Decentralized, on-demand course facilitation
Building upon these existing frameworks, we propose another instructional model, one that leverages a decentralized, on-demand facilitator network. Specifically, students progress through interactive online course modules that include audio and video presentations, practice activities, reading assignments, quizzes, tests and other graded assessments. The student then decides when to access one-to-one tutoring from a network of professional online educators who are available 24/7 (see www.straighterline.com).
The self-regulated learning (SRL) model
Students self-select asynchronous and/or synchronous instruction provided by an online network of professional educators when they need supplemental assistance with the online course content. This model is not unlike one-to-one longitudinal tutorial instructional models, regulated largely by students’ individual progress. These models do not rely on small group classrooms or lecture hall formats as the main teaching mechanism (see, for example, McCallum 1947; University of Oxford n.d.). Such a model allows students to receive tailored one-to-one online instructional support from professional educators throughout their progression of course modules (also see Twigg 2003, 2009).
On-demand assistance between online tutor and student occurs in a virtual whiteboard environment. The whiteboard is a real-time interactive learning environment where both tutor and student alike use text, colors, and graphical tools to discuss ideas and solve problems (Hewett 2006)(Hewett 2006). Specialized mathematical and language tools enable students to more easily input data like fractions and accented characters onto the whiteboard. For courses that require written work, such as essays or reports, students submit their writing to online tutors who provide qualitative critiques and, when applicable, final quantitative scores on student essays.
While a single course instructor, much like traditional face-to-face courses, facilitates most online college-level courses, our model is grounded in a self-regulated learning (SRL) framework (see Willem 2006). This model promotes a student’s self-governance of the variables within the courses, offering a relatively high level of flexibility for the student.
SRL process
Within the SRL framework, students access on-demand tutorial instruction when desired as they set individual goals for assessments and assignment submissions. Through the course materials and the assistance from on-demand tutors, students proceed through each of the stages of the SRL process:
- the planning-analysis phase, where students observe the learning objectives and customize their approach to learning the course material;
- the reflection-monitoring stage, where students examine whether the approach to learning the material is working; and
- the evaluation-application phase, when students finally evaluate whether the approach has worked (Willem 2006).
Studies supporting SRL
Supporting the SRL approach is Lee (2008), who reports that a class of students who regularly use SRL averaged 67.09 on three online modules that require problem-based learning whereas a class of students who did not use SRL only averaged 56.75. If students determine, with the help of various learning activities and course indicators, that they have not mastered the learning objectives in step three of the self-regulated learning sequence, they may seek on-demand assistance and adjust their approach on future self-regulated learning tasks appropriately.
Individual components of this instructional course delivery model have been studied and indicate efficacy from both a process and outcomes perspective. Numerous independent studies and reports demonstrate that on-demand facilitation via online tutoring opportunities can foster student achievement in various subject areas and help improve student retention (see, for example, BCC 2005; Langer 2008).
Calfee (2007) also reported that students earned significantly higher grades as a result of using online tutoring. Similarly, in a trial study conducted in the Fall of 2007, researchers at Open Universities Australia concluded that students using the online tutoring service experienced higher success rates and course completions (DeFazio and Deden 2008).
Process-based analytic studies of writing sample archives (synchronous and asynchronous sessions, as well as essay critiques) indicate enhancement of student learning. For example, Hewett’s (2006) process-based analytic study of a sample of high school and college students who used asynchronous online writing tutoring found that students used approximately 40% of the advice that tutors provided to them to improve their writing, as shown through textual iteration and presupposition analysis. Seeming to apply their own authority in subsequent revisions, students decided which advice to implement and which advice to omit.
Hewett also found that students tended to be “non-responsive” to certain types of comments that, interestingly, the online tutors themselves reported may have been problematic and difficult to understand. After evaluating the most challenging kinds of tutor statements, Hewett concluded that online writing tutors (and by extension online writing instructors) needed specific training targeting the phraseology and construction of their written commentary.
In a similar study of synchronous online interactions, Hewett (2004-2005) reported that two-thirds of student and online tutor talk was directly tied to revision in subsequent student writing. The kinds of revisions, however, differed significantly from revisions tied to asynchronous interaction. Linguistic analysis of student draft changes determined that students used such synchronous instruction for broader, more global changes to their writing. Asynchronous interactions yielded more local revisions.
