prior knowledge and online inquiry-based science reading: evidence from eye tracking

HSIN NING JESSIE HO, MENG-JUNG TSAI, CHING-YEH WANG andCHIN-CHUNG TSAI

PRIOR KNOWLEDGE AND ONLINE INQUIRY-BASED SCIENCEREADING: EVIDENCE FROM EYE TRACKING

Received: 28 July 2013; Accepted: 15 November 2013

ABSTRACT. This study employed eye-tracking technology to examine how students withdifferent levels of prior knowledge process text and data diagrams when reading a web-based scientific report. Students’ visual behaviors were tracked and recorded when theyread a report demonstrating the relationship between the greenhouse effect and globalclimate change in 2 diagrams and 4 textual sections. Based on the pretest scores, 13participants were categorized into high and low prior knowledge (PK) groups. Eye-tracking measures including the total reading time, total fixation duration, and totalregression number on each area of interest of the 2 groups were compared. A heat mapwas further used to show the overall visual distribution of each group. In addition, theinter-scanning transitions between the textual and graphical information of the 2 groupswere compared and further confirmed by the patterns of the scan paths. The resultsrevealed that overall students spent more time reading the textual than the graphicalinformation. The high PK students showed longer fixation durations and more regressionson the graphics than the low PK students. Meanwhile, the high PK students showed moreinter-scanning transitions than the low PK students not only between the text and graphicsbut also between the 2 data diagrams. This suggests that the high PK students were moreable to integrate text and graphic information and inspect scientific data which is essentialfor online inquiry learning. This study provides eye-tracking evidence to show that lowPK students have difficulties integrating scientific diagrams with expository texts andinspecting scientific data diagrams that are commonly shown in websites. Suggestions aremade for future studies and instructional design for online inquiry-based science learning.

KEY WORDS: eye tracking, inquiry-based data inspection, prior knowledge, text-graphicintegration, web-based science reading

INTRODUCTION

Searching and reading web-based information has become one of theinformation literacies required for all citizens in the twenty-first century.Web-based science reading is not only a part of school curricula but also apart of daily life for all future citizens (Ikpeze & Boyd, 2007; NationalResearch Council, 2000). The information presented in websites or digitaldevices is somewhat different from traditional paper-based readingmaterials (Sutherland-Smith, 2002). Web-based information is basicallypresented in a non-linear hyperlinked structure which allows online

International Journal of Science and Mathematics Education 2013# National Science Council, Taiwan 2013

readers to develop alternative reading strategies. In addition, comparedwith traditional paper-based reading material, web-based informationrelies more heavily on multimedia cognitive representations such aspictures, videos, animations, simulation, or interactive games. Althoughstatic pictures have often been used in traditional science textbooks, thedesign of layout of images and text in textbooks is quite different fromthat demonstrated in websites; for example, the proportion of image areasis usually larger in websites than that in textbooks. These features of web-based reading are actually challenges for all students to face if they are tobe equipped with information literacy.

Particularly in science education, online inquiry learning usuallyrequires students to interact with online scientific information such assearching (Hoffman, Wu, Krajcik & Soloway, 2003) and readingscientific-related issues via the Internet, observing relevant scientific dataor argumentation, and making judgments (Tsai, Hsu & Tsai, 2012).Reading and examining the data presented in diagrams on websites is alsoa required scientific skill for scientific literacy. Therefore, it is importantto explore the visual behaviors or the approaches the students use tointeract with online scientific information with data diagrams. Somerecent studies (Mason, Pluchino, Tomatora & Ariasi, 2013; Mason,Tomatora & Pluchino, 2013) have reported the relationship between text-and-graphic integration and learning performance as well as examined therole of prior knowledge; however, how students inspect online datadiagrams with expository texts is still not clear in the literature. Forexample, how do students with different levels of prior knowledge (PK)process the web-based science information with data diagrams? Do highPK and low PK students pay the same amount of attention to the sameareas of information in a webpage with two or more data diagrams? Thisstudy used eye-tracking technology to explore how students process ascientific report with two data diagrams drawn from the United NationsEnvironment Program (UNEP)’s website. Specifically, this study tried toexplore the role students’ prior knowledge plays in distributing theirvisual attention on this scientific information for typical online inquiry-based science learning.

Eye-tracking methodology has been used to examine the cognitiveprocess in reading, visual perception, and information processing formany decades (Clark & Clark, 2010; Rayner, 1998, 2009). This methodhas rarely been used in educational research until recently and especiallyin studies related to multimedia learning (Hyona, 2010; Mayer, 2010; vanGog & Scheiter, 2010). Such a direct measure is considered meaningful inthe analysis of the underlying interpretations of learners’ cognitive

HSIN NING JESSIE HO ET AL.

process and media representation effects associated with the providedtasks (Grant & Spivey, 2003; Meyer, Rasch & Schnotz, 2010). Forinstance, de Koning, Tabbers, Rikers & Paas (2010) used eye fixationduration to measure participants’ attention allocation on animations thatdisplayed the human cardiovascular system. The longer the duration timeswere, the greater the amount of attention that was allocated. Althougheye-tracking technology has been used to examine how students processtext and graphic information for science learning (such as Mason et al.,2013a, b), it has not been used to reveal how students inspect web-basedscientific reports which are usually illustrated with text and data diagrams.Student visual behaviors for observing scientific data diagrams could berevealed from eye-tracking methods. The current study was designed forparticipants to engage in web-based reading material, which waspresented with both text and diagram representations for scientificreasoning. Therefore, the eye-tracking technique is deemed to beappropriate to use for analyzing the learners’ attention allocations duringtheir viewing process.

Viewing Behavior in Comprehending Scientific Text and GraphicInformation

Paivio’s (1986) dual coding theory may be one of the earliest theoriesaddressing the benefits of text–diagram representations. The dual codingtheory describes that text and diagram representations are stored indifferent cognitive systems due to their different physical forms. As aresult, information displayed with both text and diagrams provides abetter chance for more cognitive elaborations than solely using text orpictures. To investigate how students comprehend the information inlearning a scientific system and how the system works, Hegarty and Just(1993) gave both low- and high-mechanical ability students instructionalmaterial that conveyed diagrams about a pulley system and textdescriptions about how the system works. They examined students’comprehension performance based on three assigned groups—text only,diagram only, and text–diagram integration—and found that learningfrom both text and diagrams delivered better task performance thanlearning from text or diagrams only. In terms of the students’ viewingbehavior, the results showed that they tended to inspect the diagram afterfinishing reading a clause or sentence; low mechanical ability students re-read and re-inspected the diagram as well as making more switchesbetween processing the text and diagrams more often than did the high

PK AND EYE MOVEMENT IN ONLINE INQUIRY SCIENCE READING

mechanical ability students. The results inferred that the studentsconsulted the diagram in an attempt to construct the relationship betweensystem components after reading the text. The system diagram alsohelped low mechanical ability learners to locate and visualize textinformation, such as system components and the way the system worked,which enhanced their mental model construction. Other similar studies onlearning scientific systems replicated these findings (Kalyuga, Chandler &Sweller, 1998; Mayer & Gallini, 1990). Mayer (2003) then proposed acomplete framework for describing how both words and pictures acrossdifferent media contribute to learning. It explains that learners’ cognitiveintegration process involves selecting relevant aspects of words orimages, building coherent visual and verbal mental models from eachrepresentation, and finally integrating both mental models based onlearners’ prior knowledge to generate learning. As a result, if addingdiagrams to text can help connect the correspondence of the depictedobjects or functions, it fosters meaningful learning.

