This study explores 465 postsecondary students’ experiences in online classes through the lens of connectivism. Downes’ 4 properties of connectivism (diversity, autonomy, interactivity, and openness) were used as the study design. An exploratory factor analysis was performed. This study found a 4-factor solution. Subjects indicated that autonomy and openness had a presence in their online course experiences. However, diversity was markedly lacking, and interactivity failed to distinguish itself as a factor although interaction was present in the courses. A third factor, suggesting an out-of-class component, also was found.
Introduction
In 2002 a Pew Research Center report predicted little interest among traditional college students to abandon the classroom and take courses online. The report concluded, “It is clear that for students already enrolled in traditional college courses, online education has a long way to go before it might challenge the traditional classroom” (Jones, 2002, p. 12).
Evidently Pew failed to anticipate the immense technological changes to befall education this past decade. The increase in numbers of students taking online courses has been dramatic, while the growth of online course offerings at postsecondary institutions has increased exponentially.
For example, the number of students taking at least one online course during the fall 2010 term surpassed 6 million (Allen & Seaman, 2011). And today nearly one third of all students in higher education are taking at least one online course (Allen & Seaman, 2011). Also:
The 10% growth rate for online enrollments far exceeded the less than 1% growth in the overall higher education student population.
Academic leaders believe that the level of student satisfaction is equivalent for online and face-to-face courses.
Sixty-five percent of higher education institutions say that online learning is a critical part of their long-term strategy (Allen & Seaman, 2011).
As these numbers illustrate, online education has achieved widespread acceptance among students and educators alike. Still, some academic leaders are concerned that the quality of online instruction is not equal to courses delivered face to face (Allen & Seaman, 2011).
Recognizing that technology has changed forever the ways in which people think and learn, it is imperative, therefore, that notions about the nature of knowledge and its attainment be reconsidered. Likewise, education would seem to require a realignment of curriculum and teaching strategies in order to sustain learning in the digital age. The questions then become: what works best for students and learning in an online environment? And are educators changing their approach from the traditional strategies used in face-to-face classrooms to accommodate their courses in cyberspace? This study sets out to explore these issues by investigating postsecondary students’ experiences in online classes through the lens of connectivism, a theoretical model with potential for better understanding learning in a digital age.
Literature Review
As the number of online course offerings has increased over the years, so has grown the scholarly attention to online education—although much of the empirical research has been limited in focus. As noted in 2004 by Reeves, Herrington, and Oliver, “Much of the existing research related to collaborative online learning continues in the same vein, that is, comparing online courses with traditional classroom courses” (p. 58). However, the focus has shifted and expanded over time as researchers investigate the tools used in online classes, faculty support in online classes, course design and, most prominent, interaction and collaboration. Results have been varied and conflicting. The review of literature for this study focuses on the most recent studies that explored students’ perceptions, technology and tools used in online courses, interaction variables in online settings, and online learning environments.
Student Perceptions
Students’ perceptions of online learning are well documented in the literature. Reynolds, Rice, and Uddin (2007) found that student perceptions of e-learning experiences increased positively as well as improved communication technology skills from 2001 to 2004. In another study, the majority of online students said teacher education courses met their academic needs and improved their technological skills (Leonard & Guha, 2001). Wyatt (2005) found students believed they received quality online academic experiences that were more academically demanding than traditional classroom instruction.
In 2010 undergraduate industrial design students indicated positive attitudes and perceptions about the approach to an online design learning environment (Chen & You, 2010), and science and technology students showed significant improvements in reading skills and strategies as well (Thang & Bidmeshki, 2010). Seok, DaCosta, Kinsell, and Tung's (2010) findings concluded that students and instructors had positive perceptions of online course effectiveness; findings that support previous studies that considered students’ and instructors’ opinions of online courses (for example, see Richardson, 2005). Research also has shown positive perceptions from students about technology and tool availability.
Technology and Tools
The literature reveals that advances in technologies and tools allow learners to collect, produce, share, and use new knowledge and information to communicate and learn—which improves online course experiences. For example, in a study conducted by Dringus, Snyder, and Terrell (2010), the use of mini audio presentations in discussion forums enhanced understanding of course content as well as satisfaction in the course. Pata (2009) examined a system of tools and resources and concluded that students involved in selecting social software tools are able to incorporate those tools in course activities. Menchaca and Bekele (2008) noted, “Faculty and students identified the importance of using multiple tools appealing to diverse learning styles” (p. 247).
As the past decade drew to a close, abundant research considered the uses of emerging Web 2.0 technologies in online courses. O'Reilly (2005) and Cifuentes, Xochihua, and Edwards (201l) provide a concise description of Web 2.0, which they say refers to the design and technological characteristics of web pages that feature two-way communication and dynamic content. Lee, Miller, and Newnham (2008) concluded that the use of RSS and other Web 2.0 technologies could be used to filter and manage the growing volume of web-based information to aid online course interactions and collaboration. Technology tools also help define the interaction and learning environment. However, Twu (2009) concluded that guidelines are needed to support student uses of online collaboration tools, such as wikis, because of social cultural differences. Dunlap and Lowenthal (2009) demonstrated how Twitter enhanced social presence in an online course by providing a mechanism for immediate social interactions.
Interaction
Previous research underscores how technology used in online courses can support the interaction available in the online course. For instance, Yiping, Bernard, and Abrami (2006) found that distant education students significantly outperformed classroom students when media-supported collaborative discussion tools were used among students in asynchronous undergraduate courses. Similarly, Swan et al. (2000) found that students with higher levels of interaction with classmates through online discussions reported higher levels of learning. Increased online interaction also has benefits over traditional classrooms. Coleman (2009) notes: “In an online environment, attendance to class is only evident if the student actually participates in classroom discussion. This increases student interaction and the diversity of opinion because everyone gets a say, not just the most talkative” (para. 5). She adds, “Because online institutions often offer ‘chat rooms’ for informal conversation between students, where student bios and nonclass discussions can take place, there appears to be an increased bonding and camaraderie over traditional class environments” (para. 9).
