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Students' self-regulation for interaction with others in online learning environments Moon-Heum Cho a, , B. Joon Kim b a Lifespan Development & Educational Sciences (LDES), Kent State University-Stark, United States b Department of Public Policy, Indiana University-Purdue University Fort Wayne, United States abstract article info Article history: Accepted 6 November 2012 Available online 13 November 2012 Keywords: Self-regulation Self-regulation for interaction with others Online interaction Online learning The purpose of this study was to explore variables explaining students' self-regulation (SR) for interaction with others, specically peers and instructors, in online learning environments. A total of 407 students participated in the study. With hierarchical regression model (HRM), several variables were regressed on students' SR for interaction with others. These variables included demographic information, perceived importance of mastering content, perceived importance of interacting with the instructor, perceived importance of interacting with peers, and perceived instructor scaffolding for interaction. The results show that all the variables proposed above signicantly explain 43% of the variance for SR for interaction with others. The combined variables show that instructors' scaffolding for interaction with others most signicantly explains students' SR for interaction with others. Along with individual variables (e.g., perceived importance of mastering content), the results suggest that instructor scaffolding is critical for students' SR for interaction with others in online learning settings. Published by Elsevier Inc. 1. Introduction Interaction with others, specically peers and instructors, is one of the important variables determining students' successful learning experiences in an online learning environment (Cho & Jonassen, 2009; Garner & Bol, 2011; McIsaac, Blocher, Mahesh, & Vrasidas, 1999; Moore, 1989; Richardson & Swan, 2003). A number of online educators and researchers have reported that interaction with others signicantly and positively relates to student satisfaction with the course (Bolliger & Martindale, 2004; Driver, 2002; Richardson & Swan, 2003), perceived learning (Richardson & Swan, 2003), and social presence (Kim, Kwon, & Cho, 2011; Shen, Nuankhieo, Huang, Amelung, & Laffey, 2008). In ad- dition, because a major portion of online assignments require students to interact with others, skillful and effective interaction with others is very important not only for individual learners' success, but also for cultivating positive learning environments (Cho & Jonassen, 2009; Cho & Summers, 2012; Dabbagh & Kitsantas, 2005). Student interaction with others is, therefore, critical in online learning settings. However, active interaction with others does not occur automatically in an online learning environment, where interaction is mediated by technology and dynamic instructor scaffolding for interaction is different from what takes place in traditional learning environments (Garner & Bol, 2011; Hiltz & Goldman, 2005; Moore, 1989; Mullen & Tallent-Runnels, 2006). Active interaction with others in online settings requires students to have a certain degree of self-regulation (Cho & Jonassen, 2009; Cho, Shen, & Laffey, 2010; Garner & Bol, 2011). Despite the recognition that online learning requires students to self-regulate their interaction with others, research on what makes students self-regulate for interaction with others in online is scarce. Thus, determining variables that explain students' SR for interaction with others is signicant to improving support for online students and providing guidelines for effective online teaching and learning practices. The current study aimed to explore variables related to SR for interaction with others in online learning contexts. 2. Denition of self-regulation for interaction with others A number of models of self-regulated learning (SRL) have emanated from various views of learning, but self-regulation is most commonly dened as students' proactive management in two areas of learning: motivation and cognition (Pintrich, 2000; Winne & Hadwin, 1998; Zimmerman, 2000). We also view that SR for interaction with others involves proactive management of motivation and cognition for inter- action (Cho & Jonassen, 2009). With regard to motivation for interacting with others, self-regulated online learners tend to enjoy and have high self-efcacy for interacting with others (e.g., peers and instructors). Cognitively, self-regulated online learners use effective writing strate- gies. They intentionally write messages, monitor the interaction pro- cess, and reect their interaction by reading others' messages (Cho & Jonassen, 2009; Cho et al., 2010). 3. Research in SRL in online learning environments Online learning is understood to involve three aspects of interac- tion, including interaction between student and content, interaction between student and student, and interaction between student and Internet and Higher Education 17 (2013) 6975 Corresponding author at: Lifespan Development & Educational Sciences (LDES), Kent State University-Stark, 407 Main Hall, North Canton, OH 44720, United States. E-mail address: [email protected] (M.-H. Cho). 1096-7516/$ see front matter. Published by Elsevier Inc. http://dx.doi.org/10.1016/j.iheduc.2012.11.001 Contents lists available at SciVerse ScienceDirect Internet and Higher Education

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Page 1: Students' self-regulation for interaction with others in online learning environments

Internet and Higher Education 17 (2013) 69–75

Contents lists available at SciVerse ScienceDirect

Internet and Higher Education

Students' self-regulation for interaction with others in online learning environments

Moon-Heum Cho a,⁎, B. Joon Kim b

a Lifespan Development & Educational Sciences (LDES), Kent State University-Stark, United Statesb Department of Public Policy, Indiana University-Purdue University Fort Wayne, United States

⁎ Corresponding author at: Lifespan Development & EdState University-Stark, 407 Main Hall, North Canton, OH 4

E-mail address: [email protected] (M.-H. Cho).