Nevertheless, scientific texts are also frequently presented in exposi-tory writings that use logic connectives between sentences to describeabstract causal if–then relationships (Yore & Shymansky, 1991).Sequential flows are normally accompanied with visual organizers thathelp learners to identify the main ideas or meanings of the texts. Forexample, Holsanova, Holmberg & Holmqvist (2009) examined howreaders processed a medical article presented with both text and graphicsand found that the article presented in logical sequence with navigationguidance (i.e. guidance about the specific reading path and entry point)drew readers’ attention and interest in reading and integrating both textualand graphic information. Conversely, the article displayed in a non-logical order led to decreased reading time and less reliable reading pathsin connecting the logic flow of the article. It may be true that whenprocessing complex material such as scientific topics, expository writingflow generally helps direct learners’ attention to the relevant informationand enhances deeper mental processing. However, the study did notexamine whether the readers’ prior knowledge played a role in processingsuch structured texts and the accompanying illustrations.

To understand how graphics guide learners’ attention in learningbiology, Hannus and Hyona (1999) used authentic biology textbookpassages as testing materials for instructing elementary school studentsin different descriptions and images of biological life forms. Diagramswere provided to show the image of a fly, the names of its body parts,or its major nutrition source, whereas the text described the growth of afly without or with diagram explanations. They found that the


illustrations benefited the learning of the illustrated text content but notthe learning of the non-illustrated text content. Learners mainly spenttime on the text with occasional inspection of the diagrams. Only highability students were able to answer the comprehension questions andacquired biological principles with the inclusion of the diagrams.Moreover, high ability learners spent longer on the text reading and re-reading and paid more attention to pertinent segments of the text andillustrations than the low ability students. The high ability studentsseemed to be more able to utilize the graphics in learning the textbookinformation. These findings were consistent with Yore & Shymansky(1991) in that learners’ comprehension levels in reading scientific textsare also determined by their ability to connect the scientific prose, thetextual structures, and the adjunct organizer (i.e. the supportingillustrations). Hegarty, Carpenter & Just (1991) categorized diagramsas iconic diagrams (i.e. used to depict a concrete object or presentphysical relationships) and schematic diagrams (i.e. those diagramsapplied for illustrating abstract relationships) and claimed that when thecomplexity of iconic diagram structures increases, learners are requiredto possess more domain knowledge and cognitive abilities to compre-hend and integrate the information from diagrams and texts. As a result,learners’ prior knowledge and accompanying texts should be morecrucial for learning schematic diagrams as readers are required tounderstand the abstract visual conventions for mental elaborations andinterpretations.

However, recent research in learning scientific problem-solving taskshas claimed that learning with abstract visual representations is notnecessarily a disadvantage to learners since well-designed abstractdiagrams have been proved to help reduce the complexity of iconicdiagrams that help students concentrate on the relevant information(Butcher, 2006; Mareno, Ozogul & Reisslein, 2011; Sloutsky, Kaminski& Heckler, 2005). Butcher (2006) found that both high and low domainknowledge students performed better with the aid of simplifieddiagrams, which only illustrated crucial information about blood flowfrom the atria into the ventricles, compared to the students with text-only or with detailed diagram support (i.e. diagrams which presentedthe actual anatomy of the heart). The students’ self-explanation dataalso indicated that a simplified diagram enhanced low domainknowledge students’ cognitive process for generating different typesof inferences than a detailed diagram. Mareno, Ozogul & Reisslein(2011) explained that abstract diagrams induce less tracking demandbetween the referred objects by clearly showing the abstract relations


between the referents. Thus, the abstract diagrams help learners to focuson task-relevant information and support them in better mapping themajor functional relationships. Based on these findings, it may be truethat either abstract or iconic diagrams can be effective if their designsare closer to learners’ cognitive representations for task-specificlearning or are necessary for learners to understand the texts (Salomon,1979; Schnotz & Bannert, 2003). Therefore, it would be interesting toexamine how expository texts (i.e. texts which explain abstract scientificrelationships) and their accompanying diagrams (i.e. diagrams whichillustrate this abstract relationship) interact in terms of learners’visualization patterns.

Viewing Behavior of Experts and Novices

Studies explain that the visualization difference (as described in theprevious section) between experts and novices is largely from long-termmemory (Ericsson & Kintsch, 1995). Experts possess a large number ofdomain-specific schemas and are able to bypass working memorycapacity limitations because many of their schemas are highly automated(Kalyuga, Ayres, Chandler & Sweller, 2003). The difference between anexpert and a novice is that a novice has not acquired the same relevantschematic knowledge as an expert. The reduction in the demand ofworking memory capacity then leads to an improvement in performance.Therefore, the definitions of experts may range from good or experiencedtask performers to “well-performing students who are somewhat moreadvanced of the goal population ……who have not yet automatedperformance procedures, are much closer to the knowledge base of thegoal population of learners and could, therefore, be more effective” (vanGog, Kester, Nievelstein, Giesbers & Paas, 2009b, p. 326).

Extensive evidence has shown that experts are more capable ofattending to domain-relevant structure information and rapidly process-ing such information than novices due to the experts’ schemas beinghighly refined (Chase & Simon, 1973; Liu, Gale & Song, 2007; Weber& Brewer, 2003). For instance, Schmidt-Weigand, Kohnert & Glowalla(2010) used the eye-tracking method to examine learners’ attentionallocations and concluded that the differences in learners’ readingability were inferred as an important factor to determine theirinstructional duration times as different learners should require differentlengths of time to comprehend the texts. On the other hand, the limitedworking memory capacity and less refined schema of novices restrict


them from processing and integrating the information across differentrepresentations (Kozma, 2003; van Gog, Jarodzka, Scheiter, Gerjets &Paas, 2009a). Kozma (2003) reported that when inspecting multiplerepresentations, expert chemists were able to coordinate the informationwithin and across the representations, whereas chemistry students haddifficulty doing so. As a result, providing cues or task instructions tonovice learners is important for them to focus on task-relevant contentsacross different media. In short, learners’ domain knowledge andcomprehensibility may determine their attention allocations rather thanvice versa (Chi, Feltovich & Glaser, 1981; Clark & Salomon, 1986).