In 2007, Herring and Clevenger-Schmertzing's in-depth study of high school students’ experiences in online courses supported previous research finding that regardless of the academic results, interaction is important in the online environment—because students expect it. However, when the researchers compared the association between students’ levels of interaction in online courses to their academic performance they found that the quality of interaction was more significant than the quantity of the interaction. Interestingly, this study also found that the interaction with classmates was not as important to the participants as their interaction with the teacher. Student-provided quantitative and qualitative data supported that student-teacher interaction leads to higher student perceptions of learning, while the data indicated that peer interaction helped shape a more favorable impression of the course (Herring & Clevenger-Schmertzing, 2007). Another study also investigated instructor interaction with students. Zhao, Lei, Yan, Lai and Tan (2005) suggested that learning outcomes were positively related to the amount of instructor participation. However, the researchers noted that when instructor involvement dominated, the effect size began to decline. Young and Norgard (2006) also found that student satisfaction with online instruction was positively correlated to quality and timely interaction between students and the professor.
A study exploring the relationship between higher order learning outcomes and collaboration confirmed previous research that found structured online group activities support deeper, more meaningful learning. As the researchers observed, “Group collaboration or knowledge construction could potentially improve students’ perceived learning and their final grades” (Akyol & Garrison, 2011 p. 245). Likewise, after studying an online MBA program, Kim, Lui, and Bonk (2005) said that virtual group experiences were valuable but challenging for preparing the students for the burgeoning global business environment. Young and Bruce (2011) concluded:
When instruction is designed to actively involve learners in meaningful tasks, students’ sense of engagement may be elevated. Student engagement and sense of classroom community are closely related to one another; students who feel a sense of connectedness rather than isolation are very likely better prepared to become more actively involved with course learning, successfully persist, and experience real world success. (p. 227)
Additionally. Web 2.0 technologies increase students’ opportunities to interact. Bernsteiner, Ostermann, and Staudinger (2008) studied the learners’ online participation, contributions, communication, and collaboration based on Web 2.0 technologies. They concluded that effective learning environments include tools such as wikis and discussion forums that can clearly support collaboration and, therefore, learning.
Learning Environments
Even before the new millennium when online education was relatively new, studies referring to the concept of learning environments appeared in the literature (see Dringus & Terrell, 1999; McLoughlin & Oliver, 2000; Teh, 1999). Since that time, research has examined learning environments from a variety of perspectives. For example, Herbert (2007) and Mandernach (2009) both concluded that instructors must move beyond content-oriented online delivery and instead create supportive learning environments to produce positive outcomes. Bekele and Menchaca (2008) reviewed 29 studies published through 2006 to explore the key trends and patterns of effective Internet-supported learning (ISL) environments. In their review, ISL included both online and blended learning. The studies that used score and/or grade outcomes in the literature review suggested ISL positively affected student achievement and student participation (Bekele & Menchaca, 2008). Menchaca and Bekele's study (2008) also identified five factors that related to successful online learning environments. These included technology-related factors, user characteristics, course-related factors, learning approach, and support services. Their results concluded that collaborative and process-oriented learning was mandatory for success.
More recent research tends to refer to online learning settings specifically as personal learning environments (PLEs). Rosenfeld (2008) recommended that tools used as personal portals be referred to as a personal learning environment (PLE). According to Johnson and Liber, (2008) a PLE is a personal organizational framework that brings all learning together. Downes (2010) describes PLEs as an atmosphere containing tools that allow engagement “in a distributive environment consisting of a network of people, services and resources” (p. 20). He added, “Educators play the same sort of role in society as journalists. They become aggregators, assimilators, analysts, and advisors” (p. 21).
Summary
Scholarship that examines online education has grown and expanded. The focus of the most recent research has centered on students’ perceptions of online courses, current technologies and tools used in online courses, the interaction—or lack thereof—present in the course, and online learning environments. Further scholarly attention, however, is needed to explore learning in an online environment. This is important for a number of reasons, as supported in the literature: first, students generally have positive perceptions of online education and its effectiveness and continue to enroll in online courses as educational institutions increasingly offer them. Second, technology, specifically Web 2.0 interactive tools, will enhance the online learning environment. These tools will continue to advance and expand opportunities for interaction and collaboration. Third, the new communication tools used by students in their everyday lives may be integrated into online courses to enhance interaction among students and professors, which, according to the literature, produces positive outcomes. Finally, previous studies have found that the concepts of Web 2.0 and personal learning environments are fundamental to the online learning environment.
Theoretical Framework
In 2008 Kop and Hill concluded that a paradigm shift is occurring in educational theory—a shift driven largely by technology that increasingly has moved students to a position of autonomous learning. Thus, Kop and Hill (2008) support an emerging theoretical model, referred to as connectivism, as fruitful for examining learning within education's vast technological revolution. In fact, they note that the connectivist model is “ripe for further studies” (Kop & Hill, 2008, p. 4). Yet, thus far, no research has empirically tested connectivism, which this study sets out to achieve.
Indeed, the role of technology in education and learning is replete with theories (Siemens, 2008). However, some of the more recent thinking regarding the new so-called e-learning is centered on the theory of connectivism, which asserts:
Knowledge—and therefore the learning of knowledge—is distributive, that is, not located in any given place (and therefore not “transferred” or “transacted” per se), but rather consists of the network of connections formed from experience and interactions with a knowing community. (Downes, 2010, p. 1)
Siemens (2004) argues that the traditional learning theories of behaviorism, cognitivism, and constructivism inadequately explain the process of learning in an educational climate of rapid, vast, and shifting technology. Moreover, according to Siemens (2004), technology has altered the very nature of knowledge by increasingly shortening the time span from when knowledge is gained to when it becomes obsolete (referred to as the half-life of knowledge). Other social changes confronting educators today include:
High volumes of data and information are driving learning needs;
The rapid growth of information has shifted the act of learning from acquisition to assimilation;
Many learners are moving into a variety of different—frequently unrelated—fields throughout their lifetimes;
Learning no longer takes place in a formal educational setting, but occurs through work, play, communities of practice, and personal networks;
Learning is a continual, lifelong process;
“Know-how” and “know-what” are being supplanted by “know-where” (Siemens, 2004, 2005, 2008).
