Internet and Higher Education 13 (2010) 37–44

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Internet and Higher Education

Subject matter effects and the Community of Inquiry (CoI) framework: Anexploratory study

J.B. Arbaugh a,⁎, Arthur Bangert b, Martha Cleveland-Innes c

a University of Wisconsin Oshkosh, College of Business, 800 Algoma Blvd., Oshkosh, WI 54901, United Statesb Montana State University, United Statesc Athabasca University, Canada

⁎ Corresponding author. Tel.: +1 920 424 7189; fax:E-mail address: Arbaugh@uwosh.edu (J.B. Arbaugh)

1096-7516/$ – see front matter © 2009 Elsevier Inc. Aldoi:10.1016/j.iheduc.2009.10.006

a b s t r a c t

a r t i c l e i n f o

Keywords:

Community of InquiryDisciplinary differences in teaching andlearningOnline learning

This paper integrates the emerging literatures of empirical research on the Community of Inquiry (CoI)framework and disciplinary effects in online teaching and learning by examining the disciplinary differencesin perceptions of social, teaching, and cognitive presence of over 1500 students in seven disciplines at two U.S.institutions. Our results found significant disciplinary differences, particularly regarding cognitive presence,in soft, applied disciplines relative to other disciplines. These initial results suggest the possibility that theCoI framework may be more applicable to applied disciplines than pure disciplines. Our findings suggestinteresting opportunities for future researchers to consider how the individual elements of the CoI frameworkmay influence and be influenced by academic disciplines and how the framework may need to be refined ormodified to explain effective course conduct in pure disciplines.

+1 920 424 7413..

l rights reserved.

© 2009 Elsevier Inc. All rights reserved.

1. Introduction

In spite of the explosion of empirical research on online learningeffectiveness over the last decade (Sitzmann, Kraiger, Stewart, &Wisher, 2006; Tallent-Runnels et al., 2006), the emergence of adominant theoretical framework that explains online learningeffectiveness has yet to occur (Larreamendy-Joerns & Leinhardt,2006). One framework that has attracted increasing attention duringthe last decade is the Community of Inquiry (CoI) frameworkdeveloped by Garrison, Anderson and Archer (2000). Google Scholarshows that Garrison et al.'s initial article describing the framework hasbeen cited in other works at least 518 times as of February 2009,making it by far the most cited article from the journal The Internetand Higher Education. However, although the CoI framework is nowsomewhat familiar among education scholars (De Smet, Van Keer, &Valcke, 2008; Han & Hill, 2007; Schrire, 2006; Shea, 2006; Ho & Swan,2007), studies that examine the framework's generalizability toonline learning in other disciplines still is somewhat limited. Arapidly emerging stream of empirical research on the CoI model(Arbaugh, 2008; Shea & Bidjerano, 2009) suggests that its elementsare distinct, measureable constructs, addressing Garrison andArbaugh's (2007) recent call for this stream of research to movefrom early exploratory and descriptive studies toward rigorousempirical analysis. To date, however, these empirical studies havenot examined disciplinary impacts on the CoI. Also, the CoI framework

only considers course conduct and participant behaviors, whereasrecent research suggests that characteristics such as the coursemanagement system, academic discipline, and course design andpedagogy also may be significant predictors of course outcomes inonline and blended learning (Alavi, Marakas, & Yoo, 2002; Arbaugh,2005b; Arbaugh & Rau, 2007; Cao, Crews, Lin, Burgoon, & Nunna-maker, 2008; Hansen, 2008; Johnson, Hornik, & Salas, 2008; Webb,Gill, & Poe, 2005). For these reasons, examining the dimensions of theCommunity of Inquiry (CoI) framework in multi-disciplinary, multi-institution, graduate course-level research settings appears to bewarranted.

This article's other focus is to examine subject matter effects inonline learning and education. Somewhat surprisingly, research onthis topic is just beginning to receive empirical attention (Arbaugh,2005a, Arbaugh & Rau, 2007; Hornik, Sanders, Li, Moskal, & Dziuban,2008; Smith, Heindel, & Torres-Ayala, 2008). Even conceptual modelsof online learning tend to address content issues only in generalitiesbased upon technological approaches to content delivery (Anderson,2003; Benbunan-Fich, 2002), the development and flexibility ofcontent (Rungtusanatham, Ellram, Siferd, & Salik, 2004), or the extentto which content is able to generate participant interaction (Brandon& Hollingshead, 1999; Garrison et al., 2000; Roblyer & Wiencke,2004). This lack of attention to subject matter effects can be attributedin part to the methodological approaches used in online learningresearch. Historically, empirical research in online education hasrelied extensively upon single-course or single-discipline studies(Berger, 1999; Brower, 2003; Ellram& Easton, 1999; Piccoli, Ahmad, &Ives, 2001). Studies that have considered multiple disciplines eitherhave incorporated them as part of the background while examining

38 J.B. Arbaugh et al. / Internet and Higher Education 13 (2010) 37–44

other characteristics (Coppola, Hiltz, & Rotter, 2002; Shea, 2006), orhave argued that process-based approaches could be used to applysimilar teaching methods across dissimilar course content (Helmi,Haynes, & Maun, 2000).