In sum, there are many studies examining the relationship betweengraphic representations and text usage in science learning (e.g.Anisworth, 2006; Hannus & Hyona, 1999; Hegarty & Just, 1993;Levie & Lentz, 1982; Mayer, 2003). However, as argued by Hyona(2010), rare studies were found for examining how these tworepresentations influence learners’ viewing behavior when using themto depict abstract relationships or principles. Even if we can find littlerelevant research (e.g. Butcher, 2006; Mason et al., 2013a, b), thelearning materials in these studies were mainly developed with onevisual graphic and some text narrations that is not identical to inquiry-based science learning material in which two or more data diagramswere needed to investigate for a better reasoning of the textualargumentations or vice versa. Using eye-tracking technology to examinehow students process the abstract data diagrams for inquiry-basedscience learning is still limited in the literature.

PURPOSE

Web-based science information usually uses text and graphic represen-tations to illustrate abstract scientific concepts or explain complicatedscientific phenomena. Nevertheless, it is not clear how text and datadiagrams used for scientific inquiry are processed during learners’mentally elaboration process. Based on the above literature review, thepurpose of this study was to use the eye-tracking technique to examinehow learners with different levels of expertise cognitively process the textand diagrams shown in web-based science reports. Two researchquestions were proposed in this study: How do students with differentlevels of prior knowledge pay attention to textual and diagrammedinformation in terms of total reading time, total fixation duration, andtotal regression numbers? And, how do students with different levels of


prior knowledge integrate information between text and diagrams in termsof patterns of scan path and inter-scanning counts?

METHODOLOGY

Participants

A total of 13 university and graduate students, with different majorsincluding engineering, mechanics, education, and social science, wererandomly selected from a selective course of a teacher educationprogram in Taiwan. About half of them had taken a fundamentalearth science course in high school or college. Most of them werenon-science majors. All participants passed the eye-tracking calibra-tions. They all consented and were included in an eye-trackingexperiment with a text–graphic reading task used in online inquiry-based science learning.

Apparatus

An eye-tracking system (FaceLAB 4.5) with a sampling rate of 60 Hzwas used to track each participant’s eye movements while they read thematerials about global climate change. While collecting the data, theparticipants were free to move their heads. The accuracy of FaceLAB’seye measurement is reported between 0.1 and 0.05°, which is sufficientfor this experiment. GazeTracker 8.0, MATLab programming, andSPSS software were further utilized to store or analyze the eyemovement data.

Material

The reading stimuli material was a one-page scientific report slightlymodified from the material provided on the UNEP’s website (http://www.grida.no/publications/vg/climate/page/3057.aspx). The UNEP re-port was chosen for two reasons. First, this study focuses on web-basedscience learning and intends to adapt web-based science material.Second, the UNEP’s website is a typical website displaying scientificdata with explanation text which is suitable for online inquiry learning.The topic of the report was the relationship between global climatechange and the greenhouse effect. The revised scientific report (seeFig. 1) consisted of two conceptual representations (text vs. graphics).The graphics included two data diagrams demonstrating the densities of


http://www.grida.no/publications/vg/climate/page/3057.aspx

http://www.grida.no/publications/vg/climate/page/3057.aspx

CO2 in the atmosphere (the upper one) and the temperature of theatmosphere (the lower one) during the last 200,000 years. The text partis a paragraph of Chinese expository text including four sections toexplain the concepts as demonstrated in the graphics, i.e., the possiblerelationship between the global climate change and the greenhouseeffect (the Chinese content is translated back to English and is availablein the “APPENDIX”). Specifically, the first section briefly introduces thegreenhouse effect. The second section describes the data shown in thetwo diagrams. Based on the above data observation, the third sectionmakes links by proposing the possible relationship between the twoevents shown in the two diagrams. And the fourth text section providesan argumentation based on the observation of the text and diagraminformation and generates an inference about the relationship betweenthe CO2 concentration in the current age and the global climate change.The two abstract diagrams and text sections are complementaryexplanations of the possible relationship between the greenhouse effectand global climate change and are a typical example of online learningresources in web-based inquiry learning environments. The text andgraphic material is laid out as two columns on a computer screen. Two

Figure 1. A sample layout of the material used in eye-tracking experiments


versions of material layout (the text on the right and the graphics on theleft and vice versa) were designed and randomly assigned asparticipants’ reading tasks to counterbalance the effect of individualpreferences in reading directions.

Data Collection

The procedure of this study included a pretest and an inquiry-basedreading task. A paper-and-pencil pretest was used to measure eachparticipant’s PK about the issue of the greenhouse effect on globalclimate change. The question was “Do you agree that the greenhouseeffect is causing the change in global climate? Why?” All participantsreceived the same pretest and wrote down their responses on paper. Afterpassing an eye-tracking calibration, each participant was asked to performthe inquiry-based task individually in which they were asked to read themodified online scientific report with text and diagrams shown on acomputer screen. The participants passed the eye-tracking calibrations ifthe angular errors were less than 1.0 for both eyes. No time limit was setfor the task. It took about 30 min to finish the whole process from thepretest to the end of the task. Each participant’s eye movements were

Figure 2. Definitions of the areas of interests


tracked and recorded by FaceLAB 4.5 and GazeTracker 8 during thewhole reading process.

Data Analysis

A total of nine areas of interest (AOIs) were defined (see Fig. 2) for theeye-tracking data analyses. Four graphic zones, LZ01, LZ02, LZ03, andLZ04, indicate the overall graphic, the graphic title, the upper (CO2)diagram, and the lower (temperature) diagram, respectively. Based onthe sections of the text semantic structure (i.e. the introduction section,the explanation section, the correlation section, and the inferencesection, respectively), five text zones, LZ05, LZ06, LZ07, LZ08, andLZ09, refer to the overall text paragraph and the above four sections(see Chinese and English translations in Fig. 1 and the “APPENDIX,”respectively).

For each AOI, eye-tracking measures including total reading timeand total fixation duration were gathered from the GazeTrackersoftware. Total reading time is defined as the length of time forparticipants to finish the entire reading task, whereas total fixationduration reveals the duration the participants spent on fixations (Lai,Tsai, Yang, Hsu, Liu, Lee, Lee, Chiou, Liang & Tsai, 2013). Thesetwo measures were used to examine the participants’ attention focus(de Koning, Tabbers, Rikers & Paas, 2010), i.e., the longer thedurations were, the greater the amount of attention allocation wasassumed in this study. The total regression number of each AOI andthe inter-scanning counts between zones (i.e. between any two pairs ofAOIs) were further calculated by the MATLab programming. The totalregression number is defined as the participants’ total revisitingnumber for each AOI, and the inter-scanning counts between zonesrepresents the participants’ total fixation numbers across or betweenAOIs (Lai et al., 2013). These two measures were applied to reflecttheir mental process in integrating the text–graphic information(Rayner, 1998; Mason et al., 2013a).

