Students’ perceptions of collaboration, self-regulated learning, and information seeking in the context of Internet-based learning and traditional learning

Computers in Human Behavior 27 (2011) 905–914

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Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh

Students’ perceptions of collaboration, self-regulated learning, and information seeking in the context of Internet-based learning and traditional learning Silvia Wen-Yu Lee a,⇑, Chin-Chung Tsai b a b

Graduate Institute of Science Education, National Changhua University of Education, Taiwan Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taiwan

a r t i c l e

i n f o

Keywords: Internet-based learning Perception Collaboration Self-regulated learning Information seeking

a b s t r a c t This study aims to investigate students’ perceptions of three aspects of learning – collaboration, selfregulated learning (SRL), and information seeking (IS) in both Internet-based and traditional face-to-face learning contexts. A multi-dimensional questionnaire was designed to evaluate each aspect in terms of perceived capability, experience, and interest. The analyses explore (1) potential differences of students’ perceptions between Internet-based and face-to-face learning environments and (2) potential differences in the three aspects in relation to learners’ attributes and the use of the Internet and enrollment in online courses. This study surveyed students in a higher education institute who had had experiences with Internet-based and face-to-face learning. The results showed that students perceived higher levels of collaboration (capability only), SRL (capability and experience) and IS (capability, interest, and experience) in Internet-based learning than in traditional learning environments. In terms of students’ education level, graduate students perceived higher levels of capabilities and interests in some of the aspects, than undergraduate students. In addition, for Internet-based learning, significant differences in collaboration and SRL were found derived from time spent on the Internet related to learning; and students’ perceptions of collaboration, SRL, and IS were all positively correlated to students’ online course-taking experience. Implications for online learning practices and instructor’s facilitation are discussed. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction In the last decade, educational researchers reported increasing Internet use for academic purposes in higher-education institutes (Jones, 2002; Jones, Johnson-Yale, Millermaier, & Perez, 2008). The Internet has been implemented widespread for higher education in Taiwan. In a survey conducted in the academic year of 2005, the results showed 80% of the higher-education institutes in Taiwan used Internet-based asynchronous communication for teaching, 70% of the institutes used online course management systems, 30% of the institutes used synchronous online communications, and 23% of the institutes utilized Internet-based assessments (National Science Council, 2006). For educators, it is no longer a question of whether students should use the Internet for academic work or not; rather, it is a question of how students can benefit the most from Internet-based learning. As the Internet has become more central to students’ experiences in higher education, researchers have attempted to unravel what better describes these experiences and how these experiences may impact learning. Earlier research of online learning focused on the debate of whether students using the Internet envi⇑ Corresponding author. Tel.: +886 4 7232105; fax: +886 4 7275891. E-mail address: [email protected] (S.Wen-Yu Lee). 0747-5632/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.chb.2010.11.016

ronment performed better than their counterparts in a traditional learning environment (e.g., Bonham, Deardorff, & Beichner, 2003; Cole & Todd, 2003; Lin, Cheng, Chang, & Hu, 2002; Russell, 2001). Rather than merely emphasizing academic performance, recent investigations of online learning stressed more on students’ perceptions of Internet-based learning (e.g., Federico, 2000; Liaw, 2002; Song, Singleton, Hill, & Koh, 2004), attitudes towards the Internet (e.g., Durndell & Haag, 2002; Ginns & Ellis, 2007; Tsai, Lin, & Tsai, 2001), or computer or Internet self-efficacy (e.g., Joo, Bong, & Choi, 2000; Peng, Tsai, & Wu, 2006; Torkzadeh & Dyke, 2002). It is imperative to gain insights into these indicators because students’ perceptions of the learning environments, under the influence of other factors in the environments, can serve as benchmark for effective learning and teaching (Ginns & Ellis, 2007; Robertson, Grant, & Jackson, 2005). Despite much research, effort has been devoted to investigating students’ perceptions, attitudes, or self-efficacy. Few studies have compared students’ perceptions between online and face-to-face learning environments. In one of the studies, a comparison of students’ perceived learning experience was made between the group taking online courses and the group in campus-based courses (Robertson et al., 2005). Among various variables, Robertson et al., found that online students perceived a greater amount of time utilizing learning materials than campus-based students. In

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a comparative study of students’ comments of online and oncampus courses, Rovai, Ponton, Derrick, and Davis (2006) found that fully online courses received more negative ratings than traditional on-campus courses in the overall evaluation of the course. Each of the aforementioned studies presented a partial view of the comparison of the two learning modes. Thus, further investigations of students’ perceptions are needed to provide a more thorough image of the differences in students’ perceptions between the two learning contexts.

2. For Internet-based learning, to which extent do students’ perceptions of collaboration, SRL, and IS vary by students’ attributes, their use of the Internet, and their enrollment in online courses? 3. Learning aspects studied Collaboration, SRL, and IS have been central of importance in traditional learning context and have also attracted much attention for Internet-based learning. In the following, each learning aspect is introduced in relation to research of Internet-based learning.