1096-7516/$ – see front matter. Published by Elsevier Ihttp://dx.doi.org/10.1016/j.iheduc.2012.11.001

a b s t r a c t

a r t i c l e i n f o

Article history:Accepted 6 November 2012Available online 13 November 2012

Keywords:Self-regulationSelf-regulation for interaction with othersOnline interactionOnline learning

The purpose of this study was to explore variables explaining students' self-regulation (SR) for interaction withothers, specifically peers and instructors, in online learning environments. A total of 407 students participatedin the study. With hierarchical regression model (HRM), several variables were regressed on students' SR forinteraction with others. These variables included demographic information, perceived importance of masteringcontent, perceived importance of interacting with the instructor, perceived importance of interacting withpeers, and perceived instructor scaffolding for interaction. The results show that all the variables proposedabove significantly explain 43% of the variance for SR for interaction with others. The combined variables showthat instructors' scaffolding for interaction with others most significantly explains students' SR for interactionwith others. Along with individual variables (e.g., perceived importance of mastering content), the results suggestthat instructor scaffolding is critical for students' SR for interaction with others in online learning settings.

Published by Elsevier Inc.

1. Introduction

Interaction with others, specifically peers and instructors, is one ofthe important variables determining students' successful learningexperiences in an online learning environment (Cho & Jonassen, 2009;Garner & Bol, 2011; McIsaac, Blocher, Mahesh, & Vrasidas, 1999;Moore, 1989; Richardson & Swan, 2003). A number of online educatorsand researchers have reported that interaction with others significantlyand positively relates to student satisfactionwith the course (Bolliger &Martindale, 2004; Driver, 2002; Richardson & Swan, 2003), perceivedlearning (Richardson & Swan, 2003), and social presence (Kim, Kwon,& Cho, 2011; Shen, Nuankhieo, Huang, Amelung, & Laffey, 2008). In ad-dition, because a major portion of online assignments require studentsto interact with others, skillful and effective interaction with others isvery important not only for individual learners' success, but also forcultivating positive learning environments (Cho & Jonassen, 2009;Cho & Summers, 2012; Dabbagh & Kitsantas, 2005). Student interactionwith others is, therefore, critical in online learning settings.

However, active interaction with others does not occur automaticallyin an online learning environment, where interaction is mediated bytechnology and dynamic instructor scaffolding for interaction is differentfromwhat takes place in traditional learning environments (Garner &Bol,2011; Hiltz & Goldman, 2005; Moore, 1989; Mullen & Tallent-Runnels,2006). Active interaction with others in online settings requires studentsto have a certain degree of self-regulation (Cho & Jonassen, 2009; Cho,Shen, & Laffey, 2010; Garner & Bol, 2011).

ucational Sciences (LDES), Kent4720, United States.

nc.

Despite the recognition that online learning requires students toself-regulate their interaction with others, research on what makesstudents self-regulate for interaction with others in online is scarce.Thus, determining variables that explain students' SR for interactionwith others is significant to improving support for online studentsand providing guidelines for effective online teaching and learningpractices. The current study aimed to explore variables related to SRfor interaction with others in online learning contexts.

2. Definition of self-regulation for interaction with others

A number of models of self-regulated learning (SRL) have emanatedfrom various views of learning, but self-regulation is most commonlydefined as students' proactive management in two areas of learning:motivation and cognition (Pintrich, 2000; Winne & Hadwin, 1998;Zimmerman, 2000). We also view that SR for interaction with othersinvolves proactive management of motivation and cognition for inter-action (Cho& Jonassen, 2009).With regard tomotivation for interactingwith others, self-regulated online learners tend to enjoy and have highself-efficacy for interacting with others (e.g., peers and instructors).Cognitively, self-regulated online learners use effective writing strate-gies. They intentionally write messages, monitor the interaction pro-cess, and reflect their interaction by reading others' messages (Cho &Jonassen, 2009; Cho et al., 2010).

