In Depth Analysis: Analyzing Analytics; A Systems Approach
Farhad (Fred) Saba, Ph. D.
Founder and Editor, Distance-Educator.com
In recent years administrators are paying increasing attention to studying the discrete components of colleges and universities through collecting and analyzing massive amount of data on student behavior, number of courses offered and taken, time students spend in each course, completion rates, etc. While analytics sheds light on such discrete components, and is necessary to bring to fore adequate information, decision making also requires understanding how the behavior of each component is affecting all the others in the university. For example, if students begin to complete courses at a higher rate what will that do to demand for new courses in subsequent semesters or quarters? If more faculty decide to include instructional design principles in developing new courses, and add social media to their courses will the instructional technology services on campus be able to respond to this increasing demand for instructional design and technology support services?
At first glance universities are composed of administrative and academic units. The administrators are in charge of managing the day-to-day affairs of the institution as well as thinking ahead in terms of planning and budgeting for different organizational units. The faculty in academic units are in charge of teaching and research and upholding the academic standards of the institution. However, complexity arises when we realize that faculty in most institutions are also directly involved in setting policies for the institution through various bodies such as academic senates or faculty unions. Students are also becoming more involved in making decisions on how to structure their learning to maximize the benefit they receive relative to the investment they must make not only in terms of time to study, but also paying for tuition and other costs. Complex systems are those that have a group of interacting parts that affect each other and are affected by each other. In fact complex systems are not things that we can touch and feel; they consists of interactions and inter-relationships that are not tangible, and that is why they are difficult to define. (Waldorp, 1992, Verma, 1998).
In defining complexity, the website Principa Cybernetica stated:
“Let us go back to the original Latin word complexus, which signifies “entwined”, “twisted together”. This may be interpreted in the following way: in order to have a complex you need two or more components, which are joined in such a way that it is difficult to separate them. Similarly, the Oxford Dictionary defines something as “complex” if it is “made of (usually several) closely connected parts”. Here we find the basic duality between parts which are at the same time distinct and connected. Intuitively then, a system would be more complex if more parts could be distinguished, and if more connections between them existed. More parts to be represented means more extensive models, which require more time to be searched or computed. Since the components of a complex cannot be separated without destroying it, the method of analysis or decomposition into independent modules cannot be used to develop or simplify such models. This implies that complex entities will be difficult to model, that eventual models will be difficult to use for prediction or control, and that problems will be difficult to solve. This accounts for the connotation of difficult, which the word “complex” has received in later periods. “(p. 1)
If taken to extreme analytics leads to reducing the entire university to a lifeless collection of departments and divisions that show up so many times in organization charts in the form of boxes that are connected with lines but do no shed any light on how the organization functions, what are its goals and what is its worth and importance to the community.
Therefore, in addition to analytical approaches for understanding the behavior of each organizational unit, a systems approach is also necessary to understand the meaning of the data in terms of the goals of the university or the department or division which is the subject of study and planning. In doing so, however, one can see a conflict between time and effort that should be put into collecting ans analyzing data, in contrast to time and effort it takes to create comprehensive models of an institution as a whole. Verma (1998) presented a thorough explanation of the analytic and systemic approaches to planning and management and demonstrated that the analytic approach provides rigor to the process of decision making, while the systemic approach offers the comprehensive vision that is required. He further asserted that: the analytic approach is “strong in formal method but, it achieves this strength by discarding questions that cannot be answered rigorously” (PP. 11). If taken to extreme analytics leads to reducing the entire university to a lifeless collection of departments and divisions that show up so many times in organization charts in the form of boxes that are connected with lines but do not shed any light on how the organization functions, what are its goals and what is its worth and importance to the community. On the other hand, Verma went on to say: “Comprehensiveness is about recognizing the importance of preparedness, sharing, trust, loyalty, entrepreneurship, and risk-taking ability in decision making. These are normative values that demand a theory of ethics, not criteria that can be feasibly optimized within an analytic calculus.” (PP 54).
…the analytic approach provides rigor to the process of decision making, while the systemic approach offers the comprehensive vision that is required.
Ultimately, the solution is striking the right balance between rigor in analytics, and the vision for comprehensiveness. In future articles we will demonstrate how a dynamic systems approach, developed by the author, can guide you to achieve such a balance.
Principa Cybernetica (1996). What is complexity. Retrieved from http://pespmc1.vub.ac.be/COMPLEXI.html
Verma, N. (1998). Similarities, connections and systems: The search for a new rationality for planning and management. Lanham, MD: Lexington Books.
Waldrop, M. M. (1992). Complexity: The emerging science at the edge of order and chaos. New York, NY: Simon & Schuster.