2. Rationale and design of the study 3.1. Collaboration In this study, students’ perceptions of Internet-based learning and traditional learning were investigated and compared through the use of a multi-dimensional questionnaire. This study was designed based on the following rationales. First, a within-subject comparison of students’ perceptions of various aspects of learning is needed for higher education research. This kind of comparison has been increasingly important and relevant to higher education due to the growth of courses that blend Internet-based and faceto-face learning. When blended and hybrid courses become more common in the future (Allen, Seaman, & Garrett, 2007), one might expect both online and face-to-face learning to be central to students’ experience in higher-education institutes. This study has chosen three aspects of learning – collaboration, self-regulated learning (SRL), and information seeking (IS) and has further investigated the extent to which one’s perceptions toward the two kinds of learning contexts may differ. Second, some educational researchers of Internet-based learning suggested the identification of different characteristics of the learners that may contribute to within group differences in nonexperimental studies (e.g., Abrami & Bernard, 2006). The results of within group differences can help educators to understand which group adapts better to the Internet-based learning environment and why. In this study, the analysis aimed to examine students’ perceptions in relation to students’ attributes, selfreported usage of the Internet for learning purposes and enrollment in online courses. The indicators for students’ attributes included gender and educational level. Finally, most studies in the past treated students’ perception as a single dimensional scale, such as self-efficacy. This study suggested a convenient tool that allowed researchers to measure multiple dimensions at one time and to make comparison between them, directly. A multi-dimensional questionnaire, Participant Perception Inventory-Internet versus Traditional Learning version (PPI-IvT) was developed based on the Participant Perception Indicator created by Berger & Carlson, 1988 (Kerner, Penner-Hahn, Berger, & Dershimer, 1997; Lee, Kerner, & Berger, 1999). The original design of PPI devised three dimensions to measure participants’ cognitive, behavioral, and affective responses (Berger & Carlson, 1988). Previous studies (Berger & Carlson, 1988; Kerner et al., 1997; Lee et al., 1999) showed that in a computer-assisted learning environment, students’ perceptions in the three dimensions varied and changed to different degrees throughout the course of the training. Therefore, it is necessary to have various dimensions assessed at the same time. In this study, three similar dimensions were created for the measurement of perceived capability (self-efficacy), experience (behavioral response), and interest (affective response). In sum, this study attempted to answer the following research questions: 1. On which dimensions (i.e. capability, experience, and interest) do students’ perceptions of Internet-based learning differ from their perceptions of traditional face-to-face learning regarding collaboration, SRL, and IS?

Reported positive effects of collaboration include better engagement in the learning process, retainment of information for a longer time period, and the gain of higher-order skills (as cited in Kirschner, Paas, & Kirschner, 2009a). Recent psychological studies also suggested that students can benefit from the reduction of individuals’ cognitive load when working on different collaborative tasks (Kirschner et al., 2009a; Kirschner, Paas, & Kirschner, 2009b). Working on group projects, exchanging notes, and studying for examinations are all common reasons why students contacted and attempted to collaborate with others through the Internet (Jones et al., 2008). Research, however, showed mixed results of whether the Internet promotes collaboration and communication among students (Kreijns, Kirschner, & Jochems, 2003; Reeves, Herrington, & Oliver, 2004). In studying the impact of collaborative mode on students’ achievement, some researchers suggested that students in the face-to-face condition performed significantly better than students in the online condition (Tutty & Klein, 2008). From a social-cognitive point of view, however, researchers commented on the potential benefits of Internet-based learning environments in promoting collaborative inquiry, collaborative knowledge building, negotiations, and argumentation (e.g., Hara, Bonk, & Angeli, 2000; Jeong & Joung, 2007; Lee & Tsai, in press; Schrire, 2006; Tisdell et al., 2004). Why is research still inconclusive about the role of the Internet in collaboration? The most likely explanation is that there are many different forms of collaboration. Collaboration represents a set of interactions with various levels of complexity based on the learning goals and activities involved. Another possible reason is that numerous factors impact or mediate online communication and collaboration. Factors such as collaborative structure of the lesson, type of task, conceptual approaches used by students, students’ perceptions of online environment, students’ sense of belonging, communication styles, and relationships with others influenced students’ online interactions with others (Kreijns et al., 2003; Tutty & Klein, 2008). It was difficult to disentangle and control these factors when comparisons were made. In this study, rather than applying an experimental design, students were asked to compare their own experiences in the two different contexts (i.e. face-to-face versus online). As for the definition of collaboration, this study focused on collaborative situations initiated by students voluntarily, such as sharing notes, asking for or providing help, or working on homework together on solutions. Thus, collaboration does not refer to any scripted collaborative projects. 3.2. Self-regulated learning SRL stresses students’ ability to actively construct meanings during learning and to monitor and control their cognition, motivation and behavior of learning (Zimmerman, 2001). SRL is particularly important for learning in non-linear learning environments, such as hypermedia or the Internet, where students are required

