Epistemic Beliefs, Online Search Strategies, and BehavioralPatterns While Exploring Socioscientific Issues

Chung-Yuan Hsu • Meng-Jung Tsai •

Huei-Tse Hou • Chin-Chung Tsai

� Springer Science+Business Media New York 2013

Abstract Online information searching tasks are usually

implemented in a technology-enhanced science curriculum

or merged in an inquiry-based science curriculum. The

purpose of this study was to examine the role students’

different levels of scientific epistemic beliefs (SEBs) play

in their online information searching strategies and

behaviors. Based on the measurement of an SEB survey, 42

undergraduate and graduate students in Taiwan were

recruited from a pool of 240 students and were divided into

sophisticated and naı̈ve SEB groups. The students’ self-

perceived online searching strategies were evaluated by the

Online Information Searching Strategies Inventory, and

their search behaviors were recorded by screen-capture

videos. A sequential analysis was further used to analyze

the students’ searching behavioral patterns. The results

showed that those students with more sophisticated SEBs

tended to employ more advanced online searching

strategies and to demonstrate a more metacognitive

searching pattern.

Keywords Epistemic belief � Online search �Searching strategy � Searching behaviors �Socioscientific issues � Sequential analysis

Introduction

With World Wide Web (commonly known as the Web, a

system that is interlinked and accessed via the Internet,

contains text, images, videos, and multimedia) becoming

accessible to everyone and with its abundant and diverse

resources, it offers an adequate context for science stu-

dents to conduct inquiry so as to promote their knowledge

construction and meaningful learning (Butler and Lumpe

2008; Tsai et al. 2012). The act of search can be both a

learning experience about the content and about the pro-

cess for future endeavors. The design of searching tasks

can be either unstructured or highly scaffolded. The for-

mer relies heavily on the user characteristics in the pro-

cess, which may be aligned with many classroom

implementations of search (e.g., the searching activity of

the present study). The latter can be intentional activities

that not only promote activation of prior knowledge and

development of strategies (Greene et al. 2010), but also

foster metacognitive awareness (Azevedo et al. 2004).

Previous studies have indicated that students’ online

searching strategies (Lin and Tsai 2008) and criteria to

evaluate online information (Mason et al. 2010) are gui-

ded by their epistemic beliefs. Epistemology, originating

from Piaget’s theories of cognitive development and

Perry’s studies of students’ intellectual development,

exists in a form of beliefs about how a person views the

C.-Y. Hsu (&)

Department of Child Care, National Pingtung University of

Science and Technology, # 1, Shuefu Road, Neipu,

Pingtung 912, Taiwan

e-mail: jackohio@gmail.com

M.-J. Tsai � C.-C. Tsai

Graduate Institute of Digital Learning and Education, National

Taiwan University of Science and Technology, #43, Sec. 4,

Keelung Rd., Taipei 106, Taiwan

e-mail: mjtsai99@mail.ntust.edu.tw

C.-C. Tsai

e-mail: cctsai@mail.ntust.edu.tw

H.-T. Hou

Graduate Institute of Applied Science and Technology, National

Taiwan University of Science and Technology, #43, Sec. 4,

Keelung Rd., Taipei 106, Taiwan

e-mail: hthou@mail.ntust.edu.tw

123

J Sci Educ Technol

DOI 10.1007/s10956-013-9477-1

nature of knowledge and the process of knowing (Hofer

and Pintrich 1997). Hofer and Pintrich (1997) further

suggested four dimensions to better represent the core

structure of individuals’ epistemic beliefs, including

Certainty of knowledge, Simplicity of knowledge, Source

of knowledge, and Justification for knowing. The first two

refer to the nature of knowledge, while the last two

represent the process of knowing.

A close relationship between epistemic beliefs and

metacognition is widely recognized by researchers (Hofer

2004; Mason et al. 2010; Tsai 2004b). Kitchener (1983)

proposed a three-level model of cognitive processing to

explain how people solve ill-structured problems (e.g., the

dispute over the safety concern of electromagnetic waves).

Consisting of cognition, metacognition, and epistemic

cognition, the first level of the model refers to cognitive

processes such as memorizing, reading, and acquiring basic

information. A question one may ask at this level is what is

an electromagnetic wave? The second level includes

metacognitive processing such as monitoring strategies or

progress in cognitive tasks of the first level (i.e., cognition).

One at this level may prompt how do I search for infor-

mation related to electromagnetic waves effectively? The

third level, epistemic cognition, involves reflection on the

certainty of knowledge and the criteria of knowing.

Questions one may ask include is the information credible?

