TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
The recent release of the Next Generation Science Standards (NGSS) offers a new challenge and opportunity for science. Science practices are the social interactions, tools and language that scientist use as they construct, evaluate and communicate scientific knowledge. The effective integration of science practices into classrooms can better support a wide range of students, including those typically underrepresented in science, to develop greater scientific literacy.
Effective integration of science practices in classrooms requires instructional leadership to support that change. Instructional leaders can include a variety of different individuals including, but not limited to, school principals, district leaders, coaches and lead teachers. The ILSP team is developing tools to support instructional leaders in the science practices.
Vision
Our vision for supporting instructional leaders in their work with teachers to improve science teaching and learning stems from our approach to instructional supervision and science instruction.
Our orientation to supervision is rooted in the importance of strong instructional leadership. We seek to support leaders in their work with teachers as they promote a growth mindset, foster frequent and ongoing opportunities for feedback, sustain a commitment to teacher development over time, and engage in collaborative practices.
K. Juuti, und J. Lavonen. NorDiNa, (2006)Construction of research based teaching sequences through Developmental research (Linsje, 1995), Educational reconstruction (Duit, Komorek & Wilbers, 1997), or Ingenierie Didactique (Artigue, 1994), can be considered very similar with design-based research. On the one hand, these approaches take into careful consideration students’ previous knowledge and emphasise basic scientific concepts and how they are related to the teaching sequence (Méhuet, 2004) and on another hand they aim to design the artefacts. For example, Andersson and Bach (2005) produced a teacher guide as an artefact describing the research-based sequence for teaching geometrical optics. However, these approaches focus on research-based design and the adoption of the innovations needs, for example, teachers’ in-service training.
(p 56).
A. DiSessa. Designing Interaction: Psychology at the Human-Computer Interface (Cambridge Series on Human-Computer Interaction), Cambridge University Press, New York, NY, (1991)