Meanwhile, a content analysis of the pretest responses was used todetermine the participants’ levels of prior knowledge for the readingtask used in this experiment. Three science educators with physicsand earth science backgrounds conducted the analysis independentlyby using the same criteria which considered the number of claimsand the number of scientific reasons provided when answering thepretest question. If there was any disagreement regarding thestudents’ prior knowledge level, all panel experts discussed the


response to reach a consensus. Finally, to explore the effects of PKon scientific text and graphic processing, t tests and effect sizes wereused to examine the significance of differences in the attentionallocations between the high and low PK groups, in terms of all eye-tracking indices mentioned above.

RESULTS

Content Analyses of Levels of Prior Knowledge

A content analysis of the pretest answers was used to determine the levelsof participants’ prior knowledge of the reading task. The criteria used inthe content analysis were based on the number of claims and the numberof scientific reasoning shown in answering the pretest question: “Do youagree that the greenhouse effect is causing the change in global climate?Why?” The responses and analyses are shown in Table 1 in which a boldsentence indicates a claim and an italicized sentence refers to a reasoningprocess. A total score was calculated by summing the number of claimsand the number of scientific reasons provided by each participant. If theparticipant got a total score of 0 or 1, then he/she was assigned to the lowPK group. Otherwise, any participant with a total score of 2 or more wasassigned to the high PK group. For example, subject #13 responded to thequestion with “Yes. The weather was good a long time ago. We did nothave so many factories and cars” in which no claims and no reasoningwere given. Thus, subject #13 got a total score of 0 and was assigned tothe low PK group. On the other hand, subject #5 answered with “I thinkso. The greenhouse effect resulted in global warming because too muchCO2 prevented the solar energy transmitting. This phenomenon thencaused the melting of icebergs around the Arctic and Antarctic zones; andfinally, the sea level rose. So, I think the greenhouse effect should be oneof the reasons causing the global climate change.” This responsedescribed possible underlying cause–effect or correlations between thegreenhouse effect and global climate change by providing one claim andtwo scientific reasoning points. Therefore, subject #5 got a total score of 3and was included in the high PK group. Finally, six students werecategorized into the high PK group and seven into the low PK group(shown in Table 1). The final results reached an agreement of 92 %among the three experts which means all of the three experts had aconsensus for the 92 % of the categorizations. Then the experts discussedfurther for contentious items to arrive a consensus.


TABLE1

Categorizations

ofpriorknow

ledgelevelsbasedon

analyses

ofpretestanswers(N

=13)

Subject

Respo

nseto

thepretestqu

estio

n:“Doyouthinktheclimatechan

geon

theEarth

iscaused

bythe

greenh

ouse

effect?Why?

Claim

Reason

Total

PK

level

#1Yes,itison

eof

thereason

s.In

recent

centuries,thegreatam

ount

ofCO2ha

schan

gedtheflo

wof

heat

tran

sfer

ontheEarth

andthischan

geresultedin

abno

rmal

climateph

enom

ena;

forexam

ple,

itbecomes

warm

inthewinterandcold

inthesummer,which

isdu

eto

aba

lanceof

energy,i.e.,

theph

enom

enaof

ElNinoor

LaNina.

02

2Higher

#2Yes.Alth

ough

therearemanyfactorsthat

causetheclim

atechange,theireffectsaremanifestedin

long

cyclicalperiod

sov

erthou

sand

sof

years.Theclim

atechan

geddramatically

duringthelast

twocenturies

whichshou

ldbemainly

causedbyhuman

beings.T

hetemperature

may

nothave

changedalot,bu

tthecyclehaschangedgreatly

.

10

1Low

er

#3Yes.In

general,thegreenhou

seeffect

increasestheglob

altemperature

andhas

caused

weather

chan

gesin

man

yplaces.

10

1Low

er

#4Yes,itsho

uldbe

oneof

thereason

s.In

manyho

tplaces,thegreenhou

seeffect

mad

etheweather

hotterthan

itwas

before.

Then,

thesealevelhaschangedandalteredtheecosystem

foranim

als

allov

ertheworld.So,

theim

pact

was

observed.

10

1Low

er

#5Ithinkso.The

greenhou

seeffect

resulted

inglob

alwarmingbecausetoomuchCO2prevented

thesolarenergy

tran

smitting.

Thisph

enom

enon

then

caused

themeltin

gof

icebergs

arou

ndthe

Arctic

andAntarctic

zones;

andfin

ally,thesealevelha

srisen.

So,

Ithinkthegreenh

ouse

effect

shou

ldbe

oneof

thereason

scausingtheglob

alclim

atechange.

12

3Higher

#6Yes.The

human

beings

mad

eahu

geam

ount

ofCO2which

caused

thegreenh

ouse

effect.The

density

oftheatmosph

erebecameun

balanced.The

clim

ateon

theEarth

changedin

orderto

balanceit.

01

1Low

er

#7Yes.Itison

eof

thereason

s.Theincrease

ofglob

altemperature

causedtheclim

ateto

chan

gesign

ifican

tly.

10

1Low

er

#8Yes,b

ecau

sethegreenh

ouse

effectincreasedtheglob

altemperature,m

eltedtheicebergs,a

ndthen

12

3Higher


raised

thesealevel.The

Earth

islik

ehaving

afeverthataffectsits

interior

andsurface.Overall,

the

climateisinflu

encedby

multip

lefactors,such

astheflo

wof

oceans,the

atmosph

ere,an

dthewater

cycles.So,

thegreenhou

seeffect

shou

ldbeon

efactor,butnot

theon

lyon

e.#9

Yes,becausegreenh

ouse

effect

resultedfrom

theincreasing

amou

ntof

CO2,which

locked

agreat

deal

ofheat

energy

into

theatmosph

ere;

then

theicebergs

melted,

thesealevelrose,an

dfin

allythe

climatechan

ged.

Since

theoceanoccupies

themostsurfaceon

theEarth,the

climatechan

geson

cetheoceanchan

ges.

02

2Higher

#10

Yes,becausetheincrease

ofCO2wou

ldab

sorb

moreheat.Thisincrease

ofheat

energy

naturally

raised

theglob

altemperature.

02

2Higher

#11

Theoretically,Ido

notkn

owmuch.