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to set their own learning goals, to decide their learning pace and learning sequences, and to adjust their learning strategies based on their progress (Azevedo & Cromley, 2004). Students who are more adapted to SRL, learn more effectively in learning settings with non-linear representations (Azevedo, Moos, Greene, Winters, & Cromley, 2008). Similarly, research of Internet-based learning suggested that students with higher SRL efficacy not only have better perceived learning strategies but also are more sensitive to the effectiveness and usefulness of a learning management system (Lee & Lee, 2008). Despite it being imperative for students to become self-directed learners, the Internet can be a double-edged sword for learning because of the requirement of a higher level of autonomy. On the one hand, some students enjoyed the flexibility of learning at their own pace when engaged in Internet-based learning activities. For example, when comparing asynchronous discussions with face-to-face discussions, students were in favor of the self-paced, self-regulated feature of asynchronous discussions (Tiene, 2000). On the other hand, the demand of SRL can be challenging and overwhelming for some students who were not prepared for this kind of learning mode (Azevedo and Cromley,2004; Smith, Heindel, & Torres-Ayala, 2008). Some researchers argued that traditional learning environments lack the support for SRL and Internet-based learning environments may possess the capacity to foster SRL skills (Vighnarajah, Bakar, & Bakar, 2009). Evidence showed that students were able to adapt the SRL used in a traditional environment to Internet-based courses (Whipp & Chiarelli, 2004). In reviewing 33 studies of SRL in relation to Internet-based learning environments, Winters, Greene, and Costich (2008) suggested that student and task characteristics and types of learner support are most relevant to SRL. In general, researchers suggested that SRL is critical for success in online learning (e.g., Williams & Hellman, 2004; Yukselturk & Bulut, 2007) but argued that SRL has not been received enough attention in educational research (Barnard, Lan, To, Paton, & Lai, 2009; Winters et al., 2008). 3.3. Information seeking An Internet-based learning environment, powered by interconnected databases and online search engines, allows students to search for educational materials that are supplementary to traditional textbooks or course materials. Recently, increasing studies investigated the information seeking or searching practices for learning. Several factors were identified in past studies for their impact on online searching strategies. Researchers found students with higher Internet self-efficacy tend to have better information searching strategies (Tsai & Tsai, 2003). Students who used advanced evaluative standards toward the accuracy and usefulness of information tended to use more sophisticated searching strategies (Wu & Tsai, 2005). Finally, with support in the online learning environment, it was likely that students’ performance of information searching improved; students perceived the activity more important; and students tended to activate the searching cycle more often (Saito & Miwa, 2007). 4. Method 4.1. Participants In this study, students in a higher education institute in Taiwan were asked to recall and compare their perceptions of Internetbased learning and traditional learning based on their recent learning experiences in a higher education setting. The participants of this study were recruited from blended-learning courses in the

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Business School at a University in Taiwan. Students drew their experiences of Internet-based learning mainly on the blendedlearning courses in which students utilized a combination of online video conferencing, synchronous and asynchronous discussion boards, and the course management system, such as the online homework system in conjunction with face-to-face lectures. Over 80% of the teaching time and almost all course materials were digitized and available on the Internet in such courses. By traditional learning, we referred to any face-to-face meetings or lectures without the support of any computer-based or Internet-based learning environments. The students were informed about these definitions before responding to the questionnaire. All participants answered the surveys voluntarily. A total of 157 students filled out the questionnaires. Among all returned questionnaires, 7 questionnaires were omitted from the data pool because each had more than 5% of missing data. Of all of the students who returned a valid questionnaire (n = 150), 56 of the participating students are female. Fifty-seven percent of the students were at master’s level, 38% of students were at college level, and 5% were at doctoral level. In terms of students’ academic background, 80% of students were enrolled in the business and management related programs, 14% were in computer science, the rest were distributed in social science, science, medicine, and other fields. Regarding time spent on the Internet for courses, 32% of students reported between 1 and 7 h per week, 42% of student reported between 8 and 14 h and the rest of the students reported more than 14 h per week. In this study, emphasis was placed in courses where the majority of content was delivered in Internet-based learning environments. When part or all face-to-face meetings in a course are substituted by real-time video conferences, it is likely most of the content of the course was delivered online. Courses with online video conferencing (OVC) communication were therefore chosen purposefully as proxies of courses with intense use of the Internet. In this study, the students were asked to report the number of courses taken with the use of OVC. Students (62%) had taken only one course (including the current course) and 31% of the students had taken more than one course. The rest of the students (n = 10) did not answer this question in the questionnaire. 4.2. Research instrument The PPI-IvT questionnaire consists of 21 statements (see Appendix A), seven for each learning aspect. That is, seven items for collaboration, SRL and IS respectively. For example, ‘‘sharing class notes or learning materials with peers’’ is an item for the collaboration aspect. ‘‘Learn at my own pace’’ is a sample statement for the SRL aspect, and ‘‘trying different searching approaches for finding new learning materials’’ is an example for the IS aspect. The design of the SRL items were mainly based on the theoretical framework proposed by Pintrich (2004), in that the SRL consists of the planning, monitoring, control and reaction, and reflection phases. For each statement, students have to mark their response on a five-point Likert scale in relation to three dimensions: perceived capability, perceived experience, and perceived interest (Berger & Carlson, 1988). Capability is defined as ‘‘ability or power to do something (‘‘Capability., n.d.’’). The self-perceived capability is a self-efficacy variable (Bandura, 1993). Interest is defined as ‘‘a feeling that accompanies or causes special attention to an object or class of objects’’ (‘‘Interest., n.d.’’) and it is an intrinsic motivation variable (Hoskins & van Hooff, 2005). Finally, experience is ‘‘direct observation of or participation in events as a basis of knowledge’’ and it is considered a behavioral variable (‘‘Experience., n.d.’’). The same statements were repeated twice, once for online learning and the second time for traditional learning in the classroom (see Fig. 1). Students were asked to recall their overall experiences