This article examines the extent to which perceptions of social,teaching, and cognitive presences vary across disciplines. After a briefreview of the literatures on the Community of Inquiry and disciplinaryeffects in online education, we propose discipline-related differencesin student perceptions of CoI elements. We then report the pre-liminary results of studies of subject matter effects on perceptions ofthe CoI presences from two American institutions.

2. Theoretical frameworks and literature review

2.1. The Community of Inquiry framework

It is important that multiple learning domains are a foundation in amodel explicating online teaching practice and student learning. Onemodel with such an orientation is the Community of Inquiry (CoI)framework developed by Garrison et al. (2000). Lipman's (1991)initial work on communities of inquiry is a central foundation of theCoI model, and key to the integration of online and mobile learning.According to Lipman, engagement that supports learning is critical,creative and caring. All three of these criteria must co-exist with thefacilitation process of letting the argument lead in order forengagement to emerge. In demonstrating care for the discussionprocess, the dialogue becomes critical and creative.Whatever deliverymode, whatever context, and for any content, these premises hold.The CoI framework provides a process-oriented, comprehensivetheoretical model that can inform both research in online learningand the practice of online instruction. It assumes that effective onlinelearning requires the development of a community (MacDonald &Thompson, 2005; Rovai, 2002; Shea, 2006) supporting meaningfulinquiry and deep learning along all three domains.

The model views community as something that emerges insupport of online learning. It emerges in the relationship betweenthree elements: social presence, teaching presence and cognitivepresence. Social presence is defined as the degree to which learnersfeel socially and emotionally connected with others in an onlineenvironment; cognitive presence describes the extent to whichlearners are able to construct and confirmmeaning through sustainedreflection and discourse. The central organizing element is teachingpresence: the design, facilitation, and, most importantly the directionof cognitive and social processes for the realization of personallymeaningful and educationally worthwhile learning outcomes. The CoIframework provides a process-oriented, comprehensive theoreticalmodel that can inform both research in online learning and thepractice of online instruction. Each of these elements has multiplecomponents that are described in further detail below.

2.1.1. Social presenceThe categories of social presence are affective expression, open

communication and group cohesion. Affective expression specificallyrefers to mechanisms for injecting emotion into the environment inlieu of visual or oral cues, such as emoticons or parentheticalmetalinguistic cues such as “hmmm” or “yuk” (Gunawardena, 1995;Hiltz, 1994; Walther, 1992). Of the three types of presence in the CoIframework, the role of social presence in educational settings hasbeen the most extensively studied, both in online and face-to-facecourse settings (Gunawardena & Zittle, 1997; Richardson & Swan,2003; Rourke, Anderson, Garrison, & Archer, 2001; Walther, 1992).

2.1.2. Teaching presenceAnderson, Rourke, Garrison, & Archer, 2001 conceptualized

teaching presence as having three components: (1) instructionaldesign and organization; (2) facilitating discourse (originally called

“building understanding”); and (3) direct instruction. Althoughrecent empirical research debates whether teaching presence hastwo (Shea, 2006) or three (Arbaugh & Hwang, 2006) sub elements,the general conceptualization of this CoI element has been supportedby subsequent research (Coppola et al., 2002; LaPointe & Gunawar-dena, 2004; Stein, Wanstreet, Calvin, Overtoom, & Wheaton, 2005).

2.1.3. Cognitive presenceGarrison, Anderson, and Archer (2001) argued that cognitive

presence in online learning is developed as the result of a four phaseprocess. These phases are: (1) a triggering event, where some issue orproblem is identified for further inquiry; (2) exploration, wherestudents explore the issue both individually and corporately throughcritical reflection and discourse; (3) integration, where learnersconstruct meaning from the ideas developed during exploration; andfinally (4) resolution, where learners apply the newly gainedknowledge to educational contexts or workplace settings. Garrisonet al. (2001) proposed that participant interactions primarily reside inthe first two phases and that moving beyond the exploration phasetypically requires enhanced teaching presence to probe and diagnoseideas so that learners will move to higher level thinking in developingtheir ideas (Pisutova-Gerber & Malovicova, 2009; Schrire, 2006). Ofthe three CoI elements, cognitive presence has been the least studiedempirically to date (Garrison & Arbaugh, 2007). Although recentresearch debates whether such higher order learning can take place invirtual learning environments (Pisutova-Gerber & Malovicova, 2009;Rourke & Kanuka, 2009; Schellens & Valcke, 2006; Schrire, 2006), it islikely that much more research into the interaction of the CoIelements is needed before the question of higher order learning canbe definitively resolved.