Or is there any alternative solution? Kitchener further

indicated that each level offers a foundation for the next,

and the last level, epistemic cognition, may influence

monitoring processing or strategies adopted in the tasks of

the first two levels. According to Hofer (2004), it is

essential for more research to investigate the epistemic

processes while students are conducting online scientific

inquiry and activating metacognitive awareness during

their knowledge construction.

In addition, researchers of epistemology concur that

individuals’ epistemic beliefs are related to their experi-

ence in disciplinary contexts (Buehl et al. 2002), that is,

epistemic beliefs regarding the science domain, for

instance, could vary from those regarding history. Thus, in

order to investigate students’ strategies and behaviors when

searching for online science information, it is necessary for

researchers to explore learners’ scientific epistemic beliefs

(SEBs). Some research efforts have indicated that students’

epistemic orientations toward science may guide the

acquisition of scientific information on the Web (Lin and

Tsai 2008; Mason et al. 2010), that is, students who view

scientific knowledge as more dynamic in nature tend to

employ a more comprehensive evaluation of the online

information (Lin and Tsai 2008).

In recent years, there have been a growing number of

studies highlighting the utilization of socioscientific issues

to promote students’ science learning. These issues refer to

social dilemmas that include moral or ethical problems as

well as having conceptual or technological connections

with science (Sadler 2004). Due to being open-ended and

ill-structured in nature, socioscientific issues require one to

take multiple perspectives or solutions into consideration

when encountering these issues (Sadler and Zeidler 2005).

This process not only enables learners to actively practice

evaluating, analyzing, and reflecting on information, but

also engages them in decision making and justifying claims

(Sadler 2004). In addition, educational researchers (Mason

and Boscolo 2004; Schommer-Aikins and Hutter 2002;

Yang and Tsai 2010) believe that the effects of individuals’

epistemic beliefs become obvious when they are dealing

with open-ended and ill-structured problems requiring the

application of high-order reasoning and reflective thinking.

Similarly, many studies also suggested that the students’

reasoning in socioscientific issues is guided by their epi-

stemic beliefs (Liu et al. 2011; Mason and Boscolo 2004;

Schommer-Aikins and Hutter 2002). Since the Internet

offers abundant and diverse resources as well as supporting

claims of various perspectives, online searching activities

are an ideal and potential context for students to explore

socioscientific issues (Wu and Tsai 2011).

To profile students’ cognitive and metacognitive strat-

egies during online information searching, Tsai and Tsai

(2003) proposed a framework classifying the searching

strategies into three domains, they are follows: behavioral,

procedural, and metacognitive domains. The behavioral

domain described skills required for basic Internet manip-

ulation and navigation, including control and disorienta-

tion aspects. The procedural domain indicated content-

general searching approaches on the Internet, including

trial and error as well as problem-solving aspects. The

metacognitive domain concerned with skills involved in

higher-order and content-related reflective on the Internet,

including purposeful thinking, select main ideas, and

evaluation. Based on this framework, Tsai (2009) devel-

oped an instrument, namely the Online Information

Searching Strategies Inventory (OISSI), to investigate

students’ online information searching strategies. Although

previous studies have noticed intimate relationship

between students’ online information searching strategies

and their SEBs (Lin and Tsai 2008; Mason et al. 2010),

rarely does the research investigates differences in online

searching strategies and behaviors in terms of different

levels of SEBs. Thus, underlying Tsai and Tsai’s (2003)

framework and using the OISSI instrument (2009), the

present study attempted to explore the role that the stu-

dents’ SEBs played in online searching activities while

exploring a socioscientific issue. The findings were

expected to provide information for practitioners and

J Sci Educ Technol

123

researchers to improve students’ abilities and understand-

ing of scientific inquiry. The purpose of this investigation

was to investigate the following questions:

1. Is there any significant difference in students’ online

information searching strategies between sophisticated

and naı̈ve SEB students?

2. Are there different patterns of online information

searching behaviors for different SEB students while

exploring socioscientific?

Methodology

Participants

This study initially recruited 240 undergraduate and grad-

uate students from four universities in northern Taiwan.

These students were studying in colleges of science

(30 %), electrical engineering and computer science

(16.1 %), liberal arts and social sciences (46.9 %), and

management (7 %). In all, 46 % of the students were sci-

ence-oriented majors, while the remaining 54 % were

social science-oriented majors. This percentage corre-

sponds to that of the whole population (43 % in science-

oriented majors; 57 % in social science-oriented majors) in

Taiwan (Ministry of Education 2012). All the participants

were requested to fill out the SEBs survey developed by

Conley et al. (2004) (described later). The students with

total scores within the top 25 % were defined as sophisti-

cated SEB searchers, while those with full scores within the

bottom 25 % were considered as naı̈ve SEB searchers.

