Teaching presence transfer between classroom and online MBA learning environments John E. Wisneski Indiana University 1
Abstract Introduction Advances in technology have made online education an increasingly common occurrence across most collegiate programs, including business schools where internet-based courses are seen as vital alternatives or supplements to traditional classroom-based courses (Rossin et. al., 2009). Online courses afford the student greater convenience and flexibility through anytime, anywhere learning (Bocchi et al., 2004), and have allowed individuals participating in MBA programs the ability to remain in the workforce and keep working while completing their degree requirements. In 2011, more than 6 million college students enrolled in online courses in the US alone (Allen & Seaman, 2012). Given the undeniable popularity of these courses in U.S. MBA programs, it is no longer a question of if this delivery model is here to stay, but rather how can programs organize and leverage their existing assets to transition from relying solely on the traditional classroom experience to attract students. Problem Statement In a 2011 survey conducted by the Sloan Consortium, 58% of faculty members at institutions of higher education in the U.S. indicated the growth of online learning at their institution filled them with more fear than excitement (Allen & Seaman, 2012). Despite numerous studies to contrary (Warren & Holloman, 2006, Harmon & Labrinos, 2007, Toper, 2007, Brownstein et al., 2008, Arbaugh, 2009), nearly two-thirds of these faculty members believed the learning outcomes for an online course are inferior or somewhat inferior to those for a comparable face-to-face course, and only 40% agreed that online education could be as effective in helping students learn as in-person instruction. Despite this skepticism among faculty, in the same study conducted by the Sloan Consortium, two-thirds of chief academic officers described online learning as a critical component of their institutions long-term strategy. Indeed, online education is here to stay, and as a result, it is becoming increasingly important for the scholarly community to organize and leverage their existing assets to transition from relying solely on the traditional classroom experience to attract students. This includes asking more and more instructors to shift from leading in-residence to online courses. As recently as 2010, however, nearly 20% of institutions did not provide any training (not even informal mentoring) for their faculty teaching online courses (Allen & Seaman, 2010). Given that many faculty members must transition to online teaching with little or no training, it becomes imperative that the scholarly community explore the effectiveness of their skill transfer and identify opportunities to improve the success of this transition. Literature Review Research on the efficacy and effectiveness of online business education began in earnest at the turn of the century. Scholars have tended to focus in three areas of research: what we know about the online student learner, what we know about the online instructor, and what we know about the technology platform used to deliver instruction. What follows is a summary of the current literature, organized by these three categories. What we know about the student Program Selection Criteria 2
Only recently have researchers attempted to describe the motivations of a typical MBA student in selecting online degree programs. In a 2010 qualitative study, Rydzewski et al., found several important selection criteria amongst both recent alums and current students in the federated Georgia WebMBA program. The importance of characteristics fall into four categories: availability, quality, program length and cost, and courses offered. It appears more experienced students view availability to be more important criteria than those with fewer years of previous work experience. However, we still do not know how students determine the strength of each factor when making the selection decision, nor do we have any comparison of a students motivation visa-vie the decision to attend a traditional MBA Program. Demographic Predictors There exists a close relationship between the sense of learning community and perceived learning of the individual student (Liu et. al., 2007). There may be a gender-based difference in class participation, with females participating far more in the online setting versus the traditional classroom (Arbaugh, 2000). If in fact female students prioritize building relationships, and as a result, contribute more actively in online courses, that may allow us to draw conclusions to support improved performance for all learners. Additional research on the role gender plays in online learning suggests that the effort women put into building a sense of community often goes unnoticed by their peers (Arbaugh, 2005). Further research on gender bias in online MBA programs may help resolve this apparent contradiction in the literature. Age, skill level, attitudes, number of courses, and