Nevertheless,from

thenews,media,andon

linenewsrepo

rts,

CO2hashadagreatim

pacton

theglob

alclim

ateandhu

man

bodies.B

ecause

CO2isem

itted

from

carsandmotorcycles,the

smok

eandwasteem

itted

from

factoriescanim

pacthu

mansandtheEarth

environm

ent;therefore,afteralong

period

oftim

e,theem

ission

sof

CO2have

impacted

theclim

ate

oftheEarth.O

ntheotherhand

,intheancientera,therewereno

tmanytechno

logies,sotherewere

notmanyproblems.Technologyadvances

rapidlybecausetim

eismoney.Mostpeople

use

motorcycles

astransportatio

nvehicles

andthequ

ality

ofgasvaries.Ifthego

vernmentdo

esno

tcontrolseriou

sly,

certainlyitwill

prod

ucepo

llutio

n.So,

ifwewantto

save

theEarth

from

air

pollu

tion,

weshou

lddo

ourbest(especially

itshou

ldbe

thego

vernment’srespon

sibility).The

greenhou

seeffect

shou

ldnot

betheon

lyreason

forclim

atechan

ge;itshou

ldbethehuman

being’srespon

sibility

toprotect

theenvironmentso

asto

preventchan

gingtheclim

ate.

Idon

’tthinkthegreenhou

seeffect

isthemajor

reason

.

10

1Low

er

#12

Yes,becausethegreenh

ouse

effect

caused

thetemperature

toincrease,icebergs

intheno

rthan

dsouthpo

lesbega

nto

melt,an

dfin

ally

thesealevelrose.Sincetheoceancanregu

latetheclimate,

once

theoceanchan

ged,

theclimatechan

ged.

02

2Higher

#13

Yes,theweather

was

good

along

timeago.

Wedidno

thave

somanyfactoriesandcars.

00

0Low

er


Overall Descriptive Eye Movement Analyses

The descriptive analysis results showed that all participants (N = 13) spentlonger on text reading (M = 24.58 s) than on viewing the graphics(M = 9.48 s). When examining the graphics, they spent longer studyingthe CO2 diagram than the temperature diagram (M = 5.30 s, 2.22 s,respectively). In terms of their text reading, the participants spent thelongest time on the first text section (M = 9.46 s) and the least time on thefourth (i.e. the last) section (M = 2.52 s). Regarding the analyzed eyefixation data, the participants also had longer fixation durations(M = 15.81 s) on the text than on the graphics (M = 5.72 s). Their totalfixation durations on the CO2 diagram (M = 3.53 s) were also more thanthe durations on the temperature diagram (M = 1.00 s).

To understand how the students compared the graphics and text, thenumbers of inter-scanning counts between pairs of zones were analyzed. Theresults then indicated that all participants switchedmost between the first textsection and the overall graphic (M = 1.85) and switched least between thethird text section and the overall graphic (M = 0.15). In addition, allparticipants’ total regression numbers were analyzed to reflect how theyrevisited the texts and the two diagrams. The results revealed that allparticipants re-read the text sections more often than re-examining thediagrams (M = 23.85, 12.31, respectively) and re-read the text most in thefirst text section (M = 12.00) and least in the fourth text section (M = 7.69).Finally, all participants repeatedly inspected the CO2 diagram more oftenthan the temperature diagram (M = 8.62, 6.38, respectively).

Visual Distribution Within the Graphics and the Texts in the Different PKGroups

Independent sample t tests were employed to examine whether there was anysignificant difference in the participants’ viewing behavior within thegraphic zones and within the text zones between the lower PK and higherPK groups. If a significant result was found, an effect size of Cohen’s d wasthen further calculated. It should be noted that one participant’s data has beencarefully examined and removed from all of the following data analyses.And the reason to remove it has been explained in detail in the next section ofdata analyses (i.e. “Inter-scanning Across the Graphics and the Texts in theDifferent PKGroups” section). The results in Table 2 (N = 12) reveal that thehigh PK group had more total reading time and total fixation duration on thewhole graphic area (i.e. the graphic AOI or LZ01) than the low PK group(t = 2.84, p = 0.04, d = 1.78; t = 3.25, p = 0.03, d = 1.99, respectively).


TABLE

2

Eye-trackingmeasurescomparedbetweenthehigh

erandthelower

PK

grou

ps

Zon

e(AOI)

Eye-trackingmeasure

PK

tp

Coh

en’s

db

Higher(n

=5)

Low

er(n

=7)

