University World News

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Dr. Fred Saba

Farhad Saba, Ph. D.
Founder and Editor, Distance-Educator.com

In this series of articles, I presented a hierarchical model of distance education consisting of seven interrelated nested systems levels. These systems have been present in most distance education organizations that I observed, or planned and built over the past 30 years. In the previous weeks, I discussed Hardware, Software, Telecommunications, Instructional, Educational, Societal and Global Systems Levels. Last week I started to explain the process of system modeling so that you could start the planning process for your organization. I hope that conducting the environmental scan as presented in a previous article has given you a better appreciation of the components of the technology-based educational programs in your organization and the interrelationships among such components. But before I went any further on the process of modeling itself, I explained certain important concepts in system methodology in this article and showed how these principles can be applied in this article titled Planning and Managing Distance Education Systems: Applying system dynamics. In a subsequent article, I presented a step-by-step application of system dynamics for model building and described how these steps can be implemented in your institution. Also in an article titled Institutional Realities, I explained the inverse relationship between complexity and the process of planning. In more complex institutions, it is difficult to agree on a set of common goals among students, faculty, administrators, taxpayers, and decision makers. In the following article the roles of the team members in the planning process are explained.

The process of model building for planning the future of an institution with multiple programs, constituencies, and stakeholders requires undertaking several tasks. Some of these tasks relate to clarifying current policies and procedures that are already in place in an organization, and the common assumptions about how they work and affect the life cycle of projects and programs underway. Others are related to developing consensus for the goals an institution would like to reach in 3, 5 or 10 years in the future. In addition there are tasks that are technical in nature, such as, developing flow diagrams, writing equations for the model, and testing the model on a computer. The following reflects the experience of the authors in model building as well as the contributions of Schoenberg and Bean (2012) who proposed a process for model building and identified members of a team necessary to design develop and implement the modeling process in higher education institutions.

The Planning Team

The team for the planning and modeling process should include representatives of stakeholders in the institution. Not all team members may be involved in all stages of the planning process; however, it is imperative that faculty, students, administrators, parents, alumni and community leaders within and outside of the institutions be invited to take an active role in conceptualizing the future of the institution. In order to achieve this inclusive process, certain roles are necessary in forming a team:

Planning manager: The planning manager is in charge of the entire process of planning and modeling. The role involves not only assuring that all the stakeholders are actively involved in the planning and modeling process, but their voices are adequately heard and included in arriving at a consensus at each stage of the process. The team manager also must ensure that the process is proceeding in a timely manner towards a set deadline. S/he must move the project forward according to a predetermined schedule for each stage and see to it that the project would be completed on the selected deadline.

Facilitator(s)- Depending on the size of the project one or more facilitators are required to engage all stakeholders in planning meetings, focus groups, brain storming sessions, and other similar activities as they are called for in the planning process. They also must assist the data collector (see below for a description of this role) in collecting and organizing all the documents and data from organizational units and individuals that are involved in the planning process. If collecting new data is necessary, they must see to it that the surveys, questionnaires or other similar data collecting instruments are created and made available online and encourage potential participants to respond to such data collection instruments.

The facilitator’s major deliverable is the definition of the problems that the group as a whole will address in the entire planning process.

Model Designer- The model designer’s tasks is to facilitate participating planners in the modeling process for explicating their common understanding of the components that constitute the institution and how these components relate to each other by developing a causal loop diagram. In projects implemented by the authors, it has always become clear that the more time and effort is spent on designing the causal loop diagram the better the outcome of the planning process would be. Designing a causal loop diagram may seem simple at the outset but as discussion in the planning process unfolds it becomes apparent that deciding on the model boundary and the number and nature of constituting components for the model is subject to the differing and at times incongruent ideals, goals and preferences of participating members of the planning team. As it was demonstrated in Chapter One an institution may have multiple goals and it may not be possible or even desirable to develop a single mission statement for an institution and arrive at a consensus on a single objective to reach in future years. Designing a model is more of an art than a technique in the sense that the causal loop diagram that is developed by the planning team must reflect the contending ideals, preferences, and goals in terms of allocating resources for them and realistic timelines to achieve them. If the relationships among the components reflect the complex reality of the institution with multiple visions for the future, and even paradoxical and dichotomous agendas then the modeling and planning process is set on a good standing to succeed.