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one-way ANOVA tests for examining differences of the perceptions of Internet-based learning due to participants’ gender, educational level, and time spent on the Internet per week in relation to learning. Finally, correlational analyses were conducted to examine the relationship between students’ perceptions of Internet-based learning and the number of courses with the use of OVC.

in both contexts. The traditional learning is defined as delivering learning material face-to-face with no use of the Internet for teaching and learning. This questionnaire design resulted in a total of 18 sub-scales (i.e., three learning aspects  three dimensions  two learning contexts). In addition, proceeding to the PPI-IvT, a short demographic survey consisting of four questions was given to the participants. The questions surveyed the participants’ gender, academic background, highest degree, time spent on the internet for educational purposes, and number of courses taken with the use of OVC. The validity and reliability of the surveys were verified by the authors in a pilot study for developing the instruments (Lee, Tsai, & Yang, 2009). For content and construct validity (Carmines & Zeller, 1979), both surveys were reviewed by 3 external experts who had experiences in developing surveys related to Internetbased learning or teaching. The surveying dimensions and the language in the statements were revised based on the comments from the experts. Exploratory factor analysis, principle component analysis with varimax rotation, verified the three facets of student’s perceptions. Based on 345 valid surveys in the pilot study, the three factors with 21 items in the PPI-IvT survey (all loading factors were above 0.63) are all retained and they accounted for on average 77% of the data (Lee et al., 2009). Based on a same criterion used in a previous study (Tsai et al., 2001), we repeated the principle component analyses with varimax rotation and retained an item when its factor loading is greater than 0.40. Six rounds of factor analyses were conducted for verifying the constructs for each of the six dimensions: online-capability, online-experience, onlineinterest, face-to-face-capability, face-to-face-experience, faceto-face-interest. All factor analyses resulted in the same three factors corresponding to the three facets of this study. Main factor loadings corresponding to the three factors were included in Appendix B. As shown in Appendix B, most loading factors in this study were above .60 and three items were between 0.40 and 0.60. Therefore, all items were retained. They accounted for on average 69% of the data. The Cronbach’s alpha coefficients (Cortina, 1993) for all 18 sub-scales in this study range between .844 and .905 and show high reliability for the assessment (please see Appendix B).

5. Results 5.1. Differences between students’ perceptions between Internet-based learning and traditional learning The results of paired t-test (see Table 1) show that statistically significant differences were found between Internet-based learning and traditional learning in all three dimensions. It appears that students perceived better collaborative ability in the Internet than face-to-face learning environments (t = 2.07, p < .05). Students also perceived that they were more capable (t = 2.95, p < .01) and more interested (t = 3.16, p < .01) in SRL in the Internet-based learning environment than in the traditional learning environment. In terms of IS, students perceived better capability (t = 7.35, p < .001), more experiences (t = 7.24, p < .001), and more interests (t = 5.87, p < .001) in Internet-based environments than in the traditional environments. According to the Cohen’s d coefficients (Cohen, 1988, p. 25), the three sub-scales of IS are of medium to large effect sizes, the three scales of SRL are of small to medium effect sizes, and the three sub-scales of collaboration are all of small effect sizes (see Table 1). 5.2. Differences of students’ perceptions by learners’ attributes No significant differences were found between genders in all 18 scales of online or face-to-face learning. The results of ANOVA tests show significant difference between students of different educational levels in their perceived capabilities and interests for Internet-based learning (F(2, 139) = 3.61, p < .05 for collaboration capability; F(2, 139) = 5.52, p < .01 for SRL capability; F(2, 139) = 4.01, p < .05 for IS capability; F(2, 136) = 3.35, p < .05 for collaboration interest) as well as face-to-face learning (F(2, 136) = 4.86, p < .01 for collaboration capability; F(2, 136) = 4.84, p < .01 for IS capability; F(2, 136) = 3.95, p < .05 for collaboration interest). With further post hoc analyses (Bonferroni) the results show for Internet-based learning, students at master’s level perceived higher level of interest in collaboration and higher capability of SRL (please see Table 2) than students in undergraduate levels. In addition, students at the Ph.D. program perceived higher level of IS