3. Research in SRL in online learning environments

Online learning is understood to involve three aspects of interac-tion, including interaction between student and content, interactionbetween student and student, and interaction between student and

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70 M.-H. Cho, B.J. Kim / Internet and Higher Education 17 (2013) 69–75

instructor (Moore, 1989). These three types of interactions are wellknown to be significant for students' successful learning experience inonline learning environments (Hiltz & Goldman, 2005; Moore, 1989);however, many online studies have investigated variables predictingthe academic achievements represented by interaction between stu-dent and content (Garner & Bol, 2011). These variables include motiva-tion (e.g., goal orientation, task value, or self-efficacy) (Chang, 2007),use of learning strategies (e.g., metacognitive and cognitive strategies)(Hu & Gramling, 2009;Whipp & Chiarelli, 2004), and resourcemanage-ment (e.g., timemanagement, environmentmanagement, or help seek-ing) (Hu & Gramling, 2009; Song, Singleton, Hill, & Koh, 2004;Vighnarajah, Luan, & Bakar, 2009).

Online learning is explained with three aspects of interaction, butresearch on two types of interaction—between student and student andbetween student and instructor—is scarce in online SRL research(Garner & Bol, 2011). Very few online studies have been conducted onthe interaction between student and student as well as the interactionbetween student and instructor from the perspective of SRL except fora few studies (Cho & Jonassen, 2009; Cho et al., 2010; Yang, Tsai, Kim,Cho, & Laffey, 2006). For example, Cho and Jonassen (2009) investigatedSR for interaction with others (e.g., peers and instructor) in onlinelearning environments and found SR for interaction with others can beexplained with motivation and cognition. Motivation constructs includeenjoyment of human interaction, self-efficacy for interaction with in-structors, concern for interaction with students, and self-efficacy forcontributing to the online community. Cognition constructs includewrit-ing, responding, and reflection strategies. In addition, they found thatmotivation constructs are positively and significantly associated withstudents' cognition constructs. More specifically, it was found that stu-dents who enjoy interactions with others are more likely to have a highself-efficacy for interaction with their instructor and display a highself-efficacy for contribution to the only learning community. These stu-dents aremore likely to usewriting, responding, and reflecting strategies.In addition, Cho et al. (2010) found that self-monitoring for interactionwith others statistically and significantly predicts students' perceivedpeer social presence, instructor social presence, sense of connectedness,and sense of learning in online learning environments. Cho et al. foundthat self-regulation occurring between student and content does notpredict types of social presence and sense of community, although it ex-plains the most variances of SRL. This study shows that self-regulationoccurring between student and content is different from self-regulationoccurring between student and student or between student and instruc-tor. Although these studies are meaningful to investigate the interactionbetween student and student and between student and instructor fromthe perspective of SRL, more empirical studies are necessary.

3.1. Possible variables explaining online SR for interaction with others

We explored possible variables associated with students' SR for in-teraction with others in online learning environments. Through a liter-ature review, possible variables were identified, including demographicinformation, mastery goal orientation, the importance of interactingwith others (e.g., peers and instructor), and instructors' scaffolding forinteraction.

3.1.1. Demographic informationDemographic information was hypothesized to be associated with

students' SR for interaction with others. It included age, gender, grade,and prior online learning experience. Developmental differences canbe assumed depending on age and grade. Azevedo, Cromley, Winters,Moos, and Greene (2005) found developmental differences in SRLamong college, high school, and middle school students in a web-based learning environment. They found that college students usemore effective monitoring and learning strategies than high school andmiddle school students. Furthermore, Zimmerman and Martinez-Pons(1990) found developmental differences in academic self-efficacy and

self-regulatory strategies betweenfifth, eighth, and11th-grade students.In addition, grade level is assumed to be related to students' SR for inter-action with others. Artino and Stephens (2009) compared undergradu-ate and graduate students' self-regulation profiles. They found thatgraduate students are more likely to use critical thinking skills and lesslikely to procrastinate about their learning.

Furthermore, online learning experiences are assumed to be related toSR for interaction with others. Vrasidas and McIsaac (1999) interviewedonline students and found prior experience with online courses relatedto students' interaction with others in synchronous discussions. Theyalso found that experienced learners are more likely to post messagesand interact with online instructors. Holcomb, King, and Brown (2004)found that previous experience with online learning has a significantimpact on students' self-regulation skills represented by interaction be-tween student and content; however, no gender difference in interactionwith others was found.

