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Simulation as a Strategy in Teacher Education  

David Kaufman and Alice Ireland

Simulations provide opportunities to extend and enhance the practice, feedback, and assessment provided during teacher education. A simulation is a simplified but accurate, valid, and dynamic model of reality. A simulation allows users to encounter problem situations, test decisions and actions, experience the results, and modify behavior cost-effectively and without risking harm. Simulations may or may not be implemented using digital technologies but increasingly take advantage of them to provide more realism, flexibility, access, and detailed feedback. Simulations have many advantages for learning and practice, including the ability to repeat scenarios with specific learning objectives, practice for longer periods than are available in real life, use trial and error, experience rare or risky situations, and measure outcomes with validated scoring systems. For skills development, a simulation’s outcome measures, combined with debriefing and reflection, serve as feedback for a formative assessment cycle of repeated performance practice and improvement. Simulations are becoming more common in preservice teacher education for skills such as lesson planning and implementation, classroom management, ethical practice, and teaching students with varying learning needs. Preservice teachers can move from theory into action, with more practice time and variety than would be available in limited live practicum sessions and without negatively affecting vulnerable students. While simulations are widely accepted in medical and health education, examples in teacher education have often been research prototypes used in experimental settings. These prototypes and newer commercial examples demonstrate the potential of simulations as a tool for both preservice and in-service teacher education. However, cost, simulation limitations, and lack of rigorous evidence as to their effectiveness has slowed their widespread adoption.

Article

Computing in Precollege Science, Engineering, and Mathematics Education  

Amy Voss Farris and Gözde Tosun

Computing is essential to disciplinary practices and discourses of science, engineering, and mathematics. In each of these broad disciplinary areas, technology creates new ways of making sense of the world and designing solutions to problems. Computation and computational thinking are synergistic with ways of knowing in mathematics and in science, a relationship known as reflexivity, first proposed by Harel and Papert. In precollege educational contexts (e.g., K-12 schooling), learners’ production of computational artifacts is deeply complementary to learning and participating in science, mathematics, and engineering, rather than an isolated set of competencies. In K-12 contexts of teaching and learning, students’ data practices, scientific modeling, and modeling with mathematics are primary forms through which computing mediates the epistemic work of science, mathematics, and engineering. Related literature in this area has contributed to scholarship concerning students’ development of computational literacies––the multiple literacies involved in the use and creation of computational tools and computer languages to support participation in particular communities. Computational thinking is a term used to describe analytic approaches to posing problems and solving them that are based on principles and practices in computer science. Computational thinking is frequently discussed as a key target for learning. However, reflexivity refocuses computational thinking on the synergistic nature between learning computing and the epistemic (knowledge-making) work of STEM disciplines. This refocusing is useful for building an understanding of computing in relation to how students generate and work with data in STEM disciplines and how they participate in scientific modeling and modeling in mathematics, and contributes to generative computational abstractions for learning and teaching in STEM domains. A heterogeneous vision of computational literacies within STEM education is essential for the advancement of a more just and more equitable STEM education for all students. Generative computational abstractions must engage learners’ personal and phenomenological recontextualizations of the problems that they are making sense of. A democratic vision of computing in STEM education also entails that teacher education must advance a more heterogeneous vision of computing for knowledge-making aims. Teachers’ ability to facilitate authentic learning experiences in which computing is positioned as reflexive, humane, and used authentically in service of learning goals in STEM domains is of central importance to learners’ understanding of the relationship of computing with STEM fields.

Article

Simulations in Teacher Education  

Stefan Schutt, Rebecca Miles-Keogh, and Dale Linegar

For decades, simulations have helped educators build students’ skills and workplace readiness in professions such as health care and medicine. Historically, teacher education has been slower in its take-up of simulations, but the value of practice for pre-service teachers (PSTs) has become more widely recognized as digital technologies have become ubiquitous. Simulations, however, are not only digital. Although their long history incorporates technology-based platforms such as virtual worlds, “serious games” and online scenarios, they also include resource-intensive face-to-face activities such as role plays involving teachers, student peers or paid actors. In teacher education a range of pedagogies support the use of simulations by recognizing the complexities of classroom practice and emphasize targeting specific aspects for skill development and supporting opportunities for deconstruction, reflection and feedback. An overview of these developments provides social practice theories as a theoretical framework for exploring the potential of simulations to help PSTs practice targeted skills in risk-free environments, followed by a potted history of simulations in education, identifying limitations, and concluding with thoughts about future directions. Examples of contemporary simulations are used throughout to illustrate specific points.