prior work experience were not viewed in any of the reviewed publications to be a predictor of perceived learning or satisfaction in the online environment. However, as Liu et al. (2010) point out, international students face significant learning barriers when it comes to language, cultural differences, time zone differences, academic conduct assumptions, and the lack of multi-cultural content. What does matter, however, is the students familiarity with the online learning environment. Online learning quality increases significantly as students take subsequent online courses, but much of this increase occurs between the first and second online course (Arbaugh, 2004). Learning Styles One major distinction that has often been studied between online and traditional business education is the type of interaction students have with their peers, the instructor, and the online learning platform. Although learner-learner interactions enhance teaming skills, only learner-instructor and learner-system interactions predict higher perceptions of online learning (Arbaugh et al., 2007). The impact learner-learner interaction has on student satisfaction with online learning is less clear. Although some scholars have predicted a strong positive relationship between learner-learner interaction and satisfaction (Eom, 2006), others have found just the opposite to be true (Arbaugh et al., 2007). We also know that course structure influences student satisfaction (Arbaugh et al., 2007). Lee et al., (2009) examined the case method in-depth, and found that both students and instructors value case-based learning. Although whole class discussion was the most common instructional activity, students were equally satisfied with asynchronous discussion forums, question and answer formats, round robin discussions, role play, and simple reflection activities. From the literature, we also see that blended classroom and virtual-based interaction may lead to more effective learning (Arbaugh et al., 2009). Generally, online courses are at least comparable to classroom courses in achieving the desired learning outcomes, but students report a more positive experience from learning in a high dialogue, low structure environment online (Arbaugh et al, 2009). 3
With the emphasis students placed on high levels of dialog online, it becomes imperative the moderator of such discussions work to employ strategies that manage over-participation, or risk losing the sense of community so strongly correlated to higher levels of academic achievement. Curricula There has also been an emerging discussion in the literature with respect to the role content or subject matter plays in both student perceived learning and satisfaction. Inquiry into the appropriateness of specific disciplines for the online learning environment has yielded several results. Perceived learning tends to be higher for students in less quantitative courses (Arbaugh et al., 2007). The results regarding disciplines effect on student satisfaction, however, are less convincing. The only study that investigated the question found only a weak correlation between discipline and student satisfaction (Arbaugh et. al., 2007). It appears Finance and Economics are two of the most underrepresented disciplines in the research of online learning strategies, while Information Systems and Management have the highest volumes of published research (Arbaugh et al., 2009). What we know about the instructor Instructor Demographics Although not surprising, instructor experience with online teaching is a strong predictor of both students perceived learning and satisfaction (Arbaugh, 2005). The typical online MBA student tends to be more experienced, and many have full-time employment responsibilities competing for their attention throughout their study. For this reason, there is very little patience for instructors who are unfamiliar with the technical resources, and/or are perceived to be learning online interaction styles as they go. Online students expect the instructor to be comfortable with the capabilities of employed technology, and how to moderate asynchronous discussions. Additionally, a point of divergence between student expectations and instructor attitudes has been identified (Liu et al., 2007). Although students reported a close relationship between the sense of a learning community and perceived learning, overall the instructors in this study reported to not be community building minded. Instructors appear to be far more focused on material coverage, than on the process of collaborative learning online. Important research remains to confirm this discrepancy, and identify guidelines for instructors to strike a balance between content and process. Instructional Strategies In perhaps a nod to the efficiency that students seeking in choosing online MBA Programs (Mujtaba, 2007), students expressed perceived learning gains when instructors choose an objectivist learning approach (Arbaugh, 2006). As opposed to a constructivist approach, which provides students the latitude to construct knowledge for