Education Case Studies: Introduction | MinSIM | College Decision Making | High School Quality

Minnesota System Information Modeling (MinSIM) Project

A collaboration with the Transforming Schools Consortium, Ramsey MN (Dr. Ralph Brauer, Executive Director):

Problem Statement: Student outcomes or performance have always been the primary focus of thoughtful school reform and improvement efforts. Student performance, however, is the result of myriad influences acting directly or indirectly on the students. Further, these influences interact with and influence each other in complex combinations of feedbacks, often non-linear and often with significant delays. There is little wonder that reform efforts have been contentious and have, at best, a mixed record of achievement. Within that context we sought to use and refine our current understanding of educational data and relationships to construct a simulation of such a system to aid educators in addressing a fundamental question: How can we better manage and improve student performance?

Approach: Using our Ladder of Engagement to structure our approach to this problem:

• We collaborated with Dr. Brauer, faculty at the University of Minnesota, school administrators, and school teachers to establish a shared baseline of KNOWLEDGE of the relationships that influence the dynamics of student learning and performance.
• Working with those insights, we then probed and captured the participants’ UNDERSTANDING of the feedbacks that control the evolution of those factors. These feedbacks were then utilized to construct a computer simulation to allow objective exploration of those relationships.
• Finally the model was fine-tuned and user interfaces constructed so that school personnel could define policies and explore theirability to positively INFLUENCE the student outcomes.

KNOWLEDGE

What do we know about the “behavior(s)” of the system?

The initial conversations, as well as much of the recent literature, identified an extensive laundry list of factors that influence student performance. For clarity in communication, we clumped those factors into the sectors illustrated here. For any given school district, each of those factors can be explored and its behavior plotted and, often speculatively, projected into the future.

Realizing that each of the sectors would influence other sectors in addition to the primary focus on student performance, we needed to develop an understanding of the complex set of feedbacks that would control those relationships. Perhaps the greatest challenge there was to identify a “common currency” with which to express those relationships. That led to the Understanding rung of the Ladder of Engagement.

UNDERSTANDING

What “drives” the behavior of the system?

Focusing on student performance, participants quickly realized that the critical dynamics reflected the changing balance between “learning resources” and “learning demands.” Addressing the “common currency” issue led to realizing that the myriad elements contributing to those resources and demands could all be defined in terms of “time.”

Demands of students of varying ability levels and behavioral characteristics can be expressed in terms of the time required to meet those demands. Teachers of varying experience and ability can meet varying levels of time demands brought by their students. Those learning resources are affected by administrative (in)efficiencies, material and personnel support, and community expectations and support. As student performance changes, so too do both demands and resources respond, creating the complex web of feedbacks that make school management such a challenge. Identifying “time” as the common currency, however, enables us to move forward constructively with research and conversation to identify and quantify those feedbacks, and construction of a computer simulation for objective exploration of the influence of a wide variety of policies.

Reading the Feedback (Causal-Loop) Map:

Individual students place a variety of demands (A) on the educational system; we have divided those demands into “learning” and “behavioral” categories, but added them together here for this discussion. In the modeling, we have expressed those demands into terms of the “time” required to address them. At the same time, the educational system can bring a variety of resources (B) to bear on those issues. These are primarily represented by the teachers who work with the students; each of them will have an individually define experience and capacity to meet the student demands. Those capacities, again in expressed in the amount of “demand time” that they can address, are further affected by administrative (in)efficiencies, material and other personnel support, and community expectations. Comparing the educational demands and the educational resources allows us to define a “resource gap” (C; do we have extra, adequate, or insufficient resources to meet the demand?). That gap, in turn drives changes in Student Performance (D), leading to improvement or worsening in the student outcomes. Those outcomes in turn impact changes in both the Educational Resources (are resources added or removed? Are efficiencies enhanced or compromised?) and the Educational Demands (if students are well served, does there growth reduce the need for resources in the future; do neglected students display ever increasing needs?). These changes (E) complete the basic feedback loops that define our mental model of the system and that provide the structure for the resulting computer simulations.

INFLUENCE

Can this understanding be used to generate better policies to manage the system?

Two aspects of “influence” are evident in this project. The simulation itself has been used in a variety of venues. In Minnesota is has been the focus of work in the schools of the CAREI network of the University of Minnesota’s School of Education; as well, the Blandin Foundation has featured it as an integral part of their school leadership program. These programs use the simulation as a guide for better understanding the feedbacks and their complex interaction in controlling the behavior of school systems, rather than seeking specific quantitative answers to school management questions or problems. In addition, the resulting simulation itself, but ALSO the systems-based and collaborative PROCESS by which it was developed, has resulted in publication in the newsletter of The Creative Learning Exchange and an article in The School Administrator, the journal of the American Association of School Administrators.