4.3. Data Analysis Two statistical analyses were employed for this study. The first analysis utilized the matching paired-t test for within-subject comparison between perceptions of Internet-based learning and perceptions of traditional learning. The second analysis used

In Internet-based learning environments

When learning, I

1.Discussing problems encountered in learning with peers

In traditional face-to-face learning environments

Capability

Experience

Interest

Capability

Experience

Interest

Low <---> High

Low <---> High

Low <---> High

Low <---> High

Low <---> High

Low <---> High

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2. Share class notes or learning 1 materials with peers

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3. Provide feedback to ideas suggested by peers

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Fig. 1. A snapshot of the PPI-IvT questionnaire. For each statement, three dimensions, capability, experience, and interest, are devised. Respondents are asked to mark their perceived level on a five-point Likert scale. The three dimensions repeat twice. The three columns on the left are for the Internet-based learning environments and the other three columns on the right are for the traditional face-to-face learning environments.

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S.W.-Y. Lee, C.-C. Tsai / Computers in Human Behavior 27 (2011) 905–914 Table 1 Results of matching paired-t test comparisons of all sub-scales between Internet-based learning and traditional learning. Dimension/sub-scale Collaboration Capability Experience Interest Self-regulated learning Capability Experience Interest Information seeking Capability Experience Interest

* ** ***

Learning context

Mean

SD

Internet Face-to-face Internet Face-to-face Internet Face-to-face

3.68 3.54 3.56 3.59 3.62 3.46

0.74 0.88 0.81 0.92 0.79 0.97

Internet Face-to-face Internet Face-to-face Internet Face-to-face

3.62 3.43 3.51 3.41 3.63 3.41

Internet Face-to-face Internet Face-to-face Internet Face-to-face

3.86 3.32 3.80 3.26 3.75 3.26

t

df 2.07⁄

Cohen’s d

149

0.17

-0.37

149

-0.03

1.89

145

0.16

0.74 0.87 0.78 0.90 0.81 0.91

2.95⁄⁄

149

0.24

1.36

149

0.11

3.16⁄⁄

145

0.26

0.87 0.98 0.88 0.98 0.96 1.05

7.35⁄⁄⁄

149

0.60

⁄⁄⁄

149

0.59

⁄⁄⁄

146

0.48

7.24 5.87

p < .05. p < .01. p < .001.

capabilities than undergraduate students. For face-to-face learning, students at the master’s level were more capable and more interested in collaboration than undergraduate students (please see Table 3). Moreover, for IS, students at the Ph.D. level perceived higher capability than students at master’s level, and students at Ph.D. level also perceived higher capability than undergraduate students. In both Internet-based learning and face-to-face learning, there were no significant differences of students’ experiences in terms of collaboration, SRL, or IS between students at different educational levels. Even though students studying for an advanced degree did not perceive more experiences regarding the three aspects, they still perceived a higher level of capabilities or interests in the aforementioned aspects. 5.3. Differences of students’ perceptions of Internet-based learning by use of the Internet and enrollment in online courses In the first ANOVA analysis, students were divided into three groups based on their time spent on the Internet per week in relation to learning. Students in the first group (n = 48) spent less than 7 h a week on the Internet; students in the second group (n = 62) spent between 8 and 14 h per week; and the third group (n = 39) of students spent more than 14 h a week. One student did not provide a valid answer to this question. The results show (see Table 4) significant differences in terms of both capability and experience in collaboration (F(2, 142) = 5.08, p < .01 for capability; F(2, 140) = 3.87, p < .05 for experience) and SRL (F(2, 142) = 3.90, p < .05 for capability; F(2, 140) = 3.76, p < .05 for experience) in the context of Internet-based learning. A series of post hoc tests (Bonferroni) were performed to further compare the differences between groups. The significance level was set to .017, based on Bonferroni adjustment. The results showed the second group scored significantly higher than the first group in the perceived capability of collaboration and the third group scored higher than the first group in the perceived capability and experience of collaboration (see Table 4). In addition, in terms of SRL, the second group perceived higher level of capability than the first group while the third group perceived more experience of SRL than the first group. In other words, students who spent