Another factor that may predict the likelihood of higher orderlearning is the nature and level of the course content. To date, therelationship between subject matter and the CoI remains unexam-ined. In the next section, we will examine the emerging literature ondisciplinary effects in online learning to see how subject matter mightinteract with the CoI elements.

2.2. Subject matter effects in online learning

Historically, researchers and practitioners of online learning have,for the most part, tended to treat course content as a constant(Coppola et al., 2002; Hartman, Dziuban, & Moskal, 2000; Palloff &Pratt, 2001) and seek approaches to online learning effectiveness thatare applicable regardless of discipline (Davis & Wong, 2007; Gorski &Caspi, 2005; Hornik et al., 2008). This practice reflects a broaderomission of failing to consider the implications of disciplinarycharacteristics on teaching regardless of the medium (Neumann,2001; Shulman, 1993). Assuming course content to be a constant maybe problematic for several reasons. First, subject matter could be aconfounding variable in the comparison studies of online vs. face-to-face courses (Sitzmann et al., 2006; Zhao et al., 2005). Since mostcomparison studies have focused on a single course or courses withina single discipline, it is impossible to make definitive assertions on theimpact of disciplinary effects. Second, much of online learningresearch to date has focused on participant perceptions and behaviorsrather than on course content effects. While this research hasproduced some useful findings, unless there is further study of therelationship between the instructor's knowledge of course contentand the delivery medium (Anderson, 2003), one might conclude thatas long as an instructor cultivates participant interaction, establishes aclear course structure, and engages in conduct to reduce the socialdistance between him/herself and his/her students, that instructor isqualified to teach anything from engineering to the liberal arts tobusiness in an online setting (May & Short, 2003; Shea, Fredericksen,Pickett, & Pelz, 2003; Swan, 2003). Third, several authors have raisedconcerns about the viability of teaching more quantitatively or

Table 1Demographic characteristics of online students—School A.

n Percent

GenderMale 310 26Female 863 74

StatusUndergraduate 820 70Graduate 353 30

Age18–25 542 4326–30 162 1431–35 116 1035 and older 353 33

Type of online courseFully online 669 57Blended 504 43

Largest enrollments by disciplineEducation 368 31.4Nursing 295 25.1Business 110 9.3Allied Health and Technical 103 8.8Engineering 81 7.0Science and Math 80 6.8Enrollments across all other disciplines 136 11.6

39J.B. Arbaugh et al. / Internet and Higher Education 13 (2010) 37–44

technically-oriented subject matter via the internet (Anderson &Jackson, 2000; DiBiase, 2000; Dyrud, 2000; Smith et al., 2008).However, empirical support for this contention is somewhat limitedto date.

In response to increasing interest in the integration of content andpedagogical knowledge (Mishra & Koehler, 2006), discipline-specificapplications of technology to education (Neumann, 2001; Neumann,Perry, & Becher, 2002), and Wallace's (2002) call for research onsubject matter effects in online learning, online teaching and learningresearchers are beginning to examine the influence of disciplinaryeffects on course outcomes. Initial studies of disciplinary effectsspecific to business courses suggest that they may not have as largean effect on learning outcomes as do instructor experience andbehaviors (Arbaugh, 2005a; Drago, Peltier, & Sorensen, 2002), butthat disciplinary effects may have a strong effect on studentsatisfaction with online learning. However, recent studies havefound evidence of more pronounced effects. Arbaugh and Rau(2007) recently found that disciplinary effects explained 67% of thevariance in student satisfaction with the educational deliverymedium in a sample of forty online MBA courses. Smith et al.(2008) recently compared changes in course management tool usageand course evaluations between 2002 and 2007. Using Biglan's(1973) taxonomy to categorize disciplines, they found that the use ofdocument creation, dropbox, and messaging tools increased mark-edly in courses from applied disciplines (Engineering, Nursing,Education) and declined markedly in courses from pure disciplines(Natural Sciences, Humanities, Mathematics). This finding promptedthe authors to suggest that online courses in pure disciplines weremoving toward commoditization, while courses in applied disciplineswere moving toward customization. In arguably the most compre-hensive cross-disciplinary study to date, Hornik et al. (2008)examined data from 13,000 students in 167 courses taught during1997–2003. The sample included undergraduate courses in disci-plines within MIS, the hard sciences, nursing, social sciences, and thehumanities. Using Kuhn's (1970) model of paradigm development toframe disciplines, they found that student grades were higher andwithdrawals were lower for subjects with high paradigm develop-ment (hard sciences, nursing, health services) than for those withlow paradigm development (social sciences, humanities, MIS, PolySci), and that these differences were particularly evident in advancedlevel courses. Initial evidence also suggests that non-quantitativecourses may be better received than quantitative courses online, butwhether this is due to the delivery medium, the subject matter, orboth still is unclear (Anstine & Skidmore, 2005; Arbaugh & Rau,2007).