3.1.2. Mastery goal orientationAnumber of studies reported thatwhen students aremotivated, they

are more like to self-regulate their learning (Mullen & Tallent-Runnels,2006; Wolters, 1998). Pintrich (1999) consistently reported that stu-dents' setting mastery goals is positively related to their use of effectivemetacognitive strategies, cognitive strategies, and resource manage-ment. Yang et al. (2006) found that students' academic motivationrepresented by mastery goal orientation, self-efficacy, and task value ispositively associated with peer social presence, instructor social pres-ence, comfort with sharing personal information, and social navigation.Yang et al. showed thatmastery goal orientation is related to SR for inter-action with others.

3.1.3. Perceived importance of interaction with peers and an instructorAlthough little research has been conducted about the relationships

between students' perceived importance of interaction and SR for inter-action with others, some studies provided evidence for positive rela-tionships between them. For example, Su, Bonk, Magjuka, Liu, and Lee(2005) interviewed 26 online faculty members and 10 second-year on-line MBA students. They found all online faculty viewed interactionsamong students or between student and instructor critical for students'success in an online MBA course. However, students' perception of theimportance of interaction with others was mixed. Su et al. found thatsome students thought that they neededmore interaction, while othersthought that they did not need much interaction in an online course.

3.1.4. Instructors' scaffolding for interaction with othersInstructors' support is important to promote students' SR for inter-

action with others. Although little research has been conducted toinvestigate how instructors' scaffolding promotes students' SR for inter-action with others, literature on effective teaching commonly suggeststhat instructors' scaffolding for interaction is critical to promotestudents' interaction with others (Mullen & Tallent-Runnels, 2006;Ryan & Patrick, 2001). For example, Mullen and Tallent-Runnels(2006) found that students' perception of academic and affectivesupport was positively related to perceived learning, task value, andcourse satisfaction in online courses. They concluded that instructors'support and demands are important to increase students' engagementin learning. Ryan and Patrick (2001) found that students' perceivedinstructor support, instructors' scaffolding to promote interaction, andmutual respect are related to the improvement of students' motivationand engagement.

4. Method

4.1. Participants

A total of 407 students enrolled in online courses at a Midwesternuniversity in the US participated in the study. Data were collected

Page 3: Students' self-regulation for interaction with others in online learning environments

Table 2Descriptive statistics.

Variables M (SD)

Age 35.54 (9.92)Gender N (%)

Female 326 (80.1%)Male 81 (19.9%)

Grade 4.80 (0.69)Number of online course taken prior 4.21 (3.70)How important is it to you to masterthe content in this course?

5.86 (1.15)

How important is interaction with theinstructor to accomplish your learning?

5.00 (1.48)

How important is interaction with otherstudents to accomplish your learning?

4.87 (1.59)

SR for interaction with others 5.57 (0.60)Instructor scaffolding for interaction 5.50 (1.17)

Note. N=407.

71M.-H. Cho, B.J. Kim / Internet and Higher Education 17 (2013) 69–75

from 69 online courses representing 11 departments (see Table 1). Theaverage number of participants in each course was 5.90 (SD=5.31).Most of the participants were female (N=326) and graduate students(N=357). Participants' average age was 35.54 (SD=9.92), and theaverage number of online courses taken was 4.21 (SD=3.70). Demo-graphic information is presented in Table 2.

4.2. Learning contexts

All the online courses were delivered via a course management sys-tem and no face-to-face meetings took place in each course. Interactionsamong students and between students and instructors occurred solelythrough asynchronous communication tools (e.g., discussion boards oremails). Students engaged in a diverse range of learning tasks. Accordingto the students' self-report, 31.4% (N=128) of them did group projects,54.8% (N=54.8) completed an individual project with peer feedback,88.5% did individual projects, 71.3% (N=290) were required to do afinal project, 17.9% (N=73) took exams, and 20.6% (N=84) took quiz-zes. In addition, 100% of the courses required participation in discussion.

4.3. Measurements

The survey comprised two parts. The first part asked about stu-dents' demographic information, types of learning tasks they partici-pated in an online course, perceived importance of mastering content(mastery goal orientation), perceived importance of interaction withan instructor, and perceived important of interaction with peers. Oneitem was constructed to measure students' mastery goal orientationin an online course: “How important is it to you to master the contentin this course?” Another item was created to measure students' per-ceived importance of interaction with the online instructor: “Howimportant is interaction with the instructor to accomplish your learn-ing?” A third item was created to measure students' perceived impor-tance of interaction with online peers: “How important is interactionwith other students to accomplish your learning?” A seven-point Likertscale was used, where 1 denoted “not important at all” and 7 denoted“very important.”