Among the students in both groups, 42 volunteered to

participate in this study and were given an appropriate

participation fee as compensation for their time. Finally, 22

(13 females and 9 males) high (sophisticated) and 20 (11

females and 9 males) low (naı̈ve) SEB students were

selected in an online searching activity to explore a so-

cioscientific issue.

The SEB survey includes 26 items on a 5-point scale,

and its full score is 130. The range of SEB scores in this

study was from 75 to 124. In our study sample, the par-

ticipants expressed relatively high agreement with the SEB

questionnaire statements. The mean scores are 117.2

(SD = 3.9) for the high SEBs and 93.7 (SD = 5.8) for the

low SEBs. It should be noted that the categorization of the

high (sophisticated) and the low (naı̈ve) SEB groups was

based on their comparative scores on the questionnaire

only within the study sample. The participants (n = 35,

83 %) self-reported that they used the Internet almost every

day. The average time for each Internet usage was more

than 1 h (n = 39, 93 %). The participants searched for

online information at least once a week, and 62 % of the

students (n = 26) performed online searches at least three

times a week. Thus, it could be supposed that the partici-

pants of this study had sufficient experience in performing

online-related activities.

Instruments

This study used Conley et al.’s (2004) SEB questionnaire

to assess students’ SEBs. Based largely on Hofer and

Pintrich’s (1997) framework, the instrument focuses on the

dimensions of epistemic beliefs regarding science, which

include Source (a sample item is, ‘‘Everybody has to

believe what scientists say.’’ [scored in reverse]), Certainty

(‘‘All questions in science have one right answer.’’ [scored

in reverse]), Development (‘‘Ideas in science sometimes

change.’’), and Justification (‘‘It is good to try experiments

more than once to make sure of your findings.’’). As

Conley et al.’s study presented the statements of the Source

and Certainty dimensions as less advanced SEB perspec-

tives (i.e., assessing the students’ agreement with the

authority and certainty of scientific knowledge), these two

factors were reversed so that, for each factor, higher scores

reflected more advanced SEBs. The questionnaire consists

of 26 items rated on a 5-point Likert scale (1 = strongly

disagree; 5 = strongly agree).

Conley et al.’s questionnaire was originally designed for

fifth graders. However, the instrument has been applied to

assess different sample subjects, such as measuring

undergraduate students’ SEBs in the studies of Liang et al.

(2010) as well as in Liang and Tsai (2010). Due to dif-

ferences in the targeted participants, a series of processes

was conducted to examine its reliability and validity, which

included translation of the instrument and reviewing,

approving, and verifying with two science education

experts. The overall coefficients of internal consistency

reliability (Cronbach’s alpha) for the SEB survey are 0.81

and 0.80, reported, respectively, in Liang et al.’s and Liang

and Tsai’s studies, showing the reasonable reliability of

this instrument. Using undergraduate and graduate students

as the participants, the present study adopted Liang et al.’s

modified questionnaire. The alpha reliability for the total

scale was 0.79, suggesting a sound reliability in assessing

the students’ SEBs.

The instrument, OISSI, was administered to assess the

participants’ self-perceived online searching strategies. Its

theoretical framework was established in Tsai and Tsai’s

(2003) study, which used a multiple-case study followed by

cross-case comparisons (see Tsai and Tsai 2003 for more

detailed information) to profile students’ cognitive strate-

gies for conducting a Web search task into seven aspects

(Control, Disorientation, Trial and Error, Problem-

Solving, Purposeful Thinking, Selecting Main Ideas, and

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Evaluation) categorized into three domains (Behavioral,

Procedural, and Metacognitive). Based on this framework,

Tsai (2009) further developed and validated the OISSI

instrument that consists of seven subscales corresponding

to the seven search strategy aspects. The OISSI includes a

total of 25 items measured by a six-point Likert scale

(1 = not like me at all; 6 = very much like me). The

Cronbach’s reliability coefficient was 0.91 for the total

scale and ranged from 0.64 to 0.88 for the seven subscales

(Tsai 2009), which was good enough for testing. In the

current study, the reliability was 0.92 for the total scale and

ranged from 0.67 to 0.91 for the seven subscales. Also, the

reliability coefficients for the Behavioral, Procedural, and

Metacognitive domains were 0.87, 0.75, and 0.93, respec-

tively. Table 1 shows the sample items for the seven aspect

strategies categorized according to the three domains of the

OISSI.