themselves, there is an expressed need for both clarity and discernible correctness when instructors elect clear and reasonable learning objectives for the course. Along with objectivist approaches, students also report higher perceiving learning when group work selected in favor of individual assignments. This goes hand in hand with the students desire to build a learning community, and suggests that online MBA students enjoy interacting with their peers in support of achieving the identified learning outcomes. Not only is interaction with peers important in building teaming skills in online MBA courses, students also report that an interactive teaching style may be most appropriate for online courses as well (Arbaugh, 2002). Learner-instructor interactions show some of the strongest predictors of online 4
student s perceived learning (Arbaugh et. al., 2009). Students want the instructor to be engaged, and not simply load the course software with lecture slides and stand aside. Although the interactivity desired does not always have to be driven by the instructor, students express the need to feel activity and a sense of presence from the instructor in a virtual classroom. Virtual Classroom Behaviors As an indication of the vital role an instructor plays in online learning, students report the behavioral characteristics of the instructor as a strong predictor not only of their perceived learning, but also of their overall satisfaction with the course (Arbaugh, 2002). Several consistent themes have emerged regarding the instructors behaviors in an online course that led to both higher levels of perceived student learning, as well as increased student satisfaction. First, it is clear from the literature that instructors are expected to provide a facilitative, yet expert role in the virtual classroom. Online students do not expect a sage on the stage, or someone who blunts the process of discovery. In fact, somewhat surprisingly, instructor login was found to be a negative predictor of learning (Arbaugh, 2010). Instead, Bower (2003) offers three principles for instructor behavior: 1) silence is golden, 2) don t answer but promote discovery, and 3) encourage and inspire. Immediacy behaviors that provide timely, relevant feedback were positive predictors of both learning and satisfaction, but results indicate this is a small, yet significant explanation in the variance of student responses (Arbaugh, 2010). Secondly, and equally as important, there is also evidence that instructors need to structure and organize their courses before the course begins, focusing on the efficient engagement with the enrolled students while the class is in session. There is heavy emphasis in the literature on the need for the instructor to communicate goals, provide clear instructions, and set deadlines for online MBA students (Arbaugh, 2010). This approach allows students with competing demands for their time at work and home to more effectively manage the additional workload commensurate with taking an online course. Once structure is provided upon initial setup of the course, ongoing process interventions by the instructor are necessary to promote discovery and understanding. What we know about the learning environment Course technology When evaluating the role technology plays in online business education, three important variables have been considered: usefulness, ease of use, and flexibility. The flexibility of the medium and the ability to develop an interactive course environment play a larger role in determining student satisfaction than the ease or frequency with which the medium is used by students (Arbaugh, 2000). Further, it appears current technologies are not detrimental to the learning process, although further research by discipline may uncover obstacles with quantitative courses. Given the centrality of course technology as a delivery medium for online instruction, many scholars have called for more institutional support that educates faculty on the use of technology prior to teaching online (Arbaugh et. al., 2009). Media Variety The variety of media used in an online course has been consistently evaluated as being positively associated with both perceived learning and student satisfaction (Arbaugh et. al., 2007). Online students expect the instructor to utilize the full capability of online software, but expect consistent use of these capabilities throughout their program. Recent qualitative research from one 5
