Working with Student Teachers: A Mixed Methods Study to Examine the Roles and Self-Identified Dispositions of Cooperating Teachers

A critical component of teacher education is the field experience during which candidates practice under the supervision of experienced teachers. Programs use the InTASC Standards to define the requisite knowledge, skills, and dispositions for teaching. Practicing teachers are familiar with the concepts of knowledge and skills, but they are less familiar with dispositions. Practicing teachers who mentor prospective teachers are underrepresented in the literature, but they are critical to teacher preparation. The research goals were to describe the self-identified dispositions of cooperating teachers, identify what cooperating teachers consider their role in preparing prospective teachers, and explain challenges that cooperating teachers face. Using a mixed methods design, I conducted a quantitative survey followed by a qualitative case study. When I compared survey and case study data, cooperating teachers report possessing InTASC critical dispositions described in Standard 2: Learning Differences, Standard 3: Learning Environments, and Standard 9: Professional Learning and Ethical Practice, but not Standard 6: Assessment and Standard 10: Leadership and Collaboration. Cooperating teachers assume the roles of modeler, mentor and advisor, and informal evaluator. They explain student teachers often lack skills and dispositions to assume full teaching responsibilities and recommend that universities better prepare candidates for classrooms. Cooperating teachers felt university evaluations were not relevant to teaching reality. I recommend modifying field experiences to increase the quantity and duration of classroom placements. I suggest further research to detail cooperating teacher dispositions, compare cooperating teachers who work with different universities, and determine if cooperating teacher dispositions influence student teacher dispositions.

Self-Organizing Map Neural Architectures Based on Limit Cycle Attractors

Recent efforts to develop large-scale neural architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason is that most conventional SOMs use a static encoding representation: Each input is typically represented by the fixed activation of a single node in the map layer. This not only carries information in an inefficient and unreliable way that impedes building robust multi-SOM neural architectures, but it is also inconsistent with rhythmic oscillations in biological neural networks. Here I develop and study an alternative encoding scheme that instead uses limit cycle attractors of multi-focal activity patterns to represent input patterns/sequences. Such a fundamental change in representation raises several questions: Can this be done effectively and reliably? If so, will map formation still occur? What properties would limit cycle SOMs exhibit? Could multiple such SOMs interact effectively? Could robust architectures based on such SOMs be built for practical applications? The principal results of examining these questions are as follows. First, conditions are established for limit cycle attractors to emerge in a SOM through self-organization when encoding both static and temporal sequence inputs. It is found that under appropriate conditions a set of learned limit cycles are stable, unique, and preserve input relationships. In spite of the continually changing activity in a limit cycle SOM, map formation continues to occur reliably. Next, associations between limit cycles in different SOMs are learned. It is shown that limit cycles in one SOM can be successfully retrieved by another SOM’s limit cycle activity. Control timings can be set quite arbitrarily during both training and activation. Importantly, the learned associations generalize to new inputs that have never been seen during training. Finally, a complete neural architecture based on multiple limit cycle SOMs is presented for robotic arm control. This architecture combines open-loop and closed-loop methods to achieve high accuracy and fast movements through smooth trajectories. The architecture is robust in that disrupting or damaging the system in a variety of ways does not completely destroy the system. I conclude that limit cycle SOMs have great potentials for use in constructing robust neural architectures.