Data Collection Procedure

The targeted participants (42 volunteers in either a high or

low SEB groups) were brought into the laboratory indi-

vidually and were first asked to read two news items

regarding a scientific dispute about the risk of electro-

magnetic waves. One news item holds a position indicating

that electromagnetic waves are dangerous, while the other

considers them to be safe. The searching task was

unstructured and required the students to justify their

positions on this issue through seeking online information

as well as answering two questions including:

1. After searching the Web for information, which of the

two news items that you read before the task do you

trust more? Why?

2. Why did you change or why do you insist on your

position? What problem(s) may the position contrary

to yours have?

The participants entered their responses in the left panel

of the browser. No time limitation was imposed on the

searching task. Since this task fulfills the characteristics of

a socioscientific issue (e.g., open-ended, ill-structured,

involving a social dilemma having conceptual or techno-

logical connections with science in nature), it can promote

one to search online information and trigger epistemic

process by actively evaluating, analyzing, reflecting,

decision making as well as justifying while conducting this

task. Each student’s searching process (including Web

sites browsed sequentially, as well as links or buttons

clicked) was recorded by the screen-capture software,

Camtasia. Upon completing the task, they had to fill out a

survey, the OISSI, to assess their self-perceived searching

strategies.

Data Analysis

Based on the SEB survey, the participants were divided

into high (sophisticated, top 25 %) and low (naı̈ve, bottom

25 %) SEB groups. After the searching tasks, the two types

of data collected, including searching strategies (via the

OISSI) and searching behaviors (via the screen-capture

software), were set as dependent variables and analyzed in

terms of the students’ different SEB levels. To make a

Table 1 Seven aspect strategies categorized into three domains on

the OISSI

Domain Aspect Definition Sample Item

Behavioral Control The skills

required for

manipulating

online

searching

applications

I know how to

utilize

advanced-

search

functions

provided by

search engines

Disorientation Individuals’

awareness of

searching

orientation

I do not know

how to start

my online

searching

(scored in

reverse)

Procedural Trial and

error

The skills of

adopting

various

solutions

when facing

difficulties

I try some other

search engines

when my

search is not

successful

Problem-

solving

The

commitment

to overcoming

difficulties

during the

searching

process

I do my best to

resolve any

problems

occurring

while

searching

Metacognitive Purposeful

thinking

The skills of

self-

monitoring the

goals of

searching

I usually make

sure of the

goals before

starting my

online

searching

Select main

ideas

The skills of

identifying

and retrieving

critical ideas

from the

information

found

I look through

titles or

hyperlinks in a

Web site in

order to

capture the

major

information

Evaluation The skills of

judging and

integrating

online

information

I compare

information

that has been

collected from

different Web

sites

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comparison between the high and low groups, independent

t tests (two-tailed) were adopted to examine the differences

in searching strategies, whereas sequential analysis was

used to compare the searching behaviors of both groups,

that is, the main purpose was to examine the role of stu-

dents’ SEBs in their searching strategies and searching

behaviors.

A sequential analysis, according to Bakeman and Gott-

man (1986), provides a systematic observation for

researchers to understand how behaviors are sequenced

moment to moment so that they can investigate the

dynamic aspects of interactive behaviors. The merit of this

approach is that it enables researchers to more accurately

examine whether sequential relationships among searching

behaviors reach a statistically significant difference.

Sequential analysis has been widely utilized in numerous

studies, such as investigating behavioral patterns of online

collaborative discussion (Hou 2011), analyzing sixth

graders’ sequential patterns of using mobile devices in a

museum-learning context (Sung et al. 2010), exploring

students’ navigation in a hypermedia program (Rezende

and de Souza Barros 2008), and examining message

response exchanges in online group debates (Jeong 2003).

In this study, the participants’ video records were ana-

lyzed through sequential analysis (Bakeman and Gottman

1986) to visualize their online information searching pro-

cess. In the initial stage of conducting the sequential ana-

lysis, the participants’ behaviors during the online

searching task were first categorized and then a coding

scheme was developed according to the categorization. In

the present study, the coding scheme, including seven main

behaviors, is presented in Table 2. Based on this scheme,

each participant’s video record was coded in chronological

order by a researcher who had completed a video analysis

training course. For instance, a user has a certain sequence

of QRWB during a certain period of time, indicating that

he/she first enters a query (Q) and then browses the result

page (R). Upon finding a relevant page, the user clicks its

hyperlink to read the information on the page (W) and

finally adds it as a bookmark (B). After coding, 42 sets of

data with 1 057 behaviors were gathered. We calculated

each participant’s total transfer frequencies from one

behavior to another (this study analyzed Lag 1 sequence)

according to lag sequential analysis method (Bakeman and

Gottman 1986). Through a series of computations of the

sequence transfer matrices (including the frequency trans-

fer matrix, the conditional probability transfer matrix, and

the expected value matrix), the computation of adjusted

residuals (z-scores) was used to identify those transitional

probabilities that were significantly higher or lower than

the expected probability (Bakeman and Gottman 1986).