Thus, according to Siemens:
The starting point of connectivism is the individual. Personal knowledge is comprised of a network, which feeds into organizations and institutions, which in turn feeds back into the network, and then continues to provide learning to the individual. This cycle of knowledge development allows learners to remain current in their field through the connections they have formed.… The amplification of learning, knowledge, and understanding through the extension of a personal network is the epitome of connectivism. (Siemens, 2004, para. 5)
The importance of learning communities in online education is not new, and they are an essential part of connectivism. However, the connectivism model refers to learning communities as nodes, which surface out of the larger network's connection points (Kop & Hill, 2008). Knowledge, distributed across the network, resides in a variety of digital formats as well as in the creative process ongoing within the nodes. Thus, according to Kop and Hill's (2008) summary of the theory, connectivism stresses that students possess the abilities to seek out information as well as filter secondary and extraneous information—both crucial skills that contribute to learning. In other words, connectivism
frames learning in terms of learners connecting to nodes on a network, suggesting that knowledge does not reside in one location, but rather that it is a confluence of information arising out of multiple individuals seeking inquiry related to a common interest and providing feedback to one another. (Kop & Hill, 2008, p. 4)
Connectivism also provides a theoretical context for the increasingly popular concepts of Web 2.0 and PLEs. As noted, Web 2.0 refers to the design and technological characteristics of web pages featuring two-way communication and dynamic content (Cifuentes et al., 2011; O'Reilly, 2005). In PLEs students control their learning experiences (Milligan, 2006). The evolution of Web 2.0 and PLEs has empowered learners to think and interact in new and different ways. Specifically, PLEs involve learners in the consumption and management of course content as well as in the production and control of resources/content utilizing the tools of Web 2.0. In fact, Downes (2010) warns against thinking about PLEs as merely content management devices or file managers, noting:
the heart of the concept of the PLE is that it is a tool that allows a learner (or anyone) to engage in a distributed environment consisting of a network of people, services, and resources.… A learning activity is, in essence, a conversation undertaken between the learner and other members of the community. This conversation, in the Web 2.0 era, consists not only of words but of images, video, multimedia and more [forming] a rich tapestry of resources, dynamic and interconnected, created not only by experts but by all members of the community, including learners. (pp. 19, 20)
Although not all scholars have embraced connectivism as a bona fide theory, connectivism is recognized by many for its contribution in helping to understand learning in a digital environment (Kop & Hill, 2008). Moreover, connectivism encompasses a practical dimension. In fact, Downes (2010) used the connectivism model to identify specific properties in order for networks to optimize learning. These four primary characteristics—(1) diversity, (2) openness, (3) autonomy, and (4) interactivity—are essential as connections to the nodes on the network transpire, and knowledge development and learning are fully supported.
Given that education is inexorably anchored in the digital age, connectivism's implications for educational practice could be monumental, and we, therefore, draw upon the model as a framework for this study.
Purpose Statement
If learning—which transpires via connections to nodes on the network—is maximized when networks comprise the properties that Downes (2010) identifies as diversity, openness, autonomy, and interactivity, then it follows that courses must be designed to incorporate these very characteristics. And, given that postsecondary institutions have increasingly been offering online programs and courses for more than 12 years, it is possible that online courses today may comprise elements of connectivism. Thus, the purpose of this study was to explore online courses, as experienced by postsecondary students, to determine if Downes’ properties of connectivism were revealed.
Research Questions
Three research questions guided this study:
Which, if any, of the properties that Downes identifies for effective online learning were revealed through students’ experiences in online courses?
Is one or more of the properties that Downes identifies for effective online learning more prevalent than others?
Do students’ experiences in online courses reveal other characteristics that affected learning?
Method
Instrument
We used Downes’ (2010) properties of connectivism to frame our study. From the works of Downes (2010) and Siemens (2006a, 2008), we extrapolated and streamlined the definitions of the four properties to form the design constructs for our survey, which follow:
Diversity: Diversity exposes students to decentralized and varied assumptions, perspectives, and differing points of view. Diversity supports all opinions and all available information; no perspective is eliminated. Diversity is achieved with course content (textbooks, lecture notes, supplemental materials), and other people (students, professors, on- and off-campus resource people).
Autonomy: Students, based on their individual knowledge, values, beliefs, perspectives, decisions and needs, are in control of their own learning outcomes. Students are decision makers by selecting what they learn. Students are active by scanning, evaluating, and recognizing patterns to make sense among ideas and concepts that can be transferred and applied to other areas or fields. Students determine what is current and relevant for their own learning needs.
Interactivity: Interactivity is the connectedness among students and others, and with content and information. Interactivity may be physical or virtual and is ongoing, occurring continuously and without restriction.
Openness: Openness is the tools that eliminate the boundaries in the learning environment. Technology facilitates openness and provides access to information that may reside in unrelated areas or fields and is disseminated with increasing speed.
We developed a scale consisting of items representing each of Downes’ four properties. Five postsecondary level professionals with online education experience independently reviewed the items. Reviewers were asked to read a randomly ordered list of the items and to categorize each item in terms of one of the four constructs the item accurately and discretely reflected. The questionnaire underwent numerous revisions and independent reviews until face validity was established. The final survey included 60 items, which denoted Downes’ four properties equally, and numerous demographic and qualitative questions.