major Midwest online MBA program suggests student frustration can set in when instructors place common course documents such as syllabi and assignment descriptions in different locations throughout the online platform. Follow-on research has attempted to clarify what students mean by media variety. Mujtaba (2007) conducted qualitative research with online students at the H. Wayne Huizenga School of Business and Entrepreneurship of Nova Southeastern University, and found that students would like to see downloadable audio and visual lectures, CD-ROM based simulations, synchronous voice and video discussions, and external web hyperlinks all incorporated in the online environment to aid and assist in the learning process. Going forward, the implication is that instructors should spend significant time upfront designing curriculum based on clear learning outcomes, and apply the technical solution that best supports this design. Unfortunately, many faculty currently approach their curriculum in reverse; spending time with structuring content to fit within the confines of their current capabilities to deliver within the online platform. Skills and Knowledge Transfer In the broadest sense, the term transfer refers to a simple pattern of learn-it-here, apply-itthere (Perkins & Salomon, 2012). Most instructors seeking to transfer their instructional strategies from the classroom to a virtual learning environment will undoubtedly elect for simple routine transfer. Transfer situations can also be opportunities for invention and reorganization, however, and are not confined to simply carrying forward and applying the same knowledge (Lobato, 2012). Adaptive transfer involves not just routine application of knowledge in a new context, but rather suggests adapting and revising prior knowledge in the context of the transfer (Schwartz et al., 2012). In much the same way that adjustments may be made in the destination context, backward transfer refers to the phenomenon where dealing with the new situation may in fact lead to revisions in a prior conception. The similarities between traditional and online student learning are striking, and instructors will find that successful strategies employed online can often lead to similar benefits in the classroom. The process of transfer is not confined to an individual actor, however, and often is the result of the movement of knowledge within the organization that yields an exemplar performance. In most organizations, a best practice is a relevant example that produces results better than any known alternative. To the extent that stand-out online instructors are willing to share and promote their instructional strategies with their peers, best practices may be defined and incorporated into an institutions online learning platform and serve as a standard template to help new instructors become proficient in the environment. The success of best practice transfer is never guaranteed. Several barriers to best practice transfer, collectively referred to as the stickiness of knowledge transfer, have been documented in the literature (Szulanski, 2003). Examples of these knowledge barriers include the recipient s level of prior knowledge, how well the transferred practice is understood within the organization, and the recipient s receptiveness to adopting the best practice. In higher education, where autonomy often overrides collective knowledge sharing, the degree to which best practices are sticky may be quite high. Transition from Classroom to Online Instruction Instructors new to the online environment should be comforted in knowing that many strategies used by exemplary educators who teach face-to-face can in fact be replicated in the online teaching environment with similar positive results (Edwards et al., 2011). Although there is no one recipe for being successful, Pelz (2004) found that exemplary educators who make the transition successfully must 6
strive for presence in order to be effective (p. 34). Presence is more than just being responsive to forum posts or personal email interactions, however, and extends to being aware of student s individualized learning needs and responding in an empathetic and supportive manner. In addition to establishing a clear classroom presence, Edwards et al. (2011), found that challenging and affirming learners, as well as striving to be a person of influence, are also behaviors that consistently lead to success both in the classroom and online. Although the instructional design may vary significantly, there is a sufficient evidence to suggest that those teach successfully face-to-face can be equally effective online. Purpose of the Study The purpose of this study is to explore the attributes of teaching presence, identifying those skills that are easily transferrable, and those that may in fact lead to barriers in the transfer to the online environment. This study seeks to further the evidence that instructors can successfully transfer their teaching style to an online environment, and provides advance guidance regarding the stickiness associated with best practice transfer from the classroom. Theoretical Framework Perhaps the most influential of all theories to date in evaluating the effectiveness of online instruction comes to us from the work of Garrison et al. (2000), in the form of the Community of Inquiry (CoI) Framework Figure 1. The framework, built upon the earlier work of Lipman (1991), describes three categories of critical inquiry in text-based learning environments, conceived through detailed reviews of transcripts of prior online instruction examples. Social presence is the ability of participants to identify with the community (e.g., course of