Since this study more focuses on exploring the occurrences

of sequences reaching the level of significance, we only

investigated z-scores greater than ?1.96. A significant

z-score indicates a significant occurrence of behavioral

sequence. This method of analysis has been adopted in

previous studies, such as investigating the pairs of students’

interactions in tutoring (Duran and Monereo 2005) and

exploring group interaction and critical thinking in online

threaded discussions (Jeong 2003).

Based on Tsai and Tsai’s (2003) framework of classi-

fying online information searching strategies, the results of

sequential analysis were further examined to probe whether

ones’ searching behaviors (sequences with statistically

significant difference) confirmed to their searching

strategies.

Results

The participants spent around 20 min on completing the

searching task. No statistical difference between the two

SEB groups in the time spent on the task was identified.

The following sections, respectively, display the results of

the comparison regarding the high and low SEB students’

searching strategies and searching behaviors.

Table 2 Coding scheme of online searching behaviors

Code Behaviors Description

Q Enter a query A user inputs a query to the

search engine and submits it

W Browse Web information A user clicks a hyperlink from

the result page to browse the

Web information

B Add a bookmark A user clicks the ‘‘add

bookmark here’’ button to

insert the hyperlink of the

browsed Web site into the

bookmark list

R Browse the result page A searcher reads the result

page that appears after

submitting a query and

normally comes up with a

list of hyperlinks

A Answer the question A searcher types words in the

answer column

N Click on the ‘‘next page’’

hyperlink to the next page

of the results

A searcher clicks on the ‘‘next

page’’ hyperlink to the next

page of the results in order

to browse more relevant

results

P Click the ‘‘previous’’ button

of the browser

A searcher clicks the

‘‘previous’’ button of the

browser to re-read the

browsed information

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Exploring Students’ Searching Strategies in Terms

of Different Levels of SEBs

Table 3 displays the results of comparing the students’

OISSI scores by the sophisticated and naı̈ve SEB groups.

As shown, of the three domains in the OISSI, two statis-

tically significant differences were identified in the

Behavioral (t = 3.60, p \ 0.05) and Metacognitive

domains (t = 2.05, p \ 0.05). The advanced SEB search-

ers outperformed the naı̈ve searchers in both domains. This

implies that those with sophisticated SEBs were more

likely to have superior skills in Internet manipulation or

navigation than those with naı̈ve SEBs. Further, they ten-

ded to employ more reflective and higher-order cognitive

strategies. Regarding the seven aspect strategies in the

OISSI, three out of the seven were found to have statisti-

cally significant differences, including Control (t = 3.20,

p \ 0.05), Disorientation (t = 2.65, p \ 0.05), and Pur-

poseful Thinking (t = 2.31, p \ 0.05). Similarly, the stu-

dents with sophisticated SEBs outperformed those with

naı̈ve SEBs in terms of these three factors. This suggests

that the advanced SEB searchers were inclined to express

more ease with manipulating the online searching appli-

cation than the naı̈ve SEB searchers. They were less likely

to feel confused and disoriented while searching for online

information. In addition, they tended to remind themselves

of their purpose during the searching process.

Exploring Students’ Behavioral Patterns in Terms

of Different Levels of SEBs

Table 4 refers to the adjusted residuals table that offers z-

scores information. These values were computed to iden-

tify whether the sequential relationships among the

advanced SEB students’ searching behaviors reached sta-

tistically significant difference. As shown in Table 4, the

rows represent initial searching behaviors, while the col-

umns refer to the follow-up behaviors. A value that is

greater than positive 1.96 suggests that the continuity of the

sequence reaches the level of significance (p \ 0.05).