After providing informed consent, the subjects were asked to respond in terms of the most recent online course they had completed; a course that did not incorporate a face-to-face component. Subjects revealed their experiences in online courses by responding to the survey items using a 5-level Likert rating scale ranging from strongly agree to strongly disagree.
Sample
To determine which (if any and to what extent) of the Downes’ characteristics were evident in online courses, we turned to the online experiences of students. We chose not to pursue a random sampling procedure; rather, we were interested in recruiting as many students as possible who had completed an online course. We initially recruited volunteer subjects from among students participating in the American Forensic Association National Individual Events Tournament (AFA-NIET) held at a Midwest public university in the spring of 2011. Some 200 students returned surveys throughout the tournament weekend; however, only 79 surveys indicated that students had completed an online course. We then recruited participants from among students at the same Midwest public university. The university has a growing presence within the statewide university system for its online programs and course offerings across disciplines at both the undergraduate and graduate levels. Moreover, students from throughout the United States and representing several foreign countries are enrolled in the university's online programs. Others take individual online courses. In total, we collected data from 465 students (153 men, 312 women). Among the respondents, 250 were undergraduates, 192, graduates, and 23 listed themselves as “other.” The 79 completed surveys returned by the American Forensic Association National Individual Events Tournament participants are among the 465.
Procedure
Downes’ four properties of connectivism (diversity, autonomy, interactivity, and openness) provided a means for us to structure the study design. We selected factor analysis as the statistical method in order to explore the data for the presence of connectivism's theoretical variables. That being said, we rejected conducting a confirmatory factor analysis because the connectivism model is not yet well established as a theory, nor has it undergone empirical scrutiny. Thus, we conducted an exploratory factor analysis to help us gain insight into the structure and/or underlying processes that could explain a collection of variables (Pohlmann, 2004).
To determine internal consistency, the 60item survey was piloted to 165 students, who had completed an online course, by distributing the surveys in select face-to-face and online classes. Because the results from this initial factor analysis indicated that two of Downe's properties (interactivity and diversity) cross-loaded, survey items underwent further revision. Two experts in online education again reviewed the revised survey for face validity, which was achieved.
We performed an exploratory factor analysis using the Statistical Package for Social Sciences. We conducted a series of analyses using principal axis factoring with an oblimin rotation and Kaiser normalization on the 40 fivepoint Likert scale items.
Results
The overarching question addressed by researchers using exploratory factor analysis is: what are the underlying or latent constructs that could have produced the observed pattern of variances and covariances among the variables (Swisher, Beckstead, & Bebeau, 2004)? Specifically, this study set out to discern if students’ experiences in online courses indicated the existence of Downes’ four properties of connectivism—to some extent or another.
The exploratory factor analysis was performed on the correlations shown in Table 1. Our initial examination of the correlations among the four a priori constructs noted that Factor 2, diversity, negatively correlated with all of the other factors. The other three factors, autonomy, interactivity, and openness were moderately correlated; however, these can be viewed as acceptable because of the exploratory nature of the study.
Factor Correlation Matrix
| Factor | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| 1 | 1.000 | −.492 | .407 | .547 |
| 2 | −.492 | 1.000 | −.361 | −.550 |
| 3 | .407 | −.361 | 1.000 | .363 |
| 4 | .547 | −.550 | .363 | 1.000 |
| Factor | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| 1 | 1.000 | −.492 | .407 | .547 |
| 2 | −.492 | 1.000 | −.361 | −.550 |
| 3 | .407 | −.361 | 1.000 | .363 |
| 4 | .547 | −.550 | .363 | 1.000 |
Extraction method: principal axis factoring. Rotation method: Oblimin with Kaiser normalization. All coefficients are statistically significant at p < 0.000.
We used the Kaiser-Guttman rule for extracting factors, which considers factors with an eigenvalue greater than 1 as common (Nunnally, 1978). We determined that a four-factor solution was the most parsimonious model that also best explained the data conceptually. Table 2 contains the variance extracted from all the constructs, and the descriptive statistics of the mean and standard deviations of all the items in the questionnaire. The four factors accounted for 54.5% of the total variance.
Eigenvalues, Total Variances Explained
| Factor | Initial Eigenvalues | Extraction Sums of Squared Loadings | Rotation Sums of Squared Loadingsa | ||||
|---|---|---|---|---|---|---|---|
| Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | Total | |
| 1 | 15.522 | 38.806 | 38.806 | 15.058 | 37.646 | 37.646 | 10.427 |
| 2 | 2.510 | 6.274 | 45.080 | 2.043 | 5.107 | 42.753 | 11.566 |
| 3 | 2.023 | 5.058 | 50.138 | 1.463 | 3.658 | 46.411 | 6.290 |
| 4 | 1.775 | 4.436 | 54.575 | 1.263 | 3.157 | 49.568 | 9.524 |
| Factor | Initial Eigenvalues | Extraction Sums of Squared Loadings | Rotation Sums of Squared Loadingsa | ||||
|---|---|---|---|---|---|---|---|
| Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | Total | |
| 1 | 15.522 | 38.806 | 38.806 | 15.058 | 37.646 | 37.646 | 10.427 |
| 2 | 2.510 | 6.274 | 45.080 | 2.043 | 5.107 | 42.753 | 11.566 |
| 3 | 2.023 | 5.058 | 50.138 | 1.463 | 3.658 | 46.411 | 6.290 |
| 4 | 1.775 | 4.436 | 54.575 | 1.263 | 3.157 | 49.568 | 9.524 |
Extraction method: principal axis factoring. aWhen factors are correlated, sums of squared loadings cannot be added to obtain a total variance.
Additionally, we inspected the scree plot (Figure 1), which also revealed four factors.
Each item as it loaded on the factors was then evaluated in terms of the criteria recommended by Costello and Osborn (2005): (1) the items had a loading of greater than or equal to 0.30; (2) the items loaded on only one factor; (3) at least three items loaded on a factor. Four survey items did not meet the criteria, and were thus deleted while we retained 36 items (see Appendix A).