3. Methods

To increase the regional, disciplinary, and institutional generaliz-ability of our findings, we collected data from two institutions locatedin the United States. Data collection for the study began in Fall 2007and continued through Fall 2008. Descriptions of data collectionprocesses for each institution are provided in the following para-graphs. To operationalize the CoI framework and the emotionalpresence construct, we used a 34-item Community of InquiryFramework survey instrument. The instrument has tested favorablyfor construct validity and reliability (Arbaugh et al., 2008). Weconducted exploratory factor analyses to assess the empiricaldistinctiveness of the three CoI elements.

3.1. School A

The participants for this study were undergraduate and graduatestudents (n=1173) enrolled in fully online (57%) and blended onlineclasses (43%) offered throughWebCT during the spring 2008 semesterat a mid-sized western university (Table 1). Thirty-two percent of

students were enrolled in Education courses with the least number ofstudents enrolled in online courses in Social Sciences (5%) courses.Seventy percent of the online students were enrolled in undergrad-uate courses with the remaining 30% enrolled in graduate-levelcourses. Sixty-eight percent of the students surveyed indicated thatthey had taken at least one WebCT course prior to their participationin this study. The Community of Inquiry survey itemswere voluntarilycompleted by students using the WebCT quiz tool during the lasttwo weeks of the spring 2008 semester. The 34 item COI surveywas administered using the WebCT quiz through WebCT during theSpring, 2008 semester.

Guidelines suggested by Field (2000) and Fabrigar, Wenger,MacCallum, and Strahan (1999) were followed when conductingthe exploratory analysis. The 1173 student responses suppliedapproximately 35 individuals for each of the 34 variables exceedingrecommended sample size recommendations. Data screening proce-dures were also undertaken to evaluate the factorability of thecorrelation matrix. Results from the Kaiser–Meyer–Olkin Measure ofSampling Adequacy (.97) and Bartlett's Test of Sphericity (χ2

561=34,170.68, p<.001) indicated that the data were appropriate for thefactor analysis to proceed. Principal component analysis with obliquerotation (direct obliminal in SPSS) methods was used to identify themost interpretable factor structure from the data collected. The use ofoblique rotation methods is appropriate due to the interaction of thethree presences proposed by the COI framework.

Initially a four-factor solution was identified by the analysis.However, after evaluating the number of factors to extract against,Kaiser's criterion, Cattell's (1966) Scree test and the pattern matrix, athree-factor solution was found to be the more parsimonious. The firstfactor extracted captured all but one item from the cognitive presencesubscale for over half (52.1%) of the total variance. Item28was found tocrossload on both the Cognitive Presence (.426) and Social Presence(.461) factors. The second factor to emerge, Teaching Presence, includedthe 15 teaching presence items and accounted for 8.96 % of the variance.The third factor, Social Presence accounted for 4.34% of the variance andcaptured all 9 social presence items. However, Item 14 was found tocrossload on both the Social Presence (.358) and Cognitive Presence(.356) factors. Item28 alsowas found to crossload onboth the CognitivePresence (.424) and Social Presence (.453) factors. The Cronbach's alphayielded internal consistency reliabilities of .96 for Teaching Presence, .91for Social Presence and .95 for Cognitive Presence. The rotated factorloadings are presented in Table 2.

Table 2COI items and factor loadings for School A.

Components

Cognitivepresence

Teachingpresence

Socialpresence

Teaching presence1 The instructor clearly communicated important course goals. − .136 − .891 .0412 The instructor clearly communicated important course topics. − .079 − .879 .0513 The instructor provided clear instructions on how to participate in course learning activities. − .093 − .875 .0884 The instructor clearly communicated important due dates/time frames for learning activities. − .176 − .871 .0885 The instructor was helpful in identifying areas of agreement and disagreement on course topics that helped me to learn. .036 − .876 − .0256 The instructor was helpful in guiding the class towards understanding course topics in a way that helped me to clarify my thinking. .114 − .836 − .0367 The instructor helped me to keep course participants engaged and participating in productive dialogue. .215 − .700 − .0338 The instructor helped me keep the course participants on task in a way that helped them to learn. .277 − .704 − .0529 The instructor encouraged course participants to explore new concepts in this course. .283 − .555 .02510 Instructor actions reinforced the development of a sense of community among course participants. .244 .591 .07311 The instructor helped me to focus discussion on relevant issues in a way that helped me to learn. .237 − .682 − .00212 The instructor provided feedback that helped me to understand my strengths and weaknesses relative to the course' goals and objectives. .284 − .580 − .03013 The instructor provided feedback in a timely fashion. .071 − .663 − .018