The second part of the survey asked about online instructors' scaf-folding for interaction and their own SR for interaction with others.An online instructors' scaffolding for interaction was developed with10 items and measured with the seven-point Likert scale, where 1denoted “not true at all” and 7 denoted “very true.” The items askedabout instructors' specific scaffolding strategies to promote interac-tions. The 10 items were developed based on the literature abouteffective teaching strategies to promote online interaction with others(Lewis & Abdul-Hamid, 2006; McIsaac et al., 1999; Mullen & Tallent-Runnels, 2006; Ryan & Patrick, 2001; Young, 2006). For example,McIsaac et al. (1999) interviewed doctoral students who participatedin an online seminar. Based on the interviews, they suggested severalinteraction strategies to promote interactions among students or be-tween student and instructor. These strategies included the following:

Table 1Course distribution across departments.

No. Department Number of courses Number of participants

1 Curriculum & Instruction 8 222 Educational Psychology 5 273 Educational Statistics 3 104 Health Science 3 165 Information Science 14 876 Journalism 4 337 Learning Technologies 13 1018 Multimedia Development 7 609 Nursing 6 3310 School Psychology 3 1111 Special Education 3 7

Total 69 407

provide immediate feedback, participate in the discussion, promote in-teraction and social presence, and use collaborative learning strategies.According to Young (2006), students regarded the following as effectiveonline teaching practices: instructors' visible and active participationin learning, effort to establish trusting relationships with students,and facilitation of students' learning. Lewis and Abdul-Hamid (2006)interviewed 30 exemplary online instructors and found effective onlineteaching practices including providing students with constructive feed-back, fostering interaction and involvement, facilitating student learn-ing, and maintaining instructor presence.

An example of items used for the current study was, “My instruc-tor provides basic guidelines to help students become aware of theimportance of online interaction.” Specific items appear in AppendixA. Exploratory Factor Analysis indicates that one dimension of teach-ing strategy explains 57% of the total variances; therefore, 10 itemswere used to explain teaching strategy to promote students' onlineinteraction. Cronbach's alpha for the 10 items was .91.

Students' SR for interaction with others was measured with theOnline Self-Regulated Learning Inventory (OSRLI), using a seven-pointLikert Scale (Cho & Jonassen, 2009), where 1 denoted “completelyuntrue of me,” and 7 denoted “completely true of me.” Cronbach'salpha with OSRLI in our study was .86. The OSRLI, which measures stu-dents' SR for interaction with others in online learning environments,consists of 28 items andmeasures two aspects of SR for interaction rep-resented bymotivation for interaction (N=17, Cronbach's alpha=.81)and cognition for interaction such as interaction strategies (N=11,Cronbach's alpha=.78).

4.4. Procedure

One of the authors contacted online instructors via email andasked to conduct the study in their online courses. Once they grantedpermission for the researchers to administer the online survey, a briefdescription of the research and online survey link were posted in aclass discussion board. All the students voluntarily participated inthe research.

4.5. Data analysis

Analysis of variance (ANOVA), correlation analysis, and hierarchicalregression model were performed in this study. A correlation matrixwas constructed among all eight variables, based on Pearson's correla-tion coefficients for significance. The impact of variables on students’SR for interaction with others was tested using hierarchical regressionmodel (HRM). The individual students' variables, such as age, gender,grade, and prior experience of online courses, were entered into Block1, followed by the importance of mastering content in the course inBlock 2, the importance of interaction with instructor in Block 3, the

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Table 3Correlations for the impact of student characteristics on students’ SRL.

SRI 1 2 3 4 5 6 7 8

SR for interaction (SRI) 11. Age 0.16⁎⁎ 12. Gender 0.02 −0.03 13. Grade 0.21⁎⁎ 0.10⁎ −0.02 14. Number of online course taken 0.08 0.08 −0.07 0.08 15. Importance of mastering content 0.44⁎⁎ 0.21⁎⁎ 0.07 0.06 0.02 16. Importance of interaction with an instructor 0.31⁎⁎ 0.22⁎⁎ −0.02 0.11⁎ 0.08 0.36⁎⁎ 17. Importance of interaction with other students 0.44⁎⁎ 0.09 0.05 0.13⁎⁎ 0.05 0.31⁎⁎ 0.35⁎⁎ 18. Instructor scaffolding for interaction 0.55⁎⁎ 0.10⁎ −0.06 0.04 0.11⁎ 0.39⁎⁎ 0.38⁎⁎ 0.40⁎⁎ 1

⁎ pb0.05.⁎⁎ pb0.01.

Table 4Hierarchical regression model.