According to Table 4, nine significant sequences are

identified, including Q (enter a query) ? R (browse the

result page), W (browse Web information) ? B (add a

bookmark), B (add a bookmark) ? R (browse the result

page), R (browse the result page) ? Q (enter a query), R

(browse the result page) ? W (browse Web information),

R (browse the result page) ? N (click on the ‘‘next page’’

hyperlink to the next page of the results), A (answer the

question) ? A (answer the question), N (click on the

‘‘next page’’ hyperlink to the next page of the results) ? R

(browse the result page), and P (click the ‘‘previous’’ but-

ton of the browser) ? W (browse Web information). To

better visualize the connections, these sequences are further

illustrated in Fig. 1. As displayed, the sophisticated SEB

group demonstrated two bidirectional sequences: Q $ R

(i.e., the participants tended to browse the result page after

keying in the keywords, and they may also consider other

keywords to make further searches after browsing the result

page.) and R $ N (i.e., the participants tended to click on

the ‘‘next page’’ hyperlink to read more search results after

browsing the result pages.) The former implies a series of

recurrent behaviors in which the searchers might try dif-

ferent queries and scan the result pages. The latter suggests

that the students tended to explore multiple sources and

browse the results. It was reasonable that the more the

students refined their queries and assessed the results, the

more chances they would have of getting more relevant

Web pages to browse later.

In addition, the sequence P (click the ‘‘previous’’ button

of the browser) ? W (browse Web information) presents

the behavior of clicking the previous button on the browser

and browsing the Web content. It is possible that the

searchers tended to re-browse the information in order to

identify nuances between the Web sites. With regard to A

(answer the question) ? A (answer the question), since

there was no sequence connected to code A (answer the

question), it is reasonable to assume that students with

Table 3 Comparisons of the OISSI scores for the sophisticated and

naive SEBs

Subscale SEB level Mean SD Range t

Behavioral domain High 42.45 4.172 33–48 3.60*

Low 37.50 4.752 29–48

Control High 22.14 1.833 20–24 3.20*

Low 19.90 2.654 13–24

Disorientationa High 20.32 3.061 13–24 2.65*

Low 17.60 3.589 12–24

Procedural domain High 27.57 4.869 19–36 1.44

Low 25.60 3.885 17–33

Trial and error High 13.12 3.385 3–18 0.85

Low 12.30 2.716 7–18

Problem-solving High 14.45 2.577 8–18 1.68

Low 13.30 1.750 9–15

Metacognitive domain High 56.23 7.578 44–66 2.05*

Low 51.55 7.149 40–66

Purposeful thinking High 20.00 3.147 14–24 2.31*

Low 17.80 3.019 13–24

Select main idea High 15.73 2.164 11–18 1.57

Low 14.65 2.277 10–18

Evaluation High 20.50 2.956 16–24 1.57

Low 19.10 2.827 15–24

* p \ 0.05a A higher disorientation score refers to a better orientation percep-

tion for searching

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sophisticated SEBs were likely to answer the questions

continuously without the interruption of performing other

behaviors. This implies that they might get the whole

picture of the searching task before responding to the

questions.

Regarding the naı̈ve SEB group, eight values with sta-

tistical significance are identified in Table 5, consisting of

Q (enter a query) ? R (browse the result page), W

(browse Web information) ? B (add a bookmark), B (add

a bookmark) ? R (browse the result page), R (browse the

result page) ? W (browse Web information), A (answer

the question) ? Q (enter a query), A (answer the ques-

tion) ? A (answer the question), N (click on the ‘‘next

page’’ hyperlink to the next page of the results) ? R

(browse the result page), and N (click on the ‘‘next page’’

hyperlink to the next page of the results) ? N (click on the

‘‘next page’’ hyperlink to the next page of the results).

These sequences are further illustrated in Fig. 2. As shown,

the unidirectional sequence Q ? R represents that the

participants browsed the result page (R) after entering a

query (Q). This indicates that the students tended not to try

different queries. In addition, the sequence N (click on the

‘‘next page’’ hyperlink to the next page of the results) ? N

(click on the ‘‘next page’’ hyperlink to the next page of the

results) shows that the students simply kept clicking the

‘‘next page’’ hyperlink of the result pages without pausing

to read the information they had found. This might suggest

that the participants felt impatient or disoriented to a cer-

tain degree so that they performed the searching task

carelessly. Further, while answering the questions, the

sequence A (answer the question) ? Q (enter a query)

shows that the naive SEB students tended not to answer the

questions continuously, that is, they were likely to seek

relevant information for each question separately, rather

than judging and analyzing all the information until they

could confidently answer all of the questions.