Social presence14 Getting to know other course participants gave me a sense of belonging in the course. .356 − .106 .38515 I was able to form distinct impressions of some course participants. .232 − .094 .44016 Online or web-based communication is an excellent medium for social interaction. .135 .033 .60717 I felt comfortable conversing through the online medium. − .025 .004 .83718 I felt comfortable participating in course discussions. − .091 − .084 .85419 I felt comfortable interacting with other course participants. − .041 − .064 .85020 I felt comfortable disagreeing with other course participants while still maintaining a sense of trust. − .052 − .022 .81521 I felt that my point of view was acknowledged by other course participants. .090 − .072 .69022 Online discussion helped me to develop a sense of collaboration. .280 .034 .643

Cognitive presence23 Problems posed increased my interest in course issues. .545 − .052 .30824 Course activities piqued my curiosity. .671 − .055 .18025 I felt motivated to explore content related questions. .702 − .029 .16126 I utilized a variety of information sources to explore problems posed in this course. .681 .033 .13327 Brainstorming and finding relevant information helped me to resolve content related questions. .751 .008 .06728 Online discussions were valuable in helping me appreciate different perspectives. .426 .038 .46129 Combining new information helped answer questions raised in course activities. .698 − .032 .18230 Learning activities helped me to construct explanations/solutions. .717 − .105 .04331 Reflection on course content and discussions helped me understand fundamental concepts in this class.32 I can describe ways to test and apply knowledge created in this course. .774 − .098 − .06733 I have developed solutions to course problems that can be applied in practice. .797 − .080 − .05934 I can apply the knowledge created in this course to my work or other non-class related activities. .745 − .094 − .060Eignevalues 17.71 3.05 1.48Percent of variance 52.1 8.96 4.34Coefficient alpha .95 .96 .91

40 J.B. Arbaugh et al. / Internet and Higher Education 13 (2010) 37–44

3.2. School B

The sample from School B came from 35 online courses conductedin the MBA program of a Mid-Western U. S. university over foursemesters from September 2007 through December 2008. Thesecourseswere in subjects such as organizational behavior, internationalbusiness, business strategy, human resource management, projectmanagement, operations management, information systems, finance,accounting, ethics, and professional development. Eleven differentinstructors taught the courses included in the study. These instructorsranged in experience fromhaving taught one previous online course tohaving taught over fifty previous online courses during the period ofthe study. All courses were conducted using Desire To Learn (D2L)course management system. The courses were distance learningclasses with students taught primarily through asynchronous web-based interactions, although seventeen of the class sections had an on-site orientation meeting. Class sizes ranged from 4 to 36.

Data collection from students was completed in a two-stepprocess. In the first step, students were emailed an invitation toparticipate in a web-based survey during the final week of the courseregarding their perceptions of the learning environment, coursemanagement system, instructor effectiveness, the knowledge theyacquired, and their satisfaction with the internet as the coursedelivery medium. Students were sent a reminder message if they had

not responded to the invitation or fully completed the web-basedsurvey. The second step was conducted 7–10 days after the initialemail invitation was sent. In this step, students who had notresponded to the electronic survey were mailed a paper copy of theoriginal survey. 409 students provided useable responses, resulting ina response rate of 53.5% (409 of 764). The mean student age was 32.9(SD=7.7), and 59% of the respondents were male.

The 34-item CoI instrument was administered using seven-pointLikert-type scales. Factor analysis techniques employed were similarto those used for School A. Initial results yielded a five factor solution,but the fifth factor loaded upon a single item. Additional analyses ofthree- and four-factor solutions revealed that a three-factor solutionhad the highest reliability and was the most readily explained bytheory. The thirty-four items loaded on the same factors as for SchoolA except for one item that loaded onto cognitive presence rather thansocial presence. Coefficient alphas for teaching, cognitive, and socialpresence were .96, .94, and .87 respectively.

4. Results

4.1. School A

The courses for the students responding to the CoI survey fromSchool A were grouped into the following eight academic disciplines:

Table 3School A: factorial ANOVA comparisons for delivery mode by discipline.