at least a moderate amount of time on the Internet for educational purposes had higher self-efficacy and richer self-perceived experience in both collaboration and SRL, than students in the minimum group. As shown in Table 5, students’ perceptions of collaboration, SRL, IS were all positively correlated to the numbers of OVC (see Table 5). Therefore, we concluded that prior experiences with courses with intense use of Internet-based instruction may contribute to the students’ positive perceptions of collaboration, SRL, and IS in Internet-based learning environments. 6. Discussion This study showed significant differences of students’ perceptions between Internet-based learning and traditional learning in terms of collaboration, SRL and IS. A significant difference was found in terms of students’ perceived ability of collaboration between Internet-based and traditional learning but it was of only small effect size. One possible explanation is that students just do not contact other students as often through the Internet, especially when they are not required to do so. In Jones et al.’s (2008) study, after surveying students in 29 colleges in the United States, they found more than half (55%) of the respondents felt they rarely (once or twice per semester) or seldom (every few weeks) contact their classmates online. Nevertheless, the situation with collaboration may be improved when more time is spent on the Internet or more experiences with Internet-based courses are obtained. The results of ANOVA analyses showed that students who spent a moderate amount of time online for learning actually perceived higher capability and more experience of collaboration than the group who spent less time. More interestingly, students who took more intense Internet-based courses not only perceived better capability and experience but also appeared to be more interested in collaboration than those who only took one course. This may be an indication that if the students have more actual practice of collaboration in a learning environment, their overall ability, experience, and interest of collaboration in Internet-based learning environments may be readily enhanced.

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Table 2 Students’ perceptions of Internet-based collaboration, SRL, and IS among groups of different educational levels. Group

Collaboration

(1) Undergraduate (2) Master’s (3) Ph.D. F(ANOVA) Bonferronia * ** ⁄⁄⁄ a

SRL

IS

Capability (mean, SD)

Experience (mean, SD)

Interest (mean, SD)

Capability (mean, SD)

Experience (mean, SD)

Interest (mean, SD)

Capability (mean, SD)

Experience (mean, SD)

Interest (mean, SD)

3.51(0.86) 3.80(0.62) 4.14(0.68) 3.61*

3.41(0.94) 3.67(0.73) 3.97(0.75) 2.30

3.41(0.85) 3.76(0.72) 3.80(0.80) 3.35* (1) < (2)

3.45(0.68) 3.82(0.64) 3.74(0.52) 5.52** (1) < (2)

3.42(0.69) 3.66(0.75) 3.69(0.53) 1.94

3.55(0.72) 3.81(0.70) 3.46(0.37) 2.48

3.85(0.57) 4.03(0.57) 4.61(0.43) 4.01* (1) < (3)

3.80(0.60) 3.92(0.64) 4.50(0.44) 2.51

3.76(0.71) 3.91(0.73) 4.21(0.66) 1.22

p < .05. p < .01. p < .001. Bonferroni adjustment; significant level was set to .017.

Table 3 Students’ perceptions of collaboration, SRL, and IS in face-to-face learning environments among groups with different educational levels. Group

Collaboration

(1) Undergraduate (2) Master’s (3) Ph.D. F(ANOVA) Bonferronia

SRL

IS

Capability (mean, SD)

Experience (mean, SD)

Interest (mean, SD)

Capability (mean, SD)

Experience (mean, SD)

Interest (mean, SD)

Capability (mean, SD)

Experience (mean, SD)

Interest (mean, SD)

3.39(0.83) 3.76(0.62) 3.94(0.70) 4;86** (1) < (2)

3.55(0.93) 3.74(0.66) 3.83(0.70) 1.14

3.27(0.85) 3.63(0.70) 3.77(0.63) 3.95* (1) < (2)

3.49(0.73) 3.57(0.62) 3.97(0.52) 1.27

3.50(0.69) 3.58(0.64) 3.94(0.59) 1.14

3.42(0.77) 3.57(0.67) 3.86(0.45) 1.32

3.39(0.62) 3.52(0.67) 4.43(0.42) 4.84** (1) < (3) (2) < (3)

3.33(0.73) 3.46(0.65) 4.14(0.31) 2.81

3.32(0.71) 3.47(0.78) 4.14(0.42) 2.54

*

p < .05. p < .01. p < .001. a Bonferroni adjustment; significant level was set to .017.

** ⁄⁄⁄

Table 4 Students’ perceptions of collaboration, SRL, and IS among groups with different amount of time spent on the Internet in relation to learning. Group

(1) <7 h (2) 7–14 h (3) >14 h F(ANOVA) Bonferronia

* ** ⁄⁄⁄ a

Collaboration

SRL

IS

Capability (mean, SD)

Experience (mean, SD)

Interest (mean, SD)

Capability (mean, SD)

Experience (mean, SD)

Interest (mean, SD)

Capability (mean, SD)

Experience (mean, SD)

Interest (mean, SD)

3.41(0.82) 3.80(0.67) 3.83(0.66) 5.08** (1) < (2) (1) < (3)

3.31(0.91) 3.67(0.79) 3.76(0.67) 3.87* (1) < (3)

3.44(0.90) 3.70(0.72) 3.70(0.72) 1.77

3.43(0.67) 3.77(0.66) 3.75(0.68) 3.90* (1) < (2)

3.32(0.78) 3.64(0.69) 3.70(0.67) 3.76* (1) < (3)

3.52(0.81) 3.76(0.66) 3.77(0.67) 1.79

3.85(0.71) 4.00(0.50) 4.05(0.55) 1.27

3.84(0.74) 3.87(0.60) 3.98(0.54) 0.51

3.74(0.87) 3.86(0.62) 3.94(0.75) 0.79

p < .05. p < .01. p < .001. Bonferroni adjustment; significant level was set to .017.