R2

changeF ratio forR2 change

B SE β

Age 0.07 7.21⁎⁎ 0.00⁎ 0.00 0.05Gender 0.04 0.06 0.02Grade 0.13⁎⁎ 0.03 0.15Number of online course taken 0.00 0.01 0.01Importance of mastering content 0.17 88.13⁎⁎ 0.11⁎⁎ 0.02 0.22Importance of interaction withan instructor

0.02 10.66⁎⁎ −0.00 0.02 −0.00

Importance of interaction withother students

0.07 43.32⁎⁎ 0.07⁎⁎ 0.02 0.20

Instructor scaffolding forinteraction

0.10 71.84⁎⁎ 0.19⁎⁎ 0.02 0.38

Note. R2=0.43; F(8,398)=37.63, pb0.01.⁎ pb0.05.

⁎⁎ pb0.01.

72 M.-H. Cho, B.J. Kim / Internet and Higher Education 17 (2013) 69–75

importance of interaction with other students in Block 4, and instructorscaffolding for interaction in the final block.

5. Results

5.1. Description of participants' perceptions about online courses

The demographic profiles of the 407 participants and the responsesabout online learning are presented in Table 2. Students reported theirperceptions of online courses as follows: the importance of masteringcontent in their courses was rated 5.86 (SD=1.15), the importance ofinteraction with an instructor to accomplish their learning was rated5.00 (SD=1.48), and the importance of interaction with other studentsto accomplish their learning was rated 4.87 (SD=1.59) on a seven-point Likert scale. On average, students' SR for interaction was 5.57(SD=0.60), and instructor scaffolding for interaction was 5.50(SD=1.17).

5.2. Correlation predictors and students’ SR for interaction

Results from the correlation analyses are presented in Table 3. Smallto moderate positive relationships were shown among student age,grade, importance of mastering content, importance of interactionwith an instructor, importance of interaction with other students, in-structor scaffolding for interaction, and SR for interaction with others.Students who were older (r=0.16, pb0.01) and who were in a highergrade (r=0.21, pb0.01) showed significantly higher SR for interactionwith others than younger students with less experience in school.Students who placed higher weight on the importance of masteringcontent reported higher levels of SR for interaction (r=0.44, pb0.01).Students who valued interactionswith the instructor and peer studentswere more likely to self-regulate for interaction with others (r=0.31,pb0.01, and r=0.44, pb0.01). Finally, instructor scaffolding for interac-tion positively correlated with students’ SR for interaction with others(r=0.55, pb0.01); however, gender and the number of online coursestaken prior to this study were not related to students’ SR for interactionwith others in online learning settings.

5.3. Overall model: hierarchical regression model

The results of the hierarchical regression model using students’ SRfor interaction with others as the dependent variable and eight inde-pendent variables in five blocks are shown in Table 4. The overallmodel with all eight variableswas statistically significant and explained43.1% of the variance in students’ SR for interaction with othersF (8, 398)=37.63, pb0.01. In Block 1, age, gender, grade and the num-ber of online courses taken prior to the study explained 6.7% of the var-iance in students’ SR for interaction, which was statistically significant,with F (4, 402)=7.21, pb0.01. In Block 2, the importance of masteringcontent explained a statistically significant 16.8% of the variance ofstudents’ SR for interaction with others after controlling for individual

student demographic variables and prior online course experience inBlock 1, with F (1, 401)=88.13, pb0.01. In Block 3, the importance ofinteraction with an instructor explained a statistically significant 2.0%of the variance of students’ SR for interaction with others after control-ling for individual student demographic variables, prior online courseexperience, and the importance of mastering content of their onlinecourses in Block 2, with F (1, 400)=10.66, pb0.01. In Block 4, the im-portance of interaction with other students explained a statistically sig-nificant 7.3% of the variance of students’ SR for interaction with othersafter controlling variables in Block 3,with F (1, 399)=43.32, pb0.01. Fi-nally, in Block 5, after controlling for all the other independent variables,instructor scaffolding for interaction uniquely explained 10.3% of vari-ance of students’ SR for interaction with others, statistically significantwith F (1, 398)=71.84, pb0.01. All five blocks of variables, therefore,significantly contributed to the explanation of students’ SR for interac-tion. When individual independent variables using standardized betascores were examined, instructor scaffolding for interaction (B=0.19,pb0.01, β=0.38) explained themost variance in students’ SR for inter-action with others, followed by the importance of mastering content inthe course (B=0.11, pb0.01, β=0.22), the importance of interactionwith other students (B=0.07, pb0.01, β=0.20), and grade (B=0.13,pb0.01, β=0.15). Controlling for all the other variables when theinstructor scaffolding for interaction increased by 1 point, the students’SR for interaction with others increased by 0.19 points.