Business Nursing Science and Mathematics Engineering Education Social Sciences Allied Health and Technical Other

Teaching presenceOnline 62.58 63.84 63.47 55.35 65.27 64.63 68.92 59.30

(13.50)a (12.50) (10.58) (17.44) (12.51) (12.34) (8.13) (13.70)n=45b n=164 n=32 n=17 n=288 n=16 n=63 n=44

Hybrid 58.60 61.01 61.46 60.25 60.70 59.35 65.27 61.67(13.13) (12.72) (12.13) (12.18) (14.78) (13.61) (12.64) (12.28)n=65 n=138 n=48 n=64 n=90 n=40 n=40 n=36

Social presenceOnline 41.87 41.18 41.50 40.88 42.96 42.63 44.06 40.35

(7.36) (7.69) (6.62) (9.36) (8.05) (7.60) (7.25) (8.46)n=45 n=162 n=32 n=17 n=287 n=16 n=63 n=43

Hybrid 37.78 39.80 39.02 39.41 38.38 36.10 45.41 37.50(8.50) (8.60) (7.86) (8.05) (8.86) (9.53) (5.32) (9.56)n=65 n=137 n=47 n=61 n=90 n=40 n=22 n=36

Cognitive presenceOnline 56.62 57.12 57.13 55.53 59.51 56.00 61.33 55.30

(10.35) (9.49) (8.20) (15.03) (9.75) (11.64) (7.62) (10.68)n=45 n=162 n=32 n=17 n=286 n=16 n=63 n=43

Hybrid 52.49 54.90 54.57 53.13 52.94 49.88 60.64 52.28(10.54) (9.55) (10.83) (10.14) (13.18) (13.30) (6.83) (12.05)n=65 n=136 n=47 n=61 n=90 n=40 n=22 n=36

41J.B. Arbaugh et al. / Internet and Higher Education 13 (2010) 37–44

Education (n=378), Nursing (n=302), Business (n=110), AlliedHealth/Technical (n=85), Engineering (n=81), Science/Math(n=80), Social Sciences (n=56) and other courses from acrossvaried disciplines (n=82). The Allied Health and Technical coursesincluded students enrolled in certification programs such as medicaltranscription, web development and other trade courses. Online andhybrid courses in biology, chemistry, physics, ecology, geology andmathematics were classified in the science and mathematicsdisciplines category. The social science disciplines category includeda broad array of courses representing sociology, anthropology,psychology, history, and political science. The remaining courses notdescribed by one of the seven disciplines were classified as “other”.Examples of courses classified into this category included English, art,foreign languages, and general studies.

Descriptive statistics by discipline and delivery mode are reportedin Table 3. Two-way factorial ANOVAs were conducted to determine ifthere were significant differences across course disciplines anddelivery mode (i.e. online vs. blended) in each of the three CoIfactors. For the teaching presence factor, the only significant resultwas for the course discipline main effect, F(7,1156)=2.94, p=.005,η2=.017. The social presence comparisons revealed significant maineffects for both discipline, F(7,1156)=3.21, p=.002, η2=.02 anddelivery mode, F(7,1156)=18.60, p=.000, η2=.02. Significant maineffects were also found for discipline, F(7,1145)=3.74, p=.001,η2=.022 and delivery mode, F(7,1145)=3.74, p=.001, η2=.022 forthe cognitive presence factor.

Students enrolled in fully online courses rated their perceptions ofsocial and cognitive presence significantly higher than students

Table 4ANOVA comparisons of means between disciplines on CoI elements—School B.

Variables Courses

1. Macro-Management 2. Operations 3. Micro-Management

(n=64) (n=114) (n=89)

Teaching presence 5.45 5.34 5.29(1.26) (1.60) (1.02)

Cognitive presence 5.75 5.57 5.50(.77) (.98) (.74)

Social presence 5.68 5.76 5.69(.95) (.82) (.76)

Note: standard deviations in parentheses.

enrolled in blended courses. Post hoc analysis for the significantdiscipline main effect found that students enrolled in Allied Healthand Technical courses generally perceived, teaching, social andcognitive presence to be significantly higher than students enrolledin courses classified as Nursing, Business, Engineering, Science/Math,Social Sciences, and Other. However, mean scores for the teachingpresence construct were not found to differ significantly between theAllied Health/Technical and Science/Math disciplines. In addition, nosignificant differences were found on any of the CoI factors forcomparisons between Allied Health and Technical and Educationcourse disciplines. Regardless of CoI factor, students enrolled inEducation courses evidenced significantly higher mean scores thanstudents enrolled in Engineering courses.