In summary, the students with high SEBs tended to try

various queries (i.e., Q $ R), whereas those with low

SEBs rarely did. Second, after entering their queries, the

high SEB students were inclined to explore the result pages

frequently (i.e., R $ N), implying the behavior of looking

through multiple sources, while the low SEB students were

less likely to browse the result pages, or browsed them

carelessly (i.e., N $ N and N ? R). Third, when surfing

the Web content, the high SEB students tended to con-

stantly compare the information among the Web sites by

clicking the previous button of the browser (i.e., P ? W),

while the low SEBs did not display this behavior. Finally,

there is some evidence that the low SEB students seemed to

answer the questions separately and incomprehensively

(i.e., A $ Q). In general, the behavior analysis supported

that the high SEB students tended to display more recurrent

searching behaviors and more in-depth exploration of

multiple sources, implying the usage of better metacogni-

tive acts.

Table 4 Adjusted residuals table for sophisticated SEB achievers’ online searching behaviors

Q W B R A N P

Q 0.21 -0.92 -2.47 4.82* -2.42 -0.41 -0.46

W -2.25 -4.67 10.62* 1.44 -1.79 -1.83 0.48

B -0.51 -2.96 -2.52 5.88* -0.10 -1.6 1.9

R 2.36* 9.75* -4.64 -8.52 -3.37 5.02* -0.87

A 0.44 -1.91 -2.76 -4.15 11.30* -2.36 -0.51

N -0.07 -1.83 -2.16 5.02* -1.91 0.06 -0.40

P -0.34 1.98* -0.47 -0.87 -0.62 -0.40 -0.09

Q: enter a query; W: browse Web information; B: add a bookmark; R: browse the result page; A: answer the question; N: click on the ‘‘next

page’’ hyperlink to the next page of the results; P: click the ‘‘previous’’ button of the browser

* p \ 0.05

Q

WB

R

A

N

P

Fig. 1 Sequential patterns of the sophisticated SEB group. Q: enter a

query; W: browse Web information; B: add a bookmark; R: browse

the result page; A: answer the question; N: click on the ‘‘next page’’

hyperlink to the next page of the results; P: click the ‘‘previous’’

button of the browser

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123

Discussion and Implications

Conducting online information searching tasks has become

a very common learning activity when the Internet is

integrated into science classrooms (Kim et al. 2007; Songer

et al. 2002). However, students are challenged with

effectively and critically retrieving, evaluating, selecting,

judging, and integrating information gathered from the

Internet (Tsai 2004a; Walraven et al. 2008). According to

the previous studies, students’ online searching strategies

(Lin and Tsai 2008) and behaviors (Mason et al. 2010) are

guided by their epistemic beliefs, which may vary in dif-

ferent disciplinary contexts (Buehl et al. 2002). This study

explored the role of students’ SEBs in their online search

strategies and behaviors. In addition, limited research has

been conducted to utilize sequential analyses to visualize

the searchers’ online information-seeking behaviors or to

embed socioscientific issues into the searching tasks. Thus,

in addition to using a survey, this study also adopted

sequential analysis as a way to examine the searching

behaviors and probed how the students’ SEBs might guide

their reasoning in online searching activities that explore a

socioscientific issue.

The results of this study show that, in comparison with

students with naı̈ve SEBs, those with more sophisticated

SEBs perceived themselves as applying more metacogni-

tive strategies in their online searching, that is, the more

advanced SEB searchers perceived themselves as using

more high-order search strategies, such as self-reflections

and self-monitoring on the goals and process of searching.

This finding responds to the results of Muis’ study (2007)

that a person’s epistemic beliefs can transform into epi-

stemic standards that foster a metacognitive monitoring

process. In fact, our searching behavior analysis also

indicated that the high SEB students had more recurrent

searching behaviors and more in-depth exploration of

multiple sources, suggesting the usage of better metacog-

nitive acts. Thus, it is likely that during the online infor-

mation searching activities, those with advanced SEBs

might believe in complex and tentative knowledge, which

guided them to contemplate metacognitive ideas such as

‘‘how can I find supportive evidence effectively and effi-

ciently.’’ It was this process that ultimately drove them to

purposefully filter the online information. This finding is in

line with the previous studies (Lin and Tsai 2008; Tu et al.

2008), suggesting that the students of more advanced epi-

stemic beliefs might adopt more sophisticated evaluative

standards and strategies to judge online information.

In addition, this study also found that those students who

held advanced SEBs were less likely to perceive confused

and disoriented while searching for online information.

Similar to the aforementioned description, the evaluative

standards and strategies the sophisticated SEB searchers

held might steer them purposefully and metacognitively to

seek online resources of concern to them, which might

relieve their sense of disorientation.