Statistical descriptions of the 36 items are presented in Table 3. The range for all items was 1 to 5. Mean scores ranged from 1.77 to 3.36. Three of the 36 items were negatively skewed. Although this item analysis reveals some departure from normality, the principal axis factoring used in this study is not reliant on normal data (Costello & Osborne, 2005).
Descriptive Statistics
| Item | N Statistic | Mean Statistic | SD Statistic | Skewness | Kurtosis | ||
|---|---|---|---|---|---|---|---|
| Statistic | SE | Statistic | SE | ||||
| 11 | 463 | 1.92 | .703 | .639 | .113 | 1.002 | .226 |
| 1 | 462 | 2.18 | 1.013 | .913 | .114 | .352 | .227 |
| 5 | 464 | 2.28 | 1.008 | .797 | .113 | .217 | .226 |
| 8 | 460 | 2.90 | 1.158 | .069 | .114 | −.983 | .227 |
| 7 | 456 | 2.15 | .826 | .769 | .114 | .781 | .228 |
| 10 | 461 | 2.06 | .893 | .939 | .114 | .949 | .227 |
| 6 | 462 | 1.93 | .772 | 1.090 | .114 | 2.171 | .227 |
| 9 | 459 | 2.05 | 1.008 | 1.097 | .114 | .856 | .227 |
| 4 | 455 | 2.15 | 1.008 | .828 | .114 | .151 | .228 |
| 2 | 458 | 2.44 | 1.092 | .510 | .114 | -.603 | .228 |
| 3 | 457 | 2.19 | 1.019 | .808 | .114 | .031 | .228 |
| 18 | 461 | 1.82 | .898 | 1.312 | .114 | 1.895 | .227 |
| 16 | 458 | 1.89 | .921 | 1.327 | .114 | 1.909 | .228 |
| 19 | 462 | 2.13 | .960 | .826 | .114 | .320 | .227 |
| 13 | 460 | 1.77 | .807 | 1.237 | .114 | 2.200 | .227 |
| 14 | 456 | 2.00 | .933 | .865 | .114 | .438 | .228 |
| 20 | 460 | 2.12 | 1.011 | .950 | .114 | .489 | .227 |
| 28 | 462 | 3.05 | 1.158 | −.072 | .114 | -.896 | .227 |
| 17 | 466 | 2.08 | .897 | .795 | .113 | .544 | .226 |
| 21 | 455 | 2.17 | .946 | .865 | .114 | .578 | .228 |
| 24 | 457 | 2.33 | .956 | .558 | .114 | .003 | .228 |
| 15 | 454 | 1.81 | 1.017 | 1.528 | .115 | 1.949 | .229 |
| 27 | 454 | 3.36 | 1.226 | −.374 | .115 | −.884 | .229 |
| 26 | 453 | 3.26 | 1.037 | .010 | .115 | −.336 | .229 |
| 30 | 451 | 2.70 | 1.036 | .297 | .115 | −.300 | .229 |
| 29 | 446 | 3.34 | 1.180 | -.302 | .116 | −.869 | .231 |
| 22 | 450 | 2.22 | .942 | .957 | .115 | .871 | .230 |
| 23 | 444 | 2.13 | .876 | 1.004 | .116 | 1.341 | .231 |
| 36 | 447 | 2.07 | 1.075 | 1.031 | .115 | .289 | .230 |
| 33 | 445 | 1.89 | .861 | 1.158 | .116 | 1.677 | .231 |
| 34 | 452 | 1.98 | .931 | 1.100 | .115 | 1.115 | .229 |
| 38 | 452 | 2.35 | 1.040 | .601 | .115 | −.383 | .229 |
| 37 | 451 | 2.24 | .947 | .728 | .115 | .119 | .229 |
| 40 | 453 | 2.19 | .842 | .809 | .115 | .581 | .229 |
| 35 | 447 | 2.12 | .784 | .649 | .115 | .488 | .230 |
| 39 | 454 | 2.22 | .853 | .661 | .115 | .478 | .229 |
| Valid N (listwise) | 352 |
| Item | N Statistic | Mean Statistic | SD Statistic | Skewness | Kurtosis | ||
|---|---|---|---|---|---|---|---|
| Statistic | SE | Statistic | SE | ||||
| 11 | 463 | 1.92 | .703 | .639 | .113 | 1.002 | .226 |
| 1 | 462 | 2.18 | 1.013 | .913 | .114 | .352 | .227 |
| 5 | 464 | 2.28 | 1.008 | .797 | .113 | .217 | .226 |
| 8 | 460 | 2.90 | 1.158 | .069 | .114 | −.983 | .227 |
| 7 | 456 | 2.15 | .826 | .769 | .114 | .781 | .228 |
| 10 | 461 | 2.06 | .893 | .939 | .114 | .949 | .227 |
| 6 | 462 | 1.93 | .772 | 1.090 | .114 | 2.171 | .227 |
| 9 | 459 | 2.05 | 1.008 | 1.097 | .114 | .856 | .227 |
| 4 | 455 | 2.15 | 1.008 | .828 | .114 | .151 | .228 |
| 2 | 458 | 2.44 | 1.092 | .510 | .114 | -.603 | .228 |
| 3 | 457 | 2.19 | 1.019 | .808 | .114 | .031 | .228 |
| 18 | 461 | 1.82 | .898 | 1.312 | .114 | 1.895 | .227 |
| 16 | 458 | 1.89 | .921 | 1.327 | .114 | 1.909 | .228 |
| 19 | 462 | 2.13 | .960 | .826 | .114 | .320 | .227 |
| 13 | 460 | 1.77 | .807 | 1.237 | .114 | 2.200 | .227 |
| 14 | 456 | 2.00 | .933 | .865 | .114 | .438 | .228 |
| 20 | 460 | 2.12 | 1.011 | .950 | .114 | .489 | .227 |
| 28 | 462 | 3.05 | 1.158 | −.072 | .114 | -.896 | .227 |
| 17 | 466 | 2.08 | .897 | .795 | .113 | .544 | .226 |
| 21 | 455 | 2.17 | .946 | .865 | .114 | .578 | .228 |
| 24 | 457 | 2.33 | .956 | .558 | .114 | .003 | .228 |
| 15 | 454 | 1.81 | 1.017 | 1.528 | .115 | 1.949 | .229 |
| 27 | 454 | 3.36 | 1.226 | −.374 | .115 | −.884 | .229 |
| 26 | 453 | 3.26 | 1.037 | .010 | .115 | −.336 | .229 |
| 30 | 451 | 2.70 | 1.036 | .297 | .115 | −.300 | .229 |
| 29 | 446 | 3.34 | 1.180 | -.302 | .116 | −.869 | .231 |
