of students; F number of female students; M number of male students; priorknowledge contextual knowledge score; pretest and posttest scores give the mean score out of 15; gain mean posttest - mean pretest; p-value is the resultof a two-tailed, paired t-test comparing the pre- versus posttest values for each group.

J. Geosci. Educ. 63, 140–146 (2015)Using Google Earth to Improve Penetrative Visualization Abilityposttest in 2007 and 2008. Introduction of Google Earth–based exercises correlated with significantly (p 0.05)improved student gains in this category (Fig. 4 and Table II).This module occupied 2 weeks of class (laboratory andlecture), so it represented a significant change to thecurriculum. The exercises called on the ability of studentsto ‘‘see’’ the geologic structure hidden inside the threedimensional terrane image displayed by Google Earth.Students were not asked to mentally rearrange images—e.g., restore a cross section to its initial, predeformationstate—as part of this exercise. Similarly, students were notrequired to mentally rotate imagery. Therefore, it seemsreasonable that introduction of the Google Earth exerciseswould impact visual penetrative ability scores but have nomeasureable effect on spatial relations or spatial manipulation abilities of students (Fig. 4).Gains in visual penetrative ability, however, were notequally distributed across the population (Fig. 5). Studentswho began the class with greater geologic knowledge werebetter able to utilize the Google Earth exercises. There wereno statistically significant gains in visual penetrative abilityfor those students with a limited geologic knowledgebackground (Fig. 5).These results are consistent with prior publishedoutcomes, which suggest that success in a spatially intensivefield positively correlates to initial three-dimensional thinking ability primarily in novice students. Uttal and Cohen(2012) hypothesize that experts in science, technology,engineering, and mathematics (STEM) fields use mentaldepictions to solve problems without relying on their spatialvisualization skills. Similarly, Hambrick et al. (2011) found acorrelation between high spatial thinking skills and geologicmapping performance only for those with low levels ofgeologic knowledge. These authors suggest that geologistswith high levels of geologic knowledge are able to find waysto work around limitations imposed by lower spatialvisualization skills.In both studies (Hambrick et al., 2011; Uttal and Cohen,2012), increased contextual knowledge appeared to enableindividuals to overcome lower three-dimensional visualization skills. The lack of statistically significant gains in thepost–Google Earth, low-background-knowledge group inthis study could be due to the inability of these students touse mental representations to solve problems. The firstexercise required students to construct a geologic map oftilted beds. Hambrick et al. (2011) found that experts, whenmapping a series of tilted beds, will call on their priorknowledge and test out a series of possible three-dimensional configurations, such as anticline, syncline, or tiltedbeds. The experts discarded models that did not fit theobservations, anticline and syncline in this case, and kept theone(s) that worked (e.g., tilted beds). A mental representation of tilted beds was then used to imagine the areaunderground and construct an appropriate cross section.This process involves a lower cognitive load than mentallyprojecting each contact underground and attempting toassemble those projections into a geologically meaningfulgeometry. It is possible that students in this study with lowergeologic knowledge were forced to devote more cognitivecapacity to visualization than their peers with greatergeologic knowledge.Shipley et al. (2013) offered a complementary explanation for the split in low versus high geologic knowledge145present in these data. While observing students on astructural geology field trip, Shipley et al. (2013) noted thatmore experienced geologists ‘‘filter’’ their observations toconcentrate on the most meaningful information. Forexample, when tasked to sketch a cross-section of theBaraboo Syncline, an expert will focus on the orientation ofbedding at an outcrop. Many outcrops in this region,however, also exhibit strong cleavage. To successfullyconstruct a cross section, the cleavage orientations need tobe distinguished from the bedding orientations. Experts haveplenty of practice filtering their observations in this manner,while novices are forced to wade through a large volume ofinformation in search of the most pertinent information.Google Earth exercises offer a similarly data-rich environment where the terrain can be observed from a variety ofangles and orientations. While a student with greatergeologic knowledge might seek out a strike-normal viewpoint, less experienced individuals may struggle to choosethe most effective orientation to observe the structure.CONCLUSIONSResults of a 5 y pretest/posttest survey of junior- andsenior-level undergraduates enrolled in a structural geologyclass suggest that implementation of Tewksbury’s SERCteaching module ‘‘Teaching Geologic Map InterpretationUsing Google Earth’’ correlates with increased gains invisual penetrative ability. Visual penetrative ability is directlyrelated to the ability of students to mentally ‘‘cross-section’’objects and, therefore, is a key skill to develop in futuregeologists. The data suggest that gains from the GoogleEarth exercises are most pronounced for students who haveacquired sufficient geologic background knowledge. Students with less background knowledge may lack mentalrepresentations of deformed beds to help them understandthe maps (e.g., Hambrick et al., 2011), and/or those studentsmay be less efficient at filtering the data to find the mostmeaningful observations (Shipley et al., 2013). Regardless ofthe cause, students who are most vulnerable—i.e., thosewith limited background knowledge—also appear to bethose least likely to benefit from a Google Earth exerciseintervention.AcknowledgmentsThanks go to Mary Hegarty, Carol Ormand, Eric Riggs,and Tim Shipley for organizing the SERC Spatial ThinkingJournal Club in 2012. The organizers and participants in thisjournal club greatly increased my ability to both understandand contextualize the data I had been collecting. Critiques byMichelle Markley, Jeff Over, four anonymous reviewers, andan associate editor greatly improved this manuscript. LisaMeyer at SUNY Geneseo assisted with the statisticalanalysis.REFERENCESCasey, M., Nuttall, R., Pezaris, F., and Benbow, C. 1995. Theinfluence of spatial ability on gender differences in mathematics college entrance test scores across diverse samples.Developmental Psychology, 31:697–705.Cohen, C.A., and Hegarty, M. 2012. Inferring cross sections of 3Dobjects: A new spatial thinking test. Learning and IndividualDifferences, 22:868–874.

146S. GiorgisCrawford, A.B., and Burnham, P.S. 1946. Forecasting collegeachievement: A survey of aptitude tests for higher education.New Haven, CT: Yale University Press. p. 291.Ekstrom, R.B., French, J.W., Harman, H.H., and Derman, D. 1976.Manual from k

survey of the spatial visualization skills of students enrolled in an undergraduate structural geology course. The data suggest that using Google Earth correlates with increased student visual penetrative ability, but these gains are apparently limited to those students with sufficient geologic knowledg