The model designer also has the task of explaining the policy ramifications of the model to the planning team, once the model is run based on several assumptions for the initial values of key components. Depending on the results of each run, the planning team might want the model to be run again based on different sets of assumptions to see how the behavior of the model might change over a period of time. This iterative process is necessary to arrive at a clear course for action, as different runs of the model may show unintended or unforeseen consequences. It is best that the planning team become aware of the unintended or unforeseen consequences of its intuitive assumptions in a simulated environment as compared to i the real world referent organization of the model when such intuitive suppositions may result in undesirable or even catastrophic events. The design process elucidates intuitive understanding of the planning team about the institution and its operations, as well as the outcomes of policies that are based on them. It also is a means to reconcile seemingly paradoxical and conflicting goals as they may emerge during the planning process.

Model Builder- Creating the flow diagram based on the causal loop diagram and determining the equations to represent the flow diagram mathematically are primary tasks of the model builder.

Model Technician- The task of the technician is to run the test version of the model, and work with the model builder to ensure that it runs according to its design. Further the technician is tasked with implementing modifications and changes that emerge from the test runs of the model.

Data Manager- The task of the data manager is to collect, classify and code the quantitative and  qualitative data that are necessary to guide designing the model and the assumptions based on which the causal loop and the flow diagrams are constructed. The data manger is also in charge of creating the matrices that are required for collecting, and coding the quantitative data that is required for running the model, as well as assisting the model technician to enter the data in the model’s database.

Depending on the complexity of the project the tasks outlined above may be carried out by one or more persons. However, the nature of these tasks require close coordination if they are carried out by several people in the more complex organizations. For example, a common thread for these tasks is what kind of data should be collected to run the model. As such, the planning manager, facilitators the model designer and the model builder should coordinate their tasks to ensure the accuracy of the data collected.

In the next article in this series we will discuss how the results of the study of key theoretical concepts in distance education (Instructional System Level) using system dynamics directly influences universal policies for the future of higher education.

Founded in 1873 as the University of the Cape of Good Hope, the institution became the first public university in the world to teach exclusively by means of distance education in 1946. Throughout the years, Unisa was perhaps the only university in South Africa to have provided all people with access to education, irrespective of race, colour or creed. This vibrant past is mirrored in our rich history, more particularly our massive and impressive database of alumni, some of whom are to be found in the most senior levels of society across the world.

Given our rootedness in South Africa and the African continent, Unisa today can truly claim to be the African university in the service of humanity.

We have embraced the fact that we need to adapt quickly to the fast-paced higher education environment of the 21st century and this is reflected in our management style and leadership practice. In addition, one of our main aims is to harness the new and emerging potential in information and communication technology to catapult the university into a truly digital future.

We offer an unparalleled range of study choices, ranging from short courses and certificate programmes to three-and four-year degrees and diplomas, to over 350 000 current students. As one of the leading research institutions on the continent, our research efforts have won us numerous awards, recognitions and honours.

Whether you’re beginning a new journey with us or staying involved with your alma mater, we commend you on choosing a world-class institution that has been building Africa’s future for 139 years.

University of South Africa

Online Journal of Distance Learning Administration

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Online Journal of Distance Learning Administration

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Abstract

During the past decade, the convenience of online learning has afforded postsecondary students of all ages the opportunity to attend and complete online programs—especially to those students who have full and/or part-time employment, dependents, and those maintaining busy schedules. The benefits of taking online courses include flexibility, convenience, and cost-effective educational opportunities anywhere and anytime. Despite these well-known affordances, postsecondary institutions offering online courses are also fully aware of the challenges concomitant with this learning environment—most notably, student retention. Numerous studies have approached the retention, progression, and completion issue from a variety of angles attempting to predict, classify, identify, and increase opportunities for students to reach their personal academic goals. Rather than repositioning and assuming a new angle, the authors of this study chose to fuse these well established–yet isolated angles. Therefore, the purpose of this study was (1) to identify significant student demographic predictors among students who dis-enroll (“stoppers”), reenroll (“swirlers” and/or “shoppers”), and/or complete their online program of study (“succeeders”), and (2) to calculate the variance among the significant predictors.

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