study), communicate purposefully in a trusting environment, and develop inter-personal relationships by way of projecting their individual personalities (Garrison, 2009). This allows participants to present themselves as individual realities, and not faceless contributors. Liu (2007) finds that close relationships exist between the sense of a learning community and perceived learning of the individual, yet most online instructors are not community building minded when designing for online instruction. Teaching Presence is the design, facilitation, and direction of cognitive and social processes for the purpose of realizing personally meaningful and educationally worthwhile learning outcomes (Anderson, Rourke, Garrison, & Archer, 2001). As Garrison et al., point out, facilitation is not solely the responsibility of the instructor, however, and may in fact be shared among the teacher and some or all of the students in the learning environment. Cognitive Presence is the extent to which learners are able to construct and confirm meaning through sustained reflection and discourse (Garrison, Anderson, & Archer, 2001). 7
Figure 1: Community of Inquiry Framework. Adapted from Critical inquiry in a text-based environment: Computer conferencing in higher education, by Garrison, D., Anderson, T. & Archer, W., 2000. The Internet and Higher Education, 2(2-3), 87-105. Following the conceptualization of the CoI framework, Shea et al., (2003) developed a survey instrument designed to operationalize the work of Garrison et al. Although cognitive presence proved to be the most difficult category to test, a robust 34-question survey has emerged. Scholars continue to call for efforts to collect evidence to build support for the CoI survey instrument from across diverse higher education disciplines and programs to increase its value as a formative assessment tool (Bangert, 2009). Attempts to prove both the construct and external validity of the CoI framework have proven fruitful. Arbaugh (2008) tested the external validity of the framework and found the CoI framework to be a parsimonious predictor of learning in MBA courses; teaching presence is a much stronger predictor of learning than student satisfaction, social presence is a much stronger predictor of satisfaction than learning, and cognitive presence a strong predictor of learning. Following this study, Shea (2009) found the instrument used to measure the construct includes survey items that cohere into interpretable factors that represent the intended CoI framework. In order to measure teaching presence across both web-enhanced face-to-face and online classroom settings, Shea et al. developed 17-question survey instrument devised to assess effective instructional design and organization, facilitation of productive discourse, and direct instruction as described in the original CoI framework (Shea et al., 2006). The instrument, referred to as the Teaching Presence Scale (TPS), proved to be a reliable measure of the teaching presence construct, and served as a valid way to measure teaching presence across both online and web-enhanced face-to-face instructional settings. The TPS instrument is provided in Appendix A. Research Questions Specifically, this study is guided by the following research questions: 1. Which teaching presence predictors are easily transferrable between the classroom and online teaching environments? 8
2. Which teaching presence predictors represent sticky practice and are not easily transferrable between the classroom and online teaching environments? 3. Which instructor characteristics predict high levels of teaching presence in both the classroom and online teaching environments? Participants Methods The participants for this study will be volunteers selected from the faculty at a large Midwestern U.S. business school. The pool of potential volunteers will be identified by selecting faculty members across all disciplines who teach at least one course in both the full-time in-residence MBA program and the corresponding online MBA program for the 2013-14 academic year. Invitations to participate will be sent through campus e-mail, and will contain details on the purpose the study and the approach to data collection. Faculty who agree to participate will be granted access to all data collected pertaining to their classrooms, while remaining anonymous and unidentifiable in the subsequent analysis and publication of results. In order to encourage participation in the study, each instructor will be offered to participate at a level they feel comfortable with. Levels of participation are as follows: Level 1 Instructor agrees to complete a brief demographic survey, and provides a course roster allowing the researcher to solicit participation from students enrolled in his/her class. Level 2 Instructor also agrees to provide data collected from the standard course evaluation survey administered to his/her students. Level 3- Instructor also agrees to provide the researcher access to the course website for inspection of online interactions related to the course. Level 