Noteworthy of these studies is that these were the first published empirical studies in the rhetoric and composition field to prove that student writing can improve through on-demand, online writing tutorials.
Standardizing quality and consistency in SRL
Within the context of any distributed facilitator model of course delivery (whether on-demand or not), standardization of the quality of tutor-student interaction and the consistency of assigned assessment scores is imperative. This standardization can be achieved through intensive training, on-going evaluation, and professional development activities. Implementing these measures highlights both the administrative and pedagogical challenges attending to individual learner needs within an efficient and standard training program.
These complexities have been documented in various publications (e.g. Ehmann Powers in progress, manuscript under review; Ehmann Powers and Hewett 2008; Hewett and Ehmann Powers 2005, 2007). To address these issues, every tutor in the distributed network completes a subject-specific certification program that involves a combination of self-paced online modules, interactive sessions with veteran tutors, and meta-cognitive exercises. In their online practice sessions, tutors undergo an orientation to their respective technology platforms and then work with academic coordinators and veteran tutors who assume a variety of student roles. Simulated tutorial exercises represent diverse situations and student needs that typically are encountered throughout the semester (for example, scenarios involving ESOL learners, non-traditional students, as well as students with varying learning styles). Upon completion of the training program, tutors must demonstrate competence in specific content areas, a mastery of online communication and instruction, and an understanding of the values that drive the organization’s practice.
All tutors who review writing assessments complete an additional 10- to 15-hour training program, which addresses the fundamental pedagogical issues of tutoring writing in an online setting. Tutors participate in norming sessions to assure consistency and reliability across both individual tutor scores and the larger tutor pool. In addition to this orientation, academic directors review and back-read samples of completed assessments to confirm consistency of scoring. Should tutors demonstrate inconsistencies, additional training and norming sessions are required.
The aforementioned scholarship supports the use of online tutoring as an effective on-demand form of instruction and reinforces the need for sufficient training. However, engaging learners in all aspects of an online course that is fundamentally grounded in a self-regulated learning approach is difficult (Lee 2008). There is consensus in the literature that one of the primary reasons students withdraw from an online course is a feeling of isolation (Freeman n.d.). It is essential, therefore, to have a level of non-subject-specific support mechanisms for learners.
A team of “student services” course advisors who are in regular contact with students on, for example, success skills and time-management techniques can achieve this goal. Social networking sites, such as Ning (www.ning.com), can also address this need by allowing both prospective and current students to connect and form associations necessary to their engagement with the course. Students have the opportunity to join content-specific discussions that match the course content. Students can enter the student lounge and connect with others enrolled in different courses, or those who are still gathering information. In addition to conversing with other students, regular communication initiated by the course advisor can decrease perceptions of isolation that online learners often express, since, otherwise, many students remain silent, waiting for another to begin the conversation (Lorenzetti 2005).
Future development and research
We have presented an instructional model that involves interactive content and access to on-demand instruction. Although the technical logistics of progressing through the course modules and accessing the on-demand help are straightforward, questions surrounding self-regulated learning within a distributed on-demand facilitator course model still exist. Since the theory and practice of on-demand content assistance by professional educators in an entirely online environment is a relatively new phenomenon in education, a deeper understanding of which learners are best suited to this type of instructional model is needed.
In order for researchers and course developers to explore both pedagogical and operational approaches and recommend appropriate modifications, issues such as student and faculty experiences gained via surveys, course evaluations, focus groups, and student records across multiple courses must be closely analyzed. Future research may include examining which learners, as well as which content areas, are ideally served by this type of model. As there are a variety of learning styles, and the learning environment itself is nonlinear, there are many approaches that may actively engage a wider variety of future learners. With changes in the approaches, modifications will also need to be made to the support offered to students, making the support of future students a priority.
While the instructional model described in this article encourages human-to-human interactions with the course advisor, the social network, and one or more tutors, relationships may be developed with partner institutions that can address the need for face-to-face interaction that some students desire. For these individuals at local institutions and for the tutors themselves, more training may occur to help best equip current and future students with the skills necessary to succeed in their academic coursework.
Conclusion
Merging on-demand assistance with self-regulated learning provides a course environment in which students can progress according to their own needs while having repeated access to content-matter experts when they determine that help is needed. This instructional model can help adult learners for whom traditional, single instructor online courses may not be appropriate.
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