Group items tagged

the academically decentralized/administratively centralized model

Changes in the institutional landscape may magnify competition among educational providers and allow new models and leaders to emerge.

Knowledge proliferation may increase content-breadth demands on higher education, spreading distance education resources ever thinner and complicating development decisions.

An NEA survey reported that faculty members’ top concern about distance education was that they will do more work for the same amount of pay, apparently a merited concern.

As long as distance education contributions are not considered in tenure and promotion decisions, and as long as professors have their own, traditional ways of delivering their courses, many faculty members will be reluctant to participate in online courses (Oravec, 2003). Concerning this reluctance, Dunn has predicted that many faculty members will revolt against technological course delivery and the emerging expectations their institutions will have of faculty members. Dunn forecast that some of the resistance will even be manifest through unionization and strikes (Dunn, 2000). Some have suggested the labor-intensive and time-consuming demands required to develop online modules as reasons for faculty resistance (Brogden, 2002).

The results of de Alva’s 2000 survey support this trend: governors rated “maintaining traditional faculty roles and tenure” as the least desirable characteristic of a twenty-first century university (p. 34).

Faculty members tend initially to try to use their conventional classroom methods to teach at a distance and then become frustrated when attempts are unsuccessful (Dasher-Alston & Patton, p. 14). In Green’s (2002) survey of the role

Distance education teams include administrators, instructional designers, technologists, and instructors/facilitators (Miller, 2001; Williams, 2003). The functions of instructors and facilitators then include being a “facilitator, teacher, organizer, grader, mentor, role model, counselor, coach, supervisor, problem solver, and liaison” (Riffee, 2003, p. 1; see also Roberson, 2002; Scagnoli, 2001). The role of faculty members in distance education requires “some specialized skills and strategies. Distance education instructors must plan ahead, be highly organized, and communicate with learners in new ways. They need to be accessible to students [and] work in teams when appropriate” (PSU, 1998, p. 4). Distance faculty members must be experts in maintaining communication, because there is increased demand for student interaction in distance learning (NEA, 2000). Finally, they may have to assume more administrative responsibilities than is true in a residential model (PSU, 1998).

“Rather than incorporating the responsibility for all technology- and competency-based functions into a single concept of ‘faculty member,’ universities are disaggregating faculty instructional activities and [assigning] them to distinct professionals” (Paulson, 2002, p. 124). Doing this involves a “deliberate division of labor among the faculty, creating new kinds of instructional staff, or deploying nontenure-track instructional staff (such as adjunct faculty, graduate teaching assistants, or undergraduate assistants) in new ways” (Paulson, 2002, p. 126

Online students are becoming an entirely new subpopulation of higher-education learners. They are “generally older, have completed more college credit hours and more degree programs, and have a higher all-college GPA than their traditional counterparts” (Diaz, 2002, pp. 1-2). For example, Diaz has noted that online students received twice as many A’s as traditional students and half as many D’s and F’s.

One result of the highly competitive e-learning market will be institutions that specialize in meeting particular niches in the market (Gallagher, 2003). Morrison and Barone (2003, p. 4) observed, “We can see the beginnings of the trend toward the unbundling of courses, credits, services, and fee structures.” Dunn foresaw a similar trend, predicting that “courseware producers will sell courses and award credits directly to the end user and thus, through intermediation, bypass the institutional middleman” (Dunn, 2000, p. 37). The transition may also blur the distinction between two- and four-year colleges and universities (Carr, 1999). In this context of greater “portability,” more educational “brokers” (e.g., Western Governor’s University, Excelsior College, Charter Oak State College, etc.) will exist (Pond, 2003). Further, as de Alva has asserted, “Institutional success for any higher education enterprise will depend more on successful marketing, solid quality-assurance and control systems, and effective use of the new media than on production and communication of knowledge” (de Alva, 2000, p. 40).

self-efficacy can be viewed as a subjective judgment of one’s level of competence in executing certain behaviors or achieving certain outcomes in the future. Self-efficacy has been identified as the best predictor of college GPA and among the best predictors of college persistence through meta-analytic research (Robbins et al., 2004). Further, commenting on the state of the art in self-regulated learning research Winne suggested that self-regulation is contingent on positive self-efficacy beliefs, arguing that “learners must subscribe to a system of epistemological and motivational beliefs that classifies failure as an occasion to be informed, a condition that is controllable, and a stimulus to spend effort to achieve better” (Winne, 2005). This contrast of failure attribution as trait (e.g., “I’m just not good at math”) versus failure as occasion to be informed (“I can control, adapt, and learn from this”) is a classic view of maladaptive and adaptive self-efficacy beliefs.

In the current study we therefore examine the relationship between CoI constructs and elements of self efficacy in order to begin to investigate the larger theme of collaborative online learner regulation and learning presence.

Thus, self-efficacy is “concerned not with what one has but with belief in what one can do with whatever resources one can muster” (Bandura, 2007, p. 6).

Bandura has noted that slightly elevated efficacy can have a bigger impact on subsequent performance. Overestimating one’s capabilities to produce a behavior and outcome may boost performance and give rise to motivation to persist in face of obstacles and seatback, while the opposite is true for underestimating one’s capabilities, which may suppress productive goals, persistence and effort (Bandura, 2007). Thus there is an important connection between self-efficacy, effort, and subsequent performance.

Positive psychological and emotional states in the aftermath of successful execution of certain academic behaviors naturally lead to sense of competence and subsequently results in enhanced sense of efficacy.

We suggest here that elements within the CoI framework may serve as mechanisms for supporting self-efficacy. Specifically we conjecture that effective teaching presence and positive social presence should serve as sources of social persuasion and positive affect supportive of self-efficacy.