Meana

SDMeana

SD

Graph

ic(LZ01

)Total

readingtim

e15

.65

10.17

2.43

2.71

2.84

0.04

*1.78

Total

fixatio

ndu

ratio

n9.07

5.19

1.34

1.82

3.25

0.03

*1.99

Total

regression

number

17.80

15.14

3.14

2.54

2.14

0.10

–Title(LZ02

)Total

readingtim

e0.81

0.63

0.31

0.45

1.59

0.14

–Total

fixatio

ndu

ratio

n0.73

0.69

0.17

0.30

1.92

0.08

–Total

regression

number

3.40

3.97

0.71

1.11

1.47

0.21

–CO2(LZ03

)Total

readingtim

e10

.00

6.79

1.01

1.45

4.26

0.04

*1.83

Total

fixatio

ndu

ratio

n6.55

4.31

0.57

1.08

3.04

0.03

*1.90

Total

regression

number

13.40

9.45

2.42

1.62

2.57

0.06

1.62

Tem

p(LZ04

)Total

readingtim

e3.31

2.19

0.74

0.62

2.55

0.06

1.60

Total

fixatio

ndu

ratio

n1.19

0.89

0.46

0.34

1.77

0.14

–Total

regression

number

10.40

7.57

1.57

1.40

2.26

0.06

1.62

Text(LZ05

)Total

readingtim

e30

.67

11.54

15.53

13.47

2.04

0.07

–Total

fixatio

ndu

ratio

n18

.05

8.07

9.69

11.43

1.40

0.19

–Total

regression

number

24.4

11.67

15.71

11.15

1.31

0.22

–Sec1(LZ06

)Total

readingtim

e11

.91

5.74

6.83

9.24

1.08

0.31

–Total

fixatio

ndu

ratio

n7.08

3.44

5.05

8.39

0.50

0.63

–Total

regression

number

11.00

5.10

8.86

5.98

0.65

0.53

–Sec2(LZ07

)Total

readingtim

e9.02

5.36

3.25

2.45

2.54

0.03

*1.38

Total

fixatio

ndu

ratio

n6.01

3.62

1.90

2.02

2.53

0.03

*1.40


Total

regression

number

11.40

4.51

6.00

5.32

1.84

0.10

–Sec3(LZ08

)Total

readingtim

e3.85

2.16

1.46

0.95

2.32

0.07

–Total

fixatio

ndu

ratio

n2.20

1.86

0.76

0.64

1.67

0.16

–Total

regression

number

10.40

4.93

3.86

2.34

2.75

0.04

*1.69

Sec4(LZ09

)Total

readingtim

e2.76

1.93

2.33

1.15

0.48

0.64

–Total

fixatio

ndu

ratio

n1.28

1.29

1.20

0.81

0.14

0.89

–Total

regression

number

10.60

10.92

5.29

3.77

1.21

0.25

–

CO2CO2concentrationdiagram,Tem

ptemperature

diagram,Sec1

text

section1,Sec2

text

section2,

Sec3

text

section3,

Sec4

text

section4

*p≤0.05

a The

unitformeasuring

timeisseconds

b|d|9

0.8show

salargeeffect

size


Looking into the individual graphic zones, we found that this significantdifference came from the inspection on the CO2 diagram (i.e. LZ03)(t = 4.26, p = 0.04, d = 1.83, for the total reading time; t = 3.04, p = 0.03,d = 1.90, for the total fixation duration). In addition, several comparisons arearound the border line for statistical significance: The high PK group read theTemp diagram (i.e. LZ04) longer than the low PK group (t = 2.55, p = 0.06,d = 1.60). They also hadmore regressions on both the CO2 diagram (t = 2.57,p = 0.06, d = 1.62) and the Temp diagram (t = 2.26, p = 0.06, d = 1.62). Theabove data show that, regarding the graphic reading, the students with higherPK tended to pay more attention to and put more mental effort intoinspecting the whole graphic area, especially focusing on the CO2 diagram,and both diagrams may be important for them to re-investigate.

With respect to text reading behavior, no significant difference is foundbetween the groups in the whole text area (i.e. the whole text AOI or LZ05).However, there are significant differences found between the groups insection 2 (i.e. the Sec2 AOI or LZ07) in terms of total reading time (t = 2.54,p = 0.03, d = 1.38) and total fixation duration (t = 2.53, p = 0.03, d = 1.40).This shows that higher PK students paid more attention to and put moremental effort into reading the text in section 2 (i.e. the data explanationsection). Another significant finding is in section 3 (i.e. the data correlationsection) in terms of the total regression number (t = 2.75, p = 0.04, d = 1.69)indicating that the higher PK students revisited the correlation text sectionmore often than the lower PK students. In sum, regarding the text reading,the above data show that students with higher PK tended to pay moreattention to and engaged deeper in reading the descriptions of the data shownin the two diagrams; additionally, they tended to focus on reading thestatement correlating the two diagrams.

Figure 3. Heat maps for the higher PK group (left, N = 5) and the lower PK group (right,N = 7). The redder the color, the longer the fixation duration on that position. The higherPK students focused on inspecting the diagrams and reading the text explaining the data.The lower PK students seldom observed the diagrams and focused on reading the textwith definitions


To show the overall visual distribution for each group, two heat mapswere output based on the students’ fixation duration by using MATLabprogramming and are shown in Fig. 3. The redder the color, the longer thefixation duration on that position. The left map shows that the higher PKgroup read both the diagrams and the texts, especially focusing on readingthe label of x-axis in the CO2 diagram. The higher PK students alsotended to observe the unit of y-axis in diagrams. Regarding the textreading, the higher PK group focused on reading some terminologies orkeywords in the first section (e.g. “radiation”) and the second section (e.g.“CO2” and “significant change”). On the right map, it is obvious thatstudents in the lower PK group only focused on reading the text area,seldom inspecting the data shown in the diagrams. And they focusedmostly on processing some keywords such as “green house gas,”“radiation,” “absorb,” “surface,” “stable,” “inference,” “human-made,”“excel,” “return,” “before” …etc. in the first section texts. Basically, theoutputs of heat maps support the results of the statistical analyses shownabove. That is, the students with higher PK paid attentions to both the textand the diagrams but put more mental efforts on or had more mental loadin inspecting the data, while the students with lower PK paid littleattention to the diagrams but put more mental efforts in or perhapssuffered from decoding the keywords in the text.

Inter-scanning Across the Graphics and the Texts in the Different PKGroups

In order to find how the participants of each group integrated theverbal and visual representations, the independent sample t tests wereemployed for the analysis of the number of inter-scanning countbetween zones for comparing the two PK groups. It should be notedthat, when we carefully examined the descriptive data in the higherPK group, one higher PK student (subject #10) had an extremelyhigh number of inter-scanning count (36 out of 52 for all) betweenthe text and the graphic AOI, which resulted in a very large standarddeviation (M = 8.67, SD = 13.47) in the higher PK group. Thismeans that the pattern of this participant’s visual behavior is quiteunique and may be different from the pattern of all others in thisgroup. Looking into all the participant’s data and backgroundinformation, we found that this participant was the only one whohad an undergraduate Physics major and his data were almost themaximum values of all eye-tracking measures. It is very possible that,when compared with all other participants in this study, he could


have nearly reached an expert level in terms of reading scientificreports and examining scientific data. Therefore, to avoid the data ofthis particular subject dominated the results analyzed for this group,the participant was removed from the higher PK group for all dataanalyses. Table 3 shows the results of t tests on the inter-scanningcounts compared between the higher and the lower PK groups. If asignificant result was found, then its effect size (Cohen’s d) wasfurther examined.

As shown in Table 3, the higher PK group significantly switched moreoften between processing the graphics and text than the lower PK group(t = 2.64, p = 0.05, d = 1.63), suggesting that the higher PK students

TABLE 3

The inter-scanning counts compared between the higher and the lower PK groups

Pairs of AOIs

PK

t p Cohen’s da

Higher (n = 5) Lower (n = 7)

Mean SD Mean SD

Text–graphic 3.20 1.64 1.14 0.69 2.64 0.05* 1.63Sec1–graphic 1.00 1.41 0.57 0.54 0.65 0.55 –Sec1–title 0.00 0.00 0.00 0.00 N/A N/A –Sec1–CO2 0.80 1.10 0.14 0.38 1.29 0.26 –Sec1–Temp 0.20 0.45 0.43 0.54 −0.78 0.45 –Sec2–graphic 1.20 0.84 0.14 0.38 2.64 0.04* 1.63Sec2–title 0.20 0.45 0.14 0.38 0.24 0.82 –Sec2–CO2 0.60 0.89 0.00 0.00 1.50 0.21 –Sec2–Temp 0.40 0.55 0.00 0.00 1.63 0.18 –Sec3–graphic 0.20 0.45 0.14 0.38 0.24 0.82 –Sec3–title 0.00 0.00 0.00 0.00 N/A N/A –Sec3–CO2 0.20 0.45 0.00 0.00 1.00 0.37 –Sec3–Temp 0.00 0.00 0.14 0.38 −0.83 0.42 –Sec4–graphic 0.80 0.45 0.14 0.38 2.76 0.02* 1.58Sec4–title 0.00 0.00 0.00 0.00 N/A N/A –Sec4–CO2 0.80 0.45 0.14 0.38 2.76 0.02* 1.58Sec4–Temp 0.00 0.00 0.00 0.00 N/A N/A –Temp–CO2 3.20 2.04 0.57 0.79 3.13 0.01* 1.70