4.2. School B

The thirty-five courses were grouped into six categories basedupon traditional major subject areas in business schools. Thecategories were: 1) Macro-Management (Strategy and InternationalBusiness), 2) Operations (MIS, Project Management, and DecisionAnalysis), 3) Micro-Management (Organizational Behavior andHuman Resources), 4) Quantitative (Accounting and Finance), 5)Marketing, and 6) Other (courses in Business Law, Ethics, andBusiness Literature) (Table 4). Management-related courses weredivided into micro- and macro-categories because of recent researchto reflect the micro-level courses' grounding in the “pure” disciplineof psychology (Biglan, 1973; Burke & Moore, 2003). The results ofcross-category comparisons of the disciplines on the CoI factors are

4. Quantitative 5. Marketing 6. Other Results of 2-tailed post hoc tests

(n=23) (n=26) (n=91) (p>.05)

4.88 5.91 6.05 5,6 >1–4(1.58) (.84) (.71)5.03 5.91 5.95 All >4(1.40) (.48) (.72)5.51 5.98 6.00 6 >1, 3(.77) (.57) (.70)

42 J.B. Arbaugh et al. / Internet and Higher Education 13 (2010) 37–44

provided in Table 3. The most pronounced differences between thedisciplines were in teaching presence. Courses in the Marketing and“Other” categories scored significantly higher on teaching presencethan did the other disciplines. All categories scored significantlyhigher on cognitive presence than did courses in the quantitativecategory. Significant differences in social presencewereminimal, withthe only differences being the “Other” category scoring higher thanthe Macro- and Micro-Management categories.

5. Discussion

These studies identified discipline-based differences in studentperceptions of social, cognitive, and teaching presence. Results forSchool A found that students enrolled in the Allied Health andTechnical courses clearly rated the CoI dimensions higher and in mostcases significantly higher than students enrolled in the otheracademic disciplines. Courses in the “Quantitative” category fromSchool B reported significantly lower scores in cognitive presencethan courses in the other categories, with courses in the “Other”category reporting significantly higher scores in teaching and socialpresence than both the “Macro-” and “Micro-” management catego-ries. Although these findings certainly need to be further elaboratedwith additional research, this initial study provides a foundation fordeveloping discipline-specific applications of the CoI framework. Weprovide some possible explanations for the findings in the followingparagraphs.

Recent work to develop typologies of discipline-specific teachingapproaches by Neumann (2001) and Neumann et al. (2002) provideparticularly useful insights for our study. Building upon the work ofscholars such as Biglan (1973) and Becher (1994), they conceptual-ized that academic disciplines could be categorized on two dimen-sions: the extent to which the discipline had a dominant paradigm,indicated as being “hard” those that did, or “soft” for those for whichcompeting paradigms exist, and whether the discipline had interest inapplication, represented a discipline being categorized as “pure” or“applied”. Some common characteristics of these categories areprovided in Table 5.

Pure fields place more attention on knowledge acquisition, whereapplication and integration receive more emphasis in applied fields.Hard, applied disciplines call for progressive mastery of techniques inlinear sequences based upon factual knowledge. Students in hard,applied disciplines are expected to be linear thinkers. Similar to hard,pure disciplines, teaching activities will be focused and instructive,with the primary emphasis being on the teacher informing thestudent. Conversely, teaching in soft, applied disciplines will be morefree-ranging, with knowledge building being a formative processwhere teaching and learning activities tend to be constructive andreiterative, shaped by practically honed knowledge and espousedtheory. Students are expected to be more lateral thinkers. In soft,applied disciplines, essays and group projects predominate, and self-assessments are common. Because of the emphasis on developingpractical skills, there is a greater need for constructive, informative

Table 5Academic discipline categories and selected characteristics.

Emphasis on application Level of paradigm development

Hard

Pure Cumulative;Atomistic (crystalline/tree-like);Concerned with universals;Resulting in discovery/explanation

Applied Purposive;Pragmatic (know-how via hard knowledge);Concerned with mastery of physical environmResulting in products/techniques

Sources: Becher (1994), Biglan (1973), Neumann (2001), Neumann, Parry, and Becher (20

feedback on assessments. Emphasis on factual knowledge, and byextension examinations, extends from hard, pure to hard, applieddisciplines, although problem solving will be emphasized more in thehard, applied disciplines. Emphasis on widely transferrable skills willbe greater in soft, applied disciplines than hard, applied ones, as willreflective practice and lifelong learning.

These categorizations provide possible explanations for ourfindings, particularly in the area of cognitive presence. Since thefinal stage of cognitive presence, resolution, focuses on applyingnewly gained knowledge, it is not surprising that Allied Healthshowed higher scores in cognitive presence than the pure disciplinessuch as math, science and the social sciences in School A. Also, theconstructive and reiterative nature of knowledge building in soft,applied disciplines better lends itself to a process that allows forexploration and integration than is the case for hard, applieddisciplines, which could explain why Allied Health had highercognitive presence scores than Nursing and Engineering, but notEducation. The hard, applied vs. soft, applied distinction also may helpexplain differences in cognitive presence in the school B sample.Although knowledge application is a goal of the quantitative courses,the focus of courses in Finance and Accounting being acquisition ofquantitative tools and techniques to solve problems delivered by acontent expert, such an approach to learning and education does notlend itself to exploration and integration to the extent provided by thesofter disciplines of management, marketing, and human resources(Anstine & Skidmore, 2005; Arbaugh et al., 2009; Sarker & Nicholson,2005).