Table 5 Correlations between the number of courses with OVC and students’ perceptions of collaboration, SRL, and IS. Group

Courses with OVC ⁄ ** ⁄⁄⁄

Collaboration

SRL

IS

Capability

Experience

Interest

Capability

Experience

Interest

Capability

Experience

Interest

0.33**

0.29**

0.27**

0.30**

0.27**

0.25**

0.24**

0.22**

0.22**

p < .05. p < .01. p < .001.

The results also showed that students were more interested in and also perceived a higher level of capability of performing SRL in an Internet-based learning environment than the traditional learning environment. As the blended class provided students with various presentations of learning materials online, including both linear and non-linear materials, students are required to constantly

set their learning goals and plan on their learning strategies in order to perform well in the courses. While most of the face-to-face learning is still teacher-led and may place less demands for SRL, students who are adapted to SRL in Internet-based learning environments could feel more interested in and capable of SRL in such an environment.

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Finally, the results showed that students’ perceived higher capability, more interests, and more experiences in IS in the Internet-based learning than face-to-face learning. These results may be because, on the one hand, students were given more IS related tasks in Internet-based learning, hence were more capable of IS. On the other hand, it is possible that in an Internet-based environment, students tend to feel more comfortable with or enjoy more information searching due to the affordance and convenience of the Internet. In fact, many studies (e.g., Aslanidou & Menexes, 2008; Chu & Law, 2007; McDowell, 2002) have shown that searching information online is a very common learning activity for students. It is also worth noting that the results do not imply that Internet-based learning is necessarily better than face-to-face learning. Studies of blended learning emphasized the proper balance of the traditional lectures or meetings and Internet-based interactions in order to achieve the most benefit (Sharma & Barrett, 2007). Blended learning is expected to continuously grow in higher-education institutes for both campus-based and distant learning programs (Allen et al., 2007). Traditional faceto-face classroom teaching still provides the advantages of hands-on experiences, instant interactions with peers and a higher level of engagement, while online learning environments offer the opportunities for extensive varieties of learning resources, ubiquitous access and student-centered learning experiences (Reardon, 2010).

6.1. Implications for teaching and learning Previous studies have established positive relationships between learning performance and perceptions, attitudes, or self-efficacy (e.g., Ginns & Ellis, 2007; Lee & Lee, 2008; Peng et al., 2006). For example, Ginns and Ellis (2007) suggested that students’ positive perceptions of the quality of online teaching were strongly related to a higher grade for the course. Although this study did not examine the direct impact of students’ perceptions on learning performance, this study suggests a few factors that could play a role in students’ perceptions and might have some impact on students’ learning in the longer term. A better understanding for the relationships among potential factors and students’ perceptions of collaboration, SRL, and IS, can help educators plan on learning activities that foster more positive student perceptions in both online and face-to-face learning. First, instructors should encourage students to spend at least a moderate amount of time online when they participate in online learning. This study suggested that students who spent moderate amounts of time online reported higher perceived ability and experiences in at least the collaboration and SRL aspects of Internetbased learning. One possible explanation is that with the limited amount of time on the Internet allocated for studying, the first group of students who spent less than 7 h did not allow enough time for themselves to feel comfortable with collaboration with others or SRL, and perhaps did not allow themselves to develop collaboration or SRL skills. Although higher level of Internet usage did not necessarily lead to better computer self-efficacy (Sam, Othman, & Nordin, 2005), students who had a moderate amount of Internet exposure time had stronger preferences on certain aspects of the Internet-based learning environment than students who had spent less (Chuang & Tsai, 2005). Chuang and Tsai (2005) suggested that ‘‘students with minimum Internet experiences were far from being critical to the Internet-based learning environments’’ (p. 265). Chuang and Tsai (2005) study and this study both suggested the benefit of spending more time on the Internet for the course.