6. Discussion

The purpose of the study was to investigate what variables are re-lated to students' SR for interaction with others in online learning en-vironments. Those variables include age, gender, grade, the extent ofmastery goal orientation, importance of interacting with an instruc-tor, importance of interacting with other students, and instructorscaffolding for interaction. This study found that the above attributes

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73M.-H. Cho, B.J. Kim / Internet and Higher Education 17 (2013) 69–75

are positively associated with students' SR for interaction with others(see Table 5).

Regarding the demographic variables, grade level was associatedwith students' SR for interaction with others, showing that students ina higher grade level are more likely to self-regulate for interaction withothers in online learning environments. However, demographic vari-ables such as age and gender were not found to be associated withstudents' SR for interaction with others. One of the possible reasons forthis result may be attributable to the data set collected for this study.The demographic information indicates that research participantswere mature students (the average age of the students was 35.54)and most of them were females (80.1%), which may have skewed thedistribution of the results. Therefore, future researchers may considerrecruiting more balanced participation in terms of age and gender.

In addition, the average number of online courses that participantshad taken prior to this study (M=4.21) was not associated with stu-dents' SR for interaction with others. This fact implies that onlinelearning experiences do not necessarily equate to students' skillfulSR for interaction. It seems that other factors such as task structuresor required interaction activities are a more powerful predictor ofSR for interaction (Vrasidas & McIsaac, 1999). Future researchersmay consider task structures (who students interact with and howthey interact) and required interaction (e.g. interaction frequencies)when conducting research on SR for interaction.

Mastery goal orientationwas positively associated with SR for inter-action with others. Mastery goal orientation seems to be significant forall aspects of online learning representedwith interaction between stu-dent and content (Pintrich, 1999; Pintrich & De Groot, 1990; Yukselturk& Bulut, 2007), interaction between student and student (Yang et al.,2006), as well as interaction between student and instructor (McIsaacet al., 1999). Consistent with previous studies that reported positiverole of mastery goal orientation in self-regulation between studentand content (Pintrich, 1999; Pintrich & De Groot, 1990; Yukselturk &Bulut, 2007), the current study also found thatmastery goal orientationwas significantly associated with students' SR for interaction withothers. Our study results are consistent with that of Yang et al. (2006)that mastery goal orientation is related to students' perceived socialpresence with peers. Yang and her colleagues interpreted the result inthat the more mastery goal orientation students have, the more likelythey are to interact with peers. As a result, students feel that they aremore connected with their peers. Therefore, the greater the students'tendency toward mastery goal orientation, the more likely they are toself-regulate for interaction with others in online learning environ-ments where significant portions of the projects are accomplishedthrough interaction with others.

Table 5Summary of propositions and findings.

Variables Expected impacton SRfor interactionwith others

Main findings

Demographic informationAge + Not significantGender Not sure Not significantGrade + Increase in the likelihood of

students’ SR for interactionNumber of onlinecourse taken

+ Not significant

Importance ofmastering content

+ Increase in the likelihood ofstudents’ SR for interaction

Importance of interactionwith an instructor

+ Not significant

Importance of interactionwith other students

+ Increase in the likelihood ofstudents’ SR for interaction

Instructor scaffoldingfor interaction

+ Increase in the likelihood ofstudents’ SR for interaction

The findings indicated that perceived importance of interactionwith an instructor itself was not statistically significant as hypothe-sized. However, perceived importance of interaction with an instruc-tor contributed to the increase of overall R square change. In addition,students' perceived importance of interaction with other studentswas statistically significant to explain SR for interaction with othersas we hypothesized. The results also demonstrated that perceived im-portance of interaction with other students explained more varianceof SR for interaction with others (7.6%) than perceived importanceof interaction with an instructor (2%) partly because most of the stu-dent interactions were perhaps related with peers in online learningsettings. As students self-reported in Part I of the survey (see Learningcontexts in the Method section), interaction with other students wasrelated with their project in the form of group projects, online discus-sions, and individual projects with peer feedback. Students might,therefore, interact with other students more actively than they didwith the instructor in online learning environments.