| 22 | 450 | 2.22 | .942 | .957 | .115 | .871 | .230 |
| 23 | 444 | 2.13 | .876 | 1.004 | .116 | 1.341 | .231 |
| 36 | 447 | 2.07 | 1.075 | 1.031 | .115 | .289 | .230 |
| 33 | 445 | 1.89 | .861 | 1.158 | .116 | 1.677 | .231 |
| 34 | 452 | 1.98 | .931 | 1.100 | .115 | 1.115 | .229 |
| 38 | 452 | 2.35 | 1.040 | .601 | .115 | −.383 | .229 |
| 37 | 451 | 2.24 | .947 | .728 | .115 | .119 | .229 |
| 40 | 453 | 2.19 | .842 | .809 | .115 | .581 | .229 |
| 35 | 447 | 2.12 | .784 | .649 | .115 | .488 | .230 |
| 39 | 454 | 2.22 | .853 | .661 | .115 | .478 | .229 |
| Valid N (listwise) | 352 |
Items 12, 25, 31 and 32 were removed.
Our four-factor solution with survey item loadings per factor is presented in Table 4.
Rotated Four-Factor Solution, Factors and Factor Loadings
| Factors | ||||
|---|---|---|---|---|
| Autonomy | Diversity | Interaction | Openness | |
| Item | 1 | 2 | 3 | 4 |
| 1 | .735 | |||
| 2 | .723 | |||
| 3 | .715 | |||
| 4 | .674 | |||
| 5 | .633 | |||
| 6 | .541 | |||
| 7 | .540 | |||
| 8 | .493 | |||
| 9 | .489 | |||
| 10 | .473 | |||
| 11 | .419 | |||
| 13 | −.820 | |||
| 14 | −.761 | |||
| 15 | −.741 | |||
| 16 | −.732 | |||
| 17 | −.725 | |||
| 18 | −.699 | |||
| 19 | −.675 | |||
| 20 | −.674 | |||
| 21 | −.540 | |||
| 22 | −.520 | |||
| 23 | −.471 | |||
| 24 | −.400 | |||
| 26 | .628 | |||
| 27 | .587 | |||
| 28 | .520 | |||
| 29 | .501 | |||
| 30 | .475 | |||
| 33 | .820 | |||
| 34 | .678 | |||
| 35 | .601 | |||
| 36 | .584 | |||
| 37 | .521 | |||
| 38 | .457 | |||
| 39 | .440 | |||
| 40 | .432 |
| Factors | ||||
|---|---|---|---|---|
| Autonomy | Diversity | Interaction | Openness | |
| Item | 1 | 2 | 3 | 4 |
| 1 | .735 | |||
| 2 | .723 | |||
| 3 | .715 | |||
| 4 | .674 | |||
| 5 | .633 | |||
| 6 | .541 | |||
| 7 | .540 | |||
| 8 | .493 | |||
| 9 | .489 | |||
| 10 | .473 | |||
| 11 | .419 | |||
| 13 | −.820 | |||
| 14 | −.761 | |||
| 15 | −.741 | |||
| 16 | −.732 | |||
| 17 | −.725 | |||
| 18 | −.699 | |||
| 19 | −.675 | |||
| 20 | −.674 | |||
| 21 | −.540 | |||
| 22 | −.520 | |||
| 23 | −.471 | |||
| 24 | −.400 | |||
| 26 | .628 | |||
| 27 | .587 | |||
| 28 | .520 | |||
| 29 | .501 | |||
| 30 | .475 | |||
| 33 | .820 | |||
| 34 | .678 | |||
| 35 | .601 | |||
| 36 | .584 | |||
| 37 | .521 | |||
| 38 | .457 | |||
| 39 | .440 | |||
| 40 | .432 |
Extraction method: principal axis factoring. Rotation method: Oblimin with Kaiser normalization. All factor loadings are significant at p < 0.000. Rotation converged in 10 iterations. Items 12, 25, 31 and 32 were removed.
A second exploratory factory analysis from a randomly selected subset of the 36 survey items was as successful in explaining the item fit as the initial dataset, indicating the strength and stability of the four-factor solution.
Factor Interpretations
We returned to the a priori framework to compare/contrast the emerging four-factor model to answer the research questions. The constructs represented Downes’ properties of connectivism including diversity, autonomy, interactivity and openness. Research Questions 1 and 2 asked, respectively, which of the properties that Downes identifies for effective online learning were revealed through students’ experiences in online courses, and is one (or more) of the Downes’ properties more prevalent than others? In fact, students indicated that the constructs of automony and openness had a presence in their online course experiences. However, the property Downes denotes as diversity was markedly lacking, and the a priori construct of interactivity failed to distinguish itself as a factor as well.
The first factor (autonomy) consisted of 11 items (38.8% of the variance). That students were active, engaged, and viewed themselves clearly in control of their learning needs are general themes reflected in the statements loading on this factor. Students’ experiences in online classes also suggested the occurrence of the a priori construct of openness with eight items accounting for 4.4% of the variance. Openness is a primary function of technology, which, based on these students’ experiences, was not only adequate, but also ranged in variety and was trouble-free.