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However, this study did not explore why students spent more time on the Internet while others did not. Students’ characteristics such as students’ own preference of Internet-based activity and their comfort level with the Internet may influence the time spent on the Internet and their decisions of course taking. A more complex explanatory model needs to be established through future investigations. The second implication regards Internet-based learning in the span of multiple courses and suggests that positive perceptual changes may exist. This study provided evidence that more favorable perceptions toward self-efficacy, experience, and interest in collaboration, SRL, and IS are positively correlated to the number of Internet-intense courses taken by the students. The enrollment of multiple online courses seemed to be critical for perceptual changes in Internet-based learning. This finding is supported by Arbaugh (2004) study which suggests that between the first and second online courses, students’ satisfaction with the Internet, perceptions of participant interaction, and perceptions of the usefulness of the courseware had a positive shift. Thus, it is important that teachers are aware of this shift of perceptions in order to provide necessary support and design activities accordingly. Third, instructors should take into account students’ educational level when designing learning activities, for both online and face-to-face learning. This study showed students’ in advanced degree programs perceived a higher level of capabilities and interests in some aspects of learning, both online and face-to-face. It could be the general school experiences or specific training at the graduate programs that contribute to the different perceptions. Students in undergraduate programs seemed to feel less interested in or perceive a lower level of capabilities for collaboration, SRL, and IS. Thus instructors can provide more scaffoldings or training for collaborative tasks and SRL when planning on such activities for students at undergraduate levels. 6.2. Limitations and future research directions This study possesses some limitations due to participants’ background and the number of valid surveys collected. In this study, the participants were mostly from business and management related programs. It is uncertain whether academic disciplines contributed to students’ perceptions of online learning. Some researchers suggested that disciplinary differences were important factors in instructional design (Smith et al., 2008; Walker, Lee, Skov, Berger, & Athey, 2002). Moreover, we intentionally selected students with experiences of similar Internet learning environment rather than those from a wider range of learning settings. This research decision could reduce the variability of students’ experiences. Thus, future studies should investigate perceptions of students from different academic disciplines or students who have experiences with different Internet-based learning environments such as game-based learning, animation, or virtual reality. Researchers can conduct a more comprehensive survey of a larger student population. Finally, although we have collected information about the educational levels of the students, we did not ask their ages. The role of learners’ ages on online learning may be also important and further discussion based on empirical data is needed in future studies.

Acknowledgements The authors would like to thank Professor Niang-Shing Chen for his kind help in conducting this research. This study is supported in part by National Science Council, Taiwan, under grant numbers NSC 98-2511-S-011-005-MY3, NSC 99-2511-S-011-005-MY3, and NSC 99-2511-S-018-003-MY2.

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Appendix A A.1. Statements for the three evaluated aspects in the PPI-IvT Statements for collaboration 1. 2. 3. 4. 5. 6. 7.

Discuss problems encountered in learning with peers. Share class notes or learning materials with peers. Provide feedback to ideas suggested by peers. Share my learning experiences with peers. Lead my peers in discussions. Make good use of learning information provided by my peers. Review learning materials with peers prior to exams. Statements for SRL

1. 2. 3. 4. 5. 6. 7.

Set my own learning goals. Recognize inadequacy of my knowledge and skills. Explore what I want to learn further. Use appropriate learning strategies. Learn at my own pace. Evaluate or review my learning effectiveness. Improve my learning approaches when it is necessary. Statements for IS

1. 2. 3. 4. 5. 6. 7.

Search for new learning materials. Judge the trustworthiness of the newly searched information. Judge the quality and usefulness of the searched materials for learning. Integrate new information into my existing knowledge. Try different searching approaches for finding new learning materials. Organize and synthesize the searched materials. Share the learning materials with others.

Appendix B B.1. Main factor loading and Cronbach’s alphas for each sub-scale Item

Online

Face-to-face

Capability

Experience

Interest

Capability

Experience

Interest

.753 .773 .692 .750 .698 .576 .710 .883

.769 .732 .685 .703 .761 .639 .764 .895

.732 .754 .767 .785 .758 .612 .534 .885

.781 .767 .741 .483 .750 .671 .811 .849

.790 .746 .832 .692 .746 .728 .761 .891

.827 .767 .773 .472 .702 .645 .632 .881

.745 .647 .682 .794 .721 .709 .712 .867

.736 .697 .714 .776 .725 .711 .800 .888

.694 .612 .669 .772 .792 .782 .740 .880

.713 .595 .669 .788 .760 .735 .768 .883

.631 .745 .715 .783 .737 .751 .816 .873

.728 .693 .719 .802 .761 .794 .764 .886

.842 .780 .825 .773 .804 .738 .666 .844

.842 .774 .777 .721 .797 .796 .746 .861

.823 .803 .813 .842 .739 .785 .693 .905

.802 .795 .827 .777 .792 .747 .637 .884

.821 .812 .829 .825 .769 .738 .622 .870

.809 .800 .795 .813 .817 .815 .685 .897

Collaboration 1 2 3 4 5 6 7

a SRL 8 9 10 11 12 13 14

a IS 15 16 17 18 19 20 21

a

Note: Each column represents the results of factor analysis for each dimension of the questionnaire. Only main factor loadings corresponding to the three factors were included here and other data were omitted due to space constraints.

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