Of greatest importance, instructor scaffolding for interaction wassignificantly associated with students’ SR for interaction with others inonline learning environments. The results were consistent with otherstudies in which interaction between student and content was investi-gated (McIsaac et al., 1999;Mullen & Tallent-Runnels, 2006).McIsaac etal. (1999) treated interaction between student and instructor as the sin-gle most important factor to describe students' success in online learn-ing environments along with interactions among students. Mullen andTallent-Runnels (2006) found that online instructor's scaffolding forinteraction significantly related to students' self-regulation betweenstudent and content. In addition, Artino (2008) found that instructionalquality is a significant predictor for students' satisfaction with theself-paced online course. This indicates that along with students' indi-vidual variables, such asmastery goal orientation and perceived impor-tance of interaction with others, the instructor also plays an importantrole for students’ SR for interaction by providing appropriate scaffoldingin online learning environments. Instructor scaffolding for interactionmost significantly explained students’ SR for interaction with others(10.3%) among other variables. The results indicated that SR for interac-tion with others is not just an individual's effort. Instead, it is a mutualeffort between individuals and instructors; therefore, online instructorsshould provide effective interaction strategies to promote students' SRfor interaction with others (Su et al., 2005).

6.1. Implications for theory development in SRL

Traditionally, interaction with others has been understood in a lim-ited way. Existing SRL studies regarded interaction with others as alearning strategy for self-regulation between student and content(Cho& Jonassen, 2009). Zimmerman andMartinez-Pons (1990) viewedinteractionwith others as seeking social assistance frompeers, teachers,or adults. Pintrich, Smith, Garcia, and McKeachie (1993) viewed inter-action with others as peer learning and help seeking, holding thatself-regulated learners seek necessary assistance from others to accom-plish learning tasks represented with self-regulation between studentand content.

However, in online learning environments, interaction with others(peers or instructors) is not limited to self-regulatory strategies orlearning resources represented with self-regulation between studentand content. Instead, interaction with others should be understood asa separate and important aspect of learning (Cho & Summers, 2012;Dabbagh & Kitsantas, 2005; Garner & Bol, 2011; McIsaac et al.,1999; Su et al., 2005) along with the interaction between studentand content. Many online learning tasks involve student collaborationwith others or to participate in online discussions to create knowledge orcommunity of inquiry (Cho & Summers, 2012; Dabbagh & Kitsantas,2005; Garrison, 2007; Su et al., 2005). Through interaction with others,students engage in diverse interaction activities, such as asking oranswering a question, seeking or providing help, participating in a

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discussion, collaborating with others, or providing or receiving feedback(Cho & Jonassen, 2009; Su et al., 2005). SR for interaction with othersshould therefore be regarded as a significant area to explain onlineself-regulation between student and student or between student andinstructor along with self-regulation between student and content.

6.2. Significance of the study

Different from most previous SRL studies that focused on self-regulation between student and content, the current study examinedself-regulation between student and student and self-regulation betweenstudent and instructor in online learning environments. The studyextends existing SRL research by using its most commonly accepteddefinition of SRL, viewing self-regulation as interaction between motiva-tion and cognition. In addition, this study applies the definition to onlinelearning context in order to investigate self-regulation between studentand student and self-regulation between student and instructor. Thestudy ismeaningful in that it identified variables related to SR for interac-tion with others and it empirically investigated self-regulation betweenstudent and others, specifically peers and instructors. The study providesa ground work for further online research investigating the relationshipsbetween student and others.

7. Conclusion

The current study has added empirical evidence to the existing bodyof SRL research, showing that individual variables (e.g., mastery goalorientation) as well as an external variable (instructor scaffoldingfor interaction) are significantly related to students' self-regulation.Extending the existing online research, this study suggests that self-regulation between the student and others should be understood asan important aspect of online self-regulation. Furthermore, this studydemonstrated that SR for interaction with others is a mutual effortbetween students and instructors. More empirical studies investigatingself-regulation between student and others will extend the currentSRL theories to online settings and expand our understanding of self-regulation in online learning environments.

Appendix A

1. My instructor provides basic guidelines to help students becomeaware of the importance of online interaction.

2. My instructor provides regular announcements with students tocommunicate clearly what she/he expects in interaction activity

3. My instructor is positive and supportive of students' comments toencourage students to continue participating in online interactions.

4. My instructor encourages students to ask questions.5. My instructor leaves a message to thank students for contribution

to online interactions.6. My instructor provides a timeline for students' online interactions.7. If students' general interaction is low, my instructor encourages

them to participate actively in interaction by sending a note.8. My instructor actively participates in online discussion by replying to

students, summarizing discussion, or asking questions to students.9. My instructormonitors group collaboration and encourages students

to participate actively in collaboration.10. Whenever posting a message, my instructor encourages students

to share their concern or problems with her/him.

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