Text the whole text section (i.e., LZ05), graphic the whole graphic zone (i.e., LZ01), CO2 CO2

concentration diagram (i.e., LZ03), Temp temperature diagram (i.e., LZ04), Sec1 text section1 (i.e.,LZ06), Sec2 text section 2 (i.e., LZ07), Sec3 text section 3 (i.e., LZ08), Sec4 text section 4 (i.e., LZ09)*p ≤ 0.05a|d| 9 0.8 shows a large effect size


integrated the text and the graphic information more than the lower PKstudents. To acquire further insights, the number of inter-scanningbetween the text sections and diagrams was analyzed. It was found thatthe higher PK group inspected the two diagrams more often when readingthe second and the fourth text sections than the lower PK group (t = 2.64,p = 0.04, d = 1.63; t = 2.76, p = 0.02; d = 1.58). All of the above findingsshowed large effect sizes between the two groups. Furthermore, it isworth noting that the participants with higher PK significantly switchedmore often between the CO2 diagram and the Temp diagram than thestudents with lower PK (t = 3.13, p = 0.01; d = 1.70) with a large effectsize. That is, the inter-scanning data analysis shows that the higher PKstudents not only showed more integration between the text and thegraphic information but also showed more inspections between the twodiagrammed data.

The scan path output (e.g. the pictures shown in Fig. 4) fromGazeTracker 8.0 for each participant was used to further confirm thepatterns of inter-scanning counts found between the groups. Two differentpatterns were in some degree observed between the numbers of the scanpaths of the two groups. The higher PK students had five to ten scan pathsacross the text zone and the graphic zone, while the lower PK studentsonly had zero to three. The numbers of scan paths across the two datadiagrams ranged from two to seven (only one was two) in the higher PKgroup and ranged from zero to two (four of seven were 0) in the lower PKgroup. Figure 4 shows the two typical types of reading strategies in thetwo groups. That is, a text–graphic–integration and data inspectionreading strategy shown by a higher PK participant tended to read backand forth between the text and the graphic zones. And a text-based

Figure 4. Different patterns of scan paths: Participant #5 (left, high PK) showed datainspection and text-graphic-integration reading strategies; while participant #2 (right, lowPK) showed text-only reading strategies with no data inspection


reading strategy shown by a lower PK group tended to read scientificinformation based on text only. This result is consistent with theaforementioned finding that the higher PK students generally did moreinter-scanning between text and diagrams than the lower PK participants.

DISCUSSION

Overall Text and Graphics Viewing Behavior

The descriptive statistical data indicated that, overall, all participantsmainly spent their time reading the texts and spent relatively less time onviewing the diagrams. The findings are consistent with previous researchthat participants easily ignore the information that is separately presentedin text–diagram format (Sweller, van Merrienboer & Paas, 1998). Thistext-directed strategy is also generally used by students to construct amental model when diagram and text representations are independentlypresented (Hegarty & Just, 1993; Holsanova et al., 2009; Schmidt-Weigand et al., 2010). Their reading paths also support the argument thatsequential processing is usually utilized in text reading (Paivio, 1986) andgraphic inspection generally occurs after finishing the entire text reading(Hegarty & Just, 1993). The participants spent relatively longer readingand re-reading the first text section than the other sections. This findingmay suggest that either readers generally pay more attention to newlydisclosed topics than to those sentences that continue the same topic(Hayona, Lorch & Kaakinen, 2002) or need longer for reading longer textparagraphs (i.e. the first text section is longer than the other text sections).Since the first text section introduces the topic of the greenhouse effectand its possible cause (i.e. the increase in CO2 concentration), the readersmay expect that comprehending the first text section is imperative forbetter processing of the other text sections.

The data also revealed that, in general, all participants spent longer andhad longer fixation durations when inspecting the CO2 diagram than thetemperature diagram. This finding is interesting in showing thatparticipants might tend to spend much more time carefully examiningthe pattern of change in the CO2 diagram than in the temperature diagram.Some students might have related the pattern of change in the CO2

diagram to match the trends depicted in the temperature diagram;therefore, they do not need to examine the temperature diagram again.Instead, they may just focus on some features for comparison or matchingonly. When the matching process was mentally confirmed, the partici-


pants established the correlation between these two diagrams. Thistherefore resulted in less total reading time and fixation durations on thetemperature diagram. Another reason for less attention paid to thetemperature diagram could be due to no related schema existing in theirmemory for constructing the relations between the two diagrams. That is,the diagrams may be less effective in supporting the expository textcomprehension, and conversely, they may demand more cognitiveresources for learners to convert the information for further mentalelaboration. This could occur for students with insufficient priorknowledge. Future research should study whether abstract diagramssupport or impede expository text learning and how they should bedesigned to complement learners’ cognitive process in learning expositorytext content.

Visual Behavior of the Different PK Groups

The present study found that higher PK learners generally spent longerreading the graphics especially focusing on reading the data diagramsthan the lower PK learners. They also focused on reading the secondand the third text sections which explained and correlated the two datadiagrams. The lengths of total reading time and fixation duration mayreflect either deeper cognitive processing (Holsanova et al., 2009;Hyona et al., 2002; van Gog, Paas & van Merrienboer, 2005) or adifficulty understanding the information (Schmidt-Weigand et al.,2010). For the current study, we may claim that higher PK learnerswere engaged in higher cognitive activities for text elaboration andgraphic inspection. Since higher PK learners possess more refinedschema than lower PK learners, the higher PK learners’ higher workingmemory capacity allows them to devote their mental resources toadvanced thinking processes. It is also important to note that, comparedto lower PK students, higher PK students read the text for asignificantly longer time and focused on the second and third textsections. Because the second text section explains what information thetwo diagrams aim to deliver and the third section discusses how the twodiagrams may relate to each other, these two sections are regarded asthe most relevant and crucial parts for establishing the relationshipbetween the greenhouse effect and climate change. Therefore, thefinding is consistent with the previous research that individuals withhigher expertise are more capable of locating and processing the domainrelevant information than learners with lower expertise (Chase &Simon, 1973; Liu et al., 2007).