However, based on these subjects’ online course experiences, the a priori construct of diversity was sorely deficient. Twelve statements, nine of which represented the diversity a priori construct and three reflecting interactivity, negatively loaded on this factor, which accounted for 5% of the variance. The negative loadings for the statements ranged from −.820 to −.400. Not only did students indicate the absence of differing viewpoints generally, but they also noted the dearth of diversity in class activities, group work, and discussions.
As asked in Research Question 3, do students’ experiences in online courses reveal other characteristics that affected learning? Indeed, this study reveals the existence of a third factor, which is comprised of five statements. This factor accounts for 5% of the variance, and seems to indicate the existence of an “out-of-class” component; that is, some student- or professor-initiated activity or communication also was taking place during the course.
Our survey also included two questions that attempted to identify the communication and information sharing styles used in the students’ online courses. In terms of both the flow of communication and information within the class, students indicated an “open” model (Figure 2, second configuration from left). This suggests that the professor/instructor and the students actively participated as information was exchanged (n = 236, 49%) and as communication took place, back and forth, among and between them (n = 350, 70%). The configurations used to identify communication and information flows are presented in Figure 2.
Discussion
We acknowledge the limitations of this study. Regarding the study framework, Downes (2010) emphasizes that the four properties of connectivism requisite for learning in an online environment are interrelated. However, as dictated by the factor analysis method, we attempted to define and separate the survey statements into four discrete constructs. Another limitation is that as a sample of convenience, the unique qualities possessed by this group of respondents do not generalize to the population. Also, because some of the subjects did not answer every item, our sample size (n = 465) may be slightly less on some items.
However, the present study that examines students’ experiences in online courses from the perspective of the connectivist model provides initial, empirical, though exploratory, information about how online learning was supported—or thwarted. In order to denote each of the factors our study identifies, we created arbitrary labels—labels based on the interpretation of the statements as themes relative to each emerged. We referred also to qualitative data collected from subjects to aid in our understanding of the factors. Our labels are: (1) student centeredness (closely related to the autonomy a priori construct; (2) technological support (closely related to the openness a priori construct; and (3) out-of-class connections. No factor reflecting the a priori construct of interactivity was found here, and the diversity a priori construct strongly loaded as a factor—albeit negatively. Thus, we did not rename this factor; however, it is a finding that merits attention and will be presented later in this discussion. Because interactivity is such an important concept for online education, as noted frequently in the literature, we also will discuss this finding in detail. For a list of all statements that loaded on each factor see Table 4.
Student Centeredness
The student-centeredness factor was the most prevalent. These findings strongly indicate that the professors and/or designers of the courses our respondents experienced provided ample opportunities for the students to take charge of their own learning. Examples of the survey statements that loaded most strongly on student centeredness:
I apply the knowledge/skills gained from the course to other classes.
The course helped me to better understand concepts from previous classes.
I use what I learned to pursue further knowledge.
I improved my ability to use knowledge to solve problems in my field of study.
An examination of qualitative data reinforces the presence of Student Centeredness. For example:
Allowed me to go at my own pace and determine my level of involvement.
The online courses forced me to take a more active role in learning.
I enjoy working at my own pace where/when I can.
I like that online graduate work allows me to truly consider my weekly readings/essays rather than “snap” thoughts in a classroom that I formulate as someone else is talking. I feel it makes me truly consider the material in more depth.
A consensus exists in the literature: student-centered environments support learning. For example, Siemens (2004, 2005) reminds us that the starting point of connectivism is the individual. Milligan (2006) states that students control their learning experiences in effective PLE's, and Kop and Hill (2008) stress that students’ abilities to seek out information is a crucial skill that contributes to learning. As this study finds, students’ online course experiences were not only replete with opportunities to control their own learning, but the subjects also valued this aspect of their online courses.
Technological Support
Our technological support factor closely relates to the openness a priori construct, which indicates that the appropriate technological tools are available to eliminate the boundaries in the learning environment. We refer to this factor as technological support because, based on a close reading of the statements that loaded on this factor, it appears that the technology in these subjects’ online experiences both broadened their opportunities for learning as well as supported the class in general. For example:
I was encouraged to use technology to access unlimited information/resources.
I was required to use technology to access unlimited information/resources.
The technology provided unlimited access to relevant information.
I did not have problems with the technology in the course.
The following student comments that reflect a positive experience with the technology are typical of the many such statements students expressed about this topic:
The use of several technologies made the learning easier and more enjoyable. One unique system utilized was Wimba, and students were able to present information about an area of research with other students. This was very helpful.
I learned how to use … electronic communication techniques that I never knew existed.
Additionally, students most often cited Skype and Google docs as the means used to enhance communication and collaboration. However, several subjects were concerned that an overemphasis on technology added additional costs or it “was a hindrance to learning the material.”
Our findings support the many studies that note the importance of online tools to provide both access and interaction in online courses (see, for example, Cifuentes et al., 201l; Menchaca & Bekele, 2008; O'Reilly, 2005; Pata, 2009).
Out-of-Class Connections
The a priori construct of interactivity did not emerge as a factor in these findings. However, one diversity and four interaction statements loaded on a factor we refer to as out-of-class connections. Although the notion of interactivity is certainly present on this factor, we conclude these statements suggest interaction transcends communication between and among the instructor and students. Rather, the common theme implies an out-of-class component:
The professor interacted with others not enrolled in the course.
Student-formed study groups were a part of the course.
Differing perspectives were available from people not enrolled in the class.
I interacted with people not enrolled in the course.
Group/team work involved all members of the group contributing without restriction.
This factor is vital because an out-of-class experience adds meaning to the content of the class, as supported in research. Kopp and Hill (2008) describe this component as bringing people with knowledge to the learners.
