PPT-Mathematics and Language Learning: An around the world tour of

Mathematics and Language Learning An around the world tour of promising mathematics instructional practices Michele Haberlach samya matouk amp Steve Morrison Something to Think About. Ray Mooney. Department of Computer Science. University of Texas at Austin. Joint work with. David Chen. Joohyun. . Kim. Machine Learning and . Natural Language Processing (NLP). Manual software development of robust NLP systems was found to be very difficult and time-consuming.. Anne Watson . Professor of Mathematics Education. University of Oxford. February 16. th. 2009. My claim. Personal engagement in mathematics, and reflection on the nature of that engagement, is at the heart of good mathematics teaching, and may make much of what is written about pedagogy redundant.. Anne Watson. ACME/ University of Oxford. Sept 15th 2010. Evidence. Synthesis of research about what children can do in and out of educational contexts. What it means to understand and be able to do mathematics. By: . Sunhee. Kang and Karl Magnuson. Regarding Readings:. 1. ) . Richard-Amato & Snow. . Ch. . . 2. . By. . Gersten. , B. & . Hudelson. , S.. Development in second language acquisition research and theory: From structuralism to social participation.. MATHEMATICALLY RELATED BELIEFS. Peter Liljedahl. MAVI 12 – preservice elementary teachers. MAVI 12 – preservice elementary teachers. MAVI 12 – preservice elementary teachers. MAVI 12 – preservice elementary teachers. Sally Betts. sallybetts@ideas4learning.co.uk. Contextualisation and integration of maths in vocation education. .. The flipped classroom. Maths . via portable devices and other technologies. Successful teaching techniques and examples of good practice used to deliver maths in vocational education. Revision. aptitude. What is language aptitude?. „. Natural ability or skill at doing something, talent” (Oxford Advanced Learner’s Dictionary, 7th edition). „Basic abilities that are essential to facilitate foreign language learning” . Ray Mooney. Department of Computer Science. University of Texas at Austin. Grounding Language Semantics . in Perception and Action. Most work in natural language processing deals only with text.. The meaning of words and sentences is usually represented only in terms of other words or textual symbols.. Ian Galloway. T3 Europe STEM lead. T3 Netherlands. Ede 2016. The Matrix . For the whole of Recorded History. To the present day. Bits or Pits. Education. Young people have an entitlement to have some knowledge of how their digital world is constructed.. K-2. . Session. 1. Agenda. Revisions . to Standards and Purpose. Emphasis on Specific Content. Decomposing . and Composing . Numbers . Computation. : Problem . Types. Fractions. Support . for Implementation. General Session. March 21, 2017 – Williamsburg. March 22, 2017 – Fredericksburg. April 4, 2017 – Wytheville. April 5, 2017 - Roanoke. 1. VDOE General Session Agenda. Overview of 2016 Standards Adoption and Implementation. . Grades 3-5 . Session. 1. 2. Welcome and Introductions. Agenda. Revisions . to standards and purpose. Emphasis on Specific Content. Number and Number Sense - Fractions. Computation and Estimation - Problem Types. 1 Learning Natural Language from its Perceptual Context Ray Mooney Department of Computer Science University of Texas at Austin Joint work with David Chen Joohyun Kim Machine Learning and Natural Language Processing (NLP) Mathematising and C ontextualising 2 Context, perspective and mathematics The Term - Mathematisation What does it mean ? It is a process that happens when creating a mathematical model to solve a real life situation.