4- Instructor also agrees to be interviewed after course conduct is complete to review collected data and discuss impressions of the results with the researcher. Once instructors have volunteered to participate in this study, a subsequent invitation will be sent to the students enrolled in their courses at the beginning of the course. The invitation will be sent via campus email, and will outline the purpose of the study. The email will indicate that the student s participation is completely voluntary, and will not affect their standing in the course in any way. Those who volunteer to participate will receive instructions for completing a survey two weeks before the end of the course. Their responses will be anonymous, and will be captured via a secure OnCourse survey instrument that only the investigators have access to. In order to encourage participation, students will be entered into a drawing to win one of four $50 MasterCard gift cards. Survey Instrument The Teaching Presence Scale (TPS), developed by Shea et al. (2006), will serve as the survey instrument for this study. The original instrument, developed to measure teaching presence across both web-enhanced and online curricula, will have the following language modifications to enable greater clarity for face-to-face class respondents: Reference Original Question Modification A.1.5 Overall, the instructor for this course helped me take advantage of the online environment to assist my learning (e.g., provided clear instructions on how to participate in online discussion forums). Overall, the instructor for this course helped me take advantage of the learning environment to assist my learning (e.g., provided clear instructions on how to participate in online or face-to-face 9
A.1.6 A.2.3 Overall, the instructor for this course helped students to understand and practice the kinds of behaviors acceptable in online learning environments (e.g., provided documentation on netiquette i.e. polite forms of online interaction). Overall, the instructor in this course acknowledged student participation in the course (e.g., replied in a positive, encouraging manner to student submissions). discussions forums). Overall, the instructor for this course helped students to understand and practice the kinds of behaviors acceptable in the learning environment (e.g., provided documentation on netiquette i.e. polite forms of online interaction). Overall, the instructor in this course acknowledged student participation in the course (e.g., replied in a positive, encouraging manner to student contributions). In addition to responses to the TPS, the following demographic data will be collected for each of the student respondents: Gender: Male, Female, Missing Age: 20-25, 25-30, 30-35, 35-40, 40+ Registration Status: Full-time, Part-time, Missing Number of course completed to date: 0, 1, 2-5, 5-10, 10+ Course Number: For instructors, a separate demographic survey will be sent to all volunteers to collect the following data: Gender: Male, Female, Missing Age: 20-30, 30-40, 40-50, 50-60, 60+ Rank: Full Professor, Associate Professor, Assistance Professor, Clinical Professor, Clinical Associate Professor, Clinical Assistant Professor, Senior Lecturer, Lecturer, Visiting Faculty, Adjunct/Part-time Instructor) Years at Kelley: 0-5, 5-10, 10-15, 15+ Department: (Finance, Marketing, Management & Entrepreneurship, Operations & Decision Technologies, Accounting, Business Communications, Business Economics & Public Policy, Business Law and Ethics, Other: please specify) MBA courses taught in-residence in 2013-2014: Online courses taught in 2013-2014: Data Collection Before data collection begins, the researcher will seek approval from the Human Subjects Committee at the University. Appropriate procedures will be followed to gain consent from all respondents and appropriate measures will be taken to safeguard the data. A demographic survey will be sent to all instructors who volunteer to participate in this study via the online courseware used at the University at the beginning of the 2013-14 academic year. At the beginning of each course included in the study, an email will be sent to each course participant to introduce the purpose of the study and to accept their consent to participate. Students will be told that their participation is completely voluntary and will in no way impact their standing in 10
the course. Students will also be made aware that a follow-up survey will be administered two weeks prior to the end of the class, which may be in addition to the standard evaluation completed for the course. Two weeks prior to the end of each course included in the study, the TPS survey will be administered via the same online courseware used at the University to each class participant who voluntarily agreed to participate. A reminder will be sent one week prior to the end of the course to maximize the sample size. Data Analysis TBD Some ideas Item correlations of teaching presence Mean, standard deviation, maximum, minimum of teaching presence for all courses and by instructor by factor construct (instructional and design organization, directed facilitation) Correlations between in-residence and online instructor teaching presence factors Regression analysis for predicting teaching presence in-residence and online Results TBD Discussion and Conclusion TBD 11