Interesting too, although the a priori interaction construct was not a distinguishing factor in these findings, participants’ comments in open-ended questions most frequently concerned interaction; more often, in fact, than any other topic. Certainly, this is consistent with previous research, which repeatedly shows that interaction is important in the online environment—because students expect it (see, for example, Herring & Clevenger-Schmertzing, 2007). Thus, as these comments show, our respondents expressed the strongest dissatisfaction with their online classes when interaction was absent—especially from their professors:
It was very difficult because the instructor was very hard to communicate with. He never responded to e-mails or phone calls so it inhibited learning significantly.
I wish that there had been more communication between students, rather than just the professor to the student.
The almost total lack of communication became very frustrating.
I didn't like the course, mainly because that I couldn't interact with anyone.
Very hard to get a hold of the instructor. Took at least a week to e-mail back, and up to 2 or 3 weeks to update incorrect grades.
I was dissatisfied with the instructor's lack of communication and availability to the online students.
It was horrible. She rarely replied to e-mails and when she did she was rude and made us feel like she didn't have time for us.
By contrast, when the subjects indicated their online course experiences were positive, they also positively referred to the interactive component of the class. For example:
The collaboration was more than what one could have expected. It definitely prepared you for that one job you have always dreamed of having in addition to modifying the learning at a deeper and more meaningful level.
Sometimes in a sit-in classroom not everyone has a chance to contribute to the discussion; in the online format everyone participates.
I liked the way students could comment and respond to one another on discussion board.
The instructor had very good communication with the students.
The majority of our respondents selected the open model as the most appropriate configuration to demonstrate the flows of communication and information in their online courses. Thus, these students seemed to perceive the opportunity for engagement their online classes afforded them—and many obviously did so.
Diversity
These findings show that students’ online course experiences strongly lacked a diversity element. The statements loading negatively on the diversity a priori construct include:
All opinions were allowed.
All opinions were considered during class discussions.
Differing viewpoints were posted in the discussion board.
All viewpoints were considered important.
The lack of diversity, as one of the four properties of connectivism, is disconcerting; if learning is to be optimized in an online environment, students must be given the opportunity to explore varying viewpoints. As Siemens (2006b) asserts, learning and knowledge require a diversity of opinions so that the whole is presented, and students are able to select the best approach. He adds:
When we create connections between content, we create a network or aggregation of different ideas, which adds meaning to individual voices.… When the network is sufficiently large to account for diverse perspectives, it achieves a certain level of meaning that is reflective of the combined force of individual elements. (Siemens, 2006b, p. 10)
Thus, diversity should indicate the presence of varied points-of-views and widespread opinions. These differing viewpoints should have an affect on the understanding and completion of the course content. Gallagher-Lepak, Reilly, and Killion, also describe the importance of diversity. In the words of one of their research subjects:
You're maybe not all starting at the same point, and everyone has had different experiences; they work in different settings…they can be from all different fields, so you're getting all kinds of different input, which may be totally foreign to you. But you learn a lot that way too. (2009 p. 142)
The importance of diversity in online learning is supported in numerous other studies (for example, see Kop & Hill, 2008; Reeves et al., 2004).
Interestingly, one respondent in our study observed the potential for negative interaction in a class in which differing viewpoints are present, “One narrow-minded student used the anonymity of the online course to insult students who had opinions that differed from his.”
Conclusion
If educators want to optimize learning in online class environments, then harnessing the properties recommended in the connectivism model holds promise. Despite the exploratory nature of this research, our findings indicate that some professors/instructors and course designers are incorporating concepts of connectivism within their classes. Moreover, our respondents appear to have comfortably shifted to an online learning environment that is student-centered. Additionally, our subjects’ online class experiences provided abundant technological support that also expanded their access to course-related information and people. The fact that our subjects’ online courses provided opportunities for an out-of-class connectedness, which relates to the connectivism property of interactivity, also reveals an important concept that merits further study.
The importance of interactivity cannot be overstated. In terms of this study, we find its presence in the Out-of-Class Connectedness factor, although the interaction that took place transcended course boundaries. We also detect the presence of interaction in many of the statements that loaded on the technical support factor. That technical support expanded opportunities to access people and information reinforces how respondents described the information/communication flow as being open and nonlinear in their classes.
A troubling qualitative finding in this study, however, is the absence of interactivity from many of our respondents’ professors. Indeed, when professors fail to interact with their online students, they do so at their own peril because learning outcomes have been found to be positively related to the amount of time teachers participate in the class and interact with students (Zhao et al., 2005). Moreover, student satisfaction with online instruction positively correlates to both the quality and timely interaction by the professor with students (Young & Norgard, 2006). Additionally, previous research has found that interaction with the teacher trumps the importance of interaction with other students. Already there is strong support in the literature about the importance of online course interaction (Herring & Clevenger-Schmertzing, 2007; Young & Norgard 2006); however, we recommend additional research to explore interaction and its many complexities.
Yet diversity is no less important for learning. As one “connectivist” put it, learning and knowledge “rest in a diversity of opinions” (Siemens, 2005, p.7). Thus, we reiterate Compton, Davis, and Correia's call for additional research in this area because of the need to “increase the diversity of resources” (2010, p. 57). The lack of diversity found in this study not only points to the need for additional research, but also implies that online course developers are not taking the notion of diversity into account, which could compromise an in-depth understanding of the content.
The findings from this study have practical implications for both online course designers and professors/instructors. However, from a scholarly perspective, the connectivism notions espoused by Downes and others deserve further investigation too as educators strive to better understand how online learning can be fostered and supported. As Kop and Hill (2008) observe:
Using the emerging technologies … ensures that … education can secure its role of critical engager … making connections with information and knowledgeable others all over the world to enrich learners’ lives … where control is shifting from the tutor to an increasingly more autonomous learner. (p. 11)
References
Appendix A Measurement Items Used in this Study
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