Another reason for these findings may be that instructors for thesecourses tend to use more authentic and applied learning tasks ascompared to online courses in other academic disciplines that focuson more conceptual learning (Yukselturk & Yildirim, 2008). The skilltransfer to the workplace and beyond is more likely to occur when“soft”, “applied” courses feature instructional strategies are focused onsolving problems (DeFillippi & Milter, 2009; MacDonald, Bullen, &Kozak, 2007). The instructional, social and cognitive elements of theCoI framework are necessary for encouraging learner discoursecharacterized by brainstorming, consensus building and testingsolutions for problem issues under consideration. Teaching presenceis promoted when instructors “seed” or trigger critical discourse bycreating and presenting a problem-based learning activity forstudents to consider. The quality of inquiry achieved throughproblem-based learning activities that promote learning exchangesthat involve brainstorming, consensus building and validation ofproposed solutions supports the social presence or one's sense ofbeing or belonging in a course. Results from CoI ratings by studentenrolled in the Allied Health and Technical courses supports previousresearch which suggests that learner discourse reaching the highestphases of cognitive presence is associatedwith activities that promoteand sustain both teaching and social presence (Bangert, 2008).

Reasons for differences in teaching and social presence do not lendthemselves to explanation as readily. Using the hard vs. soft-appliedparadigm, it is possible that we would not see differences in teaching

Soft

Reiterative;Holistic (organic/river-like);Concerned with particulars;Resulting in understanding/interpretation

ent;

Functional;Utilitarian (know-how via soft knowledge);Concerned with enhancement of [semi-] professional practice;Resulting in protocols/procedures

02).

43J.B. Arbaugh et al. / Internet and Higher Education 13 (2010) 37–44

presence due to the characteristics of its sub elements. Given theimportance of knowledge dissemination from the content expert inhard disciplines, it is reasonable to expect that direct instructionwould be particularly important in such courses. Conversely, softapplied disciplines may warrant a higher emphasis on facilitatingdiscourse due to the more free-ranging nature of knowledgedevelopment. This free-ranging nature of knowledge constructionmay explain why topics with less defined parameters such as businessliterature and ethics may have scored higher on teaching presencethan did those more clearly defined disciplines in the school B sample.It is also possible that since social presence depends more and moredirectly upon the learners to a greater extent than do the other twopresences, social presence may not lend itself to discipline-baseddifferences to the extent of the other two elements. However, theresults and explanations provided here generate abundant opportu-nities for future researchers to extend these findings.

In addition to extending these findings, we also encourage futureresearchers to address the limitations of our study, of which we canidentify at least three. First, although we have increased generaliz-ability relative to much of the research in online learning with ourmulti-institutional sample, our coverage of the range of disciplines inhigher education is somewhat limited. We particularly encouragescholars to develop research samples where the “pure” disciplineshave greater representation. Second, our measures rely exclusivelyupon students' end of course perceptions. Although the knowledgecreation and application orientation of the elements makes such anapproach appropriate and even attractive for measuring cognitivepresence, it might also explain why we did not find more preciselydefined differences between the disciplines for social and teachingpresence. Finally, although ours is a multi-institutional sample, it is asample comprised exclusively of North American institutions. Asonline teaching and learning becomes increasingly common in otherparts of the world, we particularly encourage researchers from thoseregions to examine the generalizability of these findings to their ownsettings.

6. Conclusion

In spite of these limitations, this paper does contribute byintegrating the emerging literatures of empirical research on the CoIframework and disciplinary effects in online teaching and learning.This initial study of subject matter effects on student perceptions ofcognitive, social, and teaching presence suggests the possibility thatthe CoI frameworkmay be more applicable to applied disciplines thanpure disciplines. The CoI's assumption of a constructivist approach toteaching and learning may not align with the cumulative, instructor-oriented approaches particularly associated with hard, pure disci-plines (Garrison et al., 2000; Smith et al., 2008). The emphasis onusing inquiry to develop applicable knowledge suggests the possibil-ity that the framework may be more appropriate for disciplines suchas education, health care, and business. Our findings suggestinteresting opportunities for future researchers to consider how theindividual elements may influence and be influenced by academicdisciplines and how the framework may need to be refined ormodified to explain effective course conduct in pure disciplines.Although our findings are preliminary, they open an interesting areaof inquiry for those scholars intrigued by the interaction of academicdiscipline and the CoI framework.

Acknowledgements

An earlier version of this article was presented in the OnlineTeaching and Learning SIG track at the 2009 annual meeting of theAmerican Educational Research Association. The article's first authorwas generously supported in this research by the 2009 Faculty

Fellowship from the Graduate Management Admissions Council'sManagement Education Research Institute.

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