Appendix A. Teaching Presence Scale Items A.1. Instructional Design and Organization Setting the curriculum 1. Overall, the instructor for this course clearly communicated important course goals (for example, provided documentation on course learning objectives). 2. Overall, the instructor for this course clearly communicated important course topics (for example, provided a clear and accurate course overview). Designing Methods 3. Overall, the instructor for this course provided clear instructions on how to participate in course learning activities (e.g., provided clear instructions on how to complete course assignments successfully). 4. Overall, the instructor for this course clearly communicated important due dates/times for learning activities that helped me keep pace with this course (e.g., provided a clear and accurate course schedule, due dates, etc.). Utilizing the medium effectively 12
5. Overall, the instructor for this course helped me take advantage of the online environment to assist my learning (e.g., provided clear instructions on how to participate in online discussion forums). Establishing netiquette 6. Overall, the instructor for this course helped students to understand and practice the kinds of behaviors acceptable in online learning environments (e.g., provided documentation on netiquette i.e. polite forms of online interaction). A.2. Facilitating Discourse Identifying areas of agreement/disagreement 1. Overall, the instructor for this course was helpful in identifying areas of agreement and disagreement on course topics that assisted me to learn. Seeking to reach consensus 2. Overall, the instructor for this course was helpful in guiding the class towards understanding course topics in a way that assisted me to learn. Reinforce student contributions 13
3. Overall, the instructor in this course acknowledged student participation in the course (e.g., replied in a positive, encouraging manner to student submissions). Setting climate for learning 4. Overall, the instructor for this course encouraged students to explore new concepts in this course (e.g., encouraged thinking out loud or the exploration of new ideas). Drawing in participants, prompting discussion 5. Overall, the instructor for this course helped to keep students engaged and participating in productive dialog. Assessing the efficacy of the process 6. Overall, the instructor for this course helped keep the participants on task in a way that assisted me to learn. A.3. Direct instruction Present content/questions 1. Overall, the instructor for this course presented content or questions that helped me to learn. 14
Focus the discussion on specific issues 2. Overall, the instructor for this course helped to focus discussion on relevant issues in a way that assisted me to learn. Confirm Understanding 3. Overall, the instructor for this course provided explanatory feedback that assisted me to learn (e.g., responded helpfully to discussion comments or course assignments). Diagnose misconceptions 4. Overall, the instructor for this course helped me to revise my thinking (e.g., correct misunderstandings) in a way that helped me to learn. Inject knowledge form diverse sources 5. Overall, the instructor for this course provided useful information from a variety of sources that assisted me to learn (e.g., references to articles, textbooks, personal experiences, or links to relevant external websites). 15
References Allen, E. & Seaman, J. (2012). Conflicted: Faculty and Online Education, 2012. Needham, MA: Sloan Consortium. Arbaugh, J. B. (2000). Virtual classroom characteristics and student satisfaction with internet-based MBA Courses. Journal of Management Education, 24(1), 32-54. Arbaugh, J. B. (2000). Virtual classroom versus physical classroom: An exploratory study of class discussion patterns and student learning in an asynchronous internet-based MBA course. Journal of Management Education, 24(2), 213-233. Arbaugh, J. B. (2002). Managing the online classroom: A study of technological and behavioral characteristics of web-based MBA courses. The Journal of High Technology Management Research, 13, 203-223. Arbaugh, J. B. (2004). Learning to learn online: A study of perceptual changes between multiple online course experiences. The Internet and Higher Education, 7, 169-182. Arbaugh, J. B. (2005). Is there an optimal design for on-line MBA courses? Academy of Management Learning & Education, 4(2), 135-149. Arbaugh, J. B. (2005). How much does subject matter matter? A study of disciplinary effects in on-line MBA courses. Academy of Management Learning & Education, 4(1), 57-73. Arbaugh, J. B. & Benbunan-Fich, R. (2006). An investigation of epistemological and social dimensions of teaching in online learning environments. Academy of Management Learning & Education, 