Regarding the inter-scanning behavior across the two representations,our data also show that, compared to lower PK students, higher PKstudents switched significantly more often between the diagrams and thetexts, especially for the second (i.e. data explanation) section and thefourth (i.e. the inference-making) section in the texts. According to priorstudies (Schwonke, Berthold & Renkl, 2009; Mason et al., 2013a, b), thenumber of inter-scanning transitions between verbal to graphic represen-tations has been considered as an indicator of integrative effort. We maysuggest that the higher PK learners invested more mental effort inincorporating the texts and diagrams than the lower PK learners toestablish a relevance and coherence mental models between bothrepresentations. Therefore, the findings of this study support that higherPK students are more capable of coordinating information within andacross representations and utilizing the information from diagrams(Cromly, Snyder-Hogan & Luciw-Dubas, 2010; Hannus & Hyona,1999; Kozma, 2003). It is also important to find that the higher PKstudents inspected back and forth between the two data diagramssignificantly more than the lower PK students. This may be an evidencethat shows the higher PK students possessed better scientific dataobservation and examination skills required for online inquiry-basedscience learning.

In sum, the current study suggests that using diagrams to complementexpository texts for inquiry-based science learning is a complex cognitivelearning process. Unlike learning how a mechanical system operates bysimply locating information from the text in a diagram, learners arerequired to not only read the text argumentation but also critically inspectthe scientific data that may support such an argument. That is, greaterallocations of mental resources are expected. As a result, prior knowledgeis deemed as a crucial factor in determining the effectiveness of the use ofthe text and graphic representations. In recent years, inquiry-basedlearning has been advocated for helping students to acquire scientificknowledge and has been rapidly applied in web-based learningenvironments. Such instructional practice proclaims that learners shouldbe allowed to acquire knowledge by using their own experiences andstrategies to organize information and solve problems (e.g. Clark &Mayer, 2008). Therefore, many web-based learning materials aredesigned for individuals that allow learners to study the materials ontheir own. Nevertheless, based on the current finding, educators shouldnot expect students with low prior knowledge to be able to learn abstractscientific principles only through expository texts and diagrams. Withoutadequate instructional support, such an approach may lead to cognitive


overload that may actually impede learning (Kirschner, Sweller & Clark,2006). Conversely, students with high expertise may benefit from theaccompanying abstract diagrams with expository texts such that theycan engage in deeper cognitive elaborations and scientific reasoningprocesses.

CONCLUSION

Information literacy is an essential learning skill for all citizens in thetwenty-first century, especially for online inquiry-based science learning.This study employed eye-tracking technology to examine how learnerswith different levels of expertise engaged in reading a web-basedscientific report that was displayed as text and data diagram formats.Although all students were found to pay more attention to the text thanthe diagrams, students with different levels of expertise showed differentpatterns of visual behavior in processing the textual and graphicalinformation. That is, the lower PK learners tended to rely on readinginformation in text sections, while the higher PK learners were able to notonly integrate the text and graphic information but also to compare thedata shown in two diagrams. The findings of this study support thatreading scientific text information with abstract graphical aids (such as atext with two data diagrams, commonly used in web-based sciencereports) actually involves learners’ relevant knowledge or experience.Learners with higher PK are more able to investigate and integrate thetextual and graphic information, especially inspecting and correlating thedata in two diagrams, while lower PK learners may still suffer fromdecoding the text and graphic representations. This study providesevidence to support that, without adequate instruction, learners withinsufficient prior knowledge may have difficulties in reading in ascientific report for online inquiry learning. Science educators andcurriculum developers should consider seriously how to use text-and-diagram materials for science learners with individual differences.Learners with low expertise in science may benefit from text-basedinformation before reading text-and-diagram information, while learnerswith high expertise in science could learn more deeply and improve theirhigh order cognitive skills by integrating text and diagram information.

Based on eye-tracking analyses, the current study provides a newapproach to analyze the visual behaviors within and between abstract datadiagrams and expository texts for readers with different levels of priorknowledge. Several research directions can be further emphasized in


future studies. First, the higher PK participants identified in this study canonly be viewed as having “relatively” higher domain knowledgecompared to the lower PK participants. It would be interesting if domainexperts’ eye tracking data could also be included and be compared(Gegenfurtner, Lehtinen & Saljo, 2011). Second, it is suggested thatfuture research include other measurements (e.g. participants’ verbalreports, posttest performance scores) for further understanding therelationships between visual behaviors and learning performance. Third,the effectiveness of different science instructional methods or materialsthat are used to discuss expository texts or abstract principles can becompared for better supporting different learners. Finally, in the future,students’ visualization patterns shown in their online reading behaviormay serve as an assessment reference for diagnosing students’ levels ofexpertise. Such preliminary diagnostic information should allow web-based instructors to include individual difference factors in theirscaffolding design for different learners.

ACKNOWLEDGMENTS

Funding for this research work is supported by the National ScienceCouncil, Taiwan, under grant numbers NSC 99-2511-S-011-006-MY3,100-2511-S-011-005 and 101-2511-S-011-001-MY2. The authors wouldlike to thank Prof. Hung-Ta Pai for MATLab Programming.

APPENDIX: TRANSLATIONS OF THE CHINESE TEXTS SHOWN IN FIGS. 1 AND 2

Graphic Area

Graphic title (i.e. LZ02): the atmospheric carbon dioxide concentration(the upper diagram) and the temperature (the lower diagram) of the Earthover the past 200,000 years

Upper graphic (i.e. LZ03): carbon dioxide concentration (y-axis); yearspassed from now (x-axis)

Lower graphic (i.e. LZ04): atmospheric temperature (y-axis); yearspassed from now (x-axis)

Text Area

Text section 1 (i.e. LZ06): The greenhouse effect is the phenomenon thatthe natural system suffered from the heat energy released from the Earth’s


surface. Greenhouse gases absorb the infrared radiation on the Earthsurface that arises the temperature. The climate follows a non-linear pathwith sudden and dramatic surprises with levels of greenhouse gases reachan as-yet unknown trigger point.

Text section 2 (i.e. LZ07): The diagrams show that, over the past200,000 years, the Earth’s climate has been unstable with very significanttemperature changes. The concentration of carbon dioxide in theatmosphere has also been changed significantly in the past 200,000 years.

Text section 3 (i.e. LZ08): The information presented on this graphindicates a strong correlation between carbon dioxide content in theatmosphere and the temperature.

Text section 4 (i.e. LZ09): A possible scenario is that the anthropogenicemissions of greenhouse gases could bring the climate to a state where itreverts to the highly unstable climate of the pre-ice age period.

Bottom (hyperlink): Next page.

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Hsin Ning Jessie Ho, Meng-Jung Tsai andChin-Chung Tsai

Graduate Institute of Digital Learning and EducationNational Taiwan University of Science and Technology#43, Sec. 4, Keelung Rd., Taipei, 106, TaiwanE-mail: [email protected]

Ching-Yeh Wang

Graduate Institute of Applied Science and TechnologyNational Taiwan University of Science and Technology#43, Sec. 4, Keelung Rd., Taipei, 106, TaiwanE-mail: [email protected]


prior knowledge and online inquiry-based science reading: evidence from eye tracking

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