(Bandura, 1997). These and other studies have suggested that self-efficacy has a substantial role in predicting student engagement, motivation and performance ( [Bong, 2004], [Caraway et al., 2003], [Chemers et al., 2001], [Choi, 2005], [Smith et al., 2001] and [Vrugt et al., 2002]).

The participants in the study were a random sample of 3165 students from 42 two- and four-year institutions in New York State.

Gaining knowledge about the reasons for learning and achievement of online students has attracted a great deal of attention among both researchers and practitioners. Understanding the factors that have an influence on the success of online education has significant implications for designing productive online communities.

Reviewing studies that investigated elements of online learner self-regulation

This ongoing project to document all instances of teaching, social, and cognitive presence in complete online courses also resulted in identification of learner discourse that did not fit within the model, i.e. could not be reliably coded as indicators of teaching, social, or cognitive presence ( [Shea, 2010] and [Shea et al., 2010]).

Additional work on the CoI model (Shea, Vickers, & Hayes, 2010) suggested that past research methods may have resulted in a systematic under representation of the instructional effort involved in online education.

These exceptions represent interesting data for refining and enhancing the model as they suggest that learners are attempting to accomplish goals that are not accounted for within the CoI framework.

In this paper we examine the Community of Inquiry framework (Garrison, Anderson, & Archer, 2000) suggesting that the model may be enhanced through a fuller articulation of the roles of online learners. We present the results of a study of 3165 students in online and hybrid courses from 42 two- and four-year institutions in which we examine the relationship between learner self-efficacy measures and their ratings of the quality of their learning in virtual environments. We conclude that a positive relationship exists between elements of the CoI framework and between elements of a nascent theoretical construct that we label “learning presence”. We suggest that learning presence represents elements such as self-efficacy as well as other cognitive, behavioral, and motivational constructs supportive of online learner self-regulation.

the CoI framework attempts to articulate the social, technological, and pedagogical processes that engender collaborative knowledge construction. It therefore represents an effort to resolve the greatest challenge to the quality of online education

Learner discussions also included efforts to divide up tasks, manage time, and set goals in order to successfully complete group projects. As such they appeared to be indicators of online learner self and co-regulation, which can be viewed as the degree to which students in collaborative online educational environments are metacognitively, motivationally, and behaviorally active participants in the learning process (Winters & Azevedo, 2005).

the authors concluded that all the studies converged on advantageous outcomes for providing support for “metacognitive” learning strategies including self-reflection, self-explanation, and self-monitoring.

successfully orchestrating a dialogue demands fairly sophisticated skills. Conversational contributions need to be simultaneously parsed according to their disciplinary value, their location within the chain of collective argumentation, their relevance to the instructional goals, and their role as indicators of the student’s ongoing understanding. The outcome of this complex appraisal is a sense of the amount and quality of the guidance that specific contributions and the conversation as a whole require to support learning.” (Larreamendy-Joerns & Leinhardt, p. 591)

Zhao et al. also concluded that studies in which instructor interaction with students was medium to high resulted in better learning outcomes for online students relative to classroom learners.

Summary and Conclusions This study reports three principle findings.  First, from a survey of student opinion it is reported that 59% believe that the frequency of cheating is the same in both the online and the f2f instructional format. The proportion is significantly greater than 50% at the .05 level. It is also reported that the responses to the question of cheating and instructional format are significantly different depending on whether the student came from an online class or a f2f class, but only at a p-value of .1060.  Recalling the literature review in Table 1, which reported mixed findings by previous empirical studies, an interesting implication for future research is whether student experience with each instructional format influences student perceptions of differences in the frequency of cheating. Second, on proctoring and the frequency of cheating on essay exams and multiple choice exams, it is reported that roughly half of the respondents perceive unproctored assessments as having greater cheating risk than the same assessment in a proctored format, and half think they have equal cheating risk. These findings are consistent with the conventional perception that in a side by side comparison of two courses with comparable content and predominately multiple choice exam assessments, the course with unproctored exams is viewed as having greater cheating risk. Third, in our analysis of assessment design in 20 online courses it is reported that 70% base roughly half the course grade on unproctored multiple choice exams.     These findings imply that online courses, which have unproctored multiple choice exams, can reduce perceived cheating risk by proctoring some of their multiple choice exams without significantly altering the original mix of assessment types. Gresham’s Law suggests that online courses debased by assessment designs with high cheating risk will displace courses with relatively lower cheating risk. Institutions of higher education tone deaf to the issue of proctoring online multiple choice assessments may understandably find other institutions reluctant to accept these courses for transfer credit.  The benefit of proctoring is not without cost.  A proctored exam limits the spatial and the asynchronous dimensions of online instruction, which may have been the core reason the student enrolled in the online. These costs can be mitigated to some extent by early announcement of the time and date of the exam, by allowing for some flexibility of time of exam, and by permitting use of alternate certified proctoring centers. The costs to individual instructors are formidable but there are potentially significant economies of scale to be realized by integration of online courses with an existing system that administers proctoring of exams for f2f classes.  Proctoring of some multiple choice exam assessments will reduce cheating risk. The elephant in the room, however, is the cheating risk on non-exam unproctored assessments (for example term papers, essays, discussion, and group projects). These are widely used in f2f instruction and, as online instruction evolves, will likely become equally widely used in online courses. These assessments are valuable because they encourage learning by student-to-student and student-to-faculty interactions, and because they measure Bloom’s higher levels of learning. These assessments have higher cheating risk than proctored multiple choice exams. These assessments, more so than multiple choice exams, challenge the ability of faculty and administration to inspire students to behave ethically and to refrain from academic misconduct.