Through examining the students’ video records, the

results from the sequential analysis found that those students

who held more advanced SEBs provide some evidence of

metacognitive searching behaviors such as refining queries,

filtering the results, and comparing relevant information

among Web sites. For instance, the high SEB students ten-

ded to try different queries and evaluate multiple sources.

These behaviors, according to Kitchener’s (1983) model of

cognitive processing, represent how students with sophisti-

cated SEBs metacognitively monitor searching strategies or

processes to seek relevant information. However, the naı̈ve

SEB searchers tended not to try different queries and

browsed the search result pages superficially. These findings

are resonant with the previous studies (Kitchener 1983;

Mason et al. 2010), indicating the important role that indi-

viduals’ epistemic beliefs play in guiding their metacogni-

tive and cognitive processing. In addition to searching

Table 5 Adjusted residuals table for naı̈ve SEB achievers’ online

searching behaviors

Q W B R A N

Q -1.25 -2.18 -2.41 7.27* -2.61 -1.12

W -1.12 -5.93 11.48* 0.70 -2.22 -1.14

B 1.29 -4.05 -3.14 4.84* 1.69 -0.18

R -0.54 14.73* -5.18 -8.60 -4.75 1.27

A 2.47* -4.55 -2.84 -3.39 12.59* -0.94

N -0.26 -1.88 -2.31 3.98* -2 2.39*

Q: enter a query; W: browse Web information; B: add a bookmark; R:

browse the result page; A: answer the question; N: click on the ‘‘next

page’’ hyperlink to the next page of the results

* p \ 0.05

Q

WB

R

A

N

Fig. 2 Sequential patterns of the naı̈ve SEB group. Q: enter a query;

W: browse Web information; B: add a bookmark; R: browse the result

page; A: answer the question; N: click on the ‘‘next page’’ hyperlink

to the next page of the results

J Sci Educ Technol

123

behaviors, the present study also found that when replying to

the questions, the students with sophisticated SEBs were

inclined to get the whole picture of the information they had

found before responding. Conversely, the naı̈ve SEB

searchers tended to answer the questions separately, rather

than integrating the information until they could confidently

answer both questions. This finding is in line with the prior

studies (Muis 2007; Tsai 2000), suggesting that students

who have more advanced SEBs tend to adopt more mean-

ingful learning strategies such as elaboration and integration

of the target information.

In addition, future researchers should also be aware of

students’ engagement in searching tasks. For example, if

they do not take the task seriously, they are not likely to

reflect typical knowledge acquisition strategies. Therefore,

embedding searching activities into their subject learning

can be a beneficial way to enhance students’ engagement.

Furthermore, it is possible that learners’ characteristics

(e.g., intelligence quotient or prior knowledge of the

searching tasks) may have an impact on students’ searching

strategies and behaviors. Thus, it is suggested that future

studies pay attention to learners’ characteristics while

implementing similar research.

The current study found that the students’ epistemic

orientations toward science play an important role in their

online searching strategies and behaviors regarding science

information. It is essential for future researchers and edu-

cators to take the role of SEBs into consideration when

designing online searching activities. For instance, some

possible suggestions that may metacognitively engage

students in searching activities include offering a pop-up

window that guides students’ reflection or evaluation on

the information, or providing adaptive scaffolding. Take

Puntambekar and Stylianou’s (2005) study for example;

they designed meta-navigation prompts according to the

searchers’ navigation to support their online information

searching activities, which had a positive impact on the

implementation. Future studies, in addition to assessing

searching performance, can also investigate the influence

of implementing searching assistance on improving the

participants’ SEBs. The present study utilized a sociosci-

entific issue to engage the searchers in the online searching

tasks. Thus, for the researchers of socioscientific issues,

how the students conduct argumentation and develop

informal reasoning may arouse their interest. In addition,

one may question whether using a questionnaire to exam-

ine the participants’ SEBs in the present study might risk

not accurately representing the participants’ SEBs,

although this approach has been utilized in many studies

(Liang et al. 2010; Tu et al. 2008). Therefore, future studies

are encouraged to utilize a more complicated method, such

as the think-aloud approach (Mason et al. 2009) to probe

the participants’ SEBs.

In conclusion, promoting scientific inquiry through

online information searching activities has become a

growing trend in schools. Implications drawn from this

study include that future research can integrate online

information searching tasks into inquiry-based science

curricula so as to probe the students’ searching strategies

and behaviors. Similarly, one can also investigate the role

the searchers’ SEBs play in their searching strategies,

behaviors, and outcomes. Further, how the students’

searching outcomes map onto their searching behaviors

and how the participants’ perceived behaviors map onto

their observed behaviors are also worth investigation.

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