5(4), 435-447. Arbaugh, J. B. & Hwang, A. (2006). Does teaching presence exist in online MBA courses? The Internet and Higher Education, 9, 9-21. Arbaugh, J. B. & Benbunan-Fich, R. (2007). The importance of participant interaction in online environments. Decision Support Systems, 43, 853-865. Arbaugh, J. B. & Rau, B.L. (2007). A study of disciplinary, structural, and behavioral effects on course outcomes in online MBA courses. Decision Sciences Journal of Innovative Education, 5(1), 65-95. Arbaugh, J. B. (2008). Does the community of inquiry framework predict outcomes in online MBA courses? International Review of Research in Open and Distance Learning, 9(2), 1-21. Arbaugh, J. B., Godfrey M.R., Johnson, M., Pollack, B., Niendorf, B. & Wresch, W. (2009). Research in online and blended learning in the business disciplines: Key findings and possible future directions. Internet and Higher Education, 12, 71-87. Arbaugh, J.B. (2010). Sage, guide, both, or even more? An examination of instructor activity in online MBA courses. Computers & Education, 55, 1234-1244. Arbaugh, J.B., Bangert, A. & Cleveland-Innes, M. (2010). Subject matter effects and the community of inquiry (CoI) framework: An exploratory study. Internet and Higher Education, 13, 37-44. 16
Bangert, A.W. (2009). Building a validity argument for the community of inquiry survey instrument. Internet and Higher Education, 12, 104-111. Brownstein, B., Brownstein, D. & Gerlowski, D. (2008). Web-based vs. face-to-face MBA classes: A comparative study. Journal of College Teaching & Learning, 5(11), 41-48. Edwards, M., Perry, B., & Janzen, K. (2011). The making of an exemplary online educator. Distance Education, 32(1), 101-118. Garrison, D., Anderson, T. & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2-3), 87-105. Garrison, D. R. (2007). Online community of inquiry review: Social, cognitive, and teaching presence issues. Journal of Asynchronous Learning Networks, 11(1), 61-72. Hodges, C. B., & Cowan, S. (2012). Preservice teachers' views of instructor presence in online courses. Journal of Digital Learning in Teacher Education, 28(4), 139-145. Lee, S., Lee, J., Liu, X., Bonk, C.J. & Magjuka, R.J. (2009). A review of case-based learning practices in an online MBA program: A program-level case study. Educational Technology & Society, 12(3), 178-190. Liu, X., Magjuka, R.J., Bonk, C.J. & Lee, S. (2007). Does sense of community matter? An examination of participants perceptions of building learning communities in online courses. The Quarterly Review of Distance Education, 8(1), 9-24. Liu, X., Liu, S., Lee, S. & Magjuka, R.J. (2010). Cultural differences in online learning: International student perceptions. Educational Technology & Society, 13(3), 177-188. Lobato, J. (2012). The actor-oriented transfer perspective and its contributions to educational research and practice. Educational Psychologist, 47(3), 232-247. Morgan, T. (2011). Online classroom or community-in-the-making? Instructor conceptualizations and teaching presence in international online contexts. Journal of Distance Education, 25(1), 1-13. Mujtaba, B. (2007). Understanding the needs and wants of virtual students: Online MBA program at the H. Wayne Huizenga School of Business and Entrepreneurship of Nova Southeastern University. TechTrends, 51(6), 46-51. Nagel, L., & Kotze, T. G. (2010). Supersizing E-Learning: What a CoI Survey Reveals about Teaching Presence in a Large Online Class. Internet and Higher Education, 13(1-2), 45-51. Pelz, B. (2004). Three principles of effective online pedagogy. Journal of the Asynchronous Learning Network, 8(3), 33-46. Perkins, David N., & Salomon, Gavriel. (2012). Knowledge to go: A motivational and dispositional view of transfer. Educational Psychologist, 47(3), 248-258. Proserpio, L. & Gioia, D. (2007). Teaching the virtual generation. Academy of Management Learning & Education, 6(1), 69-80. 17
Rydzewski, D.N., Eastman, J.K. & Bocchi, J. (2010). Important characteristics in an MBA program: The perceptions of online MBA students. American Journal of Business Education, 3(4), 33-41. Schwartz, D. L., Chase, C.C. & Bransford, J.D. (2012). Resisting overzealous transfer: Coordinating previously successful routines with needs for new learning. Educational Psychologist, 47(3), 204-214. Shea, P., Li, C., & Pickett, A. (2006). A Study of Teaching Presence and Student Sense of Learning Community in Fully Online and Web-Enhanced College Courses. Internet and Higher Education, 9(3), 175-190. Shea, P. & Bidjerano, T. (2009). Community of inquiry as a theoretical framework to foster epistemic engagement and cognitive presence in online education. Computers & Education, 52, 543-553. Shea, P., Vickers, J. & Hayes, S. (2010). Online Instructional Effort Measured through the Lens of Teaching Presence in the Community of Inquiry Framework: A Re-Examination of Measures and Approach. The International Review of Research in Open and Distance Learning, 11(3), 127-153. Szulanski, G. (2003). Sticky knowledge: barriers to knowing in the firm. Thousand Oaks, CA: Sage Publications. 18